Dose–response relationship in music therapy for people with serious mental disorders: Systematic review and meta-analysis

Clinical Psychology Review 29 (2009) 193–207

Contents lists available at ScienceDirect

Clinical Psychology Review

Dose–response relationship in music therapy for people with serious mental disorders: Systematic review and meta-analysis Christian Gold a,⁎, Hans Petter Solli b,c, Viggo Krüger b, Stein Atle Lie a a b c

Unifob Health, Bergen, Norway University of Bergen, Norway Lovisenberg Diakonale Hospital, Oslo, Norway

a r t i c l e

i n f o

Article history: Received 30 June 2008 Received in revised form 6 January 2009 Accepted 12 January 2009 Keywords: Psychosis Depression Psychotherapy Dose–effect relationship Mixed-effects meta-analysis

a b s t r a c t Serious mental disorders have considerable individual and societal impact, and traditional treatments may show limited effects. Music therapy may be beneficial in psychosis and depression, including treatmentresistant cases. The aim of this review was to examine the benefits of music therapy for people with serious mental disorders. All existing prospective studies were combined using mixed-effects meta-analysis models, allowing to examine the influence of study design (RCT vs. CCT vs. pre-post study), type of disorder (psychotic vs. non-psychotic), and number of sessions. Results showed that music therapy, when added to standard care, has strong and significant effects on global state, general symptoms, negative symptoms, depression, anxiety, functioning, and musical engagement. Significant dose–effect relationships were identified for general, negative, and depressive symptoms, as well as functioning, with explained variance ranging from 73% to 78%. Small effect sizes for these outcomes are achieved after 3 to 10, large effects after 16 to 51 sessions. The findings suggest that music therapy is an effective treatment which helps people with psychotic and non-psychotic severe mental disorders to improve global state, symptoms, and functioning. Slight improvements can be seen with a few therapy sessions, but longer courses or more frequent sessions are needed to achieve more substantial benefits. © 2009 Elsevier Ltd. All rights reserved.

Contents 1.

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Introduction . . . . . . . . . . . . . . . . . . . 1.1. Music therapy in mental health. . . . . . . 1.2. Music therapy—the evidence to date . . . . 1.3. Research questions addressed in this review Method . . . . . . . . . . . . . . . . . . . . . 2.1. Criteria for selecting studies . . . . . . . . 2.1.1. Study design . . . . . . . . . . . 2.1.2. Study quality . . . . . . . . . . . 2.1.3. Participants . . . . . . . . . . . . 2.1.4. Interventions . . . . . . . . . . . 2.1.5. Outcomes. . . . . . . . . . . . . 2.2. Search strategy . . . . . . . . . . . . . . 2.3. Selection of studies and data extraction. . . 2.4. Data analysis . . . . . . . . . . . . . . . 2.4.1. Individual study results . . . . . . 2.4.2. Combination of study results . . . Description of studies . . . . . . . . . . . . . . 3.1. Selection process . . . . . . . . . . . . . 3.2. General study characteristics . . . . . . . . 3.3. Interventions: Music therapy. . . . . . . . 3.4. Comparison conditions . . . . . . . . . . 3.5. Data extraction and preprocessing . . . . .

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⁎ Corresponding author. Unifob Health, Grieg Academy Music Therapy Research Centre, Lars Hilles gate 3, 5015 Bergen, Norway. Tel.: +47 97501757. E-mail address: [email protected] (C. Gold). 0272-7358/$ – see front matter © 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.cpr.2009.01.001

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4.

Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1. Comparison of music therapy versus standard care . . . . . . 4.1.1. General mental state . . . . . . . . . . . . . . . . 4.1.2. Negative symptoms . . . . . . . . . . . . . . . . 4.1.3. Depressive symptoms . . . . . . . . . . . . . . . 4.1.4. Other symptoms: Anxiety and positive symptoms . . 4.1.5. Functioning . . . . . . . . . . . . . . . . . . . . 4.1.6. Musical engagement . . . . . . . . . . . . . . . . 4.1.7. Other outcomes: Global state, leaving the study early, 4.2. Other outcomes and comparisons. . . . . . . . . . . . . . 5. Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1. Summary of findings . . . . . . . . . . . . . . . . . . . . 5.2. The evidence base for music therapy in mental health . . . . 5.3. The dose–response relationship in music therapy . . . . . . 5.4. Limitations . . . . . . . . . . . . . . . . . . . . . . . . 5.5. Implications for practice . . . . . . . . . . . . . . . . . . 5.6. Implications for future research. . . . . . . . . . . . . . . 6. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conflict of interest . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conflict of interest Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1. Introduction Serious mental disorders are common and often long-lasting conditions with considerable impact on society and the individual. Seriousness may be defined by specific states generally considered as severe, such as psychosis or suicidal behavior, by low level of functioning or a severe global impression, or by chronicity and treatment resistance. In a comprehensive international mental health survey (Demyttenaere et al., 2004), serious mental disorders were found to be prevalent in between 0.4% in Nigeria and 7.7% in the United States. Seriousness in that study was defined as severe role impairment, severe overall functional impairment, substance dependence, or suicidality in conjunction with a mental disorder, irrespective of the particular diagnosis. Treatment options for people with serious mental disorders include psychopharmacological and psychotherapeutic approaches. Both have been shown to be efficacious in many but not in all patients, and not without limits. Many patients do not show satisfactory improvement with these traditional approaches and continue to show substantial symptom levels and impaired functioning. There is therefore a need for additional, innovative forms of therapy to help people with serious mental disorders. 1.1. Music therapy in mental health Music therapy is a special type of psychotherapy where forms of musical interaction and communication are used alongside verbal communication. It has been defined as “a systematic process of intervention wherein the therapist helps the client to promote health, using music experiences and the relationships developing through them as dynamic forces of change” (Bruscia, 1998). The types of ‘music experiences’ used in music therapy can include free and structured improvisation, other types of active music-making by patients, and listening to music. Improvisation is perhaps the most prominent form of musical interaction in music therapy. It has been described as central in many music therapy models. Client(s) and therapist improvise on musical instruments they have chosen, playing together freely or with a given structure or a musical or non-musical theme. Music therapists are specifically trained to intervene therapeutically within the medium, for example to support by providing rhythmical or tonal grounding, to clarify, to confront or to challenge the client's expression in the music (Bruscia, 1987; Wigram, 2004). Other modes of music experiences in music therapy include playing composed music on instruments, singing and writing or improvising songs (Baker & Wigram, 2005), and listening to music (Grocke & Wigram, 2006). Songs may be used by clients as a

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safe, structuring and socially acceptable form in which they can express feelings which otherwise might be too overwhelming to express. Music listening may be helpful to bring up and make available therapeutically relevant issues (emotions, associations, memories, identity issues). All these different modes of ‘music experiences’ become therapeutic by being used in the context of a therapeutic relationship. Verbal discussions, reflections, or interpretations connected to the music are important to help clients explore the potential meaning of an experience, and to relate a new experience within therapy to situations in the client's life. The degree to which the music experience itself, versus the verbal reflection connected to it, is seen as the active agent of change may vary between models of music therapy (Garred, 2004), as well as between clients. However, treatments that rely solely on the direct effects of music alone, which do not “involve or depend upon a process of intervention and change within a client–therapist relationship” (“auxiliary level”, Bruscia, 1998, p. 195), are not music therapy. The term ‘music medicine’ is sometimes used to distinguish such treatments from music therapy. In the context of treatment options for people with serious mental disorders, music therapy may fill an important gap which traditional therapies do not fill. Previous clinical reports (Rolvsjord, 2001; Solli, 2008) as well as research studies (Hannibal, 2005; Hanser & Thompson, 1994; Meschede, Bender, & Pfeiffer, 1983) have reported that music therapy has helped some patients who did not benefit—or not sufficiently—from exclusively verbal psychotherapy. Particularly some of the most severely disturbed patients may not be able to use verbal language for them to change. This may obviously concern non-verbal patients, but equally importantly verbal patients who are, for whatever reasons, unable to address their problems verbally. Some music therapy models also speculate that the preverbal qualities of music (in particular of free improvisation) may help to address early childhood traumas (Wigram, Nygaard Pedersen, & Bonde, 2002, p. 155). Research on mother–infant communication supports the notion of music as a medium which is in some ways similar to language, but less laden with referential semantic meaning and more rooted in the communication at early developmental stages (Trevarthen & Malloch, 2000). These qualities may enable its effective use by patients who are too severely disturbed for purely verbal psychotherapy. Likewise, music therapy may be effective in an area of outcome in which psychopharmacological treatments show limited success—namely in the area of negative symptoms, including affective flattening or blunting, poor social relationships, and low motivation, among others (Andreasen, 1982; Buckley & Stahl, 2007; Buchanan et al., 2007). A previous meta-analysis of RCTs comparing music therapy as an additional

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treatment to standard care alone for people with psychotic disorders, showed large effects on negative symptoms (Gold, Heldal, Dahle, & Wigram, 2005). This finding is also interesting because it may show some hints as to what may be regarded as the effective factors of music therapy (or its ‘mechanisms of change’). First, music as a medium for emotional expression may help patients to improve their expressive range and diminish affective flattening. Second, making music together is always a social endeavor, inherently connected to forming and building social relationships, and may therefore help patients to overcome deficits in this area. And third, the possibility to make music in therapy may be a central motivating factor, especially for patients who otherwise show little or no motivation (Rolvsjord, 2001; Solli, 2008), which may then generalize to other situations. These domains, summarized as negative symptoms, were first described for schizophrenia but have been shown to be a transdiagnostic phenomenon which is relevant in non-psychotic mental disorders as well, particularly major depression (Winograd-Gurvich, Fitzgerald, Georgiou-Karistianis, Bradshaw, & White, 2006). Generally, it can be said that music therapy is usually tailored to an individual patient and his/her specific needs more than to a specific clinical diagnosis. There is usually no direct link between a patient's clinical diagnosis and the specific techniques used in therapy, although the type of disorder will, as part of a larger picture, certainly play a role in forming the therapist's choices, attitudes and behaviors during a therapy. (Little research has been conducted to address this link, but findings to date are that diagnosis explains only a small fraction of the variation in techniques, e.g., Drieschner & Pioch, 2002.) Similarly, indications for music therapy in mental health may be transdiagnostic, and decisions to offer music therapy to an individual patient in a given clinical setting may be based on many aspects of which the primary clinical diagnosis is only one. Many researchers have argued that dimensional concepts are more valid to describe psychopathology than categorical systems (Maser & Akiskal, 2002; Kendell & Jablensky, 2003; Krueger, Watson, & Barlow, 2005). Continuities exist between healthy and disordered states as well as between different disorders, and notably also between psychotic and non-psychotic states (Cullberg, 2007; Maser & Akiskal, 2002). The existing evidence for a continuum of mental health therefore justifies the combination of psychotic with non-psychotic disorders in a meta-analysis (as will be done here). The notion of such a continuum also fits well with the practice of music therapy being adapted more to a patient's needs than to his diagnosis. 1.2. Music therapy—the evidence to date Several systematic reviews and meta-analyses have been conducted to examine the effects of music therapy in the field of mental health (e.g., Dileo & Bradt, 2005; Gold, Heldal, et al., 2005; Gold, Voracek, & Wigram, 2004; Gold, Wigram, & Elefant, 2006; Koger, Chapin, & Brotons, 1999; Maratos, Gold, Wang, & Crawford, 2008; Pesek, 2007; Silverman, 2003; Vink, Birks, Bruinsma, & Scholten, 2003). Many of these have found promising results; however, the quality of the included studies varied. Promising results, applying rigorous study selection criteria, have been found in two recent Cochrane reviews for psychotic disorders (Gold, Heldal et al., 2005) and for depression (Maratos et al., 2008). Both reviews suggested that music therapy has a number of beneficial effects for these people when added to standard care. The review on schizophrenia also suggested some hints towards a ‘dose–effect’ relationship: Global state, general and negative symptoms and functioning improved significantly and by large effect sizes in those studies where a sufficiently large number of sessions were offered. However, both reviews were limited by their very narrow inclusion criteria. The schizophrenia review meta-analyzed only four studies; the depression review did not include any meta-analysis and relied solely on a narrative summary. Therefore an analysis of dose–effect relationship was well beyond the scope of the previous Cochrane reviews.

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In psychotherapeutic methods such as music therapy, the term ‘dose’ or ‘dosage’ clearly must be understood metaphorically, not literally. Howard, Kopta, Krause, & Orlinsky (1986) have argued that although a therapy model's proposed active ingredients (such as interpretations, empathic reflections, etc.) might be considered as the most theoretically coherent ‘unit of treatment’, these are not easy to measure. However, the number of therapy sessions a patient has received is most likely correlated to a patient's exposure to those ingredients and can therefore be used as a readily available proxy measure. The number of therapy sessions has been widely accepted as a measure of ‘dose’ in psychotherapy since this seminal paper. The same paper also brought up a discussion on whether the dose–response relationship in psychotherapy is linear, or whether the first sessions have a greater influence than subsequent sessions. This discussion is still ongoing today, and therefore the present review aims at examining both possibilities for the field of music therapy. The previous Cochrane reviews of the effects of music therapy on schizophrenia (Gold, Heldal, et al., 2005) and depression (Maratos et al., 2008) chose very narrow inclusion criteria because they were aimed at selecting only the most reliable evidence for one particular mental disorder. This narrow focus, while helping to achieve high reliability, also necessarily limited the generalizability of its findings in several ways: • Focus on only one mental disorder: As described above, music therapy is not usually targeted at a specific diagnosis, but rather broad in its goals and methods. This is reflected in some studies that used a mixed patient sample (e.g., de l'Etoile, 2002; Thaut, 1989), which would consequently have to be excluded in any review focusing on one selected diagnosis. A transdiagnostic focus also seems appropriate given the relevance of dimensional concepts in mental health as summarized above. • Exclusion of non-randomized studies: While ensuring that the most reliable evidence is used, an exclusive focus on randomized studies also has its drawbacks. For example, external validity—the extent to which studies are generalizable to everyday clinical practice—may be higher in some of the non-randomized studies. RCTs on complex interventions are difficult to conduct, so that clinically desirable features may in some cases be given too little attention. This may for example concern the selection of subjects, the contents of therapy, and the duration of therapy and follow-up. Excluding nonrandomized studies also implies that there is less evidence to draw on, which will often make advanced statistical procedures such as meta-regression impossible to apply. The present review attempted to overcome these weaknesses by applying a wider focus, in the hope of enabling broader and clinically more useful generalizations. 1.3. Research questions addressed in this review The aim of this review was to examine the effects of music therapy for people with serious mental disorders, based on all prospective studies (randomized studies, other controlled studies, uncontrolled pre-post studies). The main research questions addressed were as follows: 1. Can the previously hypothesized influence of the number of sessions on the effects of music therapy be confirmed and quantified? What shape does this ‘dose–response’ relationship take in music therapy? Is it possible to predict the number ofsessions needed for a small, medium, or large effect, respectively? 2. Does the type of mental disorder predict the effect of music therapy? Does music therapy have a different impact on patients that are either psychotic or non-psychotic? Where would music therapy be most indicated?

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In addition we also aimed to address how the type of study design may be related to the estimated effect of music therapy. In contrast to the main research questions above, the inclusion of study design as a potential predictor was less directly of clinical importance, but was mainly related to examining the robustness of findings when including ‘weaker’ study designs than RCTs.

(preferably published) rating scale. They had to be assessed either as a self-report or by an independent (preferably blinded) rater. Ratings done by therapists were excluded as they were definitely not blinded and at serious risk of being biased.

2. Method

A comprehensive search strategy was applied to identify all relevant studies. To avoid the pitfalls of publication bias and English language publication bias, published as well as unpublished reports in any language were considered. Highly sensitive search strategies were employed in previous related reviews on music therapy for psychotic disorders (Gold, Heldal, et al., 2005), for depression (Maratos, Gold, Wang, & Crawford, 2008), and for all mental disorders (Heldal & Dahle, 2006), and the results from these searches were used for this review. Each of those previous searches included searching in relevant databases as well as hand searching. To identify any later trials, we used the following search strategies (May 2006):

2.1. Criteria for selecting studies 2.1.1. Study design Studies with any prospective group design (RCTs, CCTs, and studies without control groups) were considered relevant. Randomized controlled trials (RCTs) were defined, according to the strict criteria of the Cochrane Collaboration (Higgins & Green, 2008), as studies where participants were allocated to conditions through true randomization (e.g., using lots, dice, or computer-generated randomization lists), as opposed to quasi-randomization (e.g., using patient numbers or date of intake). Controlled clinical trials (CCTs) were defined more loosely as any study using a control group intended to be equivalent in terms of patient characteristics (including quasirandomization as well as matching techniques). Finally, an uncontrolled study was defined as any other prospective design where all participants received the same interventions and baseline values were available so that participants could be used as their own controls (e.g., case series, pre-post design). 2.1.2. Study quality Studies with more than 30% attrition rate were excluded. As other important study quality characteristics, allocation concealment (in RCTs) and blindness were assessed and reported. Outcomes were included if they were either adequately blinded or a self-report. They were also included if they were possibly blinded but the actual use of blinding was uncertain. Outcomes that are definitely non-blinded present a high risk of bias and were excluded. This is in accordance with the Cochrane Handbook (Higgins & Green, 2008).

2.2. Search strategy

(a) The trial database PsiTri, which contains structured information on published and unpublished clinical trials in mental health, based on multiple database searches as well as hand searches by several Cochrane groups, was searched for entries containing the word “music” in any field. (b) PubMed was searched using its “Clinical Queries” search strategy designed to identify scientifically strong studies of therapy outcome, which was expanded with the Medical Subject Headings (MeSH) term “Evaluation Studies”, and crossed with the MeSH terms “Music Therapy” and “Mentally Ill Persons” or “Mental Disorders”. 2.3. Selection of studies and data extraction At least two reviewers independently assessed each potentially relevant study for inclusion and extracted data from the included studies. Cases of disagreement were resolved by discussion. 2.4. Data analysis

2.1.3. Participants Study participants eligible for this review were adults with serious mental disorders diagnosed by an international classification system. This included psychotic disorders as well as some non-psychotic disorders such as borderline personality disorder, depression, bipolar disorder, and suicidality connected to a mental disorder. Serious mental disorders are characterized by significant role disability (Demyttenaere et al., 2004), which could be indicated by low GAF scores or by admittance to in-patient treatment. 2.1.4. Interventions Studies were included only if participants were offered music therapy, according to the definition above. Most importantly, this excluded interventions of the ‘music medicine’ type, where music alone is provided as a treatment, rather than using music as a medium within a psychotherapeutic process and relationship. Secondly, it had to be possible to disentangle music therapy from other therapies. Comparison conditions could be no treatment, standard care, or an active control condition (i.e., a different therapy, a ‘placebo’ therapy, or a different type of music therapy). 2.1.5. Outcomes All outcomes of clinical relevance were considered, including measures of general mental state, symptoms, and functioning, but also outcomes related to music and other patient- or service-relevant outcomes such as quality of life, medication level, or satisfaction with care. Continuous outcomes had to be assessed by a standardized

2.4.1. Individual study results For each study, odds ratios (OR) were calculated for dichotomous outcomes and standardized mean differences (Hedges' g) for continuous outcomes. For continuous outcomes we first checked if there was evidence for skewness (floor or ceiling effects), which we then attempted to remove by log-transformation if possible (i.e., if raw data were available for that study). The effect size index Hedges' g is similar to Cohen's d (and can be interpreted similarly), but corrects for small-sample bias and is therefore more conservative in small samples. For dichotomous outcomes with missing data, we assumed the negative outcome for the missing cases. Effect estimates were calculated in such a way that a beneficial effect of music therapy is always represented by a positive effect size (for continuous outcomes) or by an odds ratio smaller than 1 (for dichotomous outcomes). The different types of research designs were handled as follows in the calculation: For RCTs, only post-test means were used, as the pretest was assumed to be equal in the populations due to the randomization. For CCTs, we used the post-test mean of the experimental group, but subtracted the pretest difference between groups from the post-test mean of the control group in order to adjust for existing pretest differences. For the studies without separate control groups, we used the baseline values as control, thereby simply comparing post-test versus pretest. There is some discussion in the meta-analytical literature on whether or not the correlation between pretest and post-test values should be taken into account. However, such a procedure would give relatively larger weight to the studies with the weakest designs. We therefore decided not to make use of

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these correlations in order not to give undue weight to those studies (see the related discussion in the appendix of Gold et al., 2004). 2.4.2. Combination of study results Results for the same type of outcome were combined across studies in a meta-analysis. Results of different outcomes were not combined. If the same outcome was measured with different scales in the same study, both using equally valid methods (in terms of rater blinding and standardization and validity of instrument), the average effect size of these measures was used. For the outcomes where data were available from at least five studies, we used mixed-effects meta-analysis, an extension of metaregression, to examine simultaneously the following three predictors: study design (as a 3-level factor: RCT, CCT, uncontrolled), type of disorder (as a linear predictor: percentage of participants with psychotic vs. non-psychotic disorders), and number of sessions provided (as a linear predictor; alternatively the square root of sessions if this improved the model fit). Model fits using different combinations of predictors were compared using adjusted R2 (as recommended by Tabachnick & Fidell, 2001, p. 147), and the model yielding the best fit was selected. This was done in order to fit the data best to the figures and for the prediction of effect sizes. Mixed-effects models are usually preferred over fixed-effects models in the literature on meta-analysis and meta-regression (Everitt & Hothorn, 2006; Sutton, Abrams, Jones, Sheldon, & Song, 2000; Thompson & Higgins, 2002). In contrast to the simpler fixed-effects models, mixed-effects models take into account possible random variation between the true effects of each study (between-study heterogeneity not captured by the predictors) and are essentially more conservative and less prone to bias. The appropriate study weights for the mixed-effects models were calculated iteratively until they converged, as recommended and described in Sutton et al. (2000, p. 98). The open-source statistical software environment R, Version 2.6.1 (R Development Core Team, 2007), was used for the statistical analyses. For outcomes where data were available from at least two but less than five studies, traditional meta-analytic summaries were calculated (as described in Cooper & Hedges, 1994). Study results were pooled using a fixed effects model. When a substantial amount of statistical heterogeneity (when I2 N 50%; Higgins & Green, 2008, p. 278) was found and could not be explained, we subsequently considered a random effects model. We used R package meta, Version 0.8-2 (Schwarzer, 2007), for these analyses, which replicates the procedures in the Cochrane Collaboration's meta-analysis software.

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studies without control groups. Assessor blinding was adequate in six studies and uncertain in nine studies. Definitely non-blinded outcomes in one study (Radulovic, 1996) were excluded from the analysis. Nine countries and three continents are represented in the included studies, with six studies from Europe (Denmark, Germany, Italy, Serbia, UK), five from North and Central America (Mexico, USA), and four from Asia (China, Japan). Together, the studies enrolled a total of N = 691 patients. In terms of their primary diagnosis, about two thirds (n = 456) were diagnosed with a psychotic disorder and the remaining third (n = 235) with a non-psychotic disorder, most often depression. There were three studies (de l'Etoile, 2002; Radulovic, 1996; Thaut, 1989) that included both types of disorders; however, there were several further studies that included the various forms of overlaps such as schizoaffective or schizotypal disorder. Severity was indicated in various ways, including psychoticism (11 studies), institutionalization (10 studies), classification as ‘chronic’ (6 studies), lack of response to other therapy (2 studies), and/or suicidality (1 study; see Table 1). 3.3. Interventions: Music therapy

The various searches yielded initially 166 potentially relevant studies for any mental disorder (Heldal & Dahle, 2006), 34 for schizophrenia (Gold, Heldal, et al., 2005), and 16 for depression (Maratos et al., 2008). The updated database searches did not identify any newer studies. Studies were excluded if the design, participants, interventions, or outcomes, as assessed by two reviewers independently, did not meet the inclusion criteria for this review. In addition, some potentially relevant studies had to be excluded where we were unable to retrieve the full text of the study report (Castilla-Puentes et al., 2002), where no usable outcome data were reported and attempts to retrieve additional data directly from authors failed (Meschede et al., 1983; Schmuttermayer, 1983), or where the drop-out rate exceeded 30% (Steinberg et al., 1991).

Music therapy was offered between one and six times per week over a period of one to six months. The maximum number of sessions offered in each study varied from six to 78 (if not specified directly in a report, this was calculated by multiplying frequency with duration—a potential overestimate as it does not take into account cancellations and holidays). Some of the studies (Troice & Sosa, 2003; Hayashi et al., 2002; Talwar et al., 2006; Zerhusen et al., 1995) also reported how many of this maximum number actually were received by the patient, ranging from 59% to 90% with a median of 73%. In the further calculations we used sessions received if reported, and assumed 75% otherwise. Music therapy was provided in group settings in two thirds of the studies. Three studies (Hanser & Thompson, 1994; Pavlicevic, 1994; Talwar et al., 2006) used exclusively individual sessions; two studies (Thaut, 1989; Yang et al., 1998) combined group and individual sessions. Most studies used a combination of different working modes, such as improvisation (described in 8, central in 4 studies), other forms of playing music on instruments (described in 8, central in 1 study), singing and/or writing songs (described in 6 studies), listening to music (described in 10, central in 6 studies), and verbal reflection around the music experiences (described in 11, central in 4 studies; Table 1). In all studies, music therapy was provided with some degree of processorientation as well as some degree of structure; there seemed to be an agreement that both elements were necessary in working with this population. Although there may have been some variation along this dimension, we did not find an example that was extreme on either end of the scale (either extremely open or very rigidly structured). One study (Ceccato et al., 2006) compared approaches with more versus less structure. Information concerning the theoretical background which informed the approach was sparse. Some studies described a psychodynamic (Moe et al., 2000; Radulovic, 1996) or cognitive background (Hanser & Thompson, 1994), but most studies were less explicit in this respect and appeared to be eclectic in their theoretical orientation. Similarly, information concerning the qualification level of the music therapist was infrequent, although this may reflect the different state of development of the profession across countries. Studies from countries where formalized registration requirements exist reported such board or state registration (Hanser & Thompson, 1994; Talwar et al., 2006; Troice & Sosa, 2003); in other studies, therapists were more generally described as trained, skilled, or experienced.

3.2. General study characteristics

3.4. Comparison conditions

In result, fifteen studies were retained and included in the metaanalysis (Table 1). These included eight RCTs, three CCTs, and four

The most basic comparison, where music therapy is added to standard care or minimal therapeutic contact, was available in all

3. Description of studies 3.1. Selection process

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Table 1 Characteristics of included studies Study

Design and study quality

Duration (months)

Participants Clinical condition and setting

a) Randomized studies Chen Design: Parallel (1992) Allocation concealment: Unknown Blindness: Not reported

Proportion of psychotic disorders

Demographicsc

Interventionsa

Typec

No. of sessions

Comparisonb,c

Outcome scalesd

2

Diagnosis: Depression Setting: Inpatients Country: China

0%

N = 68 Age: 60-77 (M = 64) Sex: 46% male

MT (P⁎, S), 6 sessions pr. wk. of 60 min., plus antidepressants. N = 34

Offered: 48

Antidepressants. N = 34

C) HAMD D) HARS G) Global state: Overall improvement Unable to use: Confinement in bed

Design: Parallel Allocation concealment: Unknown Blindness: Not reported

2

Diagnosis: Major or minor depressive disorder History: 90% were insufficiently improved after previous psychotherapy Setting: Outpatients Country: USA

0%

N = 32 Age: 61-86 (M = 68) Sex: 23% male

MT (L⁎, V⁎, O), 1 session pr. wk. of 60 min. N = 11

Offered: 8

1.) Minimal therapeutic contact, consisting of weekly phone talks of 20 min. N = 10 2.) No treatment. N = 11

A) BSI GSI C) Geriatric Depression Scale, GDS Not used (secondary measure): Depressed mood scale on Profile of Mood States, POMS Not used (data not reported): BDI D) Anxiety, Profile of Mood States, POMS; Hostility, POMS G) Rosenberg Self-Esteem Scale, RSE

Radulovic (1996)

Design: Parallel Allocation concealment: Unknown Blindness: Inadequate in therapist ratings (selfreports usable)

1.5

Diagnosis: Mood disorders, adjustment disorder, schizoaffective disorder Setting: Inpatients Country: Serbia

2%

N = 60 Age: 21-62 Sex: 33% male

MT (L⁎, V⁎), 2 sessions pr wk. of 20 min., plus standard care. N = 30

Offered: 12

Standard care. N = 30

C) BDI Not used (non-blinded therapist ratings): HAMD D) Not used (non-blinded therapist ratings): HARS

Talwar et al. (2006)

Design: Parallel Allocation concealment: Adequate Blindness: Adequate (assessors blinded)

3

Diagnosis: Schizophrenia Setting: Inpatients Country: UK

100%

N = 81 Age: 18-64 (M = 37) Sex: 74% male

MT (I⁎, V), 1 session pr. wk. of 50 min., plus standard care. N = 33

Offered: 12; Attended: MdN = 8

Standard care. N = 48

A) PANSS B) PANSS D) Positive symptoms, PANSS E) GAF G) Quality of Life, SFQ Satisfaction with care, CSQ Engagement with services, HAS Unable to use: EPEX

Tang et al. (1994)

Design: Parallel Allocation concealment: Unknown Blindness: Adequate (assessor blinded)

1

Diagnosis: Schizophrenia History: Chronic (residual subtype) Setting: Inpatients Country: China

100%

N = 76 Age: Unknown Sex: Unknown

MT (L⁎, P, S, V), 5 sessions pr. wk. of 1 hr., plus standard care. N = 38

Offered: 19

Standard care. N = 38

B) SANS E) Unable to use: Disability, DAS

Ulrich et al. (2007)

Design: Parallel Allocation concealment: Adequate Blindness: Adequate (assessors blinded)

1

Diagnosis: Schizophrenia, schizoaffective psychosis, schizotypal disorder, druginduced psychosis, depression with psychotic symptoms Setting: Inpatients Country: Germany

100%

N = 37 Age: 22-58 (M = 38) Sex: 54% male

MT (I, P, S, V), 2 sessions pr. wk. of 60-105 min., plus standard care. N = 21

Attended: 7.5

Standard care. N = 16

B) SANS E) Social functioning, Giessen Test (self-report and observer rating) G) Quality of life, SPG Satisfaction with care, unpublished scale

Yang et al. (1998)

Design: Parallel Allocation concealment: Unknown Blindness: Unknown

3

Diagnosis: Schizophrenia History: Chronic (mean duration of illness 13 yrs.) Setting: Inpatients Country: China

100%

N = 72 Age: 21-55 Sex: 59% male

MT (I, P, S, L, V), 6 sessions pr. wk. of 2 hrs., plus standard care. N = 41

Offered: 78 (6 per week over 3 months)

Standard care. N = 31

A) BPRS B) SANS E) Social functioning, SDSI G) Global state: Clinically important improvement

Zerhusen et al. (1995)

Design: Parallel Allocation concealment: Unknown Blindness: Unknown

2.5

Diagnosis: Depression Setting: Nursing home residents Country: USA

0%

N = 60 Age: 70-82 (M = 77) Sex: ca. 25% male

MT (L⁎, P), biweekly sessions of unknown length. N = 20

Offered: 20; Attended: 11.8 (59% of 20)

1.) Cognitive therapy. N = 20; 2.) Standard care. N = 20

C) BDI

C. Gold et al. / Clinical Psychology Review 29 (2009) 193–207

Hanser & Thompson (1994)

b) Other controlled studies Ceccato Design: Parallel et al. Matching: Age, sex, (2006) education, clinical history, cognitive deficits Blindness: Unknown

4

Diagnosis: Schizophrenia Setting: Day patients Country: Italy

100%

N = 16 Age: M = 34 (SD = 10) Sex: 81% male

1.) MT (L⁎), 1 session pr. wk. of 55 min. N = 8. 2.) MT (I⁎), 1 session pr. wk. of 55 min. N = 8.

Offered: 16

–

B) (Attention: Paced Auditory Serial Addition Test, PASAT) (Memory: Wechsler Memory Scale, WMS) E) Social functioning: Life Skills Profile, LSP

Design: Parallel Matching: Age, education, marital status, clinical history, work status, medication dose Blindness: Not reported

4 (plus 8-month follow-up in experimental group only)

Diagnosis: Schizophrenia or schizoaffective psychosis History: Chronic (ward for long-stay patients) Setting: Inpatients Country: Japan

100%

N = 66 Age: 43-84 Sex: 0% male

MT (P, S, L, V), 1 session pr. wk of 1 hr., plus standard care. N = 34

Offered: 15; Attended: M = 11.8, range 3-15

Standard care. N = 32

A) PANSS B) PANSS D) Positive symptoms, PANSS E) Unable to use (incompletely reported): Ward life activity and adjustment, unpublished scale F) Musical experiences, unpublished scale G) Quality of Life Scale, QLS; Medication level

Pavlicevic et al. (1994)

Design: Parallel Matching: Age, sex, social class, clinical history, severity, musical experience Blindness: Adequate (assessor blinded)

2.5

Diagnosis: Schizophrenia History: Chronic Setting: Day patients Country: UK

100%

N = 41 Age: M = 38 (SD = 9) Sex: 80% male

MT (I⁎), 1 session pr. wk. of 30 min., plus standard care. N = 21

Offered: 10

Minimal therapeutic contact (2 sessions) plus standard care. N = 20

A) BPRS B) SANS C) HAMD F) Music Interaction Rating for Schizophrenia, MIR(S)

1.5

Diagnosis: Schizophrenia, bipolar disorder History: All had previous therapy, 63% (5/8) had a clinical history of 10-20 years Setting: Day patients Country: USA

88%

N=8 Age: 30-45 Sex: 75% male

MT (I, P, S, L, V, O), weekly sessions of 60 min. N = 8

Offered: 6

Baseline

A) SCL-90R GSI C) Depression subscale of SCL-90R D) Obsessive-compulsive, Hostility, Paranoid deation, all SCL-90R subscales G) Not used: Attitude to seeking help (4 factors), Fisher & Turner Attitude Scale Not used: Helpfulness of therapeutic factors (10 factors), unpublished scale

6

Diagnosis: Schizotypal disorder, schizophrenia, schizoaffective disorder Setting: 89% (8/9) inpatients/day patients Country: Denmark

100%

N=9 Age: 23-40 (M = 29) Sex: 78% male

MT (L⁎, V⁎), 1 session pr. wk. of 90 min. N = 9

Attended: Range 23-32

Baseline

E) GAF G) Unable to use: Qualitative rating of therapy contents

c) Studies without control groups de l'Etoile Design: Pre-post (2002) Blindness: Not reported

Moe et al. (2000)

Design: Pre-post Blindness: Not reported

Thaut (1989)

3 Design: Pre-post Blindness: Not applicable (self-reported outcomes only)

Diagnosis: Schizophrenia, bipolar disorder, depression, adjustment disorder, suicidal tendencies Setting: Forensic patients Country: USA

70%

N = 50 Age: 18-45 Sex: 100% male

MT (I, P, L, V, O), 3 weekly sessions of 60-90 min. N = 50

Offered: 39 (3 per week over 13 weeks)

Baseline

C) (Mood, 1-item rating) G) Relaxation, 1-item rating; Positive thoughts, 1-item rating

Troice & Sosa (2003)

Design: Pre-post Blindness: Not reported

Diagnosis: Schizophrenia History: Chronic (mean duration of illness 8 yrs.) Setting: Outpatients Country: Mexico

100%

N = 15 Age: M = 32 (SD = 8) Sex: 67% male

MT (I⁎, V⁎), biweekly sessions of 1 hour. N = 15

Offered: 40; mean 35.8

Baseline

A) PANSS B) PANSS D) Positive symptoms, PANSS F) Unable to use (incompletely reported): Experiences with music G) Unable to use (incompletely reported): Subjective well-being

6

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a Including all music therapy interventions. MT – music therapy; working modes in MT: I – improvisation, P – playing music on instruments (excl. improvisation), S – singing songs, L – music listening, V – verbal reflection, O – other. Central working modes are marked with ⁎. b Including all non-music therapy interventions. c N = participants included in the study (including any who may have dropped out after inclusion). d Outcomes were categorized as follows: A) General mental state; B) Negative symptoms; C) Depressive symptoms; D) Other symptoms; E) Functioning and related; F) Music-related; G) Other. Outcomes partly related to a category are listed in brackets. Abbreviations of common outcome measures are explained in the text.

C. Gold et al. / Clinical Psychology Review 29 (2009) 193–207

Hayashi et al. (2002)

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C. Gold et al. / Clinical Psychology Review 29 (2009) 193–207

studies (Table 1). Standard care, in whatever specific way this was defined by the authors, included any form of treatment as usual which was provided to all participants (i.e. both experimental and control group). Only two studies included other comparisons: One study compared to cognitive behavior therapy (Zerhusen et al., 1995), one study compared two types of music therapy approaches (Ceccato et al., 2006). Meta-analyses were therefore only calculated for the comparison between music therapy and standard care. When various types of music therapy were provided simultaneously (Thaut, 1989), so that this prevented separation of the effects of each of these types, the study was included as an uncontrolled (pre-post) study, although it may have originally been described as a CCT. When a CCT comparing different types of music therapy also allowed for a pre-post comparison, but not a controlled comparison, of music therapy versus standard care (Ceccato et al., 2006), it was included as a CCT but treated as an uncontrolled study for the respective comparison. 3.5. Data extraction and preprocessing Data were reported in varying ways in the studies. When necessary, study authors were contacted to retrieve additional data. For some studies, we received from the study authors either individual patient data (Ceccato et al., 2006; Talwar et al., 2006; Ulrich et al., 2007) or unpublished summary data (Radulovic, 1996) which we were able to use. Log-transformation to remove skewness, based on individual patient data, was performed in one instance (negative symptoms in Ulrich et al., 2007). In two instances (negative symptoms in Ceccato et al., 2006, functioning in Ulrich et al., 2007), we calculated and used the average effect size of two equally valid measures for the same outcome category. In one instance (global state in Yang et al., 1998) we encountered missing values in a dichotomous outcome and inserted the negative event. 4. Results 4.1. Comparison of music therapy versus standard care For the comparison of music therapy versus standard care, there were four outcomes where we were able to estimate a dose–response relationship. In addition, there was a range of other outcomes where simple meta-analysis was performed. 4.1.1. General mental state Seven studies (Table 1), including 315 participants, measured general mental state on a continuous scale, using one of the following standardized measures: The Symptom Checklist SCL-90R General Severity Index (SCL-90R GSI), the Brief Symptom Inventory General Severity Index (BSI GSI), the Brief Psychiatric Rating Scale (BPRS), or the Positive and Negative Syndrome Scale (PANSS). The model selection process for this outcome is shown in Table 2. It can be seen that the number of sessions alone explained 78% of the variance in this outcome (p b .01). Design and disorder alone were not useful predictors. We also examined a full model adjusting

for all predictors simultaneously, which yielded no additional information (data not presented). Therefore, the number of sessions was selected as the only predictor for this outcome. The dose– response relationship is illustrated graphically in Fig. 1. An increasing trend can be seen in the symbols for the individual studies, as well as in the regression line from the mixed-effects model. From the regression model, it can be predicted how many sessions will be needed on average to achieve a certain effect. Table 3 shows that a small effect on general symptoms will be expected after ten sessions, a large effect after 39 sessions. 4.1.2. Negative symptoms Eight studies, with a total of 404 participants, measured negative symptoms on a continuous scale, typically using either the Scale for the Assessment of Negative Symptoms (SANS) or the negative symptoms subscale of the PANSS. One study (Ceccato et al., 2006) measured sub-domains of negative symptoms (attention and memory) using other scales (Table 1). Again, Table 2 shows the model selection process. As all studies involved exclusively participants with psychotic disorders, only design and dosage could be examined as potential predictors. As in the previous outcome, design was not a useful predictor, whereas dosage was highly significant and explained a large proportion of the variance. In this outcome, the square root of sessions turned out to be a better predictor than the untransformed number of sessions, and was therefore selected as the only predictor in the model. Fig. 2 illustrates the dose–response relationship for negative symptoms. Here, the regression line is curvilinear, showing a steep increase of effect for the first sessions and a moderate but continuing increase for later sessions. This is also reflected in Table 3, which shows that a small effect on negative symptoms can be expected already after as little as three sessions, whereas it takes 42 sessions to produce a large effect. 4.1.3. Depressive symptoms Data for depressive symptoms, measured on a continuous scale, were available from seven studies (319 participants). Measures used included the Hamilton Rating Scale for Depression (HAMD), the Beck Depression Inventory (BDI), and other related measures (Table 1). As for the previous outcomes, design and disorder showed no relation to the effect size, but dosage was a highly significant predictor, with the number of sessions explaining 73% of the variance in effects (Table 2). The steep linear relationship is shown in Fig. 3. Although there is one positive outlier (from an RCT—Hanser & Thompson, 1994), most studies fall into the confidence range of the prediction line, which appears to be equally valid for psychotic (white boxes) and nonpsychotic disorders (black boxes). Table 3 reflects the steep regression line, showing that small effects on depressive symptoms are expected after four sessions, and even large effects may occur after relatively few (16) sessions. 4.1.4. Other symptoms: Anxiety and positive symptoms Simple meta-analyses were applied for other symptom domains which were measured on continuous scales in less than five studies.

Table 2 The model selection process—explained variance (adjusted R2) for all possible mixed-effect models Variance explained by each model (Adjusted R2)

Outcome

N of studies

N of participants

Design

Disorder

Sessions

Square root of sessions

General symptoms Negative symptoms Depressive symptoms Level of functioning

7 8 7 5

315 404 319 215

.23 .03 .00 .00

.00 NAa .16 NAa

.78⁎⁎ .69⁎⁎ .73⁎⁎ .66⁎

.70⁎⁎ .77⁎⁎ .66⁎⁎ .74⁎

Note. The table shows explained variance (adjusted R2) and significance levels (⁎p b .05, ⁎⁎p b .01, ⁎⁎⁎p b .001) for each model. Negative values of adjusted R2 were set to zero. Full models including all predictors simultaneously were also examined but not presented as they did not improve the prediction for any of the outcomes. The selected models (i.e. the ones with the highest explained variance, if significant) are highlighted in bold font. a Not available (the predictor was constant for this outcome).

C. Gold et al. / Clinical Psychology Review 29 (2009) 193–207

201

Fig. 1. Dose–effect relationship of music therapy for general symptoms. Note. Each individual study is plotted at the position indicated by the number of sessions provided and the effect size found in that study. The box symbol for each study is filled white if the majority of participants had a psychotic disorder, and black otherwise. The size of the box represents each study's weight in the analysis. The vertical line added to each individual study indicates the 95% confidence interval (CI) of the observed effect; the line type (solid, dashed, or dotted) indicates the strength of the study's design. Finally, the dashed regression line shows the result of a mixed-effects meta-regression analysis, indicating the relationship between the number of sessions provided and the predicted effect size. The 95% CI of the regression is shown by the dotted lines around the regression line.

Anxiety was measured in three studies (108 participants) with the Hamilton Anxiety Rating Scale (HARS) or other related scales (Table 1). An initial meta-analysis of the three studies suggested a large and significant effect size, but also a high amount of statistical heterogeneity (Table 4). Visual inspection of the results revealed that the study with the weakest design (de l'Etoile, 2002, an uncontrolled study) was responsible for the heterogeneity. Therefore, the analysis was repeated with this study excluded. Meta-analysis of the two remaining, methodologically strong studies (Chen, 1992; Hanser & Thompson, 1994, both RCTs) yielded a large and significant effect (g = 1.31, p b .001) and no statistical heterogeneity (I2 = 0%). It should be noted that both studies included in this meta-analysis concerned people with depression. Positive symptoms were measured in four studies (170 participants), using the respective subscale of the PANSS or a related scale (Table 1). A meta-analysis of these studies did not reveal a significant effect; however, the confidence interval was wide enough to include potential effects of clinically meaningful size (Table 3).

4.1.5. Functioning Five studies (215 participants) had usable data on the effects of music therapy on functioning, using the Global Assessment of Functioning (GAF) or related scales (Table 1). The mixed-effects meta-analytic models shown in Table 2 suggested that design was not related to the effect. Type of disorder could not be examined as a predictor because all studies concerned people with psychotic disorders only. As for the previous outcomes, therapy “dosage” was the only strong and significant predictor of the effect of music therapy compared to standard care. The square-root model explained 74% of the variance (p b .05) and was selected as the best model. Fig. 4 shows that effects increase with the number of therapy sessions provided, most steeply during the first sessions. Table 3 shows the estimated number of sessions necessary for each effect size. 4.1.6. Musical engagement Two studies (107 participants) had usable data on music-related outcomes, measured on continuous scales (Table 1). These form a

Table 3 Model formulae and prediction of numbers of sessions needed to achieve relevant effects Outcome General symptoms Negative symptoms Depressive symptoms Functioning

Regression model .02 × sessions .12 × √ (sessions) .05 × sessions .11 × √ (sessions)

Number of sessions needed Small effect

Medium effect

Large effect

10 3 4 3

24 16 10 20

39 42 16 51

Note. This table shows the regression parameters of the previously described mixed-effects meta-regression models and predicted values based on these parameters. Small, medium, and large effects are defined according to Cohen's (1988) guidelines for the interpretation of the effect size index Cohen's d. The effect size index Hedges' g which was used in the calculation is comparable but corrected for small-sample bias (i.e., it is more conservative when studies are small, but asymptotically identical to Cohen's d).

202

C. Gold et al. / Clinical Psychology Review 29 (2009) 193–207

Fig. 2. Dose–effect relationship of music therapy for negative symptoms. Note. Explanations see under Fig. 1.

relatively heterogeneous category which might be summarized as “musical engagement”. One study (Pavlicevic, 1994) assessed musical interaction in a music therapy assessment session, the other study

(Hayashi et al., 2002) assessed musical experiences in daily life. Metaanalysis of these studies (Table 4) showed a medium-sized effect (g = 0.49, p b .05) with no heterogeneity (I2 = 0%), suggesting that music

Fig. 3. Dose–effect relationship of music therapy for depressive symptoms. Note. Explanations see under Fig. 1.

C. Gold et al. / Clinical Psychology Review 29 (2009) 193–207

203

Table 4 Meta-analyses for outcomes measured in less than five studies Outcome

N of studies

N of participants

Effect sizea

Heterogeneityb

a) Dichotomous outcomes Global state

2 (Chen 1992; Yang et al., 1998)

140

Odds ratio (95% CI) 0.03 (0.01 to 0.09)⁎⁎⁎ NNT = 1.59

I2 0%

4 (Hanser & Thompson, 1994; Pavlicevic et al., 1994; Talwar et al. 2006; Yang et al., 1998)

226

1.11 (.42 to 2.92)

0%

3 (Chen 1992, de l'Etoile 2002, Hanser & Thompson, 1994)

108

Hedges' g (95% CI) 1.05 (0.63 to 1.48)⁎⁎⁎

I2 73.8%⁎

Anxiety (excluding weak design)

2 (Chen 1992; Hanser & Thompson, 1994)

100

1.31 (0.85 to 1.78)⁎⁎⁎

0%

Positive symptoms

4 (de l'Etoile 2002; Hayashi et al., 2002; Talwar et al. 2006; Troice & Sosa, 2003)

170

0.18 (−0.12 to 0.48)

0%

Musical engagement

2 (Hayashi et al., 2002, Pavlicevic 1994)

107

0.49 (0.09 to 0.88)⁎

0%

Quality of life

2 (Hayashi et al., 2002; Ulrich et al., 2007)

103

0.16 (−0.24 to 0.56)

0%

Satisfaction

2 (Talwar et al. 2006; Ulrich et al., 2007)

118

Fixed: 0.13 (−0.28 to 0.53) Random: 0.06 (−0.57 to 0.68)

52%

Medication level

2 (Hayashi et al., 2002; Tang, 1994)

142

−0.25 (− 0.58 to 0.08)

41%

Leaving the study earlyc

b) Continuous outcomes Anxiety (initial analysis)

a

Effect sizes are shown for the fixed-effects models where no unexplained heterogeneity was found, and for both fixed and random-effects models where unexplained heterogeneity was found. For dichotomous outcomes where a significant effect was found, the number-needed-to-treat statistic (NNT) is also shown. All effect sizes were coded such that OR b 1 and g N 0 represent a positive effect. b 2 I describes the percentage of variability in effect estimates that is due to heterogeneity, rather than sampling error (Higgins & Green, 2008). Significance of heterogeneity is shown for the Q test. c Only calculated for controlled studies (RCTs and CCTs) with at least one drop-out in any group.

therapy improved the musical engagement of those receiving music therapy, compared to standard care. Rather than as a clinical endpoint in itself, the outcome might best be understood as an indicator of mechanisms of change in music therapy. 4.1.7. Other outcomes: Global state, leaving the study early, quality of life, satisfaction, and medication level Five further outcomes, two dichotomous and three continuous ones, were available from at least two but less than five studies. Metaanalyses for each of these outcomes are shown in Table 4. Global state (2 studies, 140 participants) was rated as a dichotomous outcome (no overall improvement as rated by a psychiatrist). The results were clearly in favor of music therapy, with a very low and significant odds ratio (OR = 0.03, p b .001) and no statistical heterogeneity (I2 = 0%). To improve interpretability, the result was also translated into the number needed to treat (NNT), which indicated that less than two patients need to be referred to music therapy so that one will benefit. It should be noted that this result was based on RCTs, that it concerned psychotic as well as depressed patients, and that (although dose–response relationship was not addressed for this outcome) both included studies provided a large number of sessions. The odds of leaving the study early (as a proxy measure of tolerability) were calculated from four controlled studies. The metaanalysis suggested no difference, indicating good tolerability of music therapy as well as of standard care. Quality of life, satisfaction with care, and medication level were each available from two studies. No significant effects were found for these outcomes. 4.2. Other outcomes and comparisons All outcomes with usable data from at least two studies were included in the analyses presented above, but we chose not to metaanalyze non-replicated outcomes with usable data from only one study. For the comparison of music therapy versus standard care, these included hostility, self-esteem (Hanser & Thompson, 1994), and engagement with services (Talwar et al., 2006). Non-replicated results concerning other comparisons included structured versus improvisa-

tional music therapy (Ceccato et al., 2006) and music therapy versus cognitive behavior therapy (Zerhusen et al., 1995). Later follow-ups (some months after termination of therapy) or intermediate assessments (during therapy) were included in some studies (Hanser & Thompson, 1994, Hayashi et al., 2002), but not frequently and consistently enough across studies to be included in a meta-analysis. 5. Discussion 5.1. Summary of findings This study is the most comprehensive systematic review and metaanalysis of the effects of music therapy in adult mental health to date. It showed that music therapy, when added to standard care, has strong and significant effects on global state, level of general symptoms, negative symptoms, depression, anxiety, functioning, and musical engagement. It showed further that the effects do not depend on diagnosis, which confirms music therapy's broad applicability. Neither did the results depend on study design, confirming the robustness of our findings. In contrast, effects do depend strongly on the number of sessions provided. For all outcomes where data were available from sufficiently many studies (i.e., for general symptoms, negative symptoms, depressive symptoms, and functioning), the results of the review suggest that the ‘dosage’ of music therapy was the best predictor of its effects, explaining more than 70% of the variance. This indicates clearly that the effects of music therapy are related to the number of sessions provided. For two of the outcomes, the square root of the number of sessions seemed to be a better predictor than the untransformed number of sessions, indicating that the dose– response relationship may be non-linear (increasing more steeply with the first few sessions) for negative symptoms and functioning. However, a linear dose–response relationship also fitted the data relatively well for all of these outcomes. From the findings it was estimated that between 16 sessions (for depressive symptoms) and 51 sessions (for functioning) will be needed until large effects are seen.

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Fig. 4. Dose–effect relationship of music therapy for functioning. Note. Explanations see under Fig. 1.

5.2. The evidence base for music therapy in mental health The findings of this review demonstrate that music therapy is an effective treatment for serious mental disorders with a clear dose– effect relationship. This extends the more basic knowledge from previous related reviews demonstrating music therapy's effectiveness for schizophrenia (Gold, Heldal, et al., 2005) and depression (Maratos et al., 2008). The fact that the size of music therapy's effect was not significantly related to the type of diagnosis for any of the outcomes examined makes most sense in the context of a dimensional model of mental health, which emphasizes the commonalities of the different mental disorders rather than conceptualizing them as distinct entities. This does not imply that no differences exist—but differences may be more likely to show on other than diagnoses-related dimensions. This will be discussed further under implications for practice. The same finding lends support to a contextual model of therapy which focuses on encounter, relationship, and therapeutic process, as opposed to a medical model where specific techniques are applied to treat specific diseases or symptoms (Wampold, 2001). Music therapy appears to be indicated for a broad range of serious mental disorders. The fact that the effect sizes found in this review were also not related to the type of study design justifies and underlines the appropriateness of the range of study designs included. Although nonrandomized study designs may bear a greater risk of bias and caution is warranted, such caution was applied in the analysis and no indication of bias was found. Overall, the fact that all study designs and all types of serious mental disorder included showed the same results strengthens the confidence with which conclusions can be drawn from this review. 5.3. The dose–response relationship in music therapy The one predictor that was significant consistently across all outcomes was the ‘dosage’ of music therapy. In line with previous

findings from research in verbal psychotherapy (Howard et al., 1986), our findings indicate that the effects of music therapy increase with the number of sessions provided. The number of sessions explained high proportions of the variance in effects (between 73% and 78%), indicating a clear and strong relationship. With the findings from this review, it is now possible to predict the expected effect size from the number of sessions, or to predict the number of sessions needed to achieve a given effect size. The results indicate that small effects are seen after 3 to 10 sessions; medium effects are achieved after 10 to 24 sessions and large effects after 16 to 51 sessions. This facilitates the planning of future research in the field and may also have direct implications for practice and policy. As others have noted, “the presence of a dose–response gradient may also increase our confidence in the findings of observational studies and thereby enhance the assigned quality of evidence” (Higgins & Green, 2008, p. 367). The findings of this review therefore confirm the effectiveness of music therapy and underline the strength of causal inference from the existing evidence. 5.4. Limitations The results of a meta-analysis depend firstly on the results of the individual studies included. Therefore, their limitations should be mentioned here first. We excluded studies with very high risk of bias (e.g. with very high drop-out rates) from the review. However, we did include studies with ‘weaker’ designs than the strongest ones that exist. This was done to enhance external validity, and the choice was accompanied by a clear strategy to statistically identify the impact of a potential bias. The results showed no impact of study design, indicating that their inclusion was justified in this sample of studies. However, other limitations of the primary studies' quality, as well as the quality of their reporting, should also be mentioned. In more than half of the studies it was uncertain whether or not outcome assessment was adequately blinded. Similarly, concealment of

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allocation (relevant in RCTs) was only rarely reported and often uncertain. It was not possible to assess the impact of methodological quality in greater detail in this meta-analysis, both due to the lack of methodological transparency and due to the need for parsimony in selecting predictor variables in meta-regression models. For example, it would have been desirable to assess the impact of adequate blinding as a further methodological predictor in addition to study design. Likewise, greater transparency had also been desirable for many clinical aspects, particularly for the kind of music therapy that was applied in the studies. While some aspects, such as duration, setting, and general working modalities were fairly clear from the study reports, it sometimes proved difficult to identify clearly the theoretical orientation and the formal qualification level of the music therapist(s) who applied the therapy. This may in part be related to the stage of development of music therapy as an academic discipline and as a regulated profession, both of which vary across countries. The implication of this limitation for the present review is that conclusions can only be about music therapy in general (within the definition provided in the beginning of this article), rather than about more specific theoretical or methodological approaches within music therapy. Relatedly, as the vast majority of studies compared music therapy to standard care and did not include an active control intervention, it is not possible at this stage to make any statements about the specificity of music therapy's effects. The current evidence suggests that music therapy has an effect. Based on that, a logical and useful next step in this field of research would be to examine to what extent this effect is due to its specific ingredients—the use of music—or due to other, more general factors. The potential impact of researcher allegiance has been much debated in psychotherapy research (e.g., Luborsky et al., 1999). Although it was not the focus of this review, it should be mentioned here as a potential limitation. It is plausible to assume that most studies in this review were carried out by researchers who have a somewhat positive allegiance towards music therapy. One notable exception is the Zerhusen et al. (1995) study, where music therapy was used as a comparison condition only, which might indicate neutral or negative allegiance towards music therapy. However, the high consistency of the dose–response relationship identified in this review makes bias from researcher allegiance seem unlikely here. Researcher allegiance is presumably independent of the number of sessions in a study; we can see no plausible reason why the impact of researcher allegiance should be greater in studies where many sessions were provided. Therefore, researcher allegiance can be ruled out as a potential threat to the validity of our findings. To summarize, the existence of a clear dose–response relationship strengthens the conclusion that the results reflect the true effects of therapy rather than methodological artifacts. Finally, the small number of studies deserve mentioning as a limitation. Specifically, one might ask how conclusive the results concerning the dose–effect relationship are, given that they are based on only 15 studies. Concerning this possible criticism, it is first important to note that these 15 studies reflect information from almost 700 patients, who were offered a widely ranging number of music therapy sessions. Secondly, one should recall the unusually high proportion of explained variance that was found in this meta-analysis. According to Cohen's (1988, pp. 413–414) guidelines for interpretation of effects in the behavioral sciences, an effect expressed as explained variance is “large” when R-squared is .26. The dose–effect relationship we discovered, with an R-squared of around .75 for all outcomes examined, by far exceeds the conventions of a large effect. This lends credibility to its interpretation as a true dose–effect relationship and makes alternative explanations unlikely (Higgins & Green, 2008). Nevertheless, there is a need for further studies, as is discussed further below. 5.5. Implications for practice The findings of this review have several implications for practice. First, they underline the value of music therapy as an effective

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treatment in mental health care. Music therapy helps patients with serious mental disorders to improve their general mental state, symptom levels, and level of functioning. This has been known before for patients with schizophrenia (Gold, Heldal, et al., 2005); the current review both confirms and extends the findings from that previous review. The current review extended from the previous meta-analysis by including not only schizophrenic, but also non-psychotic serious mental disorders. Compared to the previous review, the current review was also based on a broader selection of studies, including practice-based studies, which likely improved the generalizability and clinical applicability of the findings. Particular mention should be made of the important group of patients with depression where no meta-analysis existed previously (only a narrative review of depression studies was provided by Maratos et al., 2008). This broad range of patients will benefit if music therapy is added to their usual care. Second, the findings imply that the number of sessions is an important factor for music therapy to be beneficial. Small benefits of music therapy can be seen already after a few sessions, as may be most typical on an acute inpatient ward. However, for stronger, clinically more meaningful—and potentially more lasting—effects, a considerable number of sessions will be required. The findings therefore also underline the value of either intensive or long-term engagement of patients in music therapy, the latter of which may be most typical in outpatient settings or in private practice. Mood changes seem to occur more quickly than improvements in general symptom levels. It has to be noted, however, that the extent of individual benefit from music therapy will necessarily vary from patient to patient. Some may respond rapidly after few sessions, whereas others may need more time than expected and predicted by the model. Further, the results of this review do not tell us if, the total number of sessions being equal, a higher frequency of sessions over a shorter time or a lower frequency over a longer time will be more beneficial. This may also vary across client groups. As a third implication for practice, the lack of difference of effect between psychotic and non-psychotic disorders raises the question of differential indication for music therapy. If diagnosis is not the main determinant of music therapy's effect, then what other criteria might be more fruitful in determining who should receive music therapy? Psychotherapy researchers have argued that factors such as the match between therapist and client and the client's motivation for a specific type of therapy should be recognized more (Wampold, 2001). That the use of such “soft” indications can often be more fruitful than an uncritical ‘prescription’ based on diagnosis alone, is very much in accordance with our clinical experience as music therapists. For example, clients are often referred to music therapy because they are deemed unsuitable or unmotivated for verbal psychotherapy (Hannibal, 2005; Hanser & Thompson, 1994; Meschede et al., 1983; Rolvsjord, 2001; Solli, 2008). An international multicenter RCT is currently investigating the effects of music therapy for this specific population (Gold, Rolvsjord, et al., 2005). It is important to be aware that referral based on such types of indications requires referrers to think more carefully about the individual patient and necessitates more and better communication between referrers and therapists than referral based on diagnosis. For policy makers, it will be important to know how easily music therapy can be implemented into the care of seriously mentally disordered patients. In many countries, qualified music therapists with an appropriate level of training are available; in other countries, there may be an insufficient level of training, an insufficient number of qualified music therapists, or an insufficient focus of the music therapy training on mental health care. The findings of this review suggest that music therapists in this field need to be clinically skilled to enable a range of music experiences, as well as a fruitful reflection of these experiences, in a framework that offers both sufficient structure and openness towards the patient's individual therapeutic process. This requires extensive and adequate clinical training.

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5.6. Implications for future research

6. Conclusion

This review has established the efficacy and dose–response relationship of music therapy for people with serious mental disorders. This is an important, but still fairly general finding. Studies will be needed to fill gaps in client populations and to extend our knowledge on the effects of therapy variables other than dosage. Concerning client populations, the findings of this review indicate that not all mental disorders have been covered equally well. Most of the studies identified focused on either psychotic disorders or on depression. Some important disorders where music therapy is applied, including for example borderline personality disorder and eating disorders, have not received specific attention in music therapy outcome research to date. Furthermore, as noted in the previous section, there seem to be specific subgroups across diagnoses that warrant closer investigation, such as patients with low therapy motivation. Future research should attempt to close these gaps. Concerning the further specification of the treatment, effects of music therapy approaches may vary not only by the treatment “dose” (number of sessions), but also according to theoretical background, qualification of the therapist, therapeutic setting, and working modalities within therapy. Another wide area for more specific future studies on differences in types of music therapy will be related to individual therapist variables, which may be at least as important as the more formal characteristics of therapy (Wampold, 2001). As a related but different issue, it would also be useful to compare music therapy to active control conditions in order to establish to what extent the effect of music therapy is due to using music in therapy or due to other factors. To improve the methodological quality of future outcome research in the field, researchers should adhere to guidelines such as the CONSORT statement for RCTs (Moher, Schulz, & Altman, 2001) and related statements for other study designs (as listed on www.consortstatement.org). Many of the methodological weaknesses identified in the available research to date—from design aspects such as allocation concealment and blinding through to the adequate reporting of statistics—are related to transparency of reporting, which can easily be improved by using those guidelines. Specifically and concretely, the numeric results of the metaregression models can be used directly in the planning of future research. Power calculation, an issue which was long ignored—not only—in music therapy research (Gold, 2004), is now increasingly being used by music therapy researchers to identify the required sample size for their study hypotheses. One central assumption in power calculation is the expected effect size. The results of this review are a strong reminder that this effect size again depends to the number of sessions. Researchers planning an outcome study in the field should make use of this knowledge. One can use the model formulae and predicted values (Table 3) to make an informed decision on the expected effect size, based on the number of music therapy sessions to be provided. For example, in a study with general symptoms as the primary outcome, one can see from the table that a medium effect size is expected after 24 sessions, and a large effect size after 39 sessions. Power calculation then shows that the required sample size will decrease considerably (from 64 to 26 participants per group) if the higher number of sessions is chosen.1 Our findings therefore enable researchers to make more informed decisions in planning research by using the number of therapy sessions as a parameter in power calculation.

This review has shown that music therapy is an effective therapy for serious mental disorders, which helps patients to improve global state, symptoms, and functioning. This adds to the knowledge on effective therapy for a population which often does not respond easily to traditional approaches. Music therapy appears to contribute something unique to this field, with music helping in at least three different ways—as a motivating factor, as a medium for emotional expression, and as a social endeavor. At the same time, this research is rooted in the wider field of psychotherapy research, and its findings contribute to research on contextual models in psychotherapy (Wampold, 2001) as well as to research on dose–response relationship in psychotherapy (Howard et al., 1986). It is hoped that the findings of the present review will also be fruitful for those related fields, as well as furthering the knowledge and application of music therapy as a treatment that is rooted in good clinical practice, guided by adequate theory and supported by reliable evidence.

1 Calculated for an independent samples t-test comparing two groups of equal size, with significance level α = .05 and test power 1 − β = .80, using the function power.t.test in R. The same results can be found in the sample size tables in Cohen (1988).

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