Research in Developmental Disabilities 43–44 (2015) 123–135
Contents lists available at ScienceDirect
Research in Developmental Disabilities
Music-based Autism Diagnostics (MUSAD) – A newly developed diagnostic measure for adults with intellectual developmental disabilities suspected of autism Thomas Bergmann a,*, Tanja Sappok a, Albert Diefenbacher a, Sibylle Dames b, Manuel Heinrich a, Matthias Ziegler c, Isabel Dziobek c,d a
Protestant Hospital Ko¨nigin Elisabeth Herzberge, Herzbergstrasse 79, 10365 Berlin, Germany1 Statistics – Joint Masters Program Berlin, Freie Universita¨t Berlin, Garystr. 21, 14195 Berlin, Germany c Faculty of Life Sciences/Department of Psychology, Humboldt-Universita¨t zu Berlin, Unter den Linden 6, 10099 Berlin, Germany d Berlin School of Mind and Brain, Humboldt-Universita¨t zu Berlin, Unter den Linden 6, 10099 Berlin, Germany
b
A R T I C L E I N F O
A B S T R A C T
Article history: Received 25 January 2015 Received in revised form 20 April 2015 Accepted 28 May 2015 Available online
The MUSAD was developed as a diagnostic observational instrument in an interactional music framework. It is based on the ICD-10/DSM-5 criteria for autism spectrum disorder (ASD) and was designed to assess adults on a lower level of functioning, including individuals with severe language impairments. This study aimed to evaluate the psychometric properties of the newly developed instrument. Methods: Calculations were based on a consecutive clinical sample of N = 76 adults with intellectual and developmental disabilities (IDD) suspected of ASD. Objectivity, test-retest reliability, and construct validity were calculated and a confirmatory factor analysis was applied to verify a reduced and optimized test version. Results: The structural model showed a good fit, while internal consistency of the subscales was excellent (v > .92). Item difficulties ranged between .04 pi .82 and itemtotal correlation from .21 to .85. Objectivity was assessed by comparing the scorings of two external raters based on a subsample of n = 12; interrater agreement was .71 (ICC 2, 1). Reliability was calculated for four test repetitions: the average ICC (3, 1) was .69. Convergent ASD measures correlated significantly with the MUSAD, while the discriminant Modified Overt Aggression Scale (MOAS) showed no significant overlap. Conclusion: Confirmation of factorial structure and acceptable psychometric properties suggest that the MUSAD is a promising new instrument for diagnosing ASD in adults with IDD. ß 2015 Elsevier Ltd. All rights reserved.
Keywords: Autism Diagnostics Intellectual disability Assessment Music therapy
* Corresponding author at: Evangelisches Krankenhaus Ko¨nigin Elisabeth Herzberge, Herzbergstrasse 79, 10365 Berlin, Germany. Tel.: +49 30 5472 4951; fax: +49 3091741523. E-mail address: [email protected] (T. Bergmann). 1 Tel.: +49 30 5472 0; fax: +49 30 5472 2000. http://dx.doi.org/10.1016/j.ridd.2015.05.011 0891-4222/ß 2015 Elsevier Ltd. All rights reserved.
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1. Introduction Autism Spectrum Disorder (ASD) is a frequently co-occurring condition in individuals with intellectual developmental disabilities (IDD). The prevalence of IDD within the autism spectrum is estimated to be between 30% and 35% (Centers for Disease Control and Prevention, 2012; Fombonne, 2003b). Despite the clinical relevance of this group due to high rates of comorbid challenging behaviors leading to an above-average administration of antipsychotics (McCarthy et al., 2010; Sappok, Budczies, et al., 2014), and frequent admissions to inpatient treatment (Tsakanikos, Costello, Holt, Sturmey, & Bouras, 2007), research activities focusing on adults in the low-functioning range of the autism spectrum are rare (Matson & Shoemaker, 2009). There is a lack of diagnostic standards assessing ASD in adults with IDD, especially in those with limited language skills (Bo¨lte & Poustka, 2005, 2005; Matson & Shoemaker, 2009). Generally ASD seems to be under-diagnosed in adulthood (Brugha et al., 2011): reasons for this may be the change of diagnostic criteria over the decades, increasing sensitivity to ASD in children or individual adaptation to social demands. In adults with IDD, diagnostics are further complicated by, for example, limited self-report and a lack of information about early child development due to loss of contact with families. Symptom overlap with schizophrenia, long-term hospitalization, severe sensory impairments, and IDD itself may lead to misinterpretation and wrong treatment concepts (Akande, Xenitidis, Roberston, & Gorman, 2004; Sappok, Bergmann, Kaiser, & Diefenbacher, 2010). In cases of suspected ASD, comprehensive diagnostics is the basis for adequate treatment and support, enhancing health, reducing challenging behaviors, developing social and emotional skills and leading to a better quality of life. In children and young people, a huge number of ASD screening tools and a diagnostic gold standard including a parental interview, the Autism Diagnostic Interview-Revised (ADI-R; Lord, Rutter, & Le Couteur, 1994) and a play- and interview-based behavior observational assessment, namely the Autism Diagnostic Observation Schedule (ADOS; Lord et al., 1989), allow for valid diagnostic statements in cases where ASD is suspected. While increasing numbers of specific tools and questionnaires have been developed to screen for ASD in adults with IDD in recent years, such as the Pervasive Developmental Disorder in Mental Retardation Scale (PDD-MRS; Kraijer & Bildt, 2005), the Autism Spectrum Disorders – Diagnosis for intellectually disabled Adults (ASD-DA; Matson, Wilkins, Boisjoli, & Smith, 2008), the Diagnostic Behavioral assessment for Autism Spectrum disorder – Revised (DIBAS-R; Sappok, Gaul, et al., 2014), and the Autism Check List (ACL; Sappok, Heinrich, & Diefenbacher, 2014), there is a lack of diagnostic standards and a specific measure for structured behavioral observation in adults with IDD and severe language impairment. Even though the ADOS is generally applicable in adults with IDD (Berument et al., 2005; Sappok, Diefenbacher, et al., 2013), childlike materials and prompts seem to be inappropriate in assessing adults. Additionally, limited feasibility is reported, correlating with the severity of IDD and speech impairments (Bergmann, Sappok, Diefenbacher, & Dziobek, 2015; Sappok, Diefenbacher, et al., 2013). In light of the lack of specific ASD diagnostic measures for adults on a lower level of functioning, valid procedures based on nonverbal communication are highly desirable. Musical interaction as a nonverbal means of communication and an adult form of play may build a framework to assess ASD in adults with IDD (Wigram, 2000). The strong connection between music and ASD is described in terms of exceptional musical interests and abilities early on by Kanner (1943), and has been supported recently by a Cochrane review of music therapy in the treatment of children with ASD (Geretsegger, Elefant, Kim, & Gold, 2014). But what kind of diagnostic information might music provide? Interactional skills, social affect, and reciprocity are to be observed in joint music-making, including individuals with severe language impairments. Stereotyped, restricted, and repetitive behaviors and interests may occur in musical exploration and expression. Multisensory aspects of musical instruments (auditive, visual, haptic, olfactory) allow the investigation of abnormal sensory interests. Motor coordination and mannerisms are to be seen in the ways in which instruments are handled (right-left drum beat) and in movements like tapping and dancing. Overall, most behavioral characteristics listed as core symptoms of ASD in the ICD-10 and DSM-5 can be observed in musical action and interaction (Bergmann et al., 2015). Given the background of the strong association between music and ASD, several assessment tools and two explicit music-based diagnostic instruments have been developed in the field of ASD: Wigram’s Harper House Music Therapy Assessment (1999) and the Music Therapy Diagnostic Assessment (MTDA; Oldfield, 2004). Both were developed to assess children and lack a comprehensive psychometric verification. In 2009, the Music-based Scale for Autism Diagnostics (MUSAD) was designed in a music therapeutic setting alongside further specific instruments for ASD screening in this group of patients (Sappok, Gaul, et al., 2014; Sappok, Heinrich, et al., 2014). The instrument was developed along the ICD-10 research criteria for autism (F84.0, F84.1) taking into account the latest changes made in the DSM-5 (Bergmann et al., 2015). The concept is comparable to the ADOS, using prompts to provoke diagnostic relevant behaviors that are to be coded on a 4-point Likert scale regarding the severity of symptom expression in the autistic spectrum. Ten predefined active musical interactional situations were used to create a playful, naturalistic, and age-appropriate framework, also engaging non-speakers in a diagnostic assessment. The implementation procedure is as follows: 1. Free play (warm-up); 2. Piano (joint attention); 3. Gongs (dynamic/affective attunement); 4. Congas (musical dialog); Break; 5. Sing a song (socio-emotional togetherness); 6. Ocean drum (contact via instrument, imagination); 7. Symbolic instruments (pretend play); 8. Music selection (asking for help); 9. Balloon game (turn-taking): and 10. Dancing together (bodily synchronization). The conga situation may provide an example of the entire diagnostic work-up:
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1. Implementation The Investigator initiates joint play with a common pulse, followed by slight tempo changes and again stabilization of a basic beat. The next task is to hit the drum with alternating right and left hand. Next are simple motifs and breaks in order to initiate interplay; if the client does not react or stops, he or she is supported verbally and gesturally. Finally a crescending drum roll with release of the suspense in a final blow invites the client to share affectivity. 2. Description To change perspective, the investigator is first asked to describe the client’s behaviors in free-text along predetermined observation priorities: i.e., in this case, motor coordination, imitation skills, metric synchronization and variations, turntaking skills, social reciprocity and shared joy. 3. Scoring Task-specific items like ‘‘Rhythmic synchronization of tempo changes’’ or overall items like ‘‘Joy in playing together’’ are to be scored on a 4-point scale in order to operationalize ASD-related behaviors. (For a complete description see: Bergmann et al., 2015). Eighty-eight items are grouped in five domains according to ASD main characteristics, including (1) social interaction, (2) communication, (3) stereotyped and repetitive behaviors, (4) sensory-motor issues, and (5) affective dysregulation. Temper tantrums, aggression, and self-injury are mentioned as ‘‘nonspecific problems’’ in the ICD-10, so we added the last domain (affective dysregulation) as a possible ASD marker. Regarding the future development of diagnostic criteria and diagnostic understanding of ASD, motor issues were also included. The scale consists of two modules, one for verbal and one for nonverbal individuals. However, the two modules differ only in the domain of social communication (2), involving a set of verbal items or an alternative nonverbal item set without influence for conducting the investigation. The inclusion of low-level musical interventions without the requirement to imitate (Bergmann et al., 2011; Schumacher & Calvet, 2007) and a course of tasks with increasing demands on social and physical contact was developed in order to decrease irritability and rejection in individuals with a low level of functioning facing an unfamiliar environment. In a previous study, a feasibility of 95% was achieved when applying the MUSAD in adults with IDD (Bergmann et al., 2015), which is considerably higher than the feasibility for the ADOS in this group, which was reported at 81% (Berument et al., 2005; Sappok, Diefenbacher, et al., 2013). The primary objective of the present study was to examine the MUSAD along the main criteria for test quality, i.e., objectivity, reliability, and validity based on a clinical sample. The secondary aim was the improvement of test economy by reducing the number of items. 2. Material and methods 2.1. Procedure Data collection with the newly developed MUSAD was conducted at a psychiatric department that specialized in mental health care for adults with IDD in Berlin, Germany. This service consists of an inpatient and outpatient unit and offers assessment and treatment for adults with IDD and mental disorders and/or severe challenging behaviors. Given this setting, all participants in this study had an additional mental or behavioral problem on admission. In case of suspected ASD, the diagnostic assessment, including MUSAD investigation, was made after remission of acute exacerbation of the psychiatric illness, mainly at the outpatient clinic. Diagnostic classification including ASD and severity of IDD was conducted in accordance with the diagnostic research criteria for mental disorders proposed by ICD-10 (World Health Organization, 2008). The MUSAD assessment took place in a big room equipped with a predefined set of standard music therapy instruments arranged according to the course of musical-interactional diagnostic situations (Bergmann, Sappok, Diefenbacher, & Dziobek, 2012). The admission of the procedure was based on a manual with prescribed interventions for each musical-interactional situation. The MUSAD manual is part of the unpublished test draft (for a complete description see also: Bergmann et al., 2015). All sessions were videotaped to allow better subsequent diagnostic behavioral observation and scoring as well as to gain material to assess interrater reliability. All investigations were carried out by the test developer, who was blinded for the final diagnosis, all scorings had been carried out before the final clinical diagnosis was made. Although the diagnostic team was blinded to the MUSAD scoring, single video sequences were used to support diagnostic decisionmaking in cases of diagnostic uncertainty. However, no information about the scoring of certain items or overall sumscores was provided. ASD diagnoses were assigned by a multidisciplinary team consensus conference according to the ICD-10 diagnostic research criteria for autism or atypical autism (F84.0/F84.1). If no information about the developmental history could be obtained, atypical autism was diagnosed. The multi-disciplinary team consisted of at least one psychiatrist, a clinical psychologist, a special-needs caregiver, therapists, and a member of the nursing staff who was experienced in the fields of IDD and ASD. Diagnostic classification was based on all available information, including medical histories, psychiatric and physical examinations, video-based behavior analyses across a variety of contexts, and various standardized measures such as the PDD-MRS (Kraijer & Bildt, 2005; Kraijer & Melchers, 2003), the German version of the Social Communication Questionnaire-current (FSK-aktuell; Bo¨lte, Poustka, Rutter, Bailey, & Lord, 2006), the ACL (Sappok, Heinrich, et al., 2014), and, in cases of diagnostic uncertainty, the ADOS (Bo¨lte, Rutter, Le Couteur, & Lord, 2006), and/or the ADI-R (Bo¨lte, Rutter, et al.,
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2006). The SCQ-current was completed by an informant from the patient’s private living environment; the PDD-MRS, the ADOS, and the ADI-R were completed by a psychologist (H.K.) who was not involved in the study. Existing data from diagnostic procedures were used, and these procedures were performed with the informed consent of the patients as a part of routine patient care (National Hospital Law § 25.1, version 18.09.2011). This study was part of a larger study on the development and adaptation of instruments for ASD diagnosis in this group (Sappok, Budczies, et al., 2013; Sappok, Gaul, et al., 2014; Sappok, Heinrich, et al., 2014) which was approved by the local ethics committee and was conducted according to the recommendations of the Declaration of Helsinki. 2.2. Sample In the period between 1/2010 and 12/2011 the MUSAD was applied to 91 adult patients who were consecutively included in the diagnostic procedure. Either ASD diagnostics were part of the treatment contract or ASD was suspected as a result of clinical behavioral observation in the inpatient or outpatient setting, conspicuous biographical disclosures and/or unclear previous findings. Inclusion criteria were age >18 years and the presence of an IDD (ICD-10: F70-73). There were no further selection criteria except for limitations like logistic problems or missing consent documents, resulting in an ad-hoc sample reflecting clinical reality. In our in- and outpatient unit, the study participants did not receive regular music therapy or any form of musical training allowing the development of musical skills in advance of the MUSAD investigation. During the three months preceding the study, the procedure was tested and slightly edited based on observations with 11 patients. Of the 80 cases included in this study, four were excluded due to profound sensory impairments and rejection of the procedure. All calculations were based on the remaining N = 76. For demographic and clinical characteristics of the study sample, see Table 1. ASD was diagnosed in 50 participants (66%), while the remaining 26 participants (34%) did not show ASD but were diagnosed with schizophrenia, mood disorders, attachment disorders, sensory deficits, obsessive-compulsive behaviors, attention deficit hyperactivity disorder, or challenging behaviors on a background of IDD. The gender distribution in the ASD group was 42 men to eight women, reflecting the well-known accumulation of ASD in males (Fombonne, 2003a). A similarly unequal distribution (21 to 5) was also found in the non-ASD group, suggesting an increased suspicion of autism in males. Eighteen participants were non-verbal, and 15 were able to speak in single words, indicating a high proportion (43%) of participants with profound expressive language impairments within the entire sample. Since deficits in verbal and nonverbal communication are closely associated with autism, profound speech impairments were found in 52% of the ASD group compared to 27% in individuals with IDD only. There were no significant differences in any clinical or demographic characteristic between the ASD and the IDD only group. 2.3. Measures Convergent scales to screen for ASD used in this study are listed below, followed by more elaborate diagnostic procedures and discriminant measures.
Table 1 MUSAD sample characteristics. Characteristic Gender Females Males Age Years M (SD) IQ, intellectual disability .92 (Cicchetti et al., 2011). Values of Cronbach’s Alpha >.8 have been reported for the IDD specific PDD-MRS (Kraijer & Bildt, 2005), the ASD-DA (Matson et al., 2008), and the DIBAS-R (Sappok, Gaul, et al., 2014). Item difficulties of all 37 items included in the CFA showed high variability and ranged between .04 (particular interest for parts of objects) and .82 (reactive social smile). Varying difficulties are desirable in assessing a broad spectrum of autismrelated behaviors and a divergent group of individuals with mild to profound IDD. In particular, items with high difficulties
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are probably useful in displaying differences between individuals with a high symptom load. Overall, we found high itemtotal correlations: only item MUS306 (particular interest for parts of objects) was below the threshold of >0.3 (Ferketich, 1991; Maltby, Day, & Macaskill, 2010). At the same time, this item is the most peripheral one with the highest difficulty. For reasons discussed above, we decided to keep this item under observation in further scale development. To assess objectivity, interrater agreement was calculated in terms of ICC. Agreement between the two external raters (ICC = .71) as well as between three raters including the test developer (ICC = .67) are ‘‘good’’ according to the cutoffs recommended by Cicchetti and Prusoff (1983). This is indicative of sufficient objectivity in the test developer’s ratings. However, compared to the results of the ADOS pilot study (Lord et al., 2000) with more than 80% exact agreement among raters and across all modules, the MUSAD results seem to be worthy of improvement. These improvements in interrater reliability could be achieved by training of the raters including consensus conferences, further item selections, the revision of the 4-point coding descriptions, and a more stringent execution of the MUSAD. In assessing the stability of the MUSAD over time, the mean value of four test-retest correlations (ICC = .69) was acceptable. The ADOS showed similar results, with an ICC of .82 for the SC domain and .56 for RRB (Lord et al., 2000). Assessing convergent validity by calculating correlations between the MUSAD total scores and the SCQ, the PDD-MRS and ADOS resulted as expected in positive significant correlations ranging from .32 to .85. Overall, these results support the contention that the MUSAD measures ASD symptoms. Looking more closely at convergent scales, the highest correlations were found with the ADOS modules 1 and 2 (r = .58 resp .83). This may be caused by the conceptual comparability in assessing situation-specific interactional behaviors of the MUSAD and ADOS assessments, but due to small sample sizes, the data should be interpreted with caution. A correlation of r = .55 was found with the PDD-MRS, which is similar to the convergent validity of the ASD-DA (Matson et al., 2008) and the DIBAS-R (Sappok, Gaul, et al., 2014), both IDD-specific ASD screeners. The comparatively moderate agreement of the MUSAD with the SCQ sum score may be caused by the fact that the SCQ is designed to assess children and does not catch all aspects of an adult ASD/ID phenotype or a reduced parallelism due to the postponement of MUSAD items assessing verbal communication. In assessing discriminant validity, the low correlation with the MOAS supports the independence of measured constructs. However, unexpectedly, we found a weak positive relationship between MUSAD and ABC scores. Interestingly, closer examination shows that two of five ABC domains cover ASD core features that are also covered by the MUSAD, i.e., lethargy/social withdrawal and stereotyped behavior; while another two cover additional ASD behavioral characteristics, i.e., irritability and hyperactivity/ noncompliance. The ABC is a widely used measure in ASD treatment studies (Kaat, Lecavalier, & Aman, 2014), and has been used diagnostically to identify additional ASD in individuals with Down’s Syndrome (Ji, Capone, & Kaufmann, 2011). These strong associations between behaviors assessed with the ABC and ASD in individuals with IDD suggest that the ABC is less useful to assess discriminant validity in further studies. Significant correlations between the MUSAD and ASD measures on the one hand and a negligible correlation with the MOAS indicates construct validity of the MUSAD, i.e., the potential to detect the adult IDD phenotype of ASD. To summarize, the MUSAD showed adequate psychometric properties in terms of reliability, objectivity and factorial validity. However, interrater reliability should be improved and tested in further scale development. Compared to other music-based approaches in diagnosing ASD, the development of the MUSAD allowed a comprehensive psychometric verification in this field for the first time with an adequate sample size. Wigram’s Harper House Music Therapy Assessment (1999) is documented by a single case study and is based on Bruscia’s Improvisation Assessment Profiles (IAP; Bruscia, 1987), which have not yet been validated at all. Oldfield’s Music Therapy Diagnostic Assessment (MTDA; Oldfield, 2004) did not allow a comprehensive test-theoretical verification due to a small sample size of N = 30. Both instruments were developed to assess children, and not adults, suspected of having ASD. Therefore, within music-based approaches to diagnosing ASD, the MUSAD is the first instrument which strives for adequate psychometric properties and it is the first music-based approach to assess adults with IDD. Compared to the ADOS, the strength of the MUSAD concept is its slightly more flexible approach to make the situation more suitable and to allow for assessment of individuals who are difficult to assess, such as those with severe to profound intellectual disability (Bergmann et al., 2015). Since further scale development intends to develop an algorithm, the MUSAD may become an additional valuable source for gaining relevant diagnostic information and may improve the diagnostic process in diagnosing ASD in adults with IDD, especially in those with limited language skills. 4.1. Limitations The consecutive clinical ad-hoc sample of adults with suspected ASD investigated here resulted in a blurred separation of groups by assessing ‘‘borderline autistic’’ individuals and an unbalanced ratio of n = 50 ASD vs. n = 26 non-ASD. Due to the lack of a balanced and clearly separated control group of individuals with IDD only, the investigation of diagnostic validity, including ROC analysis and the calculation of a diagnostic algorithm, was postponed. In factor analysis a case number of more than 100 is recommended (MacCallum, Widaman, Zhang, & Hong, 1999). The smaller sample size of N = 76 is explained by the monocentric study design in this early phase of test development and the high examination effort of behavioral observational instruments. Against the backdrop of only three given factors, the absence of estimation problems, excellent internal consistency, and fit indices indicating good model fit, the final CFA model seems to be sufficiently robust despite a sample size
Contents lists available at ScienceDirect
Research in Developmental Disabilities
Music-based Autism Diagnostics (MUSAD) – A newly developed diagnostic measure for adults with intellectual developmental disabilities suspected of autism Thomas Bergmann a,*, Tanja Sappok a, Albert Diefenbacher a, Sibylle Dames b, Manuel Heinrich a, Matthias Ziegler c, Isabel Dziobek c,d a
Protestant Hospital Ko¨nigin Elisabeth Herzberge, Herzbergstrasse 79, 10365 Berlin, Germany1 Statistics – Joint Masters Program Berlin, Freie Universita¨t Berlin, Garystr. 21, 14195 Berlin, Germany c Faculty of Life Sciences/Department of Psychology, Humboldt-Universita¨t zu Berlin, Unter den Linden 6, 10099 Berlin, Germany d Berlin School of Mind and Brain, Humboldt-Universita¨t zu Berlin, Unter den Linden 6, 10099 Berlin, Germany
b
A R T I C L E I N F O
A B S T R A C T
Article history: Received 25 January 2015 Received in revised form 20 April 2015 Accepted 28 May 2015 Available online
The MUSAD was developed as a diagnostic observational instrument in an interactional music framework. It is based on the ICD-10/DSM-5 criteria for autism spectrum disorder (ASD) and was designed to assess adults on a lower level of functioning, including individuals with severe language impairments. This study aimed to evaluate the psychometric properties of the newly developed instrument. Methods: Calculations were based on a consecutive clinical sample of N = 76 adults with intellectual and developmental disabilities (IDD) suspected of ASD. Objectivity, test-retest reliability, and construct validity were calculated and a confirmatory factor analysis was applied to verify a reduced and optimized test version. Results: The structural model showed a good fit, while internal consistency of the subscales was excellent (v > .92). Item difficulties ranged between .04 pi .82 and itemtotal correlation from .21 to .85. Objectivity was assessed by comparing the scorings of two external raters based on a subsample of n = 12; interrater agreement was .71 (ICC 2, 1). Reliability was calculated for four test repetitions: the average ICC (3, 1) was .69. Convergent ASD measures correlated significantly with the MUSAD, while the discriminant Modified Overt Aggression Scale (MOAS) showed no significant overlap. Conclusion: Confirmation of factorial structure and acceptable psychometric properties suggest that the MUSAD is a promising new instrument for diagnosing ASD in adults with IDD. ß 2015 Elsevier Ltd. All rights reserved.
Keywords: Autism Diagnostics Intellectual disability Assessment Music therapy
* Corresponding author at: Evangelisches Krankenhaus Ko¨nigin Elisabeth Herzberge, Herzbergstrasse 79, 10365 Berlin, Germany. Tel.: +49 30 5472 4951; fax: +49 3091741523. E-mail address: [email protected] (T. Bergmann). 1 Tel.: +49 30 5472 0; fax: +49 30 5472 2000. http://dx.doi.org/10.1016/j.ridd.2015.05.011 0891-4222/ß 2015 Elsevier Ltd. All rights reserved.
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1. Introduction Autism Spectrum Disorder (ASD) is a frequently co-occurring condition in individuals with intellectual developmental disabilities (IDD). The prevalence of IDD within the autism spectrum is estimated to be between 30% and 35% (Centers for Disease Control and Prevention, 2012; Fombonne, 2003b). Despite the clinical relevance of this group due to high rates of comorbid challenging behaviors leading to an above-average administration of antipsychotics (McCarthy et al., 2010; Sappok, Budczies, et al., 2014), and frequent admissions to inpatient treatment (Tsakanikos, Costello, Holt, Sturmey, & Bouras, 2007), research activities focusing on adults in the low-functioning range of the autism spectrum are rare (Matson & Shoemaker, 2009). There is a lack of diagnostic standards assessing ASD in adults with IDD, especially in those with limited language skills (Bo¨lte & Poustka, 2005, 2005; Matson & Shoemaker, 2009). Generally ASD seems to be under-diagnosed in adulthood (Brugha et al., 2011): reasons for this may be the change of diagnostic criteria over the decades, increasing sensitivity to ASD in children or individual adaptation to social demands. In adults with IDD, diagnostics are further complicated by, for example, limited self-report and a lack of information about early child development due to loss of contact with families. Symptom overlap with schizophrenia, long-term hospitalization, severe sensory impairments, and IDD itself may lead to misinterpretation and wrong treatment concepts (Akande, Xenitidis, Roberston, & Gorman, 2004; Sappok, Bergmann, Kaiser, & Diefenbacher, 2010). In cases of suspected ASD, comprehensive diagnostics is the basis for adequate treatment and support, enhancing health, reducing challenging behaviors, developing social and emotional skills and leading to a better quality of life. In children and young people, a huge number of ASD screening tools and a diagnostic gold standard including a parental interview, the Autism Diagnostic Interview-Revised (ADI-R; Lord, Rutter, & Le Couteur, 1994) and a play- and interview-based behavior observational assessment, namely the Autism Diagnostic Observation Schedule (ADOS; Lord et al., 1989), allow for valid diagnostic statements in cases where ASD is suspected. While increasing numbers of specific tools and questionnaires have been developed to screen for ASD in adults with IDD in recent years, such as the Pervasive Developmental Disorder in Mental Retardation Scale (PDD-MRS; Kraijer & Bildt, 2005), the Autism Spectrum Disorders – Diagnosis for intellectually disabled Adults (ASD-DA; Matson, Wilkins, Boisjoli, & Smith, 2008), the Diagnostic Behavioral assessment for Autism Spectrum disorder – Revised (DIBAS-R; Sappok, Gaul, et al., 2014), and the Autism Check List (ACL; Sappok, Heinrich, & Diefenbacher, 2014), there is a lack of diagnostic standards and a specific measure for structured behavioral observation in adults with IDD and severe language impairment. Even though the ADOS is generally applicable in adults with IDD (Berument et al., 2005; Sappok, Diefenbacher, et al., 2013), childlike materials and prompts seem to be inappropriate in assessing adults. Additionally, limited feasibility is reported, correlating with the severity of IDD and speech impairments (Bergmann, Sappok, Diefenbacher, & Dziobek, 2015; Sappok, Diefenbacher, et al., 2013). In light of the lack of specific ASD diagnostic measures for adults on a lower level of functioning, valid procedures based on nonverbal communication are highly desirable. Musical interaction as a nonverbal means of communication and an adult form of play may build a framework to assess ASD in adults with IDD (Wigram, 2000). The strong connection between music and ASD is described in terms of exceptional musical interests and abilities early on by Kanner (1943), and has been supported recently by a Cochrane review of music therapy in the treatment of children with ASD (Geretsegger, Elefant, Kim, & Gold, 2014). But what kind of diagnostic information might music provide? Interactional skills, social affect, and reciprocity are to be observed in joint music-making, including individuals with severe language impairments. Stereotyped, restricted, and repetitive behaviors and interests may occur in musical exploration and expression. Multisensory aspects of musical instruments (auditive, visual, haptic, olfactory) allow the investigation of abnormal sensory interests. Motor coordination and mannerisms are to be seen in the ways in which instruments are handled (right-left drum beat) and in movements like tapping and dancing. Overall, most behavioral characteristics listed as core symptoms of ASD in the ICD-10 and DSM-5 can be observed in musical action and interaction (Bergmann et al., 2015). Given the background of the strong association between music and ASD, several assessment tools and two explicit music-based diagnostic instruments have been developed in the field of ASD: Wigram’s Harper House Music Therapy Assessment (1999) and the Music Therapy Diagnostic Assessment (MTDA; Oldfield, 2004). Both were developed to assess children and lack a comprehensive psychometric verification. In 2009, the Music-based Scale for Autism Diagnostics (MUSAD) was designed in a music therapeutic setting alongside further specific instruments for ASD screening in this group of patients (Sappok, Gaul, et al., 2014; Sappok, Heinrich, et al., 2014). The instrument was developed along the ICD-10 research criteria for autism (F84.0, F84.1) taking into account the latest changes made in the DSM-5 (Bergmann et al., 2015). The concept is comparable to the ADOS, using prompts to provoke diagnostic relevant behaviors that are to be coded on a 4-point Likert scale regarding the severity of symptom expression in the autistic spectrum. Ten predefined active musical interactional situations were used to create a playful, naturalistic, and age-appropriate framework, also engaging non-speakers in a diagnostic assessment. The implementation procedure is as follows: 1. Free play (warm-up); 2. Piano (joint attention); 3. Gongs (dynamic/affective attunement); 4. Congas (musical dialog); Break; 5. Sing a song (socio-emotional togetherness); 6. Ocean drum (contact via instrument, imagination); 7. Symbolic instruments (pretend play); 8. Music selection (asking for help); 9. Balloon game (turn-taking): and 10. Dancing together (bodily synchronization). The conga situation may provide an example of the entire diagnostic work-up:
T. Bergmann et al. / Research in Developmental Disabilities 43–44 (2015) 123–135
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1. Implementation The Investigator initiates joint play with a common pulse, followed by slight tempo changes and again stabilization of a basic beat. The next task is to hit the drum with alternating right and left hand. Next are simple motifs and breaks in order to initiate interplay; if the client does not react or stops, he or she is supported verbally and gesturally. Finally a crescending drum roll with release of the suspense in a final blow invites the client to share affectivity. 2. Description To change perspective, the investigator is first asked to describe the client’s behaviors in free-text along predetermined observation priorities: i.e., in this case, motor coordination, imitation skills, metric synchronization and variations, turntaking skills, social reciprocity and shared joy. 3. Scoring Task-specific items like ‘‘Rhythmic synchronization of tempo changes’’ or overall items like ‘‘Joy in playing together’’ are to be scored on a 4-point scale in order to operationalize ASD-related behaviors. (For a complete description see: Bergmann et al., 2015). Eighty-eight items are grouped in five domains according to ASD main characteristics, including (1) social interaction, (2) communication, (3) stereotyped and repetitive behaviors, (4) sensory-motor issues, and (5) affective dysregulation. Temper tantrums, aggression, and self-injury are mentioned as ‘‘nonspecific problems’’ in the ICD-10, so we added the last domain (affective dysregulation) as a possible ASD marker. Regarding the future development of diagnostic criteria and diagnostic understanding of ASD, motor issues were also included. The scale consists of two modules, one for verbal and one for nonverbal individuals. However, the two modules differ only in the domain of social communication (2), involving a set of verbal items or an alternative nonverbal item set without influence for conducting the investigation. The inclusion of low-level musical interventions without the requirement to imitate (Bergmann et al., 2011; Schumacher & Calvet, 2007) and a course of tasks with increasing demands on social and physical contact was developed in order to decrease irritability and rejection in individuals with a low level of functioning facing an unfamiliar environment. In a previous study, a feasibility of 95% was achieved when applying the MUSAD in adults with IDD (Bergmann et al., 2015), which is considerably higher than the feasibility for the ADOS in this group, which was reported at 81% (Berument et al., 2005; Sappok, Diefenbacher, et al., 2013). The primary objective of the present study was to examine the MUSAD along the main criteria for test quality, i.e., objectivity, reliability, and validity based on a clinical sample. The secondary aim was the improvement of test economy by reducing the number of items. 2. Material and methods 2.1. Procedure Data collection with the newly developed MUSAD was conducted at a psychiatric department that specialized in mental health care for adults with IDD in Berlin, Germany. This service consists of an inpatient and outpatient unit and offers assessment and treatment for adults with IDD and mental disorders and/or severe challenging behaviors. Given this setting, all participants in this study had an additional mental or behavioral problem on admission. In case of suspected ASD, the diagnostic assessment, including MUSAD investigation, was made after remission of acute exacerbation of the psychiatric illness, mainly at the outpatient clinic. Diagnostic classification including ASD and severity of IDD was conducted in accordance with the diagnostic research criteria for mental disorders proposed by ICD-10 (World Health Organization, 2008). The MUSAD assessment took place in a big room equipped with a predefined set of standard music therapy instruments arranged according to the course of musical-interactional diagnostic situations (Bergmann, Sappok, Diefenbacher, & Dziobek, 2012). The admission of the procedure was based on a manual with prescribed interventions for each musical-interactional situation. The MUSAD manual is part of the unpublished test draft (for a complete description see also: Bergmann et al., 2015). All sessions were videotaped to allow better subsequent diagnostic behavioral observation and scoring as well as to gain material to assess interrater reliability. All investigations were carried out by the test developer, who was blinded for the final diagnosis, all scorings had been carried out before the final clinical diagnosis was made. Although the diagnostic team was blinded to the MUSAD scoring, single video sequences were used to support diagnostic decisionmaking in cases of diagnostic uncertainty. However, no information about the scoring of certain items or overall sumscores was provided. ASD diagnoses were assigned by a multidisciplinary team consensus conference according to the ICD-10 diagnostic research criteria for autism or atypical autism (F84.0/F84.1). If no information about the developmental history could be obtained, atypical autism was diagnosed. The multi-disciplinary team consisted of at least one psychiatrist, a clinical psychologist, a special-needs caregiver, therapists, and a member of the nursing staff who was experienced in the fields of IDD and ASD. Diagnostic classification was based on all available information, including medical histories, psychiatric and physical examinations, video-based behavior analyses across a variety of contexts, and various standardized measures such as the PDD-MRS (Kraijer & Bildt, 2005; Kraijer & Melchers, 2003), the German version of the Social Communication Questionnaire-current (FSK-aktuell; Bo¨lte, Poustka, Rutter, Bailey, & Lord, 2006), the ACL (Sappok, Heinrich, et al., 2014), and, in cases of diagnostic uncertainty, the ADOS (Bo¨lte, Rutter, Le Couteur, & Lord, 2006), and/or the ADI-R (Bo¨lte, Rutter, et al.,
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2006). The SCQ-current was completed by an informant from the patient’s private living environment; the PDD-MRS, the ADOS, and the ADI-R were completed by a psychologist (H.K.) who was not involved in the study. Existing data from diagnostic procedures were used, and these procedures were performed with the informed consent of the patients as a part of routine patient care (National Hospital Law § 25.1, version 18.09.2011). This study was part of a larger study on the development and adaptation of instruments for ASD diagnosis in this group (Sappok, Budczies, et al., 2013; Sappok, Gaul, et al., 2014; Sappok, Heinrich, et al., 2014) which was approved by the local ethics committee and was conducted according to the recommendations of the Declaration of Helsinki. 2.2. Sample In the period between 1/2010 and 12/2011 the MUSAD was applied to 91 adult patients who were consecutively included in the diagnostic procedure. Either ASD diagnostics were part of the treatment contract or ASD was suspected as a result of clinical behavioral observation in the inpatient or outpatient setting, conspicuous biographical disclosures and/or unclear previous findings. Inclusion criteria were age >18 years and the presence of an IDD (ICD-10: F70-73). There were no further selection criteria except for limitations like logistic problems or missing consent documents, resulting in an ad-hoc sample reflecting clinical reality. In our in- and outpatient unit, the study participants did not receive regular music therapy or any form of musical training allowing the development of musical skills in advance of the MUSAD investigation. During the three months preceding the study, the procedure was tested and slightly edited based on observations with 11 patients. Of the 80 cases included in this study, four were excluded due to profound sensory impairments and rejection of the procedure. All calculations were based on the remaining N = 76. For demographic and clinical characteristics of the study sample, see Table 1. ASD was diagnosed in 50 participants (66%), while the remaining 26 participants (34%) did not show ASD but were diagnosed with schizophrenia, mood disorders, attachment disorders, sensory deficits, obsessive-compulsive behaviors, attention deficit hyperactivity disorder, or challenging behaviors on a background of IDD. The gender distribution in the ASD group was 42 men to eight women, reflecting the well-known accumulation of ASD in males (Fombonne, 2003a). A similarly unequal distribution (21 to 5) was also found in the non-ASD group, suggesting an increased suspicion of autism in males. Eighteen participants were non-verbal, and 15 were able to speak in single words, indicating a high proportion (43%) of participants with profound expressive language impairments within the entire sample. Since deficits in verbal and nonverbal communication are closely associated with autism, profound speech impairments were found in 52% of the ASD group compared to 27% in individuals with IDD only. There were no significant differences in any clinical or demographic characteristic between the ASD and the IDD only group. 2.3. Measures Convergent scales to screen for ASD used in this study are listed below, followed by more elaborate diagnostic procedures and discriminant measures.
Table 1 MUSAD sample characteristics. Characteristic Gender Females Males Age Years M (SD) IQ, intellectual disability .92 (Cicchetti et al., 2011). Values of Cronbach’s Alpha >.8 have been reported for the IDD specific PDD-MRS (Kraijer & Bildt, 2005), the ASD-DA (Matson et al., 2008), and the DIBAS-R (Sappok, Gaul, et al., 2014). Item difficulties of all 37 items included in the CFA showed high variability and ranged between .04 (particular interest for parts of objects) and .82 (reactive social smile). Varying difficulties are desirable in assessing a broad spectrum of autismrelated behaviors and a divergent group of individuals with mild to profound IDD. In particular, items with high difficulties
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are probably useful in displaying differences between individuals with a high symptom load. Overall, we found high itemtotal correlations: only item MUS306 (particular interest for parts of objects) was below the threshold of >0.3 (Ferketich, 1991; Maltby, Day, & Macaskill, 2010). At the same time, this item is the most peripheral one with the highest difficulty. For reasons discussed above, we decided to keep this item under observation in further scale development. To assess objectivity, interrater agreement was calculated in terms of ICC. Agreement between the two external raters (ICC = .71) as well as between three raters including the test developer (ICC = .67) are ‘‘good’’ according to the cutoffs recommended by Cicchetti and Prusoff (1983). This is indicative of sufficient objectivity in the test developer’s ratings. However, compared to the results of the ADOS pilot study (Lord et al., 2000) with more than 80% exact agreement among raters and across all modules, the MUSAD results seem to be worthy of improvement. These improvements in interrater reliability could be achieved by training of the raters including consensus conferences, further item selections, the revision of the 4-point coding descriptions, and a more stringent execution of the MUSAD. In assessing the stability of the MUSAD over time, the mean value of four test-retest correlations (ICC = .69) was acceptable. The ADOS showed similar results, with an ICC of .82 for the SC domain and .56 for RRB (Lord et al., 2000). Assessing convergent validity by calculating correlations between the MUSAD total scores and the SCQ, the PDD-MRS and ADOS resulted as expected in positive significant correlations ranging from .32 to .85. Overall, these results support the contention that the MUSAD measures ASD symptoms. Looking more closely at convergent scales, the highest correlations were found with the ADOS modules 1 and 2 (r = .58 resp .83). This may be caused by the conceptual comparability in assessing situation-specific interactional behaviors of the MUSAD and ADOS assessments, but due to small sample sizes, the data should be interpreted with caution. A correlation of r = .55 was found with the PDD-MRS, which is similar to the convergent validity of the ASD-DA (Matson et al., 2008) and the DIBAS-R (Sappok, Gaul, et al., 2014), both IDD-specific ASD screeners. The comparatively moderate agreement of the MUSAD with the SCQ sum score may be caused by the fact that the SCQ is designed to assess children and does not catch all aspects of an adult ASD/ID phenotype or a reduced parallelism due to the postponement of MUSAD items assessing verbal communication. In assessing discriminant validity, the low correlation with the MOAS supports the independence of measured constructs. However, unexpectedly, we found a weak positive relationship between MUSAD and ABC scores. Interestingly, closer examination shows that two of five ABC domains cover ASD core features that are also covered by the MUSAD, i.e., lethargy/social withdrawal and stereotyped behavior; while another two cover additional ASD behavioral characteristics, i.e., irritability and hyperactivity/ noncompliance. The ABC is a widely used measure in ASD treatment studies (Kaat, Lecavalier, & Aman, 2014), and has been used diagnostically to identify additional ASD in individuals with Down’s Syndrome (Ji, Capone, & Kaufmann, 2011). These strong associations between behaviors assessed with the ABC and ASD in individuals with IDD suggest that the ABC is less useful to assess discriminant validity in further studies. Significant correlations between the MUSAD and ASD measures on the one hand and a negligible correlation with the MOAS indicates construct validity of the MUSAD, i.e., the potential to detect the adult IDD phenotype of ASD. To summarize, the MUSAD showed adequate psychometric properties in terms of reliability, objectivity and factorial validity. However, interrater reliability should be improved and tested in further scale development. Compared to other music-based approaches in diagnosing ASD, the development of the MUSAD allowed a comprehensive psychometric verification in this field for the first time with an adequate sample size. Wigram’s Harper House Music Therapy Assessment (1999) is documented by a single case study and is based on Bruscia’s Improvisation Assessment Profiles (IAP; Bruscia, 1987), which have not yet been validated at all. Oldfield’s Music Therapy Diagnostic Assessment (MTDA; Oldfield, 2004) did not allow a comprehensive test-theoretical verification due to a small sample size of N = 30. Both instruments were developed to assess children, and not adults, suspected of having ASD. Therefore, within music-based approaches to diagnosing ASD, the MUSAD is the first instrument which strives for adequate psychometric properties and it is the first music-based approach to assess adults with IDD. Compared to the ADOS, the strength of the MUSAD concept is its slightly more flexible approach to make the situation more suitable and to allow for assessment of individuals who are difficult to assess, such as those with severe to profound intellectual disability (Bergmann et al., 2015). Since further scale development intends to develop an algorithm, the MUSAD may become an additional valuable source for gaining relevant diagnostic information and may improve the diagnostic process in diagnosing ASD in adults with IDD, especially in those with limited language skills. 4.1. Limitations The consecutive clinical ad-hoc sample of adults with suspected ASD investigated here resulted in a blurred separation of groups by assessing ‘‘borderline autistic’’ individuals and an unbalanced ratio of n = 50 ASD vs. n = 26 non-ASD. Due to the lack of a balanced and clearly separated control group of individuals with IDD only, the investigation of diagnostic validity, including ROC analysis and the calculation of a diagnostic algorithm, was postponed. In factor analysis a case number of more than 100 is recommended (MacCallum, Widaman, Zhang, & Hong, 1999). The smaller sample size of N = 76 is explained by the monocentric study design in this early phase of test development and the high examination effort of behavioral observational instruments. Against the backdrop of only three given factors, the absence of estimation problems, excellent internal consistency, and fit indices indicating good model fit, the final CFA model seems to be sufficiently robust despite a sample size
