research on music therapy for depression

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Research Article

Effects of music therapy on depression: A meta-analysis of randomized controlled trials

Roles Conceptualization, Writing – original draft

Affiliation Bengbu Medical University, Bengbu, Anhui, China

Roles Methodology, Software

Affiliation Anhui Provincial Center for Women and Child Health, Hefei, Anhui, China

Roles Writing – review & editing

Affiliations Bengbu Medical University, Bengbu, Anhui, China, National Drug Clinical Trial Institution, The First Affiliated Hospital of Bengbu Medical University, Bengbu, Anhui, China

Roles Conceptualization, Writing – review & editing

* E-mail: [email protected]

• Qishou Tang, 
• Zhaohui Huang, 
• Huan Zhou, 

• Published: November 18, 2020
• https://doi.org/10.1371/journal.pone.0240862
• Peer Review
• Reader Comments

We aimed to determine and compare the effects of music therapy and music medicine on depression, and explore the potential factors associated with the effect.

PubMed (MEDLINE), Ovid-Embase, the Cochrane Central Register of Controlled Trials, EMBASE, Web of Science, and Clinical Evidence were searched to identify studies evaluating the effectiveness of music-based intervention on depression from inception to May 2020. Standardized mean differences (SMDs) were estimated with random-effect model and fixed-effect model.

A total of 55 RCTs were included in our meta-analysis. Music therapy exhibited a significant reduction in depressive symptom (SMD = −0.66; 95% CI = -0.86 to -0.46; P <0.001) compared with the control group; while, music medicine exhibited a stronger effect in reducing depressive symptom (SMD = −1.33; 95% CI = -1.96 to -0.70; P <0.001). Among the specific music therapy methods, recreative music therapy (SMD = -1.41; 95% CI = -2.63 to -0.20; P <0.001), guided imagery and music (SMD = -1.08; 95% CI = -1.72 to -0.43; P <0.001), music-assisted relaxation (SMD = -0.81; 95% CI = -1.24 to -0.38; P <0.001), music and imagery (SMD = -0.38; 95% CI = -0.81 to 0.06; P = 0.312), improvisational music therapy (SMD = -0.27; 95% CI = -0.49 to -0.05; P = 0.001), music and discuss (SMD = -0.26; 95% CI = -1.12 to 0.60; P = 0.225) exhibited a different effect respectively. Music therapy and music medicine both exhibited a stronger effects of short and medium length compared with long intervention periods.

Conclusions

A different effect of music therapy and music medicine on depression was observed in our present meta-analysis, and the effect might be affected by the therapy process.

Citation: Tang Q, Huang Z, Zhou H, Ye P (2020) Effects of music therapy on depression: A meta-analysis of randomized controlled trials. PLoS ONE 15(11): e0240862. https://doi.org/10.1371/journal.pone.0240862

Editor: Sukru Torun, Anadolu University, TURKEY

Received: June 10, 2020; Accepted: October 4, 2020; Published: November 18, 2020

Copyright: © 2020 Tang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript and its Supporting Information files.

Funding: The Key Project of University Humanities and Social Science Research in Anhui Province (SK2017A0191) was granted by Education Department of Anhui Province; the Research Project of Anhui Province Social Science Innovation Development (2018XF155) was granted by Anhui Provincial Federation of Social Sciences; the Ministry of Education Humanities and Social Sciences Research Youth fund Project (17YJC840033) was granted by Ministry of Education of the People’s Republic of China. These funders had a role in study design, text editing, interpretation of results, decision to publish and preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Depression was reported to be a common mental disorders and affected more than 300 million people worldwide, and long-lasting depression with moderate or severe intensity may result in serious health problems [ 1 ]. Depression has become the leading causes of disability worldwide according to the recent World Health Organization (WHO) report. Even worse, depression was closely associated with suicide and became the second leading cause of death, and nearly 800 000 die of depression every year worldwide [ 1 , 2 ]. Although it is known that treatments for depression, more than 3/4 of people in low and middle-income income countries receive no treatment due to a lack of medical resources and the social stigma of mental disorders [ 3 ]. Considering the continuously increased disease burden of depression, a convenient effective therapeutic measures was needed at community level.

Music-based interventions is an important nonpharmacological intervention used in the treatment of psychiatric and behavioral disorders, and the obvious curative effect on depression has been observed. Prior meta-analyses have reported an obvious effect of music therapy on improving depression [ 4 , 5 ]. Today, it is widely accepted that the music-based interventions are divided into two major categories, namely music therapy and music medicine. According to the American Music Therapy Association (AMTA), “music therapy is 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” [ 6 ]. Therefore, music therapy is an established health profession in which music is used within a therapeutic relationship to address physical, emotional, cognitive, and social needs of individuals, and includes the triad of music, clients and qualified music therapists. While, music medicine is defined as mainly listening to prerecorded music provided by medical personnel or rarely listening to live music. In other words, music medicine aims to use music like medicines. It is often managed by a medical professional other than a music therapist, and it doesn’t need a therapeutic relationship with the patients. Therefore, the essential difference between music therapy and music medicine is about whether a therapeutic relationship is developed between a trained music therapist and the client [ 7 – 9 ]. In the context of the clear distinction between these two major categories, it is clear that to evaluate the effects of music therapy and other music-based intervention studies on depression can be misleading. While, the distinction was not always clear in most of prior papers, and no meta-analysis comparing the effects of music therapy and music medicine was conducted. Just a few studies made a comparison of music-based interventions on psychological outcomes between music therapy and music medicine. We aimed to (1) compare the effect between music therapy and music medicine on depression; (2) compare the effect between different specific methods used in music therapy; (3) compare the effect of music-based interventions on depression among different population [ 7 , 8 ].

Materials and methods

Search strategy and selection criteria.

PubMed (MEDLINE), Ovid-Embase, the Cochrane Central Register of Controlled Trials, EMBASE, Web of Science, and Clinical Evidence were searched to identify studies assessing the effectiveness of music therapy on depression from inception to May 2020. The combination of “depress*” and “music*” was used to search potential papers from these databases. Besides searching for electronic databases, we also searched potential papers from the reference lists of included papers, relevant reviews, and previous meta-analyses. The criteria for selecting the papers were as follows:(1) randomised or quasi-randomised controlled trials; (2) music therapy at a hospital or community, whereas the control group not receiving any type of music therapy; (3) depression rating scale was used. The exclusive criteria were as follows: (1) non-human studies; (2) studies with a very small sample size (n<20); (3) studies not providing usable data (including sample size, mean, standard deviation, etc.); (4) reviews, letters, protocols, etc. Two authors independently (YPJ, HZH) searched and screened the relevant papers. EndNote X7 software was utilized to delete the duplicates. The titles and abstracts of all searched papers were checked for eligibility. The relevant papers were selected, and then the full-text papers were subsequently assessed by the same two authors. In the last, a panel meeting was convened for resolving the disagreements about the inclusion of the papers.

Data extraction

We developed a data abstraction form to extract the useful data: (1) the characteristics of papers (authors, publish year, country); (2) the characteristics of participators (sample size, mean age, sex ratio, pre-treatment diagnosis, study period); (3) study design (random allocation, allocation concealment, masking, selection process of participators, loss to follow-up); (4) music therapy process (music therapy method, music therapy period, music therapy frequency, minutes per session, and the treatment measures in the control group); (5) outcome measures (depression score). Two authors independently (TQS, ZH) abstracted the data, and disagreements were resolved by discussing with the third author (YPJ).

Assessment of risk of bias in included studies

Two authors independently (TQS, ZH) assessed the risk of bias of included studies using Cochrane Collaboration’s risk of bias assessment tool, and disagreements were resolved by discussing with the third author (YPJ) [ 10 ].

Music therapy and music medicine

Music Therapy is defined as 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. Music medicine is defined as mainly listening to prerecorded music provided by medical personnel or rarely listening to live music. In other words, music medicine aims to use music like medicines.

Music therapy mainly divided into active music therapy and receptive music therapy. Active music therapy, including improvisational, re-creative, and compositional, is defined as playing musical instruments, singing, improvisation, and lyrics of adaptation. Receptive music therapy, including music-assisted relaxation, music and imagery, guided imagery and music, lyrics analysis, and so on, is defined as music listening, lyrics analysis, and drawing with musing. In other words, in active methods participants are making music, and in receptive music therapy participants are receiving music [ 6 , 7 , 9 , 11 – 13 ].

Evaluation of depression

Depression was evaluated by the common psychological scales, including Beck Depression Inventory (BDI), Children’s Depression Inventory (CDI), Center for Epidemiologic Studies Depression (CES-D), Cornell Scale (CS), Depression Mood Self-Report Inventory for Adolescence (DMSRIA), Geriatric Depression Scale-15 (GDS-15); Geriatric Depression Scale-30 (GDS-30), Hospital Anxiety and Depression Scale (HADS), Hamilton Rating Scale for Depression (HRSD/HAMD), Montgomery-sberg Depression Rating Scale (MADRS), Patient Reported Outcomes Measurement Information System (PROMIS), Self-Rating Depression Scale (SDS), Short Version of Profile of Mood States (SV-POMS).

Statistical analysis

The pooled effect were estimated by using the standardized mean differences (SMDs) and its 95% confidence interval (95% CI) due to the different depression rate scales were used in the included papers. Heterogeneity between studies was assessed by I-square ( I 2 ) and Q-statistic (P<0.10), and a high I 2 (>50%) was recognized as heterogeneity and a random-effect model was used [ 14 – 16 ]. We performed subgroup analyses and meta-regression analyses to study the potential heterogeneity between studies. The subgroup variables included music intervention categories (music therapy and music medicine), music therapy methods (active music therapy, receptive music therapy), specific receptive music therapy methods (music-assisted relaxation, music and imagery, and guided imagery and music (Bonny Method), specific active music therapy methods (recreative music therapy and improvisational music therapy), music therapy mode (group therapy, individual therapy), music therapy period (weeks) (2–4, 5–12, ≥13), music therapy frequency (once weekly, twice weekly, ≥3 times weekly), total music therapy sessions (1–4, 5–8, 9–12, 13–16, >16), time per session (minutes) (15–40, 41–60, >60), inpatient settings (secure [locked] unit at a mental health facility versus outpatient settings), sample size (20–50, ≥50 and <100, ≥100), female predominance(>80%) (no, yes), mean age (years) (<50, 50–65, >65), country having music therapy profession (no, yes), pre-treatment diagnosis (mental health, depression, severe mental disease/psychiatric disorder). We also performed sensitivity analyses to test the robustness of the results by re-estimating the pooled effects using fixed effect model, using trim and fill analysis, excluding the paper without information on music therapy, excluding the papers with more high biases, excluding the papers with small sample size (20< n<30), excluding the papers using an infrequently used scale, excluding the studies focused on the people with a severe mental disease. We investigated the publication biases by a funnel plot as well as Egger’s linear regression test [ 17 ]. The analyses were performed using Stata, version 11.0. All P-values were two-sided. A P-value of less than 0.05 was considered to be statistically significant.

Characteristics of the eligible studies

Fig 1 depicts the study profile, and a total of 55 RCTs were included in our meta-analysis [ 18 – 72 ]. Of the 55 studies, 10 studies from America, 22 studies from Europe, 22 studies from Asia, and 1 study from Australia. The mean age of the participators ranged from 12 to 86; the sample size ranged from 20 to 242. A total of 16 different scales were used to evaluate the depression level of the participators. A total of 25 studies were conducted in impatient setting and 28 studies were in outpatients setting; 32 used a certified music therapist, 15 not used a certified music therapist (for example researcher, nurse), and 10 not reported relevent information. A total of 16 different depression rating scales were used in the included studies, and HADS, GDS, and BDI were the most frequently used scales ( Table 1 ).

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• TIFF original image

PRISMA diagram showing the different steps of systematic review, starting from literature search to study selection and exclusion. At each step, the reasons for exclusion are indicated. Doi: 10.1371/journal.pone.0052562.g001.

https://doi.org/10.1371/journal.pone.0240862.g001

https://doi.org/10.1371/journal.pone.0240862.t001

Of the 55 studies, only 2 studies had high risks of selection bias, and almost all of the included studies had high risks of performance bias ( Fig 2 ).

https://doi.org/10.1371/journal.pone.0240862.g002

The overall effects of music therapy

Of the included 55 studies, 39 studies evaluated the music therapy, 17 evaluated the music medicine. Using a random-effects model, music therapy was associated with a significant reduction in depressive symptoms with a moderate-sized mean effect (SMD = −0.66; 95% CI = -0.86 to -0.46; P <0.001), with a high heterogeneity across studies ( I 2 = 83%, P <0.001); while, music medicine exhibited a stronger effect in reducing depressive symptom (SMD = −1.33; 95% CI = -1.96 to -0.70; P <0.001) ( Fig 3 ).

https://doi.org/10.1371/journal.pone.0240862.g003

Twenty studies evaluated the active music therapy using a random-effects model, and a moderate-sized mean effect (SMD = −0.57; 95% CI = -0.90 to -0.25; P <0.001) was observed with a high heterogeneity across studies ( I 2 = 86.3%, P <0.001). Fourteen studies evaluated the receptive music therapy using a random-effects model, and a moderate-sized mean effect (SMD = −0.73; 95% CI = -1.01 to -0.44; P <0.001) was observed with a high heterogeneity across studies ( I 2 = 76.3%, P <0.001). Five studies evaluated the combined effect of active and receptive music therapy using a random-effects model, and a moderate-sized mean effect (SMD = −0.88; 95% CI = -1.32 to -0.44; P <0.001) was observed with a high heterogeneity across studies ( I 2 = 70.5%, P <0.001) ( Fig 4 ).

https://doi.org/10.1371/journal.pone.0240862.g004

Among specific music therapy methods, recreative music therapy (SMD = -1.41; 95% CI = -2.63 to -0.20; P <0.001), guided imagery and music (SMD = -1.08; 95% CI = -1.72 to -0.43; P <0.001), music-assisted relaxation (SMD = -0.81; 95% CI = -1.24 to -0.38; P <0.001), music and imagery (SMD = -0.38; 95% CI = -0.81 to 0.06; P = 0.312), improvisational music therapy (SMD = -0.27; 95% CI = -0.49 to -0.05; P = 0.001), and music and discuss (SMD = -0.26; 95% CI = -1.12 to 0.60; P = 0.225) exhibited a different effect respectively ( Fig 5 ).

https://doi.org/10.1371/journal.pone.0240862.g005

Sub-group analyses and meta-regression analyses

We performed sub-group analyses and meta-regression analyses to study the homogeneity. We found that music therapy yielded a superior effect on reducing depression in the studies with a small sample size (20–50), with a mean age of 50–65 years old, with medium intervention frequency (<3 times weekly), with more minutes per session (>60 minutes). We also found that music therapy exhibited a superior effect on reducing depression among people with severe mental disease /psychiatric disorder and depression compared with mental health people. While, whether the country have the music therapy profession, whether the study used group therapy or individual therapy, whether the study was in the outpatients setting or the inpatient setting, and whether the study used a certified music therapist all did not exhibit a remarkable different effect ( Table 2 ). Table 2 also presents the subgroup analysis of music medicine on reducing depression.

https://doi.org/10.1371/journal.pone.0240862.t002

In the subgroup analysis by total session, music therapy and music medicine both exhibited a stronger effects of short (1–4 sessions) and medium length (5–12 sessions) compared with long intervention periods (>13sessions) ( Fig 6 ). Meta-regression demonstrated that total music intervention session was significantly associated with the homogeneity between studies ( P = 0.004) ( Table 3 ).

A, evaluating the effect of music therapy; B, evaluating the effect of music medicine.

https://doi.org/10.1371/journal.pone.0240862.g006

https://doi.org/10.1371/journal.pone.0240862.t003

Sensitivity analyses

We performed sensitivity analyses and found that re-estimating the pooled effects using fixed effect model, using trim and fill analysis, excluding the paper without information regarding music therapy, excluding the papers with more high biases, excluding the papers with small sample size (20< n<30), excluding the studies focused on the people with a severe mental disease, and excluding the papers using an infrequently used scale yielded the similar results, which indicated that the primary results was robust ( Table 4 ).

https://doi.org/10.1371/journal.pone.0240862.t004

Evaluation of publication bias

We assessed publication bias using Egger’s linear regression test and funnel plot, and the results are presented in Fig 7 . For the main result, the observed asymmetry indicated that either the absence of papers with negative results or publication bias.

A, evaluating the publication bias of music therapy; B, evaluating the publication bias of music medicine; BDI = Beck Depression Inventory; CDI = Children’s Depression Inventory; CDSS = depression scale for schizophrenia; CES-D = Center for Epidemiologic Studies Depression; CS = Cornell Scale; DMSRIA = Depression Mood Self-Report Inventory for Adolescence; EPDS = Edinburgh Postnatal Depression Scale; GDS-15 = Geriatric Depression Scale-15; GDS-30 = Geriatric Depression Scale-30; HADS = Hospital Anxiety and Depression Scale; HRSD (HAMD) = Hamilton Rating Scale for Depression; MADRS = Montgomery-sberg Depression Rating Scale; PROMIS = Patient Reported Outcomes Measurement Information System; SDS = Self-Rating Depression Scale; State-Trait Depression Questionnaire = ST/DEP; SV-POMS = short version of Profile of Mood Stat.

https://doi.org/10.1371/journal.pone.0240862.g007

Our present meta-analysis exhibited a different effect of music therapy and music medicine on reducing depression. Different music therapy methods also exhibited a different effect, and the recreative music therapy and guided imagery and music yielded a superior effect on reducing depression compared with other music therapy methods. Furthermore, music therapy and music medicine both exhibited a stronger effects of short and medium length compared with long intervention periods. The strength of this meta-analysis was the stable and high-quality result. Firstly, the sensitivity analyses performed in this meta-analysis yielded similar results, which indicated that the primary results were robust. Secondly, considering the insufficient statistical power of small sample size, we excluded studies with a very small sample size (n<20).

Some prior reviews have evaluated the effects of music therapy for reducing depression. These reviews found a significant effectiveness of music therapy on reducing depression among older adults with depressive symptoms, people with dementia, puerpera, and people with cancers [ 4 , 5 , 73 – 76 ]. However, these reviews did not differentiate music therapy from music medicine. Another paper reviewed the effectiveness of music interventions in treating depression. The authors included 26 studies and found a signifiant reduction in depression in the music intervention group compared with the control group. The authors made a clear distinction on the definition of music therapy and music medicine; however, they did not include all relevant data from the most recent trials and did not conduct a meta-analysis [ 77 ]. A recent meta-analysis compared the effects of music therapy and music medicine for reducing depression in people with cancer with seven RCTs; the authors found a moderately strong, positive impact of music intervention on depression, but found no difference between music therapy and music medicine [ 78 ]. However, our present meta-analysis exhibited a different effect of music therapy and music medicine on reducing depression, and the music medicine yielded a superior effect on reducing depression compared with music therapy. The different effect of music therapy and music medicine might be explained by the different participators, and nine studies used music therapy to reduce the depression among people with severe mental disease /psychiatric disorder, while no study used music medicine. Furthermore, the studies evaluating music therapy used more clinical diagnostic scale for depressive symptoms.

A meta-analysis by Li et al. [ 74 ] suggested that medium-term music therapy (6–12 weeks) was significantly associated with improved depression in people with dementia, but not short-term music therapy (3 or 4 weeks). On the contrary, our present meta-analysis found a stronger effect of short-term (1–4 weeks) and medium-term (5–12 weeks) music therapy on reducing depression compared with long-term (≥13 weeks) music therapy. Consistent with the prior meta-analysis by Li et al., no significant effect on depression was observed for the follow-up of one or three months after music therapy was completed in our present meta-analysis. Only five studies analyzed the therapeutic effect for the follow-up periods after music therapy intervention therapy was completed, and the rather limited sample size may have resulted in this insignificant difference. Therefore, whether the therapeutic effect was maintained in reducing depression when music therapy was discontinued should be explored in further studies. In our present meta-analysis, meta-regression results demonstrated that no variables (including period, frequency, method, populations, and so on) were significantly associated with the effect of music therapy. Because meta-regression does not provide sufficient statistical power to detect small associations, the non-significant results do not completely exclude the potential effects of the analyzed variables. Therefore, meta-regression results should be interpreted with caution.

Our meta-analysis has limitations. First, the included studies rarely used masked methodology due to the nature of music therapy, therefore the performance bias and the detection bias was common in music intervention study. Second, a total of 13 different scales were used to evaluate the depression level of the participators, which may account for the high heterogeneity among the trials. Third, more than half of those included studies had small sample sizes (<50), therefore the result should be explicated with caution.

Our present meta-analysis of 55 RCTs revealed a different effect of music therapy and music medicine, and different music therapy methods also exhibited a different effect. The results of subgroup analyses revealed that the characters of music therapy were associated with the therapeutic effect, for example specific music therapy methods, short and medium-term therapy, and therapy with more time per session may yield stronger therapeutic effect. Therefore, our present meta-analysis could provide suggestion for clinicians and policymakers to design therapeutic schedule of appropriate lengths to reduce depression.

Supporting information

S1 checklist. prisma checklist..

https://doi.org/10.1371/journal.pone.0240862.s001

S1 Dataset.

https://doi.org/10.1371/journal.pone.0240862.s002

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Effects of music therapy on depression: A meta-analysis of randomized controlled trials

• November 2020
• 15(11):e0240862
• This person is not on ResearchGate, or hasn't claimed this research yet.

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ORIGINAL RESEARCH article

Music therapy for depression enhanced with listening homework and slow paced breathing: a randomised controlled trial.

• 1 Faculty of Information Technology, University of Jyväskylä, Jyväskylä, Finland
• 2 NORCE Norwegian Research Centre AS, Bergen, Norway
• 3 Department of Clinical and Health Psychology, Faculty of Psychology, University of Vienna, Vienna, Austria

Introduction: There is evidence from earlier trials for the efficacy of music therapy in the treatment of depression among working-age people. Starting therapy sessions with relaxation and revisiting therapeutic themes outside therapy have been deemed promising for outcome enhancement. However, previous music therapy trials have not investigated this issue.

Objective: To investigate the efficacy of two enhancers, resonance frequency breathing (RFB) and listening homework (LH), when combined with an established music therapy model (trial registration number ISRCTN11618310).

Methods: In a 2 × 2 factorial randomised controlled trial, working-age individuals with depression were allocated into groups based on four conditions derived from either the presence or absence of two enhancers (RFB and LH). All received music therapy over 6 weeks. Outcomes were observed at 6 weeks and 6 months. The primary outcome was the Montgomery Åsberg Depression Rating Scale (MADRS) score.

Results: There was a significant overall effect of treatment for the primary outcome favouring the breathing group ( d = 0.50, 95% CI 0.07 to 0.93, p = 0.02). The effect was larger after adjustment for potential confounders ( d = 0.62, 95% CI 0.16 to 1.08, p = 0.009). Treatment effects for secondary outcomes, including anxiety (anxiety scale of Hospital Anxiety and Depression Scale) and quality of life (RAND-36), were also significant, favouring the breathing group. The homework enhancer did not reach significant treatment effects.

Conclusion: We found that the addition of RFB to a music therapy intervention resulted in enhanced therapeutic outcome for clients with depression.

Introduction

Impact of depression.

Depression is one of the most disabling of diseases, causing a serious individual and societal burden ( Sobocki et al., 2006 ). In Europe, major depression and specific phobia are the most common psychiatric disorders ( Alonso et al., 2004 ). Almost 13% of the population report a lifetime history of major depressive disorder, with around 4% having experienced major depression in the past 12 months. Depression is often connected to other disabling disorders, such as generalised anxiety disorder and somatoform disorder, all of which show an excess comorbidity leading to higher psychosocial disability, increased suicidality, and worse clinical outcome and treatment response ( Maier and Falkai, 1999 ). According to Turunen (2020) , the prevalence of mental problems in Finland has been growing continuously in recent years; the number of anxiety diagnoses, for instance, was 25% higher in 2019 compared to the year before. Also the effect of COVID-19 can be clearly seen in the use of mental health services, the number of short-term psychotherapy referrals across countries having increased four times at the beginning of 2020, compared to the same period one year before ( Khan et al., 2020 ). In the light of these trends, offering the best possible evidence-based treatments and improving existing therapeutic approaches has become more important than ever. The aim of this study was to investigate whether an effective form of music therapy could be further enhanced in terms of clinical outcomes.

Treatments for Depression

Pharmacotherapy and psychotherapy—used alone or in combination—are currently the main treatments for depression ( Masennus: Käypä hoito-suositus, 2020 ), and both have been found equally efficacious ( De Maat et al., 2006 ). However, when including risk of relapse, long-term outcomes, and suicidal risks in the evaluation, pharmacotherapy has been associated with higher relapse ( De Maat et al., 2006 ), poorer long-term outcomes ( Hengartner et al., 2018 ), and increased suicidal risks ( Baldessarini et al., 2017 ), making psychotherapy an appealing and valuable option among the treatment modalities. Interestingly, a recent meta-analysis ( Weitz et al., 2018 ) reports that psychotherapy is almost as effective at reducing comorbid anxiety symptoms as it is at reducing depressive symptoms. Furthermore, besides the reduction in depressive symptoms, psychotherapy also has a positive impact on quality of life (QoL), especially its mental health component ( Kolovos et al., 2016 ).

Forms of Psychotherapy

When comparing the most common forms of verbal psychotherapy used in the treatment of depression, Cuijpers et al. (2014) found no significant difference in terms of response and remission rate, which suggests that the various forms of verbal psychotherapy might be largely interchangeable. A common challenge for verbal psychotherapy is the fact that major depression typically leads to psychomotor regression in the area of speech ( Flint et al., 1993 ), noticeable in the form of retardation of speech and prolongation of quiet episodes ( Hoffman et al., 1985 ). Consequently, verbal expression and processing during therapy may be difficult or insufficient for some individuals with depression. Psychotherapy forms that allow non-verbal expression – such as arts therapies – may offer a potential alternative. For instance, there is an increasing number of randomised controlled trials (RCT) and two Cochrane systematic reviews ( Maratos et al., 2008 ; Aalbers et al., 2017 ) on the effect of music therapy for depression. According to Aalbers et al. (2017) , music therapy provides short-term beneficial effects for people with depression. More specifically, music therapy added to treatment as usual (TAU) appears to be more efficacious than TAU alone. Furthermore, music therapy is not associated with more or fewer adverse events than TAU alone. Similarly, a systematic review on the effectiveness of dance and movement therapy (DMT) in the treatment of adults with depression also concludes that DMT is an effective intervention ( Karkou et al., 2019 ).

Improvisational Music Therapy

We previously conducted an RCT on the effectiveness of music therapy for working-age people with depression ( Erkkilä et al., 2011 ). In that trial, only one specific music therapy technique was used, called improvisational psychodynamic music therapy (IPMT) ( Erkkilä et al., 2012 ). This decision was influenced by the first systematic review on music therapy for depression ( Maratos et al., 2008 ), which concluded that one weakness of the existing RCTs was the variety of music therapy methods included in the same study, making it difficult to draw any conclusions on the effect of a single method, such as clinical improvisation. In that RCT, based on 20 bi-weekly music therapy sessions of 60 min each, we found that the clients in the IPMT + TAU group improved significantly more in terms of depression, anxiety, and general functioning, compared to the TAU group. Furthermore, the treatment response of the IPMT + TAU group was almost twice as high as in the TAU group, based on the primary outcome measure (depression). We concluded that IPMT is an effective treatment for depression when added to TAU, with the added benefit of significantly reducing comorbid anxiety and improving general functioning. The core element of IPMT, free improvisation, can be described as a means of “self-projection and free association” and may enable clients thereby “to connect with emotional memories and images” ( Erkkilä et al., 2011 , p. 132). Emphasising the creative process rather than the end product, it has also been described as “playing around with sounds until they form whatever patterns, shapes or textures one wants them to have, or until they mean whatever one wants them to mean” ( Bruscia, 1998 , p. 5). In the present study, we aimed to build on the positive results of our previous RCT, and investigate whether the effectiveness of integrative improvisational music therapy (IIMT; based on IPMT with certain modifications, as described in “Methods”) can be further enhanced through the addition of carefully selected elements. The two elements we chose were a slow-breathing technique called resonance frequency breathing (RFB), and a homework task where clients were encouraged to listen to the improvisations created during therapy.

Enhancement 1: Resonance frequency breathing (RFB)

Resonance frequency breathing is the core element of a method called heart rate variability biofeedback (HRVB). With the help of biofeedback equipment displaying heart and respiration patterns in real-time, clients learn to breathe at their resonance frequency, which corresponds to a specific breathing speed that is unique to each person, and is typically located between 4.5 and 6.5 breaths/min in adults ( Vaschillo et al., 2006 ). When breathing at resonance frequency, heart, respiratory, and blood pressure rhythms become highly synchronised, and heart rate variability (HRV) substantially increases ( Lehrer and Gevirtz, 2014 ). Within a very short time, the autonomic nervous system shifts to parasympathetic dominance (rest-and-digest), resulting in relaxation and lower stress levels. RFB is a simplified form of HRVB, as it does not involve any biofeedback equipment. In RFB, the resonance frequency is determined beforehand through a single breathing assessment. Subsequently, clients are doing paced breathing at their previously determined resonance frequency, using a breath pacer set at the right speed, according to the results of the breathing assessment. In terms of application, HRVB has proven beneficial for a wide range of physical and psychological conditions ( Gevirtz, 2013 ; Moss and Shaffer, 2017 ), as well as for the enhancement of artistic creativity ( Gruzelier et al., 2014 ) and sport performance ( Jiménez Morgan and Molina Mora, 2017 ). More relevant to the topic of the present trial, a recent meta-analysis, based on 24 studies and 484 participants, revealed that HRVB was associated with a large reduction in stress and anxiety ( Goessl et al., 2017 ). HRVB has also been found beneficial for the treatment of depression, both in open-label studies ( Karavidas et al., 2007 ; Siepmann et al., 2008 ) and in controlled studies ( Caldwell and Steffen, 2018 ; Lin et al., 2019 ). In a systematic review and meta-analysis investigating the effect sizes of HRVB for specific health conditions, the authors conclude that HRVB would be a useful addition to clinicians’ existing skill-sets, because of its proven efficacy and the ease with which it can be used alongside other forms of therapy ( Lehrer et al., 2020 ). However, to date, very few attempts have been made to fully integrate HRVB into an existing form of (psycho)therapy, so as to create a synergy effect in support of the latter. In most studies we have come across, HRVB is used as an additional and separate treatment modality, for example alongside cognitive behavioural therapy or acceptance and commitment therapy ( Reiner, 2008 ; Caldwell and Steffen, 2018 ). At the Music Therapy Clinic for Research and Training (University of Jyväskylä, Finland), we have developed and tested our own therapy format, whereby each session of IIMT begins with 10 min of RFB. Our pilot studies suggest that the inclusion of RFB helps clients upregulate and downregulate their emotions during music therapy, depending on their clinical status and current needs ( Brabant and Erkkilä, 2018 ). These preliminary findings require follow-up with a between-group study such as the present one, to determine whether the observed effects on therapy processes also lead to better outcomes. Generally, it should be noted that RFB is an active field of research. The mechanisms behind RFB are incompletely understood, but may include baroreflex gains ( Shaffer and Meehan, 2020 ); vagal nerve stimulation ( Gerritsen and Band, 2018 ); enhancement of functional connectivity in brain areas associated with emotion regulation ( Mather and Thayer, 2018 ); and the complex interplay of several neurophysiological processes ( Noble and Hochman, 2019 ). However, there is consensus that the resonance frequency is stable in adults, around 0.1 Hz or 6 bpm, and that breathing at a frequency near 0.1 Hz promotes relaxation and other physical and mental benefits ( Mather and Thayer, 2018 ; Noble and Hochman, 2019 ; Shaffer and Meehan, 2020 ). Slow-placed breathing may provide a parsimonious explanation of the physical and mental benefits of a number of contemplative activities such as meditation or yoga ( Gerritsen and Band, 2018 ), but it is less clear whether breathing at the individual’s precise resonance frequency is more effective than breathing at 6 bpm ( Shaffer and Meehan, 2020 ). Procedures for frequency assessment have been reviewed recently ( Shaffer and Meehan, 2020 ), based on previous work by Lehrer and colleagues ( Lehrer and Gevirtz, 2014 ; Lehrer et al., 2020 ).

Enhancement 2: Listening homework (LH)

The idea of the LH task arose from our earlier clinical observations, where some clients seemed to benefit from listening back to the recorded music improvisations, both during the sessions and at home. We hypothesise that, because music improvisations evoke emotions and imagery with specific therapeutic meanings, providing clients with the chance to further process these emotions at home may improve the effect of therapy. The therapeutic potential of homework is already known in the context of verbal psychotherapy ( Kazantzis et al., 2000 ; Kazantzis et al., 2010 ; Mausbach et al., 2010 ), where it has been used for the treatment of depression ( Thase and Callan, 2006 ). According to the meta-analysis by Mausbach et al. (2010) , clients’ compliance to homework is a crucial factor, with higher compliance being associated with better therapeutic outcomes. While this body of research supports the plausibility of homework in psychotherapy in general, it is not directly related to LH in this study. First, the previous research involved predominantly cognitive and behavioural therapy (CBT), which is quite distant from IIMT. Second, LH is rather different from the types of homework assignments typically given in these other types of psychotherapies. However, the idea of LH is closely connected to a category of music therapy methods called receptive music therapy. In receptive music therapy, listening to music is used to stimulate the verbal dialogue between client and therapist, and to evoke emotions, memories, images, associations, and so on. The music is often precomposed, but can also be improvised by a therapist in a given situation. In this context, music is often seen as a catalyst and enhancer. In one of the best-known examples of receptive methods – the Bonny Method of Guided Imagery and Music (BMGIM) ( Grocke and Bruscia, 2002 )– pre-designed programmes of Western classical music are used to shape and support the client in experiencing unfolding imagery. The client listens to the programme while in an altered state of consciousness and simultaneously dialogues with the therapist. From a therapeutic perspective, the BMGIM approach and the experiences in altered state as an essential element of it have been found beneficial and effective ( Hammer, 1996 ; McKinney et al., 1997 ; McKinney and Honig, 2017 ). In contrast to BMGIM, however, in our study there was no therapeutic guidance during the home listening, although there were opportunities to discuss the listening experiences when being back in the therapy room.

In this RCT, we examined two hypotheses concerning the efficacy of RFB and LH when combined with IIMT to enhance therapeutic outcome. Hypothesis 1 suggested that RFB would reduce depressive symptoms and that we would observe a significant overall treatment effect over time for RFB, together with significant treatment effects post-intervention and at follow-up. Hypothesis 2 suggested that LH would similarly reduce depressive symptoms and yield significant treatment effects. These hypotheses are rationalised by the aforementioned findings, which indicate positive treatment effects of both HRVB and psychotherapeutic homework assignments in depressed clients. In addition to this, we were interested in exploring potential interaction effects between the RFB and LH interventions, although due to insufficient literature we did not have an a priori hypothesis on the efficacy of this combination for the treatment of depression.

Materials and Methods

We conducted a 2 × 2 factorial randomised controlled trial in which all clients received IIMT ( Erkkilä et al., 2019 ). The trial was registered (ISRCTN11618310) before recruitment. Clients were randomly allocated to one of four groups (IIMT alone, IIMT + LH, IIMT + RFB, IIMT + LH + RFB) following a 2 × 2 factorial design. Conditions were derived from either the presence or absence of LH (LH yes , LH no ) and RFB (RFB yes , RFB no ).

Participants

Eligible participants were adults with a primary diagnosis of major depressive disorder (F32/F33, ICD-10 criteria). The diagnosis was made by a psychiatric nurse with an MA degree in nursing science and assessment qualification. Musical skills were not required from participants. Exclusion criteria were a known history of psychosis, bipolar disorder, personality disorder, other combined psychiatric disorders in which depression cannot be defined as primary disorder, acute and severe substance misuse, and depression severity impeding clinical measurements or verbal conversation.

Randomisation and Blinding

After screening and diagnosis, a computerised block randomisation with randomly varying block sizes of 4 and 8 was conducted by an external person (C.G.) who had no direct contact with the patients. To ensure group allocation concealment, randomisation was conducted at another site (NORCE Norwegian Research Centre). Thus, assessor, therapists, and participants were unaware of allocation until therapy started. As this was a single-blind trial, only the outcome assessor remained blinded to allocation throughout the trial.

Assessment Procedure

Outcome measures were collected by a specialist in psychiatric assessment at three measurement points: (1) baseline, i.e., during recruitment (T0); (2) post-intervention, i.e., 6 weeks after randomisation (T1); (3) and follow-up, i.e., 6 months after randomisation (T2). The time point of primary interest was post-intervention. Demographic information was obtained at the beginning of the intervention.

Interventions

All participants were offered 12 bi-weekly sessions of IIMT over a period of 6 weeks. Each session lasted one hour. The therapeutic approach and its additional components (LH and RFB) are described in the following sections.

Integrative Improvisational Music Therapy (IIMT)

In music therapy, music experiences are used to enrich and enhance a client’s expression and interaction. Essential to music therapy is the client-therapist relationship, in contrast with music and medicine, where music can be used without that relationship. IIMT, developed at the Music Therapy Clinic for Research and Training (University of Jyväskylä, Finland), is based on clinical improvisation, which is one of the major methods of music therapy ( Bruscia, 1987 ). IIMT is based on the interplay and alternation between free music improvisation and verbal discussion ( Erkkilä et al., 2011 , 2012 ). It was originally anchored in the psychodynamic music therapy tradition ( Priestley, 1994 ; Bruscia, 1998 ), and later on, adopted elements from the integrative psychotherapy tradition ( Norcross and Goldfried, 2005 ) as well. The fundamental aim of IIMT is to encourage clients to engage in expressive musical interaction with the therapist. The experiences arising from this interaction are then conceptualised and further processed in the verbal domain ( Erkkilä et al., 2011 ). In IIMT, improvising is primarily understood both as a symbolic representation of abstract mental content, and as an expressive medium able to evoke emotions, images, and memories ( Erkkilä et al., 2012 ), but other human processes–such as cognitive, behavioural, and physiological–may be involved as well.

We standardised the clinical setting so that every therapy process involved identical instruments and a similar arrangement of the two music therapy clinics. Two identical digital pianos placed opposite each other (one for the client, another one for the therapist) were used for melodic and harmonic improvisations. Two identical djembe drums placed next to the pianos were used for non-melodic, rhythmic improvisations. No other instruments or music therapy methods were used. The improvisations were digitally recorded, which made it possible to listen back to them anytime afterwards. Eleven qualified and clinically experienced music therapists (five female, six male) were responsible for conducting the therapy sessions.

Added Component: Resonance Frequency Breathing (RFB)

Each client’s resonance frequency was determined through a breathing assessment conducted before the beginning of therapy. We opted for a single assessment for the sake of simplicity, relying on the finding that adults’ resonance frequency appears to be very stable ( Vaschillo et al., 2006 ). The assessment followed the protocol developed by Lehrer (2007) , and consisted of two parts. First, the client was instructed in how to perform RFB (abdominal breathing, inhalation through the nose and exhalation through the mouth, no holds or pauses, and breathing slower without breathing deeper). Once the technique was sufficiently mastered, the client was asked to breathe at six different rates for 3 min each, while wearing a heart rate monitor. The breathing rates ranged from 7 to 4.5 breaths/min, starting from the fastest until the slowest, in 0.5 steps. Heart rate data for each breathing segment was then analysed using Kubios HRV 3.1 ( Tarvainen et al., 2014 ). The optimal breathing rate was defined as the rate producing the highest peak in the low frequency (LF) component of the power spectrum (0.04–0.15 Hz), as obtained through a fast Fourier transform analysis of the heart beat intervals.

Following the assessment, each client’s optimal breathing speed was communicated to their respective therapist, who used this information for the RFB task. At the beginning of each therapy session, clients assigned to RFB yes performed 10 min of RFB at an inhalation/exhalation ratio of 40/60 in a seated position, while following visual cues provided by a breathing app called Kardia ( Tache, 2017 ), installed on a tablet computer placed in front of the client. Longer exhalations are known to promote parasympathetic activation ( Strauss-Blasche et al., 2000 ) and, in a slow-breathing scenario, a 40/60 ratio has been shown to induce higher levels of relaxation than its opposite ratio ( Diest et al., 2014 ).

Added Component: Listening Homework (LH)

Listening homework was conducted outside the therapy context, in the client’s own time, based on the clinical improvisations created in music therapy sessions using two digital pianos and two djembes. These improvisations were recorded by the therapists using Pro Tools 11.3.1. Each client had personal access through their personal computers to all of their improvisation recordings. Recordings were stored on a University server and automatically synchronized with the clients’ home computers using the continuous file synchronization program Syncthing ( The Syncthing Foundation, 2017 ) in order to be available for listening immediately after the music therapy session. All improvisations created during the music therapy process were available to the client for listening throughout the therapy process. Clients were instructed to use headphones to listen, whenever they felt like doing so and as many times as they wished, to any of the available improvisations and could decide when and how many times they wanted to listen to the improvisations. A dedicated music player, Cantata (2017) , which automatically displayed all available improvisations to clients, was installed in clients’ computers for this purpose. Software installation and guidance to clients on how to use the music player was performed shortly before the first music therapy session. Client’s mean total listening time was 02h:28m:59s (SD = 03:03:34; median = 01:10:32; Q1 = 00:26:20; Q3 = 03:34:29; range 0 to 12:11:21).

At the beginning of the trial, the clinicians were advised to encourage clients to listen to the improvisations after each session. In addition, the therapists were advised to recommend particular improvisations to be listened to at home when they were connected to specific, clinically important themes. Clients’ experiences while listening back to improvisations could be discussed and reflected upon with the therapist in subsequent therapy sessions.

Treatment Fidelity

To ensure treatment fidelity, the selected clinicians were offered intensive training in the music therapy model and in the two added components. All the clinicians were qualified music therapists. Regular clinical supervision was used for monitoring and maintaining the quality of the clinical work.

Primary Outcome

The Montgomery-Åsberg Depression Rating Scale (MADRS) ( Montgomery and Åsberg, 1979 ) was the primary outcome of the study. At the beginning of the study, MADRS was used to determine participant eligibility. The MADRS has high joint-reliability, has been shown to be sensitive to change, and has been demonstrated to have predictive validity for major depressive disorder ( Rush et al., 2008 ).

Secondary Outcomes

The anxiety subscale (HADS-A) of the Hospital Anxiety and Depression Scale (HADS) ( Aro et al., 2004 ) was used to assess anxiety. QoL was assessed using the RAND-36 ( Aalto et al., 1999 ), whose results were aggregated into two summary scales, physical component sum (PCS) and mental component sum (MCS) ( Ware and Kosinski, 1994 ). A detailed explanation of this procedure can be found in the Supplementary Material . The Global Assessment of Functioning (GAF) ( Jones et al., 1995 ) was used for assessing how mental health symptoms affected the clients’ daily life and general functioning. The measures of general functioning and QoL were chosen based on widespread use in psychological intervention studies concerning people with mental health problems.

Sample Size

Following a previous IIMT intervention, we assumed that no more than 10% of clients would leave the study early. We aimed to recruit 68 participants and allocate them into 4 conditions in a factorial design ( n = 34 in each condition; n = 17 in each group) ( Erkkilä et al., 2019 ). For each condition, the selected sample size provided statistical power of 0.80 for detecting a medium standardised effect size of Cohen’s d = 0.60 in a mixed-model analysis (see Twisk, 2013 , p. 281, equation 13.3), with a 2-tailed significance level of p < 0.05 and intra-participant correlation of ρ = 0.6.

Statistical Analysis

An intention-to-treat (ITT) approach was followed, using all available data regardless of whether the treatment was received as intended. Clients who left the study before completion of the intervention were considered dropouts. All tests used two-tailed 5% significance level, with no adjustments for multiplicity. Baseline, post-intervention and follow-up outcome measures served as continuous dependent variables. Repeated-measures linear mixed-effects models (see Supplementary Material ) were used to assess RFB and LH effects for each continuous outcome. An advantage of the utilised repeated measures design is that clients with missing data can be retained in the model, and thus all clients were used in the analysis. RFB and LH were entered as predictors and a random intercept term grouped by client was added to adjust for the dependency of repeated observations within each client. To adjust for baseline differences between conditions, the treatment terms were removed from the model ( Twisk et al., 2018 ). Hence, the effects of RFB and LH were calculated from the interaction between each factor and time. As an exploratory investigation to examine potential interaction effects between RFB and LH interventions, the repeated-measures linear mixed-effects models were subsequently expanded by adding an RFB x LH interaction.

Besides treatment effect post-intervention and follow-up, we obtained an overall treatment effect over time B as an estimate of the raw mean difference between presence and absence of each factor; B was calculated as the sum of the regression coefficients between each condition and time points ( Erkkilä et al., 2019 ). To estimate effect sizes for a given outcome, its overall treatment effect over time was divided by the standard deviation of the measure across all clients at baseline.

For each client, a dichotomous treatment response variable was calculated, defined as a reduction in MADRS of at least 50% between the pre- and post-intervention measurements. For dichotomous variables (leaving the study early, treatment response), missing data were imputed and a negative outcome was assumed for those clients (left the study early, no-response) for a conservative estimate. Fisher’s exact test and odds ratio were calculated separately for RFB and LH. To determine clinical significance, risk difference and number needed to treat (NNT) were calculated for effects that were statistically significant.

Besides the crude efficacy analysis, an adjusted efficacy analysis and two sensitivity analyses were carried out. The repeated-measures linear mixed-effects model of each continuous outcome was adjusted for prognostic covariates by adding them as random effects (random slopes) in the model: age group (i.e., grouped every 10 years), gender, medication (use of antidepressants, anxiolytic or hypnotic medication), and therapist. Two sensitivity analyses were conducted for the primary outcome: a single imputation method (Last Observation Carried Forward) that assumes no change for missing data, and a per-protocol approach (treatment as received). All statistical analyses were performed in Matlab 2019b (MathWorks, Natick, Massachusetts).

Data Sharing

The study’s data-set, except for the data that could compromise the privacy of research participants, is available from the corresponding author upon request.

The study was conducted at the Music Therapy Clinic for Research and Training (University of Jyväskylä, Finland). Figure 1 shows the patient flow during the trial.

Figure 1. Flow of participants through the trial.

Recruitment started on February 1, 2018 and ended on October 31, 2018. Participants were recruited in central Finland through newspaper announcements. Of 102 people who were initially invited for screening, 14 declined, 11 were no-shows and 7 met an exclusion criterion. This left 70 eligible participants (74% female), their age ranging from 19 to 57 years ( M = 39). Baseline characteristics in each condition are shown in Table 1 .

Table 1. Demographic and clinical characteristics of 70 clients at baseline.

According to the results of the treatment effect analysis, there was a significant main effect of time both post-intervention and at follow-up in the expected direction (i.e., improvement of clients’ condition) on all outcome measures (see Table 2 ).

Table 2. Effects of music therapy with or without resonance frequency breathing or listening homework.

Figures 2 , 3 show mean outcome scores across time points, separately for presence and absence of RFB and LH. An overall improvement over time for all secondary measures can be observed, regardless of condition.

Figure 2. Mean scores of continuous outcome for presence and absence of RFB across timepoints. Error bars denote confidence intervals at 95%. T0: baseline; T1: post-intervention (6 weeks after the beginning of the intervention); T2: follow-up (6 months after the beginning of the intervention).

Figure 3. Mean outcome measure scores for presence and absence of LH across timepoints. Error bars denote confidence intervals at 95%. T0: baseline; T1: post-intervention (6 weeks after the beginning of the intervention); T2: follow-up (6 months after the beginning of the intervention).

Table 2 shows the results of crude and adjusted treatment efficacy analyses post-intervention and at follow-up. The crude treatment efficacy analyses revealed significant differences between RFB yes and RFB no for all outcome measures, in all cases favouring RFB yes . The differences between most outcome measures both post-intervention and at follow-up reached statistical significance. Regarding LH, although the results for most outcome measures favoured LH yes (with the exception of HADS), none of them reached significance. Adjusted treatment efficacy analyses yielded similar results to those obtained in the crude analyses, except that the adjusted analyses for RFB reached significance at both time points for all outcome measures. Potential interactions between RFB and LH were examined by subsequently adding an RFB x LH interaction. This factor interaction, however, did not yield significance at any time point for any outcome measure, neither in the crude nor in the adjusted analysis.

Crude and adjusted overall treatment effect over time and resulting effect sizes are presented in Table 3 . According to the crude treatment efficacy analysis, the overall effect of treatment for RFB was significant for all measures except GAF, with RFB yes clients invariably improving more than RFB no clients. The adjusted treatment efficacy analysis yielded similar results, except for two differences. First, while the overall effect of treatment for GAF did not reach significance for RFB in the crude analysis, all outcome measures yielded significant differences for RFB in the adjusted analysis. Second, differences between RFB yes and RFB no increased after covariate adjustment of the treatment efficacy analysis, especially for MADRS.

Table 3. Effect sizes of music therapy with or without resonance frequency breathing for continuous outcomes.

Montgomery-Åsberg Depression Rating Scale scores decreased in all conditions post-intervention, as shown in Figures 2 , 3 . An overall improvement in MADRS from moderate (20-34 points) to mild depression (7-19 points) can be observed for all conditions. Overall, the post-intervention remission rate (defined as MADRS ≤ 9) was 31%, and the post-intervention response rate (defined as a MADRS reduction of 50% or more) was 39%.

Regarding treatment effect post-intervention and follow-up for the RFB factor (see Table 2 ), there was no significant difference between conditions in MADRS ( p = 0.103) post-intervention (6 weeks). However, at follow-up (6 months), the decrease in MADRS score was significantly larger in the RFB yes condition than in the RFB no condition ( p = 0.04). No significant differences were found between the LH factor levels, neither at post-intervention ( p = 0.485) nor follow-up ( p = 0.297).

Overall treatment effect analyses [3] (see Table 3 ) showed a significantly higher decrease in MADRS for RFB yes than for RFB no (Crude B [SE] = −3.55 [1.53], p = 0.02 ∗ ). These differences increased after adjustment for potential confounders (Adjusted B [SE] = −4.35 [1.64], p = 0.009 ∗∗ ). No significant differences were found between LH factor levels (Crude B [SE] = −1.70 [1.53], p = 0.27; adjusted B [SE] = −1.42 [1.76], p = 0.42). Medium effect sizes for RFB were observed in both crude and adjusted analysis, although they were higher in the adjusted analysis ( d [95% CI] = 0.62 [0.16−1.08]) than in the crude analysis ( d [95% CI] = 0.50 [0.07−0.93]). For LH, small effect sizes (Crude d [95% CI] = 0.24 [−0.19−0.67]; Adjusted d [95% CI] = 0.20 [−0.29−0.70]) were observed.

Results for dichotomous variables are presented in Table 4 . There were fewer dropouts in RFB yes compared to RFB no but the odds ratio was not significant. MADRS response rates were significantly greater in RFB yes ( p < 0.05) post-intervention (6 weeks), but were not significant at follow-up (6 months). A risk difference of 0.26 and NNT of 3.9 were observed, favouring RFB yes condition. There were no significant differences between the LH factor levels in any of the dichotomous variables.

Table 4. Attrition and response rates in 70 participants randomised to music therapy with or without resonance frequency breathing or listening homework.

The crude treatment efficacy analyses resulted in a significant improvement in secondary measures for RFB yes either at follow-up, post-intervention, or both time points (see Table 2 ). HADS scores decreased in all conditions during the intervention. In regards to RFB, there was a significant difference between conditions in HADS ( p = 0.027) post-intervention. At follow-up, differences did not reach significance ( p = 0.054). No significant differences were found between the LH factor levels neither at post-intervention nor follow-up; similar results regarding LH were observed for the other three secondary measures (RAND-36 MCS, RAND-36 PSY and GAF). Adjusted treatment effect analyses yielded comparable results, albeit of higher significance; this was also observed for the rest of the secondary outcomes. Also, in the adjusted analysis there was a significant difference in HADS ( p = 0.017) between RFB no and RFB yes at follow-up.

For all conditions, both RAND-36 MCS and RAND-36 PCS decreased during intervention. For RAND-36 MCS, RFB results showed a significant difference between conditions, both post-intervention ( p = 0.027) and at follow-up ( p = 0.012), in favour of RFB yes . Significant differences were also observed between RFB yes and RFB no for RAND-36 PCS, both post-intervention ( p = 0.012) and at follow-up ( p = 0.01).

All conditions exhibited a decrease in GAF scores. There was no significant difference between conditions in GAF for RFB ( p = 0.257) post-intervention (6 weeks). However, GAF scores at follow-up (6 months) were significantly higher in RFB yes than in RFB no ( p = 0.042).

Regarding the overall crude treatment effect of secondary measures (see Table 3 ), we observed significant differences between RFB conditions for HADS (B [SE]: −1.68 [0.67], p = 0.01 ∗ ), RAND-36 MCS (B [SE]: 1.63 [0.56], p = 0.004 ∗∗ ) and RAND-36 PCS (B [SE]: 1.41 [0.46], p = 0.003 ∗∗ ). No significant differences in GAF were observed for RFB. With respect to LH, overall treatment effect analyses did not yield significant differences for any of the secondary measures. The adjusted overall treatment effect analysis yielded similar findings, although the differences between RFB yes and RFB no were larger, and GAF results reached significance. Crude effect sizes for RFB were medium or above medium for RAND-36 MCS and RAND-36 PCS, and close to medium for HADS and GAF. Adjusted effect sizes for RFB were close to large for RAND-36 MCS and above medium for HADS, RAND-36 PCS, and GAF. Regarding the LH factor, crude and adjusted effect sizes were trivial (d ≤ 0.2) for all outcome measures except GAF, which yielded higher effect sizes (Crude d [95% CI] = 0.41 [−0.07−0.89], Adjusted d [95% CI] = 0.37 [−0.17−0.92]).

Sensitivity Analyses

Two sensitivity analyses were conducted. The first assumed no change in MADRS scores for missing observations, thus providing a conservative estimate for dropouts. Overall treatment effect for RFB was still significant in both crude ( p = 0.003 ∗∗ ) and adjusted analysis ( p = 0.002 ∗∗ ). Furthermore, a per-protocol analysis reclassified three clients from LH yes to LH no , as they did not engage in any form of listening homework. There were still no significant differences between the LH factor levels in any of the outcome measures. Reclassification of clients for the RFB factor was not needed, since they all followed protocol.

Adverse Events and Reasons for Drop-Out

Adverse events were rare, transient, and mostly unrelated to the trial interventions. Two participants (one IIMT + RFB, one IIMT + LH) experienced a worsening of problems (sleep problems) following a change in their medication. One (IIMT) had to stop therapy due to a pre-existing comorbid condition which necessitated surgery and subsequent recovery time. One (IIMT + LH) stopped therapy because a therapeutic alliance (agreement on goals and methods of therapy) could not be established. Finally, two participants (one IIMT, one IIMT + LH) stopped therapy due to scheduling issues.

In this study, we investigated whether a music therapy model called IIMT could be further enhanced by introducing additional components known to favour emotional processing and/or stress regulation (listening homework – LH, and resonance frequency breathing – RFB). In line with our previous RCT ( Erkkilä et al., 2011 ), we found that 12 bi-weekly sessions of music therapy were able to significantly improve MADRS scores in all four conditions. Furthermore, our results indicate that IIMT can indeed be further enhanced, at least with RFB. More specifically, the overall effect of treatment for RFB was statistically significant for all measures except GAF, with RFB clients consistently improving more than non-RFB clients (see Table 3 ). We also observed significant differences in all outcome measures—either post-intervention, at follow-up, or both—favouring clients allocated to RFB (see Table 2 ). In contrast, the LH factor did not yield significant differences in any of our analyses. However, for all outcome measures besides HADS, the observed changes did favour LH yes . In sum, these results strongly support the hypothesis of RFB as an enhancer of therapeutic outcome and speak for its inclusion in music therapy, and possibly in other forms of psychotherapy.

Interestingly, for RFB yes , the treatment effect at T2 was larger than at T1 for all outcome measures except HADS, and the mean improvement in RFB yes was monotonic (i.e., continued to increase between post-intervention and follow-up). Although we did not monitor whether clients kept using RFB on their own after the end of therapy, it is possible that an independent practice of RFB might have contributed to maintaining and reinforcing these positive outcomes.

In terms of clinical significance, the addition of RFB resulted in a near doubling of the MADRS post-intervention response rate, which went from 26% (RFB no ) to 51% (RFB yes ). To put these results into perspective, in our previous depression study (consisting of 20 bi-weekly sessions of music therapy without enhancers), the post-intervention response rate was 45% ( Erkkilä et al., 2011 ). It is not surprising that 12 sessions of music therapy without RFB would result in a lower response rate than 20 sessions. However, the truly interesting finding is that, in terms of response rate, 12 sessions of music therapy with RFB were equivalent to 20 sessions without enhancers. Although this is a post hoc comparison of two different trials, it suggests that integrating RBF into music therapy might allow similar results to be achieved with fewer sessions.

These results point to the existence of qualities specific to RFB and music therapy which, when combined, can create a synergy effect. In our experience ( Brabant and Erkkilä, 2018 ), clients who are starting their therapy sessions with RFB tend to have deeper and more productive sessions, which we attribute to RFB’s ability to rebalance the autonomic nervous system, reduce stress, and increase emotional resilience ( Goessl et al., 2017 ). As to improvisational music therapy, three of its unique characteristics are to offer a non-verbal way of expressing emotions, to provide an absorbing experience anchored in the present, and to allow the emergence of unconscious material ( MacDonald and Wilson, 2014 ). Thus, it stands to reason that combining the two methods would greatly facilitate the emergence of themes and emotions that usually remain unexpressed, while making it easier for the client to face these emotions and process them.

On a more general level, these findings highlight the benefits that can be derived from integrating RFB into an existing therapy method, instead of simply using it as an adjunct or complementary exercise, as is still largely the case when RFB or HRVB are being used. While searching the literature, we only found a few instances where such integration took place (e.g., Polak et al., 2015 ) or was being advocated (e.g., Gevirtz, 2020 ). Studies employing HRVB as a stand-alone intervention could serve as a baseline to determine the magnitude of possible synergy effects obtained in studies such as ours, by comparing effect sizes.

In contrast to RFB, our second added component (LH) did not yield any significant effect, in any of the analyses or comparisons that we performed. However, the changes observed at T1 and T2 were, nonetheless, always in favour of LH yes , except for HADS. In other words, the clients in the LH yes condition benefited more from therapy than the clients in the LH no condition. A more detailed analysis which is beyond the present paper will address the question whether listening duration correlated with clinical change. For such an analysis it will be important to separate extended, likely intentional listening from very short listening such as in searching for a piece.

Lastly, it should be noted that our results are in line with the existing evidence presented in the Introduction, regarding the positive effect of psychotherapy on comorbid anxiety ( Weitz et al., 2018 ) and QoL ( Kolovos et al., 2016 ). Interestingly, in this case, although the addition of RFB had a positive impact on both the physical and mental health component of QoL, the effect was more pronounced for physical health. We speculate that this was due to the nature of RFB and the regular practice thereof, which might have led to a sustained increase in autonomic flexibility and HRV, thus allowing clients to better regulate their stress levels in daily life and reduce unpleasant physical sensations.

Limitations

The main limitations of this trial include limited sample size and lack of a no-treatment or placebo control group. Although the sample was large enough to detect a significant effect of breathing added to IIMT, it was not large enough to exclude a clinically meaningful effect of listening homework. Further research with a larger sample would be required to confirm or disconfirm any effects of this component. The sample was also restricted to a single site, so that conclusions generalising to other settings or world regions cannot be drawn with confidence. Second, the study did not use a no-treatment or placebo control group. However, robust effects of IIMT compared to standard care were already demonstrated in the previous study on which the present study was built ( Erkkilä et al., 2011 ).

An issue surrounding LH is the absence of prior studies making use of this specific activity, which might have led to an incorrect estimation of the expected effect size. Although the use of homework has a long history in CBT, the kind of task given in CBT is arguably not directly comparable to what was required from the clients in the present trial. Thus, it is possible that our sample size was too small to detect a significant effect for the LH factor.

Another issue with LH might have been its possible inadequacy for the client population under investigation. Indeed, in contrast to RFB, LH was unsupervised, meaning that clients were free to perform the task or not, which led to lower task adherence compared to RFB. This raises the question of whether clients presenting with symptoms of depression should be given voluntary and unsupervised tasks in between therapy sessions, since depression typically includes a lack of initiative.

Future studies would benefit from having a larger sample size for studying LH, and being multi-centre. Furthermore, the results presented here are purely outcome-oriented, meaning it is not possible at this point to explain the results by establishing a relationship between what happened during therapy and the observed affective or behavioural changes.

Lastly, one question that remains unanswered is the extent to which the enhancement effect achieved with RFB in music therapy could be generalised to the larger field of psychotherapy. Based on our results, we presume that other forms of therapy would similarly benefit from the inclusion of RFB, especially if their approach and principles are similar to the ones used in music therapy (e.g., being emotion-focused, experiential, and integrative). Should this be the case, it would open the door to shorter and more cost-effective forms of therapy.

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 authors assert that all procedures contributing to this work comply with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975, as revised in 2008. All procedures involving human subjects/patients were approved by the Ethical board of Central Finland health care district, 07/09/2017, ref.: 17 U/2017. Written informed consent was obtained from every participant.

Author Contributions

JE did the project leadership, contribution to the study design, development and implementation of the clinical music therapy model, writing parts of abstract, introduction, methods and discussion, and finalizing the manuscript. OB did the contribution to the study design, development and implementation of the RFB component, and writing parts of the methods and discussion sections. MH did the development and implementation of the LH component, statistical analysis, and writing parts of the methods, results, and discussion sections. AM did the statistical analysis, writing parts of the methods and results sections. EA-R developed and implemented the clinical music therapy model, wrote parts of the intervention, and commented the manuscript. NS did the development of LH component and implementation of the RFB component, helping to revise the methods and discussion section. SS did the contribution to the study design, helping to draft the results section and revise the manuscript. CG did the contribution to the study design, randomisation procedure, supervision of statistical analyses and revision of the manuscript text. All authors contributed to the article and approved the submitted version.

This work was supported by funding from the Academy of Finland (project numbers 298678, 314651, and 316912).

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.

Acknowledgments

The study team acknowledges the support from the Academy of Finland and University of Jyväskylä. The authors would like to thank Inga Pöntiö for the psychiatric assessments, Markku Pöyhönen for providing support in administrative, practical, and logistical matters, Mikko Leimu for setting up the music recording platform, Jos Twisk for statistical advice and Monika Geretsegger for her support with the study.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyg.2021.613821/full#supplementary-material

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Keywords : depression, anxiety, music therapy, randomised controlled trial, resonance frequency breathing, homework

Citation: Erkkilä J, Brabant O, Hartmann M, Mavrolampados A, Ala-Ruona E, Snape N, Saarikallio S and Gold C (2021) Music Therapy for Depression Enhanced With Listening Homework and Slow Paced Breathing: A Randomised Controlled Trial. Front. Psychol. 12:613821. doi: 10.3389/fpsyg.2021.613821

Received: 03 October 2020; Accepted: 22 January 2021; Published: 16 February 2021.

Reviewed by:

Copyright © 2021 Erkkilä, Brabant, Hartmann, Mavrolampados, Ala-Ruona, Snape, Saarikallio and Gold. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Christian Gold, [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.

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Music therapy for depression

Why is this review important?

Depression is a common problem that causes changes in mood and loss of interest and pleasure. Music therapy, an intervention that involves regular meetings with a qualified music therapist, may help in improving mood through emotional expression. This review might add new information about effects of music therapy in depressed individuals.

Who will be interested in this review?

Our review will be of interest for the following people: people with depression and their families, friends, and carers; general practitioners, psychiatrists, psychologists, and other professionals working in mental health; music therapists working in mental health; and mental health policy makers.

What questions does this review aim to answer?

1. Is music therapy more effective than treatment as usual alone or psychological therapy?

2. Is any form of music therapy better than another form of music therapy?

Which studies were included in the review?

We included nine studies with a total of 421 people of any age group (from adolescents to older people). Studies compared effects of music therapy versus treatment as usual, and versus psychological therapy. Additionally, we examined the differences between two different forms of music therapy: active (where people sing or play music) and receptive (where people listen to music).

What does evidence from the review tell us?

We found that music therapy plus treatment as usual is more effective than treatment as usual alone. Music therapy seems to reduce depressive symptoms and anxiety and helps to improve functioning (e.g. maintaining involvement in job, activities, and relationships). We are not sure whether music therapy is better than psychological therapy. We do not know whether one form of music therapy is better than another. The small numbers of identified studies and participants make it hard to be confident about these comparisons.

What should happen next?

Music therapy for depression is likely to be effective for people in decreasing symptoms of depression and anxiety. Music therapy also helps people to function in their everyday life. However, our findings are not complete and need to be clarified through additional research. Future trials should study depression in children and adolescents, and future trial reports should thoroughly describe music therapy interventions, other interventions, and the person who delivers these interventions.

Findings of the present meta-analysis indicate that music therapy provides short-term beneficial effects for people with depression. Music therapy added to treatment as usual (TAU) seems to improve depressive symptoms compared with TAU alone. Additionally, music therapy plus TAU is not associated with more or fewer adverse events than TAU alone. Music therapy also shows efficacy in decreasing anxiety levels and improving functioning of depressed individuals.

Future trials based on adequate design and larger samples of children and adolescents are needed to consolidate our findings. Researchers should consider investigating mechanisms of music therapy for depression. It is important to clearly describe music therapy, TAU, the comparator condition, and the profession of the person who delivers the intervention, for reproducibility and comparison purposes.

Depression is a highly prevalent mood disorder that is characterised by persistent low mood, diminished interest, and loss of pleasure. Music therapy may be helpful in modulating moods and emotions. An update of the 2008 Cochrane review was needed to improve knowledge on effects of music therapy for depression.

1. To assess effects of music therapy for depression in people of any age compared with treatment as usual (TAU) and psychological, pharmacological, and/or other therapies.

2. To compare effects of different forms of music therapy for people of any age with a diagnosis of depression.

We searched the following databases: the Cochrane Common Mental Disorders Controlled Trials Register (CCMD-CTR; from inception to 6 May 2016); the Cochrane Central Register of Controlled Trials (CENTRAL; to 17 June 2016); Thomson Reuters/Web of Science (to 21 June 2016); Ebsco/PsycInfo, the Cumulative Index to Nursing and Allied Health Literature (CINAHL), Embase, and PubMed (to 5 July 2016); the World Health Organization International Clinical Trials Registry Platform (WHO ICTRP), ClinicalTrials.gov, the National Guideline Clearing House, and OpenGrey (to 6 September 2016); and the Digital Access to Research Theses (DART)-Europe E-theses Portal, Open Access Theses and Dissertations, and ProQuest Dissertations and Theses Database (to 7 September 2016). We checked reference lists of retrieved articles and relevant systematic reviews and contacted trialists and subject experts for additional information when needed. We updated this search in August 2017 and placed potentially relevant studies in the "Awaiting classification" section; we will incorporate these into the next version of this review as appropriate.

All randomised controlled trials (RCTs) and controlled clinical trials (CCTs) comparing music therapy versus treatment as usual (TAU), psychological therapies, pharmacological therapies, other therapies, or different forms of music therapy for reducing depression.

Two review authors independently selected studies, assessed risk of bias, and extracted data from all included studies. We calculated standardised mean difference (SMD) for continuous data and odds ratio (OR) for dichotomous data with 95% confidence intervals (CIs). We assessed heterogeneity using the I 2 statistic.

We included in this review nine studies involving a total of 421 participants, 411 of whom were included in the meta-analysis examining short-term effects of music therapy for depression. Concerning primary outcomes, we found moderate-quality evidence of large effects favouring music therapy and TAU over TAU alone for both clinician-rated depressive symptoms (SMD -0.98, 95% CI -1.69 to -0.27, 3 RCTs, 1 CCT, n = 219) and patient-reported depressive symptoms (SMD -0.85, 95% CI -1.37 to -0.34, 3 RCTs, 1 CCT, n = 142). Music therapy was not associated with more or fewer adverse events than TAU. Regarding secondary outcomes, music therapy plus TAU was superior to TAU alone for anxiety and functioning. Music therapy and TAU was not more effective than TAU alone for improved quality of life (SMD 0.32, 95% CI -0.17 to 0.80, P = 0.20, n = 67, low-quality evidence). We found no significant discrepancies in the numbers of participants who left the study early (OR 0.49, 95% CI 0.14 to 1.70, P = 0.26, 5 RCTs, 1 CCT, n = 293, moderate-quality evidence). Findings of the present meta-analysis indicate that music therapy added to TAU provides short-term beneficial effects for people with depression if compared to TAU alone. Additionally, we are uncertain about the effects of music therapy versus psychological therapies on clinician-rated depression (SMD -0.78, 95% CI -2.36 to 0.81, 1 RCT, n = 11, very low-quality evidence), patient-reported depressive symptoms (SMD -1.28, 95% CI -3.75 to 1.02, 4 RCTs, n = 131, low-quality evidence), quality of life (SMD -1.31, 95% CI - 0.36 to 2.99, 1 RCT, n = 11, very low-quality evidence), and leaving the study early (OR 0.17, 95% CI 0.02 to 1.49, 4 RCTs, n = 157, moderate-quality evidence). We found no eligible evidence addressing adverse events, functioning, and anxiety. We do not know whether one form of music therapy is better than another for clinician-rated depressive symptoms (SMD -0.52, 95% CI -1.87 to 0.83, 1 RCT, n = 9, very low-quality evidence), patient-reported depressive symptoms (SMD -0.01, 95% CI -1.33 to 1.30, 1 RCT, n = 9, very low-quality evidence), quality of life (SMD -0.24, 95% CI -1.57 to 1.08, 1 RCT, n = 9, very low-quality evidence), or leaving the study early (OR 0.27, 95% CI 0.01 to 8.46, 1 RCT, n = 10). We found no eligible evidence addressing adverse events, functioning, or anxiety.

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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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The cochrane review, conclusions, data availability, acknowledgement, declaration of interest, music therapy for depression: is it music to our ears.

Published online by Cambridge University Press:  22 February 2024

Music therapy has been a recognised form of therapy for mental illness for many years. This commentary on a Cochrane Review on music therapy for depression sheds light on the evidence. It aims to give further clinical context to the findings, to help guide practice and examine music therapy as an evidence-based practice. The review compares music therapy plus ‘treatment as usual’ (TAU) with TAU alone, music therapy with psychological therapy, and ‘active’ with ‘receptive’ music therapy (the two main types of music therapy). The review points to music therapy being beneficial for people with depression when combined with TAU (versus TAU alone) in the short term, as well as improving anxiety and functioning. We need more evidence looking at longer-term outcomes, comparing music therapy with psychological therapies and comparing different forms of music therapy.

Depression is a common illness that can severely affect people's quality of life (Malhi Reference Malhi and Mann 2018 ) and it is expected to be the leading cause of burden of disease worldwide by 2030 (World Health Organization 2011 ). Despite various treatments, people can experience recurrent and difficult-to-treat episodes (Malhi Reference Malhi and Mann 2018 ). Music therapy is one form of therapy used in depression and other mental and cognitive disorders. Previous reviews support its use, including in Parkinson's disease (Machado Sotomayor Reference Machado Sotomayor, Arufe-Giráldez and Ruíz-Rico 2021 ), dementia (Gómez-Romero Reference Gómez-Romero, Jiménez-Palomares and Rodríguez-Mansilla 2017 ), anxiety (Lu Reference Lu, Jia and Liang 2021 ), schizophrenia, post-traumatic stress disorder, post-natal depression and social anxiety (Witusik 2019 ). Music therapy is not simply asking patients to listen to music (coined ‘music medicine’), but rather involves the cultivation of a therapeutic relationship between participant and therapist (Dileo Reference Dileo 2006 ), as in other forms of psychological therapy. This is done using components of music (e.g. melody) with the goal of promoting physical, psychological and social well-being (Dileo Reference Dileo 2006 ).

This commentary looks at the Cochrane Review (Aalbers Reference Aalbers, Fusar-Poli and Freeman 2017 ) in this issue's Cochrane Corner, which synthesised evidence on music therapy for depression.

The review cited the World Federation of Music Therapy's definition of the intervention ( Box 1 ) and split the four types into two groups: receptive (listening to music) and active forms (‘making’ music). The review's inclusion criteria identified ‘well-defined’ music therapy using criteria that appeared to follow from the definition ( Box 1 ) and background literature, but they were not explicit in their formulation.

BOX 1 What is music therapy?

The World Federation of Music Therapy defines music therapy as ‘the professional use of music and its elements as an intervention in medical, educational, and everyday environments with individuals, groups, families, or communities who seek to optimize their quality of life and improve their physical, social, communicative, emotional, intellectual, and spiritual health and wellbeing’ (World Federation of Music Therapy Reference Witusik and Pietras 2023 ).

There are four main types of music therapy/method (Bruscia Reference Bruscia 2013 ):

• receptive (listening)

• composition

• improvisation

• re-creative (or performance).

The Cochrane Review outlined the following criteria to identify ‘well-defined’ music therapy (Aalbers Reference Aalbers, Fusar-Poli and Freeman 2017 ). Music therapy had to:

• include sessions with a structured therapeutic framework

• involve a musical interaction between therapist and participant or between therapist and a group of participants

• aim to improve health

• have a main therapeutic change agent that could be described as music, the relationship or reflections induced by the music.

The evidence base for music therapy has only more recently been systematically examined. This may be due to the difficulty in defining music therapy because of its heterogeneity, as it is shaped by its cultural context (Bruscia Reference Bruscia 2013 ) and has been developed from different therapies (e.g. behavioural and psychoanalytical models) (Scovel Reference Scovel and Gardstrom 2012 ). Previous reviews (Van Assche Reference Van Assche, De Backer and Vermote 2015 ; Zhao Reference Zhao, Bai and Bo 2016 ) indicate its potential to improve depression. However, of the two earlier reviews, one did not perform a meta-analysis (Van Assche Reference Van Assche, De Backer and Vermote 2015 ) and the other focused on older adults (Zhao Reference Zhao, Bai and Bo 2016 ). Aalbers et al's ( Reference Aalbers, Fusar-Poli and Freeman 2017 ) Cochrane Review aimed to include unexamined newer trials and perform a meta-analysis on populations of all ages, comparing:

(a) music therapy plus ‘treatment as usual’ (TAU) versus TAU

(b) music therapy versus psychological therapy

(c) active versus receptive music therapy.

The review examined randomised controlled trials (RCTs) or clinical controlled trials (CCTs) ( Box 2 ) and used standardised mean differences (s.m.d.) for continuous outcomes measured on different scales ( Box 3 ) with their confidence intervals (95% CI) ( Box 4 ).

BOX 2 What are clinical controlled trials?

A clinical controlled trial (CCT) is a controlled trial (i.e. a trial with a control arm) in which randomisation to the arms of the trial is not made clear or where there is quasi-randomisation (not pure randomisation), for example where the assignment method is alternation or date of birth.

BOX 3 Standardised mean difference

The standardised mean difference (s.m.d.) is:

• a summary statistic (it summarises a set of observations) in meta-analysis

• used when studies assess the same outcome but measure it in different ways (e.g. depression by different tools/scales)

• used because you need to convert results of different studies with different scales to the same scale before they can be combined.

It is calculated as the intervention effect (difference in mean outcome between groups) in the study, divided by the between-participant variability in the study (i.e. the standard deviation of outcome among participants):

BOX 4 95% confidence intervals: what do they tell us?

It is important to have not only measures of an effect, translated into effect sizes (e.g. s.m.d.), but also measures of how precise the estimate of that effect is (this is the 95% confidence interval). In other words, how sure can you be that the true effect of music therapy is around the estimated effect given in the Cochrane Review? The 95% confidence interval gives you this information, giving you a range of values in which you can expect the true effect to lie 95% of the time.

The review found that music therapy plus TAU appears to be more effective than TAU alone in reducing depressive symptoms and comorbid anxiety and improving functioning in the short term (up to 3 months). More studies are needed examining longer-term outcomes. It was not clear whether music therapy was better than psychological therapy or whether one form of music therapy was better than another.

The review outlined two objectives: (a) to assess the effects of music therapy for depression in people of any age compared with TAU and psychological, pharmacological and/or other therapies; and (b) to compare the effects of different forms of music therapy for depression in people of any age.

The review outlined five comparisons (music therapy versus TAU; music therapy plus TAU versus TAU alone; music therapy versus psychological therapies; music therapy versus pharmacological therapies; and one form of music therapy versus another form), which slightly differed from the two objectives in, for example, including a music therapy plus TAU versus TAU comparison. The review made three comparisons: comparison 1, music therapy plus TAU versus TAU alone; comparison 2, music therapy versus psychological therapy; comparison 3, active versus receptive music therapy (no studies compared music therapy versus TAU or music therapy versus pharmacological therapies). With no comparisons outlined in a protocol, this could be a source of reporting bias, whereby what is reported is decided on post hoc (after the analysis).

The review authors clearly outlined the population, intervention (music therapy), primary outcomes (depressive symptoms and adverse events) and secondary outcomes (functioning, quality of life (QoL), leaving the study early, anxiety, self-esteem, cost/cost-effectiveness and satisfaction with treatment).

The review limited the studies to RCTs and CCTs, but did not give a rationale. This limiting is appropriate, however, because the review examined one intervention and there were a limited number of RCTs.

The review authors conducted a thorough search of the literature, searching the Cochrane Common Mental Disorders Group's specialised register of RCTs for mental disorders, in addition to databases, clinical trial registers, dissertations/theses, grey literature and references, and contacting trial authors and subject experts. The review was published in November 2017, and searches were conducted between May and September 2016. Confusingly, they ran a pre-publication update search in August 2017, identifying three more studies, but did not include these in their analysis and did not provide a rationale for this – a clear source of potential bias. They also did not include two studies identified from their initial search, owing to insufficient information on study design, intervention and analysis, again a potential source of bias.

They assessed risk of bias in included studies using the Cochrane Handbook for Systematic Reviews of Interventions criteria (Higgins Reference Higgins and Green 2015 ) and assessed the overall quality of evidence using the GRADE approach (Schünemann Reference Schünemann, Broz˙ek and Guyatt 2013 : Chapter 5.2).

They performed a meta-analysis using a random-effects model, which is appropriate owing to the expected heterogeneity in music therapy. They planned to explore heterogeneity by examining subgroups (subgroup analysis) ( Box 5 ) according to participant characteristics, duration of therapy, modality of therapy and type of therapy. They also planned to undertake a sensitivity analysis ( Box 5 ), repeating the analysis after excluding studies at high risk of bias.

BOX 5 Subgroup versus sensitivity analysis

Nine studies were included, with a total of 421 participants, 411 of whom were included in the meta-analysis. They did not explain this discrepancy (a potential source of bias).

Comparison 1: music therapy plus TAU versus TAU alone

Four studies examined clinician-rated short-term depressive symptoms (up to 3 months) and found significant symptom reduction in the music therapy plus TAU group (s.m.d. = −0.98; 95% CI −1.69 to −0.27; high heterogeneity I 2  = 83%; moderate-quality evidence). One study examined clinician-rated depressive symptoms in the medium term (up to 6 months) and found no significant difference. Four studies looked at patient-reported short-term depressive symptoms and found a significant reduction in the music therapy plus TAU group (s.m.d. = −0.85, 95% CI −1.37 to −0.34,; moderate heterogeneity I 2  = 49%; moderate-quality evidence). A significant reduction in anxiety and improvement in functioning was found in the short term (music therapy plus TAU group), but not in the long term. One RCT looked at adverse events and found no significant difference. No significant difference was found for self-esteem (one study), QoL (one study) or leaving the study early. No studies looked at cost/cost-effectiveness or satisfaction with treatment.

Comparison 2: music therapy versus psychological therapy

One RCT looked at clinician-rated and four RCTs at patient-reported depressive symptoms and found no significant differences in the short/medium term. No significant differences were found in QoL (one study) or in leaving the study early. There were no data on adverse events, self-esteem, functioning, anxiety, cost-effectiveness or satisfaction with treatment.

Comparison 3: active versus receptive music therapy

One RCT looked at clinician-rated and patient-reported depressive symptoms and found no significant difference in the short/medium term. There was no difference found in QoL or leaving the study early. There were no data on adverse events, functioning, anxiety, self-esteem, cost/cost-effectiveness or satisfaction with treatment.

Duration of outcome evaluation

Significant findings in shorter-term outcomes but not medium-term outcomes may be a reflection of the paucity of studies examining medium-term outcomes rather than a true pattern of effect. Only one RCT compared music therapy plus TAU versus TAU alone in the medium term, finding a non-significant result favouring music therapy plus TAU. Similarly, only one study looked at medium-term outcomes comparing music therapy versus psychological therapy and active versus receptive music therapy.

Subgroup analyses

The review authors did not examine heterogeneity based on predefined subgroups. They examined duration of treatment but not as planned, examining months of music therapy (short: <3 months; medium: 3–6 months; and long term: >6 months). Researchers cannot always follow planned review protocols (e.g. owing to the limited number of studies found), but they should have made this explicit.

Sensitivity analyses

The sensitivity analyses, in which studies with high risk of bias were removed, did not change the results.

Clinical significance of the findings

The review used guidelines for behavioural science interventions to translate findings into meaningful outcomes, where effects sizes (s.m.d.) up to 0.2 are considered ‘small’, those around 0.5 are ‘medium’ and those at 0.8 and above are ‘large’ (Cohen Reference Cohen 1988 ). An effect size is meaningful if it translates to a clinically meaningful difference (Ranganathan Reference Ranganathan, Pramesh and Buyse 2015 ), for example in the review an s.m.d. of −0.98 was a ‘large’ effect size, translating to a difference of 9.8 points on the Hamilton Rating Scale for Depression – something clinicians may better relate to. The review authors felt this was a clinically significant difference; however, clinical significance is open to interpretation, as it requires the consideration of clinically important factors such as cost-effectiveness and treatment acceptability (Ranganathan Reference Ranganathan, Pramesh and Buyse 2015 ).

The review concluded that music therapy plus TAU may be more effective than TAU alone in reducing depressive symptoms and anxiety and improving functioning, as evidenced by large effect sizes (translating to clinically meaningful differences) and moderate-quality evidence; however, there needs to be more evidence examining longer-term outcomes, adverse effects and patient-driven outcomes (e.g. functioning). We need to consider sources of heterogeneity and how this affects our interpretation. Depression was a comorbid diagnosis alongside substance misuse or anxiety in some studies and one study included some participants with a history of bipolar disorder. This heterogeneity makes the results translatable to more patients, although simultaneously less certain in the treatment effect specific to a particular patient ( Box 6 ).

BOX 6 Generalisability versus specificity

There is a tension between the generalisability and specificity (and thus precision) of your results. On the one side, having a study with a heterogeneous population (people with very different characteristics) will make the results more generalisable (more applicable to a larger number of patients); however, it may muddy the waters in understanding the effect for a specific patient with specific characteristics. For example, music therapy may not work so well for people with depression and comorbid substance misuse. Examining diverse populations together does not allow you to identify potential differences in how well music therapy works for different patient groups. This makes you less certain about the precision of the estimate of the effect (how close the result is to the true value) for a specific patient.

Since this Cochrane Review, another review and meta-analysis of 55 RCTs (Tang Reference Tang, Huang and Zhou 2020 ) has been published examining music-based interventions (music therapy and ‘music medicine’) in depression. In keeping with the Cochrane Review, it found that music therapy significantly reduced depressive symptoms (s.m.d. = −0.66; 95% CI −0.86 to −0.46; P  < 0.001), but this effect did not last in the long term. The authors argued that this may be due to the limited number of studies examining longer-term outcomes. Another review found that music therapy significantly improved depression in people with dementia, but this did not last after the intervention ended (Li Reference Li, Wang and Lu 2019 ). Music therapy appeared beneficial in a pilot study of adolescents with depression, but again this did not last after the intervention ended (Geipel Reference Geipel, Koenig and Hillecke 2022 ). Music therapy requires a trained music therapist. Even if funds and evidence were available to support music therapy in the long term, having the trained personnel may limit its implementation. Currently, it is challenging to argue for its implementation from a policy and practice perspective, without more evidence on longer-term outcomes and cost.

This Cochrane Review did well to attempt to synthesise the evidence on something that is challenging to define and heterogeneous (music therapy), but it would benefit from including the updated studies identified – its key limitation. Nevertheless, the review is important, owing to the need to bring evidence-based medicine to music therapy, a therapy that has been used for years to treat a variety of psychiatric conditions.

Data availability is not applicable to this article as no new data were created or analysed in this study.

I would like to thank Dr Riccardo De Giorgi for providing feedback on this manuscript.

Dr Tessa Lomax has been funded to undertake this research by the National Institute for Health and Care Research (NIHR); NIHR Award number: ACF-2021-13-010.

Commentary on… Music therapy for depression (Cochrane Corner). See this issue.

No CrossRef data available.

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• Volume 30, Issue 2
• Tessa Lomax (a1)
• DOI: https://doi.org/10.1192/bja.2023.67

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The Transformative Power of Music in Mental Well-Being

• August 01, 2023
• Healthy living for mental well-being, Patients and Families, Treatment

Music has always held a special place in our lives, forming an integral part of human culture for centuries. Whether we passively listen to our favorite songs or actively engage in music-making by singing or playing instruments, music can have a profound influence on our socio-emotional development and overall well-being.

Recent research suggests that music engagement not only shapes our personal and cultural identities but also plays a role in mood regulation. 1 A 2022 review and meta-analysis of music therapy found an overall beneficial effect on stress-related outcomes. Moreover, music can be used to help in addressing serious mental health and substance use disorders. 2 In addition to its healing potential, music can magnify the message of diversity and inclusion by introducing people to new cultures and amplifying the voice of marginalized communities, thereby enhancing our understanding and appreciation for diverse communities.

Healing Trauma and Building Resilience

Many historically excluded groups, such as racial/ethnic and sexual minorities and people with disabilities, face systemic injustices and traumatic experiences that can deeply impact their mental health. Research supports the idea that discrimination, a type of trauma, increases risk for mental health issues such as anxiety and depression. 3

Music therapy has shown promise in providing a safe and supportive environment for healing trauma and building resilience while decreasing anxiety levels and improving the functioning of depressed individuals. 4 Music therapy is an evidence-based therapeutic intervention using music to accomplish health and education goals, such as improving mental wellness, reducing stress and alleviating pain. Music therapy is offered in settings such as schools and hospitals. 1 Research supports that engaging in music-making activities, such as drumming circles, songwriting, or group singing, can facilitate emotional release, promote self-reflection, and create a sense of community. 5

Empowerment, Advocacy and Social Change

Music has a rich history of being used as a tool for social advocacy and change. Artists from marginalized communities often use music to shed light on social issues (.pdf) , challenge injustices, and inspire collective action. By addressing topics such as racial inequality, gender discrimination, and LGBTQ+ rights, music becomes a powerful medium for advocating for social justice and promoting inclusivity. Through music, individuals can express their unique experiences, struggles, and triumphs, forging connections with others who share similar backgrounds. Research has shown that exposure to diverse musical genres and artists can broaden perspectives, challenge stereotypes, and foster empathy among listeners especially when dancing together. 7

Genres such as hip-hop, reggae, jazz, blues, rhythm & blues and folk have historically served as platforms for marginalized voices, enabling them to reclaim their narratives and challenge societal norms. The impact of socially conscious music has been observed in movements such as civil rights, feminism, and LGBTQ+ rights, where songs have played a pivotal role in mobilizing communities and effecting change. Music artists who engage in activism can reach new supporters and help their fans feel more connected to issues and motivated to participate. 6

Fostering Social Connection and Support

Music can also serve as a catalyst for social connection and support, breaking down barriers and bridging divides. Emerging evidence indicates that music has the potential to enhance prosocial behavior, promote social connectedness, and develop emotional competence. 2 Communities can leverage music’s innate ability to connect people and foster a sense of belonging through music programs, choirs, and music education initiatives. These activities can create inclusive spaces where people from diverse backgrounds can come together, collaborate, and build relationships based on shared musical interests. These experiences promote social cohesion, combat loneliness, and provide a support network that can positively impact overall well-being.

Musicians and Normalizing Mental Health

Considering the healing effects of music, it may seem paradoxical that musicians may be at a higher risk of mental health disorders. 8 A recent survey of 1,500 independent musicians found that 73% have symptoms of mental illness. This could be due in part to the physical and psychological challenges of the profession. Researchers at the Max Planck Institute for Empirical Aesthetics in Germany found that musically active people have, on average, a higher genetic risk for depression and bipolar disorder.

Commendably, many artists such as Adele, Alanis Morrisette, Ariana Grande, Billie Eilish, Kendrick Lamar, Kid Cudi and Demi Lovato have spoken out about their mental health battles, from postpartum depression to suicidal ideation. Having high-profile artists and celebrities share their lived experiences has opened the conversation about the importance of mental wellness. This can help battle the stigma associated with seeking treatment and support.

Dr. Regina James (APA’s Chief of the Division of Diversity and Health Equity and Deputy Medical Director) notes “Share your story…share your song and let's help each other normalize the conversation around mental wellness through the influence of music. My go-to artist for relaxation is jazz saxophonist, “Grover Washington Jr” …what’s yours?” Submit to [email protected] to get featured!

More on Music Therapy

• Music Therapy Fact Sheets from the American Music Therapy Association
• Music Therapy Resources for Parents and Caregivers from Music Therapy Works

By Fátima Reynolds DJ and Music Producer Senior Program Manager, Division of Diversity and Health Equity American Psychiatric Association

• Gustavson, D.E., 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
• Golden, T. L., et al. (2021). The use of music in the treatment and management of serious mental illness: A global scoping review of the literature. Frontiers in Psychology, 12. https://doi.org/10.3389/fpsyg.2021.649840
• Schouler-Ocak, M., et al. (2021). Racism and mental health and the role of Mental Health Professionals. European Psychiatry, 64(1). https://doi.org/10.1192/j.eurpsy.2021.2216
•  Aalbers, S., et al. (2017). Music therapy for Depression. Cochrane Database of Systematic Reviews, 2017(11). https://doi.org/10.1002/14651858.cd004517.pub3
• Dingle, G. A., et al. (2021). How do music activities affect health and well-being? A scoping review of studies examining Psychosocial Mechanisms. Frontiers in Psychology, 12. https://doi.org/10.3389/fpsyg.2021.713818
• Americans for the Arts. (n.d.). A Working Guide to the Landscape of Arts for Change. Animating Democracy. http://animatingdemocracy.org/sites/default/files/Potts%20Trend%20Paper.pdf
• Stupacher, J., Mikkelsen, J., Vuust, P. (2021). Higher empathy is associated with stronger social bonding when moving together with music. Psychology of Music, 50(5), 1511–1526. https://doi.org/10.1177/03057356211050681
• Wesseldijk, L.W., Ullén, F. & Mosing, M.A. The effects of playing music on mental health outcomes. Sci Rep 9, 12606 (2019). https://doi.org/10.1038/s41598-019-49099-9

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Individual music therapy for depression: randomised controlled trial

Affiliation.

• 1 GAMUT, Uni Health, Lars Hilles gt. 3, 5015 Bergen, Norway.
• PMID: 21474494
• DOI: 10.1192/bjp.bp.110.085431

Background: Music therapy has previously been found to be effective in the treatment of depression but the studies have been methodologically insufficient and lacking in clarity about the clinical model employed. Aims To determine the efficacy of music therapy added to standard care compared with standard care only in the treatment of depression among working-age people.

Method: Participants (n = 79) with an ICD-10 diagnosis of depression were randomised to receive individual music therapy plus standard care (20 bi-weekly sessions) or standard care only, and followed up at baseline, at 3 months (after intervention) and at 6 months. Clinical measures included depression, anxiety, general functioning, quality of life and alexithymia.

Trial registration: ISRCTN84185937.

Results: Participants receiving music therapy plus standard care showed greater improvement than those receiving standard care only in depression symptoms (mean difference 4.65, 95% CI 0.59 to 8.70), anxiety symptoms (1.82, 95% CI 0.09 to 3.55) and general functioning (-4.58, 95% CI -8.93 to -0.24) at 3-month follow-up. The response rate was significantly higher for the music therapy plus standard care group than for the standard care only group (odds ratio 2.96, 95% CI 1.01 to 9.02).

Conclusions: Individual music therapy combined with standard care is effective for depression among working-age people with depression. The results of this study along with the previous research indicate that music therapy with its specific qualities is a valuable enhancement to established treatment practices.

PubMed Disclaimer

• Music therapy for depression: it seems to work, but how? Maratos A, Crawford MJ, Procter S. Maratos A, et al. Br J Psychiatry. 2011 Aug;199(2):92-3. doi: 10.1192/bjp.bp.110.087494. Br J Psychiatry. 2011. PMID: 21804144
• Refurbishing the masked RCT design for psychological interventions. Sen D, Biswas PS, Sinha VK. Sen D, et al. Br J Psychiatry. 2011 Dec;199(6):514-5; author reply 515-6. doi: 10.1192/bjp.199.6.514b. Br J Psychiatry. 2011. PMID: 22130753 No abstract available.

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• v.5(1); 2015 Mar 22

Effects of music and music therapy on mood in neurological patients

Correspondence to: Alfredo Raglio, MT, PhD, Department of Public Health, Experimental and Forensic Medicine, University of Pavia, Via S. Boezio 24, 27100 Pavia, Italy. [email protected]

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Mood disorder and depressive syndromes represent a common comorbid condition in neurological disorders with a prevalence rate that ranges between 20% and 50% of patients with stroke, epilepsy, multiple sclerosis, and Parkinson’s disease. Notwithstanding, these conditions are often under-diagnosed and under-treated in the clinical practice and negatively affect the functional recovery, the adherence to treatment, the quality of life, and even the mortality risk. In addition, a bidirectional association between depression and neurological disorders may be possible being that depressive syndromes may be considered as a risk factor for certain neurological diseases. Despite the large amount of evidence regarding the effects of music therapy (MT) and other musical interventions on different aspects of neurological disorders, no updated article reviewing outcomes such as mood, emotions, depression, activity of daily living and so on is actually available; for this reason, little is known about the effectiveness of music and MT on these important outcomes in neurological patients. The aim of this article is to provide a narrative review of the current literature on musical interventions and their effects on mood and depression in patients with neurological disorders. Searching on PubMed and PsycInfo databases, 25 studies corresponding to the inclusion criteria have been selected; 11 of them assess the effects of music or MT in Dementia, 9 explore the efficacy on patients with Stroke, and 5 regard other neurological diseases like Multiple Sclerosis, Amyotrophic Lateral Sclerosis/motor neuron disease, Chronic quadriplegia, Parkinson’s Disease, and Acquired Brain dysfunctions. Selected studies are based on relational and rehabilitative music therapy approaches or concern music listening interventions. Most of the studies support the efficacy of MT and other musical interventions on mood, depressive syndromes, and quality of life on neurological patients.

Core tip: We conducted a search on PubMed and PsychInfo databases identifying 25 Randomized Controlled Trials or Clinical Controlled Trials regarding the effects of Music Therapy and other musical interventions on mood disorders in neurological patients. Although the Jadad score evaluation revealed a generally poor methodological quality of the research protocols, we found that almost all studies supported the effectiveness of musical interventions in improving mood, depression, quality of life, functional recovery, and neuromotor performances. Therefore Music Therapy and other musical approaches seem to be effective, inexpensive and non-invasive, being that no adverse side-effects were observed.

INTRODUCTION

Neurology and psychiatry.

Neurological diseases are often associated with several behavioral and psychological symptoms that are usually overlooked by neurologists because require diagnostic methods that differ from those used for classical somatic symptoms and are more suitable to the field of psychiatry. On the other hand, psychiatrists do not seem to give an adequate attention to these symptoms considering them as a consequence of a cerebral damage and more pertinent to neurologists. This clinical attitude is historically based on the obsolete and reductive distinction between “organic” and “functional” behavioral disorders introduced by the phrenologist George Combe in 19 th century. According to Combe, cerebral diseases were respectively classified depending on the presence or the absence of cerebral lesions and from that time on this terminology has been used to indicate that some behavioral disorders are linked to a neurological damage while others are not. However, the reductionism of Combe’s distinction clearly emerges from the clinical observation given that a wide range of nervous system’s illnesses with different etiology shows both neurological and psychiatric symptoms. Emotional and behavioral disturbances with a polymorphic symptomatology are often connected to neurological disorders such as Multiple Sclerosis (MS)[ 1 - 3 ], Parkinson’s Disease (PD)[ 4 ], stroke[ 5 ], dementia[ 6 ], traumatic brain injury[ 7 ], epilepsy[ 8 , 9 ], Amyotrophic Lateral Sclerosis (ALS) and others Motor Neuron Diseases (MND)[ 10 , 11 ], pain syndromes (like headaches) and can be observed even with or without “organic” neurological diseases, thus miming in some cases an idiopathic psychiatric disorder.

Most common psychiatric disorders in neurology are depression, anxiety, maniacal states, and thought and perception disorders. Other psychiatric syndromes that can be seen in persons with neurological disorders are alexithymia, worry, and locus of control[ 12 ]. For example, mood disorders are often associated with acute or chronic cerebrovascular pathologies where the most common complications is certainly depression, usually defined post-stroke depression (PSD). The frequency of this syndrome is variable accordingly to different studies with a mean of 40% of the cases[ 13 ] and data obtained by numerous studies seem to indicate the presence of multiple etiological factors, both structural-endogenous and environmental-external, that may change depending on the early or late onset of the depressive disorder. In addition, a bidirectional association between depression and neurological disorder may be possible being that depressive syndromes may be considered as a risk factor for certain neurological disorders. As sustained by two recent meta-analysis, depressive syndromes, particularly major depressive disorder (MDD), are associated with a significantly increased risk of stroke[ 14 , 15 ]. On the other hand, lower rates of depression in equally impaired orthopedic patients suggest that PSD may even result from a stroke-specific neurobiological change and not only from a consequence of the psychological distress or the related impairments[ 16 - 18 ].

The second most common neurodegenerative disorder is represented by PD, with a prevalence of 1% of the elderly worldwide population. About 30% of PD patients show clinically significant depressive syndromes and, again, it appears to be also an increased risk for depressed patients to develop PD[ 19 - 23 ].

As far as regard MS, depressive syndromes are psychiatric most common disorders associated to the illness. Among individuals with MS, relative to the general population, lifetime prevalence rates are elevated for MDD (36%-54%), bipolar disorder (13%), anxiety disorders (35.7%), adjustment disorders (22%), and psychotic disorders (2%-3%). Suicide may be at least twice as common[ 1 ].

Many reports of depression and its correlation with numerous variables in clinical samples of people with MS have been published. The few population-based studies have reported a high prevalence of depression, despite using different methods of data collection. The lifetime risk of major depression in people with MS has been estimated to be as high as 50% compared to 10% to 15% in the general population[ 24 , 25 ].

In a recent cross-sectional, population-based study conducted in Stockholm county, the authors reported a prevalence rate of depression of 19% [Beck Depression Inventory (BDI) > 13] among patients suffering of MS. It’s interesting to note how depressive symptoms were associated with worse self-reported functioning, with poor memory function and with weak sense of coherence (SOC) (referring to ‘‘general resistance resources’’ - capacities that facilitate coping with stressors). Moreover, the authors suggested to incorporate depressive symptoms or mental health as a standard parameter for assessment and follow-up in clinical MS management[ 26 ].

Data from the United Kingdom MS Register, those obtained directly from MS patients, confirmed a high rate of anxiety and depression: over half of the respondents (54.1%) reported anxiety and 46.9% reported a variable level of depression[ 27 ]. From this registry data were recently examined about the positive relationships between physical disability, anxiety and depression[ 28 ].

Other reports confirmed the need to recognize and treat, having widely effective treatments, several emotional disorders which may worsen functioning and quality of life, decrease treatment adherence, and increase risk of suicide[ 1 ].

The prevalence of depressive disorders is higher in MS patients than patients with other chronic disease, suggesting a possible direct effect of the illness on the pathogenesis of the depressive syndromes in addition to the reactive disorder. Some evidences suggested that depression in MS is largely biologically mediated by some of the same processes involved in the immunopathogenesis of this neurologic disease. In particular, the increase in proinflammatory cytokines, the activation of the hypothalamic-pituitary-adrenal axis, and the reduction in neurotrophic factors. Notwithstanding, depression and mood disorders still remain under-diagnosed and under-treated in neurological patients claiming for a bio-psychosocial model be used[ 29 - 32 ].

Music therapy

In last decades, a growing body of evidence in the use of musical intervention in clinical setting have been seen, concerning singing, music listening, musical improvisation, and other musical activities, as long as more structured music therapy (MT) treatments. Given that music engages a variety of brain areas involved in emotion, motivation, cognition, and motor functions, musical interventions have been used to increase socialization and cognitive, emotional, and neuromotor functioning[ 33 - 38 ]. Although the debate on what the boundaries of MT is still going on, different approaches of musical intervention are actually available referring to three principal domains: relational approaches, rehabilitative approaches and music listening.

Relational approaches refer to psychological models and involve both active and receptive techniques[ 39 , 40 ]. The former consist of different musical activities such as free or structured musical improvisation by means of simple musical instruments, singing, songwriting etc . that allow patient and therapist to directly interact building a musical relationship[ 41 ]. In receptive approaches music imagery and music listening are used to induce psychological beneficial effects and even to evoke and process emotions and thoughts[ 40 ].

Rehabilitative approaches, such as Neurologic Music Therapy (NMT)[ 42 ] refer to neuroscientific models and use primarily the potential of musical stimuli to activate perception and production areas in the human brain, providing a series of therapeutic applications to sensory, cognitive, and motor dysfunctions resulting from neurological disorders. Using directive approach based on a series of exercises, NMT may be used, for example, to improve gait and movements in post-stroke and PD patients[ 43 - 47 ] and language in persons with aphasia[ 48 , 49 ].

On the other hand, simple music listening interventions don’t require neither a specifically trained therapist nor a direct therapeutic relationship with the patient being that beneficial effects are induced by the content of the musical stimuli and by the activity of listening itself. For these reasons, this practice is sometimes defined with the term “Music Medicine” rather than “MT”[ 41 , 50 , 51 ]. Notwithstanding, listening interventions seem to be quite common in clinical literature, usually based on self-selected or other-selected music proposed individually[ 52 , 53 ] or in group, as in the case of background music[ 54 , 55 ].

As far as regard neurological disorders, MT may promote functional recovery and also improve social and psychological outcomes such as socialization, motivation, mood, and depression[ 56 ]. Literature in this field shows that most of the musical interventions are currently used in clinical practice, being that the majority of the interventions are based on a combination of rehabilitative and relational techniques. Also music listening seems to be a common practice in neurological rehabilitation. Due to the possible side effects of pharmacological treatment of depressive syndromes following neurological disease, music and MT may represent a valid support in reducing depressive symptoms, improving mood and adherence to treatment while contributing to the functional recovery at the same time.

PubMed and PsychInfo databases were considered for articles to include in the current narrative review. The research has been conducted by three independent reviewers using the following search terms: (“Music” OR “MT”) AND (name of pathology) AND (“Mood” OR “Depression”). Names of pathologies where used alone or in combination with “OR” Boolean operator and included: “Stroke”, “Parkinson”, “Dementia”, “Epilepsy”, “ALS”, “MS”, “Cerebral palsy”, “Neurological disease”, and “Acquired brain injury”.

We included only Randomized Controlled Trials (RCTs) or Clinical Controlled Trials (CCTs) studies in English language published in peer-reviewed journals between 1 st January 1997 and 31 st May 2014. Importantly, we considered only trials including outcomes concerning mood or depression where experimental conditions were clearly stated and consisted only or primarily of musical activities.

Assessments of methodological quality of selected studies have been provided using Jadad score[ 57 ]. Jadad scale is based on 7 items that evaluate three main characteristics of a clinical trial: the random assignment, the double-blinding of assessments, and the flow of participants. Scoring ranges from a minimum of 0 to a maximum of 5 points where a score of 3 indicates a good quality study. Being that 2 points on 5 are scored for double-blinding and none of the included studies had double-blinding assessment, the maximum possible score was 3. Even if it doesn’t take into account allocation concealment and has been criticized for placing too much emphasis on blinding[ 58 ], Jadad scale represents a simple, easy and common way to evaluate the methodological quality of a clinical trial with good validity and reliability[ 59 ]. Due to the heterogeneity of the outcomes, no meta-analysis was carried out.

DESCRIPTION OF SELECTED STUDIES

A total of 464 records resulted from the search of which 301 from PubMed and 163 from PsychInfo. Twenty-five articles that met the inclusion criteria have been found and were included in the current review. Most of the selected studies are related to dementia (44%) and stroke (36%) while others regard MS, ALS/motor neuron disease, PD, Chronic quadriplegia, and acquired brain dysfunctions (20%). Fourteen studies (56%) employed a relational approach including both active and receptive techniques, six studies (24%) adopted a rehabilitative approach, and five (20%) concerned music listening interventions. Activities were conducted by trained music therapists in the most part of the experimental interventions. As far as regard the methodological quality of included studies, our analysis showed that only nine on twenty-five (36%) of the included studies received a Jadad score of 3 and thus can be considered of good quality. Five studies (20%) had a Jadad score of 2, three studies (12%) a score 1, and eight studies (32%) were evaluated with a score of 0. Results of the methodological assessment pointed out a general poor rigor in research protocols.

In the following subsections, results are presented through a subdivision of the selected studies by pathology (Table ​ (Table1, 1 , Table ​ Table2, 2 , Table ​ Table3 3 ).

Characteristics of the included studies concerning effects on dementia

ADAS-Cog: Alzheimer's Disease Assessment Scale-Cognitive Subscale; ADL: Activities of daily living; BEHAVE-AD: Behavioral Pathology in Alzheimer’s Disease Scale; CCT: Controlled Clinical Trial; CMAI: Cohen-Mansfield Agitation Inventory; CSDD: Cornell Scale for Depression in Dementia; C-CSDD: Chinese Cornell Scale for Depression in Dementia; DQOL: Dementia Quality of Life; ECG Holter: Electrocardiography Holter; GDS: Geriatric Depression Scale; GMP: Good Manufacturing Practice; GQoL: Geriatric Quality of Life; HRSD: Hamilton Rating Scale for Depression; IADL: Instrumental Activities of Dailiy Living; ICEA-D: Inventory to Asses Communication, Emotional Expression and Activity in Dementia; MMSE: Mini-Mental State Examination; NPI: Neuropsychiatric Inventory; NPI-Q: Neuropsychiatric Inventory Questionnaire; RCT: Randomized Controlled Trial; SVAM: Metacognition Assessment Scale.

Characteristics of the included studies concerning effects on stroke

Characteristics of the included studies concerning effects on other neurological disorders

HM: Happiness Measure; MS; Motor Subscale; PDQL: Parkinson's Disease Quality of Life Questionnaire; UPDRS: Unified Parkinson’s Disease Rating Scale; BDI: Beck Depression Inventory; HADS: Hospital Anxiety and Depression Scale; SESA: Self-Acceptance Scale; HAQUAMS: Hamburg Quality of Life Questionnaire in Multiple Sclerosis; MSFC: Multiple Sclerosis Functional Composite; WAIS-III: Wechsler Adult Intelligence Scale-III; AVLT: Auditory Verbal Learning Test; TMT-B: Trial Making Test Part B; BSI-18: Brief Symptoms Inventory-18; MAACL: Multiple Affect Adjective Check List; SEQ: Self Efficacy Questionnaire; HADS: Hospital Anxiety and Depression Scale; ESAS: Edmonton Symptom Assessment System; HR: Heart Rate; EMG: Electromyogram; PVP: Perceptual Voice Profile; POMS: Profile of Mood State; AQoL: Assessment of Quality of Life.

Effects on dementia

Eleven studies assessed the effects of music and MT on dementia[ 60 - 70 ]. Eight studies employed a relational approach[ 60 - 61 , 63 - 65 , 68 - 70 ] based either on active or receptive techniques or a combination of both of them. Two studies concerned music listening interventions[ 62 , 67 ] and one study adopted a rehabilitative approach[ 66 ]. In most cases the results show a positive effect on mood, depression, and anxiety. Two studies revealed no significant effect of musical intervention[ 64 , 66 ] while in three studies both experimental and control group improved emotional and behavioral functioning in the same way[ 65 , 67 , 69 ]. Characteristics of the studies and main results have been summarized in Table ​ Table1 1 .

Effects on stroke

Nine studies assessed the effects of music or MT on post-stroke patients[ 71 - 79 ]. Four of them were based on a relational approach[ 71 , 72 , 76 , 77 ], three regarded music listening interventions[ 74 , 75 , 78 ], and two used a rehabilitative approach[ 73 , 79 ]. All studies show a positive effect of music or MT on mood in patients with Stroke. For a synthesis of studies and results please see Table ​ Table2 2 .

Effects on other neurological disorder

Five studies concerning other neurological disorders such as MS, ALS/motor neuron disease, PD, Chronic quadriplegia, and Acquired Brain dysfunctions, have been found[ 80 - 84 ]. Three studies concerned a rehabilitative approach[ 80 , 82 , 84 ] and two studies adopted a relational approach using an active technique[ 81 ] or both active and receptive techniques depending on what the therapist deemed appropriate in consultation with the patient[ 83 ]. All studies but one[ 83 ] reported positive effects of music and MT on outcomes as mood, depression, anxiety, and quality of life. Characteristics of the studies and main results have been summarized in Table ​ Table3 3 .

In the last few decades, the development of neuroscience demonstrated that the brain isn't a static structure only influenced by genetic determinants but it is a plastic organ that continuously reorganizes synaptic connections under the influence of inner and outer factors such as genetic programs, environmental stimulation, learning and expertise[ 85 - 87 ].

Neurological illnesses that provoke behavioral disturbances might originate from both endogenous and external causal factors thus determining, depending on the circumstances, a more “structural” or a more “environmental” etiology. The mutual interaction between these factors occurs in the brain and gives rise to a variety of psychiatric disorders that can be distributed upon a continuum, on one end of which are behavioral disturbances clearly linked to neuroanatomic and neurochemical alterations while on the opposite those more associated to the environment.

Synaptic functions and neuroanatomic structures are proper “organic” factors that determine those alterations that are usually treated by neuropsychiatry and biological psychiatry. Behavioral disorders resulting from these factors include psychiatric syndromes that are linked to alterations of the neural transmission caused by receptor’s abnormalities and by modifications of the synaptic concentrations of one or more neurotransmitters. Given that neurotransmitters regulate the neural impulse transmission processes into neurotransmitter systems, with a widespread projection in the brain, the whole emotional, motivational, and affective state of the person will be altered[ 88 , 89 ].

External causal factors related to the environment may promote and characterize those behavioral disorders that are commonly counted accordingly to a bio-psychosocial model and interfere with the cognitive and emotional state of the person thus inducing an important change in the quality of the inter-individual relationships. These disorders may be considered as a reaction to the physical disability and the psycho-social difficulties produced by the disease but also as an adjustment disorder if we consider the impact of the diagnosis on patient’s life, or the weight of a chronic illness and all the other factors that may affect patient’s quality of life[ 90 - 92 ].

Depressive syndromes in chronic neurological illness are common and disabling. Their etiology is complex and may be multifactorial. Good history taking and detailed examination of physical and mental state (including cognitive function) will usually reveal the diagnosis and the formulation.

Providing a correct diagnosis of an emotional disorder and starting an appropriate treatment may help physicians to increases in function and quality of life of their neurological patients[ 93 ].

The current review showed how MT and musical interventions can improve mood and psychological well-being in neurological patients. These clinical results are in accordance with the literature that highlights the effects that music listening and music making have on brain structures of emotion regulation[ 36 ], on various neurochemical systems[ 38 ], and on neural plasticity[ 94 , 95 ]. However, the strength of this review’s findings is limited due to a generally poor methodological quality of the studies and the restricted size of samples. Moreover, the heterogeneity of the outcomes prevented any meta-analysis. Notwithstanding, the analysis of the 25 RCTs or CCTs included in this work points out a positive effect of interventions with music on psychosocial outcomes such as mood, depression, and quality of life when compared to standard care or other treatments.

Music-based activities can represent a valid and without side effects intervention for reducing psychological and behavioral disturbances related to neurological disorders and also for promoting the functional recovery. Specifically, the most significant results of the music interventions on the psychological side can be identified in the aspects more closely related to mood, especially in the reduction of the depressive and anxiety's component, and in the improvement of the emotional expression, communication and interpersonal skills, self esteem and quality of life. As revealed in advance, the efficacy of music and MT interventions could be explained by different points of view. From the neurochemistry point of view we know that music can activate limbic and paralimbic structures, such as the amygdala, the hippocampus, the nucleus accumbens, etc. that function abnormally in patients with a high depressive component. At the psychological level music can engage several social functions, can increase communication and social cohesion and can promote empathetic relationships, especially in the active MT approaches. Finally, from the rehabilitative point of view, making music can involve and influence motor areas functioning and regulation. This effect appears to be connected to the pleasure and thereby can positively affect the mood and consequently the rehabilitative process[ 36 - 38 ].

In conclusion, a more methodological rigor and a clearer definition of music approaches are needed to improve the quality of MT research and to focus on the specific role of music-based interventions in psychological symptoms in the field of neurology.

P- Reviewer: Boehm K, Mishra J S- Editor: Ji FF L- Editor: A E- Editor: Lu YJ

Conflict-of-interest: The Authors have not any competing interests to declare and no funding was received for this research.

Open-Access: This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/

Peer-review started: September 28, 2014

First decision: December 17, 2014

Article in press: February 11, 2015