J Autism Dev Disord (2014) 44:1959–1971 DOI 10.1007/s10803-014-2071-4 ORIGINAL PAPER Age Related Differences of Executive Functioning Problems in Everyday Life of Children and Adolescents in the Autism Spectrum Sanne F. W. M. van den Bergh • Anke M. Scheeren Sander Begeer • Hans M. Koot • Hilde M. Geurts • Published online: 23 February 2014 Ó Springer Science+Business Media New York 2014 Abstract Numerous studies investigated executive functioning (EF) problems in people with autism spectrum disorders (ASD) using laboratory EF tasks. As laboratory task performances often differ from real life observations, the current study focused on EF in everyday life of 118 children and adolescents with ASD (6–18 years). We investigated age-related and individual differences in EF problems as reported by parents on the Behavioral Rating Inventory Executive Functions (BRIEF: Gioia et al. in Behavior rating inventory of executive function. Psychological Assessment Resources, Odesse 2000), and examined the association with autism severity. Inhibition problems were mostly found in the youngest group (6- to 8-year-olds), whereas problems with planning where more evident for 12- to 14-year-olds as compared to 9- to 11-year-olds. In a subsample of Electronic supplementary material The online version of this article (doi:10.1007/s10803-014-2071-4) contains supplementary material, which is available to authorized users. S. F. W. M. van den Bergh
H. M. Geurts (&) Autism Clinic, Research and Development, Dr. Leo Kannerhuis, Houtsniplaan 1, 6865 XZ Doorwerth, The Netherlands e-mail: h.m.geurts@uva.nl S. F. W. M. van den Bergh e-mail: s.f.w.m.vandenbergh@uva.nl S. F. W. M. van den Bergh
H. M. Geurts Department of Psychology, Brain and Cognition, University of Amsterdam, Amsterdam, The Netherlands S. F. W. M. van den Bergh
H. M. Geurts Dutch Autism and ADHD Research Center (d’Arc), Weesperplein 4, 1018 XA Amsterdam, The Netherlands participants meeting the ADOS ASD cut-off criteria the age related differences in planning were absent, while problems with cognitive flexibility were less apparent in 15- to 18-year-olds, compared to 9- to 11-, and 12- to 14-year olds. EF problems surpassing the clinical cutoff were only observed in 20 % (planning) to 51 % (cognitive flexibility) of the children and adolescents, and no relation was found with ASD symptom severity. This underlines the heterogeneous nature of ASD. Keywords ASD
Autism severity
Behavioral Rating Inventory Executive Functions (BRIEF)
Development
Executive functioning Introduction The theory of executive dysfunction (Damasio and Maurer 1978; Pennington and Ozonoff 1996) suggests that some autism symptoms might stem from executive functioning A. M. Scheeren
S. Begeer
H. M. Koot Autism Research Amsterdam (ARA), Amsterdam, The Netherlands A. M. Scheeren
S. Begeer
H. M. Koot EMGO Institute for Health and Care Research, Van der Boechorststraat 1, 1081 BT Amsterdam, The Netherlands S. Begeer School of Psychology, University of Sydney, Brennan MacCallum Building (A18), Sydney, NSW 2006, Australia H. M. Geurts Cognitive Science Center Amsterdam, Nieuwe Achtergracht 129, 1018 WS Amsterdam, The Netherlands A. M. Scheeren
S. Begeer
H. M. Koot Department of Developmental Psychology, VU University, Amsterdam, The Netherlands 123 1960 (EF) deficits. EF refers to cognitive skills that serve independent, purposive, goal-directed, and self-serving behavior (Lezak et al. 2012). A plethora of studies has shown that children, adolescents, and adults with autism spectrum disorders (ASD) encounter problems in executive functioning (e.g., Bramham et al. 2009; Corbett et al. 2009; Sinzig et al. 2008, for reviews see Hill 2004; Russo et al. 2007). Also, EF deficits relate positively to certain autism symptoms (e.g., repetitive behavior: de Vries and Geurts 2012; Yerys et al. 2009). However, children and adolescents with ASD do evolve in their EF skills (Christ et al. 2011; Happé et al. 2006; Luna et al. 2007; Pellicano 2010), and not all individuals with ASD have clinically significant EF deficits (Hill and Bird 2006; Pellicano et al. 2006; Towgood et al. 2009). Hence, within the population of individuals with ASD the development of EF is highly heterogeneous. Moreover, it is important to examine the development of different domains of EF separately, because research in typical development has shown that the structure of EF becomes more differentiated with age (see Hughes et al. 2009; Huizinga et al. 2006; Miyake et al. 2000), and different types of EF develop at a different pace (Best et al. 2009; De Luca et al. 2003; Hughes 2011). Four domains of EF that are traditionally referred to are inhibition, working memory, cognitive flexibility (i.e., shifting), and planning (Hill 2004; Hughes 2011; Pennington and Ozonoff 1996). These four domains have been studied extensively in psychological laboratories in both typically developing (TD) children and children with ASD of varying ages. In the current study we aim to describe developmental EF profiles of children and adolescents with ASD, based on everyday life observations by parents. The first component of EF, inhibition, refers to the ability to voluntarily and deliberately suppress responses. There are three different types of inhibition (Friedman and Miyake 2001). The first type, inhibition of prepotent responses, refers to the suppression of a dominant response: for example when a child inhibits the response to speak before it is his/her turn. Resistance to interference, the second type, refers to ignoring irrelevant information. This is, for example, required when a child tries to listen to a teacher but hears other children speak. The last type, resistance to proactive interference, refers to processes where previously learned information becomes irrelevant and interferes with new information. This happens, for example, when a teacher tries to learn new names of students, but old names interfere. Improvements of inhibition are evident from childhood to adulthood in typical development (Davidson et al. 2006; Huizinga et al. 2006; Luna et al. 2004). In ASD, deficits have been observed in each of the three inhibition domains in some (e.g., Adams and Jarrold 2011; Christ et al. 2011; Mosconi et al. 2009; Sinzig et al. 2008; Verté et al. 2005), but not all studies 123 J Autism Dev Disord (2014) 44:1959–1971 (e.g., Christ et al. 2007, 2011; Geurts et al. 2009a). However, this inconsistency might be partly due to differences in the inhibition type and age group studied, which is illustrated by two cross-sectional studies. Prepotent response inhibition seems to improve with age in ASD (Christ et al. 2011: 8 to 18 years; Luna et al. 2007: 8 to 33 years) and difficulties with resistance to interference might even fade away after the age of twelve (Christ et al. 2011) as older individuals with ASD showed better skills than younger individuals (Christ et al. 2011; Luna et al. 2007). The development of proactive interference in people with ASD seems to parallel the development of TD individuals in the age range of 8–18 years (Christ et al. 2011). Hence, in people with ASD not all aspects of inhibitory control might be equally impaired and inhibition problems might even disappear with age. In ASD the developmental trajectory of working memory, the second EF component, seems to differ from the developmental trajectory of inhibition (Luna et al. 2007). Working memory is the ability to maintain and manipulate on-line information (Baddeley 1992). A distinction is made between visual and verbal working memory processes (Smith et al. 1996). In typical development, both components show a linear development between the age of 4 and 15 years (Gathercole et al. 2004), although improvements of visual (spatial) working memory processes are seen even into young adulthood (Luna et al. 2004). Working memory deficits are commonly observed in ASD (e.g., Minshew and Goldstein 2001; Steele et al. 2007, but see Edgin and Pennington 2005), especially in spatial working memory (Williams et al. 2005). Compared to TD individuals, working memory developments seems to be intact in children, but not in adolescents and adults with ASD (Luna et al. 2007). Thus, despite parallel development during childhood, working memory deficits are evident across the lifespan in ASD. Clear improvements with increasing age are noted in typically developing children and adolescents for the two other EF components, cognitive flexibility (i.e., the ability to intentionally shift thoughts and actions in response to contextual changes: Monsell 2003) and planning (i.e., thinking ahead: Anderson et al. 2001; Davidson et al. 2006; De Luca et al. 2003; Luciana et al. 2009). Problems in both cognitive flexibility and planning have been observed in children with ASD (Hill 2004), but especially for cognitive flexibility the findings are rather inconsistent (Geurts et al. 2009b). However, cognitive flexibility does seem to improve in ASD during childhood (Happé et al. 2006) and development in planning is evident in young children (Pellicano 2010) as well as in young adolescents (Happé et al. 2006) with ASD. In sum, little is known about the developmental patterns of cognitive flexibility and planning in people with ASD, though improvements with age have been observed for these EF domains. J Autism Dev Disord (2014) 44:1959–1971 In general, there are three different hypotheses regarding the development of EF in ASD: (1) the development of EF in children and adolescents with ASD might be delayed, but parallel to the typical EF development (Christ et al. 2011; (2) there might be a deviant EF development in ASD (Ozonoff and McEvoy 1994; or (3) a delayed, but parallel EF development in childhood might be followed by a deviant EF development in adulthood (Luna et al. 2007). All of the above might be true given that different EF domains follow other developmental trajectories. This underlines the importance to focus on specific domains instead of the broad construct of EF. In everyday lives of individuals with ASD behavioral problems are observed that seem related to EF. The Behavioral Rating Inventory Executive Functions (BRIEF: Gioia 2000), a parent questionnaire that is widely used in the clinical practise, addresses those everyday behaviors. Whereas associations are evident with other attention and behavioral problems (BRIEF: Gioia et al. 2000, 2002; McAuley et al. 2010), the BRIEF is only minimally related to laboratory tasks (McAuley et al. 2010). This poses a problem for the association of BRIEF reports to pure, actual EF functioning. This has resulted in some authors arguing that the BRIEF does not actually measure EF (McAuley et al. 2010). Others (Kenworthy et al. 2008) question the ecological validity of laboratory tasks as measurements of EF, arguing that EF related problems in everyday life are observed in people with ASD, even when laboratory task performance is intact. Caution should be made stating that the BRIEF measures actual EF. However, problems described by the BRIEF take place in a social context and are relevant in the everyday lives of people with ASD. Therefore, complementary to what we already know from laboratory task studies, it is important to study these behaviors from a developmental perspective and with respect to individual differences. Several studies have shown that parents of children and adolescents with ASD consistently report EF deficits on the BRIEF (Boyd et al. 2009; Chan et al. 2009; Endedijk et al. 2011; Kalbfleisch and Loughan 2012; Winsler et al. 2007; Yerys et al. 2009; Zandt et al. 2007). Clinically significant BRIEF scores on different domains are reported in 35–70 % of children with ASD (Gioia et al. 2002; Kenworthy et al. 2005). In a study with 54 children with ASD with a mean age of 11 years inhibition problems were observed in 46 % of the children, working memory problems in 57 %, cognitive flexibility problems in 69 %, and planning problems in 70 % of these children (Kenworthy et al. 2005). The high amount of flexibility problems reported in the BRIEF even discriminates children with ASD from other clinical groups (Gioia et al. 2002). Furthermore, a higher score on the BRIEF Behavioral Regulation Index (containing the subscales inhibit, shift, and 1961 emotional control) was associated with more symptoms of communication deficits and restricted repetitive behavior (Kenworthy et al. 2009) as assessed by the Autism Diagnostic Interview-Revised (ADI-R: Lord et al. 1994), the Autism Diagnostic Observation Scale- Generic (ADOS-G: Lord et al. 2000), as well as the Repetitive Behavior ScaleRevised (Boyd et al. 2009). This is in line with the observed relations between cognitive flexibility and repetitive behavior in ASD in studies using laboratory tasks (de Vries and Geurts 2012; Yerys et al. 2009). Thus, despite the weak relations between laboratory task performance and observed behaviors, autism symptomatology is related to EF measured with neuropsychological paradigms as well as to reported EF problems in everyday life. Most studies in which the BRIEF is used, are focussed on index scores. However, since the specific EF domains provide more detailed information than a general EF measure, we focus on the four well-described and studied domains in the current study. The BRIEF has already been used to study developmental trajectories in TD children (Huizinga and Smidts 2011), and recently also in children and adolescents with ASD (Rosenthal et al. 2013). In a large TD sample (431 boys and 416 girls), BRIEF raw subscale scores of four age groups (5- to 8-; 9- to 11-; 12- to 15-; and 16- to 18-yearolds) were compared (Huizinga and Smidts 2011). Working memory and flexibility mainly seemed to develop before the age of 11 years, inhibition appeared to develop until young adulthood, whereas no development seemed evident with regard to the planning subscale. The findings with regard to inhibition fit the conclusions based on laboratory tasks performance. The working memory and cognitive flexibility findings differ from laboratory tasks studies, as development was only evident in childhood, and not during adolescence. The developmental pattern of planning is in contrast with the observed development of planning skills during childhood and adolescence based on EF laboratory tasks performance. In a comparable ASD study (158 boys and 27 girls, Rosenthal et al. 2013), BRIEF standardized (T) scores of four age groups (5- to 7-; 8- to 10-; 11- to 13-; and 14- to 18-year-olds) were compared. Working memory problems were more severe in 14–18 year olds as compared to 6–7 year olds. So, despite the improved performances on working memory tasks during childhood in ASD, parent reports indicate an increase of working memory problems. This is explained by Rosenthal et al. (2013) by the fact that higher real world demands during adolescence create a larger discrepancy with typical development. However, higher demands from the environment might also produce increasing EF problems in typically developing adolescents, which is not found in the Huizinga and Smidts study (2011). One explanation could be that children and adolescents with 123 1962 ASD are more vulnerable to changes in environmental demands than typically developing peers. Since no agerelated differences were found in other specific EF domains (e.g. inhibition, cognitive flexibility, and planning), it does not seem to be the case that reported everyday EF problems in general increase during adolescence in ASD. As Rosenthal et al. (2013) compared age-related T-scores, thereby providing information about the relative impairment of participants with ASD compared to a typical age norm; it is hard to relate these findings to the findings of Huizinga and Smidts (2011). Trajectories of reported everyday EF problems in ASD, using raw scores, would provide the information necessary for these comparisons. Furthermore, from the sample of Rosenthal et al. (2013) it is not clear how many children and adolescents actually showed clinically significant problems. Given the heterogeneity of EF problems in ASD (Hill and Bird 2006; Pellicano et al. 2006; Towgood et al. 2009), this information seems highly relevant. Although Rosenthal et al. (2013) controlled for symptomatology, they did not investigate the unique contribution of symptom severity to everyday EF. This would be of interest, given the wide range of ASD severity, and following the theory that some ASD symptoms might stem from EF problems (Pennington and Ozonoff 1996). The first and primary aim of the current study is to study developmental profiles of specific everyday EF domains in children and adolescents with ASD (6–18 years). For this purpose we use the same cross-sectional approach as was taken in TD children and adolescents (Huizinga and Smidts 2011). Hence, we focus on raw scores instead of T-scores. In line with studies addressing EF in ASD (e.g., Christ et al. 2011; Happé et al. 2006), we expect to observe agerelated improvements of the subscales inhibition and shift. We do not expect to find age-related improvements of working memory and planning, based on the larger BRIEF working memory discrepancy with TD during adolescence in ASD (Rosenthal et al. 2013), and the absence of BRIEF planning improvement in TD (Huizinga and Smidts 2011). Our secondary aims are: to explore the degree of clinically relevant EF problems, and to investigate the impact of ASD severity. Given the heterogeneity of ASD (Hill and Bird 2006; Pellicano et al. 2006; Towgood et al. 2009), we expect some, but not all participants to encounter clinically significant everyday EF deficits. The highest proportions of reported deficits are expected on the shift and planning scale (Gioia et al. 2002; Kenworthy et al. 2009). In an attempt to examine individual differences from a developmental perspective as well, the relative proportions of clinical scores are examined for different age groups. In line with Rosenthal et al. (2013), it is expected that the amount of working memory problems will be significantly higher in children than in adolescents. Finally, we expect to 123 J Autism Dev Disord (2014) 44:1959–1971 find a positive association between ASD symptom severity and EF deficits, especially with regard to inhibition and cognitive flexibility problems (Boyd et al. 2009; Kenworthy et al. 2009). Methods Participants The participants, 155 children and adolescents with ASD from 6 to 18 years, took part in a larger study (Scheeren et al. 2010, 2012, 2013) for which they were recruited from a specialized school for normally intelligent pupils with ASD. Inclusion criteria for the present study were threefold. First, ASD was diagnosed by a team of clinicians according to the criteria of the DSM-IV-TR (American Psychiatric Association APA 1994, 2000). The clinicians worked independently from the authors and were blind to the outcomes of the current study. The diagnostic process included an examination of psychiatric, neuropsychological, and speech functioning. Second, clinical diagnoses were verified by a raw score above the Dutch threshold for ASD (which is C60 for boys and C51 for girls) on the Social Responsiveness Scale (SRS: Constantino and Gruber 2007; Dutch version: Roeyers et al. 2011). The SRS is a parent questionnaire with 65 items, ranging from 0 (never true) to 3 (almost always true). It provides an index score for autism symptomatology; a higher total score indicates more autistic traits (Constantino and Gruber 2007). Third, to be included participants had to have a receptive verbal IQ score C70 on the Dutch version of the Peabody Picture Vocabulary Test-III (PPVT-III: Dunn and Dunn 2005). After the exclusion of participants with a score below the Dutch SRS threshold score (n = 27), or an IQ \ 70 (n = 6), the final sample consisted of 118 participants (16 girls, 102 boys), diagnosed with autism (n = 24), Asperger (n = 14) and PDD-NOS (n = 80). The final sample was divided into four different age groups (i.e. 6- to 8 yearolds, 9- to 11 year-olds, 12- to- 14 year-olds and 15- to 18 year olds: see for similar procedure Huizinga and Smidts 2011). Descriptives of the included participants are presented in Table 1. Supplementary Table 1 provides information about the normed performances on the BRIEF scales, other than those of interest in the current study. Although the ADOS score was used as a predictor in this study, it might concern some that over half of the participants did not score above the ASD threshold on the ADOS. However, all participants are clinically diagnosed within the spectrum, and the SRS was used to confirm this. Therefore, it might be the case that the low ADOS scores indicate a sensitivity problem of the ADOS (Bastiaansen et al. 2011; Gotham et al. 2008). Nonetheless, we repeated Range 6.3 (4.1) 63.1 (11.5) 58.5 (7.8) 64.2 (10.6) 57.7 (8.7) ADOSa Inhibition T WM T Shift T Plan Tb 6–18 36–82 29–88 39–94 40–74 0–19 61–133 72–132 8.1 (.8) 55.3 (6.8) 59.6 (10) 65 (7) 61.4 (8.4) 7.4 (4.5) 91.2 (15.5) 105.6 (14.1) 47–64 45–81 54–79 43–74 2–19 67–126 84–126 6.4–8.9 Range 55 (8.5) 65.5 (11.5) 63 (9.4) 58.9 (7) 6 (4.4) 91.3 (17.5) 98.3 (11.3) 10.8 (.9) M (SD) 4/1/9 13/1 60.5 (8.9) 65.2 (9.9) 63.1 (11.5) 58.2 (7.3) 6.3 (4.1) 87.2 (18.1) 109 (11.7) 13.6 (.8) M (SD) 8/1/20 24/5 40–82 46–83 39–94 41–73 0–19 61–133 80–130 12.1–14.8 Range 9- to 11- (29) 57.6 (8.7) 63.6 (11) 65 (7) 57.1 (8.9) 7.4 (4.5) 91.2 (15.5) 105.9 (13.7) 16.5 (1) M (SD) 6/7/29 38/4 36–71 42–84 54–79 40–71 2–19 67–126 76–132 15–18.4 Range 12- to 14- (42) – – – – .67 2.61 3.40 386.50 F* – – – – .57 .06 – – – – .02 .06 .08 .91 \.001 .02 g2p p 6/5/22 27/6 15- to 18- (33) a df = 3,112/ n = 116; * df = 3,114 b n = 117 AUT autism, AS asperger, BRI Behavior Regulation Index, BRIEF Behavioral Rating Inventory Executive Functions, IQ intelligence quotient, M mean, PDD-NOS pervasive developmental disorder not otherwise specified, Plan plan/organize, PPVT-III Peabody Picture Vocabulary Test-III, SD standard deviation, SRS Social Responsiveness Scale, T T score 38–72 29–88 43–80 44–71 0–18 66–128 72–118 9–11.8 Range 6-to 8- (14) Autism Diagnostic Observation Scale Generic communication and social reciprocity 87.2 (18.1) SRS total 13.1 (2. 9) 105.3 (12.9) PPVT-III Age M (SD) 24/14/80 M (SD) 102/16 Diagnosis (AUT/AS/PDD-NOS) Total (118) Groups, age in years (n) Gender (m/v) Measure Table 1 Descriptive statistics of the participants J Autism Dev Disord (2014) 44:1959–1971 1963 123 1964 J Autism Dev Disord (2014) 44:1959–1971 Table 2 Age-related differences on the BRIEF raw subscale scores Subscale Groups, age in years (n) Total (n = 118)a M (SD) 6–8 (n = 14)b M (SD) 9–11 (n = 29) M (SD) 12–14 (n = 42) M (SD) 15–18 (n = 33) M (SD) F p g2p Posthoc* (p) Inhibit 20.5 (4.7) 23.8 (2.4) 21.1 (3.9) 20.5 (4.6) 18.6 (5.3) 4.59 .01 .11 6–8 [ 9–11 (.04), 12–14 (.01), 15–18 (.00) WM 22.1 (3.9) 24 (4.2) 22.3 (3.5) 22.1 (3.3) 21.2 (4.7) 1.67 .18 .04 – Shift 17.5 (3.4) 17.3 (2.7) 18.2 (2.9) 17.9 (3.5) 16.3 (3.7) 2.11 .10 .05 – Plan 25.5 (4.5) 24 (3.7) 23.9 (4.6) 26.9 (4.1) 25.7 (4.8) 3.13 .03 .08 9–11 \ 12–14 (.04) BRIEF behavioral rating inventory executive functions, M mean, Plan plan/organize, SD standard deviation, WM working memory * Corrected for multiple comparisons a n for analyses is 117 b n for analyses is 13 all analyses with the subsample and describe this sample in supplementary Table 2. When results with regard tot the subsample show different patterns compared to findings concerning the total group this will be mentioned. With the exception of verbal receptive IQ and age, group descriptives did not significantly differ between age groups. In the ADOS subsample receptive IQ didn’t differ significantly between age groups. Measurements Behavioral Rating Inventory Executive Functions The Behavior Rating Inventory of Executive functions (BRIEF: Baron 2000; Gioia et al. 2000; Dutch translation Huizinga and Smidts 2012) is a parent questionnaire that includes 86 statements regarding behavior. Items can be rated on a 3-point frequency scale (1 = never; 2 = sometimes; 3 = often). Raw subscale scores can be calculated for eight subscales (inhibit, working memory, shift, emotional control, initiate, plan/organize, organization of materials, and monitoring). Aggregated subscales provide three index scores: (1) the Behavior Regulation Index: consisting of the scales inhibit, shift, and emotional control; (2) the Metacognition Index: consisting of the working memory, initiate, plan/organize, organization of materials, and monitoring scale; and (3) a total score: a composite of all subscales. For each composed score a higher score means more everyday executive problems. T-scores (M = 50, SD = 10) can be calculated to determine whether scores are potentially clinically significant (T [ 65), according to gender- and agespecific norms (Huizinga and Smidts 2012). Please note that, in line with the experimental literature concerning EF, we focused on four relevant BRIEF subscales: inhibit (10 items, e.g., ‘‘Interrupts others’’); working memory (10 items, e.g., ‘‘When given three things to do, remembers only the first or last’’); shift (8 items, e.g., ‘‘Acts upset by a change in 123 Fig. 1 Mean BRIEF planning subscale scores per age group. The error bars represent 95 % CI plans’’); and plan/organize (12 items, e.g., ‘‘Does not bring home homework, assignment sheets, materials, and so on’’). The BRIEF is a reliable and valid questionnaire (Baron 2000; Gioia et al. 2000, 2002; Huizinga and Smidts 2011). The Autism Diagnostic Observation Scale Generic The Autism Diagnostic Observation Scale Generic (ADOS-G: Lord et al. 2000) is a semi-structured instrument used to observe social interaction, communication, imagination and repetitive interests in people with supposed ASD. The developmental and language level determine which one of four modules of approximately 45 min is carried out. Behaviors are scored on a 3-point scale (0 = normal; 1 = slightly abnormal; 2 = clearly abnormal). Scores are calculated for five domains: communication; social reciprocity; fantasy; repetitive interests; and other behaviors. A threshold (C7) for the aggregated scores J Autism Dev Disord (2014) 44:1959–1971 1965 et al. 2010, 2012, 2013), in which various other questionnaires and experimental tasks were used. Outliers and Missing Datapoints Fig. 2 Mean BRIEF inhibition subscale scores per age group. The error bars represent 95 % CI of communication and social reciprocity determines whether the cut-off for ASD is met. There is a standardized continuous ADOS score (Gotham et al. 2009), which would be most suitable to measure severity. However, module 4 is not standardized. Therefore, we used the ADOS aggregated scores of communication and social reciprocity as a measure of severity. A higher score reflects more autism symptoms. Note, that in the current study module 3 (N = 39) or 4 (N = 77), for children or adolescents with fluent speech, were used. The validity and reliability of the ADOS are reported as good to excellent (Lord et al. 2000). Procedure For each participant there was parental informed consent. Participants from 12 year and older also gave informed consent for themselves. Both the ADOS and BRIEF were administered as part of a larger research project (Scheeren There were no outliers ([3 SE) in the current sample on any of the included BRIEF scales. Following the BRIEF manual (Huizinga and Smidts 2009), if no more than 2 subscale items were missing, missing items were scored as 1 and subscales were computed. SRS missing items were scored as the mean score of the completed subscale items, only when no more than 2 items per scale were missing. The SRS total score was calculated as the sum of the subscales. More than 2 items were missing for one participant on the BRIEF plan/organize scale. Consequently, this score is missing for this participant. The ADOS was only assessed with 116 participants. Therefore, for two participants the ADOS score is missing. Results Are there Differences Between the Age Groups on the BRIEF Scores? A multivariate analysis of variance (MANOVA) was conducted with the four BRIEF scale raw scores as dependent variables, and age group as between subject factor. Alpha level for the MANOVA and the subsequent ANOVAs were set on .05. Given the unequal sample sizes and, for inhibition and working memory, the failure to meet the heteroscedasticity criterion (Field 2009), the Games Howell procedure was used in posthoc procedures. Effect sizes were expressed with partial g2, representing small, medium, and large effects by values of respectively C.01, .06, and .14 (Cohen 1992). In Table 2 results of the MANOVA, and subsequent ANOVAs, were reported. A significant main effect of age was found, Wilks K = .654 F(12,291.32) = 4.23, p = \ .001, partial Table 3 Percentages of clinical scores on the BRIEF subscales Subscale Groups, age in years (n) Total (n = 118) 6–8 (n = 14) 9–11 (n = 29) 12–14 (n = 42) 15–18 (n = 33) Fisher’s exact test p Cramer’s V Inhibit 50.0 48.3 38.1 36.4 41.5 1.58 .68 .11 WM 50.0 13.8 19.0 24.2 22.9 6.77 .07 .25 Shift 28.6 51.7 57.1 51.5 50.8 3.41 .34 .17 Plana 0.0 10.3 33.3 18.2 19.7 9.21 .02 .29 ADOS autism diagnostic observation scale generic communication and social reciprocity, BRIEF behavioral rating inventory executive functions, M mean, Plan plan/organize, SD standard deviation, WM working memory a n = 117 123 1966 J Autism Dev Disord (2014) 44:1959–1971 Table 4 Regression analyses with BRIEF subscales as dependent variables BRIEF scales Predictors Inhibit DR2 Step 1 Total R2 p -.35 \.001 .05 .61 .999 .001 .00 ADOS .00 .12 DR2 b .12 Age PPVT-III Step 2 WM b p DR2 .07 .02 -.22 .02 .08 .41 .25 .05 .01 .999 .002 Shift -.11 .06 b P DR2 .28 .05 -.17 .08 .01 .95 .11 .02 .25 .09 Plan -.15 .09 p .16 .09 .14 .14 .59 .06 .00 .11 .16 b .05 .16 .59 .12 Beta’s are standardized beta’s from the full model ADOS autism diagnostic observation scale generic communication and social reciprocity, BRIEF behavioral rating inventory executive functions, PPVT-III Peabody Picture Vocabulary Test-III g2 = .13, indicating differences between the age groups on both the inhibit scale, and the plan/organize scale, but not on the working memory and the shift scale. The follow up analyses revealed that compared to 6- to 8- year-olds, inhibition problems were less severe in the older groups (see Fig. 1). However, for planning there was another pattern of results (see Fig. 2) as compared to 9- to 11-year-olds, 12to 14- year-olds showed more planning problems, p = .04. None of the other post hoc comparisons for planning were significant. As some aspects of IQ cover EF, controlling for IQ might provide overcorrected or unlikely results (Dennis et al. 2009). However, we repeated the analyses with receptive verbal IQ as a covariate: the same pattern was found, except that age-related differences in planning were only moderately significant, F(3,112) = 2.49, p = .06. In the ADOS cut-off subsample there was a main age effect as well, Wilks K = .487 F(12,108.77) = 2.83, p = .002, partial g2 = .21. However, in this sample this was caused by age differences in inhibition, F(4,43) = 2.92, p = .04, partial g2 = .17 (less problems in 15-18- year-olds, compared to 9- to 11- year olds, p = .04) and shift, F(4,43) = 5.37, p = .003, partial g2 = .27 (less problems in 15-18- year-olds, compared to 9- to 11-, p = .04; and 12- to 14- year olds, p = (\ .01). When repeating the analyses of the subsample with receptive verbal IQ as covariate the same pattern was found. What is the Proportion of Children and Adolescents with Clinical BRIEF Scores, and are Proportions Different Between Age Groups? The BRIEF scores were first categorized as clinical (T [ 65) or within normal limits (T B 65). Based on this, the proportions of actual clinical scores per age group were 123 calculated. Next, Fisher’s exact tests with adjusted alpha levels of .01 (.05/4) were used to test differences in relative proportions across age groups. Effect sizes were expressed with Cramer’s V. Effects from ±.1 represent a small effect, this is medium for ±.3, and large for ±.5 (Field 2009). Clinical scores were observed for inhibition in 42 % of the participants, for working memory in 23 %, for shift in 51 % and for plan/organize in 20 % of participants. For the plan/organize scale clinical scores were absent in the youngest group, but present in 33 % of the children from to 9- to 11-years-old. Nonetheless, no significant age group differences were found with regard to clinical percentages of all BRIEF scale scores (see Table 3). In the ADOS cutoff subsample this was 43 % for inhibition, 20 % for working memory, 47 %, for shift, and 21 % for plan/ organize and, again, there were no age differences. What is the Unique Contribution of ASD Severity to BRIEF Scores? Four multiple, hierarchical regression analyses were performed with BRIEF subscale scores as dependent variables. As receptive verbal IQ differed between age groups, we included IQ together with age in the first step, and ADOS aggregated scores in the second step. Alpha level was set at .01 (.05/4). Prior to the regression analyses, correlation analyses were performed. Alpha level was set at .01 (05/4). Correlation analyses revealed no relations between the BRIEF subscales and the ADOS: Inhibit, r (114) = .04, p = .70; working memory, r (114) = .1, p = .33; shift, r (114) = .13, p = .16; plan/organize, r (114) = .02, p = .87. In the ADOS cut-off subsample relations were absent as well: Inhibit, r (49) = .04, p = .81; working memory, r (49) = .03, p = .83; shift, r (49) = .02, p = .92; plan/organize, r (48) = .032, p = .84. Regression analysis showed that 12 % of the variance in the inhibit scale was explained. This was 16 % J Autism Dev Disord (2014) 44:1959–1971 in the ADOS cut-off subsample. However, in both cases this was fully attributable to the effect of age. Autism severity did not add uniquely to the variance of the inhibition scale, nor did IQ (see Table 4). With respect to the other BRIEF subscales (working memory, shift and plan/ organize), none of the regression models were significant. See the results and statistics in Table 4. Discussion In the current cross-sectional study we focused on agerelated differences in specific domains of everyday EF in children and adolescents with ASD as reported by their parents. Moreover, we explored age differences in proportions of clinically significant EF problems, and the relationship between ASD symptom severity and everyday EF. Age-related differences were found with regard to inhibition and planning. Compared to the 6- to 8-year-olds, inhibition problems were reported less for the older children and adolescents. For planning, an opposite pattern was found. Compared to the 9- to 11-year-olds, in 12- to 14year-olds more planning problems were observed. Hence, planning problems may be especially apparent in young adolescents with ASD during the transition period from primary to secondary education. In a subsample of children and adolescent that scored above the ASD threshold on the ADOS, differences were found between age groups for inhibition and cognitive flexibility. Cognitive flexibility problems were less apparent in the oldest group (15- to 18-year olds), compared to 9- to 11-year-olds and 12- to 14- year-olds. Consistent with former studies, everyday EF deficits were found in children and adolescents with ASD, although to a smaller extent than expected. Only 20 % (planning) to 51 % (cognitive flexibility) of the participants encountered clinical EF problems. In the ADOS cut-off subsample this was approximately the same. This highlights the heterogeneity of ASD, and underlines the importance of focusing on individual differences when studying EF in ASD. Counter to our expectation, no relations between ASD symptom severity and everyday EF were found. Developmental Profiles of Everyday EF As expected, in general, older children and adolescents with ASD showed fewer inhibition problems. However, this was only the case when comparing the older age groups with the youngest group. This might suggest a delayed or protracted development of inhibition, rather than a deviated development, which is in line with Rosenthal et al. (2013). Longitudinal research with both ASD and TD participants is necessary to test this hypothesis. 1967 Age-related differences were not found in working memory and cognitive flexibility. With respect to working memory, this contrasts the observed decrease of everyday working memory problems during childhood in TD (Huizinga and Smidts 2011), which consequently fits the increasing T-scores found by Rosenthal et al. (2013). Hence, it might be the case that working memory problems in ASD are specifically revealed during adolescence. Increased real world expectations (Rosenthal et al. 2013) do not seem to explain this though, because age-related differences of raw scores could not be found. Considering that laboratory task performance revealed a deviant development of working memory during adolescence in ASD (Luna et al. 2007) an explanation at the cognitive level might be more valid. Hence, it seems that working memory development in children and adolescents with ASD differs from that of typical development, which, with age, might lead to an increase of impairments compared to TD. Nonetheless, differences between real life observations and observed laboratory performance make it hard to draw firm conclusions. Further research could focus on the unique contribution of specific cognitive developmental processes on the one hand, and increasing environmental demands on the other hand. With respect to cognitive flexibility, the lack of age-related differences in the current study is against our expectations and inconsistent with previously observed age-related improvements in cognitive flexibility performance in ASD (Happé et al. 2006). However, in a subsample of children and adolescents that scored above the ASD threshold on the ADOS we did note age related differences, as the 15- to 18- year olds had fewer problems than the 9- to 11- and 12- to 14-year-olds. One explanation might be that children and adolescents that score above the ASD threshold on the ADOS (and have more severe ASD than those who score below the threshold) receive more professional help or help from their families with switching. However, this is only speculative. In line with Rosenthal et al. (2013), in both of the current samples an increase of flexibility problems was absent. This might suggest that, if cognitive flexibility problems are characteristic for ASD, there is at least no increase of problems during childhood and adolescence. Longitudinal research with ASD and TD participants could test this hypothesis. Contrary to findings based on laboratory tasks (Happé et al. 2006; Pellicano 2010), in the total group of children and adolescents with ASD, more planning problems were observed in 12- to 14-year-olds compared to 9- to 11-yearolds. This might well be explained by two interdependent factors. First, this could stem from changing demands of the environment. Naturally, children from 12 years and older are expected to behave more independently with regard to school tasks, homework, and everyday activities 123 1968 than younger children. In the Netherlands it is uncommon for children younger than twelve to receive homework. Although an increase of planning problems in ASD was not found in their study, this is in line with the arguments of Rosenthal et al. (2013) about increased expectations from the environment. Second, parents of younger children might consequently rate behaviors as ‘‘never a problem’’, when these behaviors are not yet asked for in real life. Thus, this does not mean that planning problems are absent, but that the BRIEF might be insufficient to detect these problems. Indeed, the observed age-related differences disappeared in an exploratory analysis with a smaller planning scale where we excluded the items referring to homework or tasks (5 items).1 To determine whether or not daily life planning abilities change across the life span in children and adolescents with ASD, it is of importance that a planning scale is developed that is more valid with respect to a broader age range. In the ADOS cut-off subsample an increase of planning problems was absent. Again, although speculative, this might be explained by the fact that the family of children and adolescents with more severe ASD or professionals provide more help compared to those supporting participants with milder symptoms, thereby preventing an increase of planning problems when homework is involved. Summing up, inhibition problems were most apparent in the 6- to 8- year-olds, whereas in the total group studied more planning problems were observed in 12- to 14- yearolds compared to 9- to 11- year-olds. With regard to working memory and cognitive flexibility no age-related differences were found, except that in the ADOS cut-off subsample cognitive flexibility problems were less apparent in the oldest group (15- to 18- year olds), compared to 9- to 11-, and 12- to 14- year-olds. This clearly underlines the importance of studying EF domains separately. The Proportion of Children and Adolescents with Clinically Significant EF Problems Compared to previous studies (Gioia et al. 2002; Kenworthy et al. 2005), we found relatively small proportions of children and adolescents with ASD with clinically significant EF problems (inhibition: 42 %; working memory: 23 %; cognitive flexibility: 51 %; planning: 20 %). This could be influenced by the fact that the samples from the previous studies came from a hospital based neuropsychology service, to which children might have been referred because of behavioral problems. The current sample might be more representative for the prevalence of 1 For this restricted planning scale we eliminated all questions with the words ‘‘homework’’, ‘‘task’’, or ‘‘assignment’’ in the in Dutch translated descriptions, F (3,112) = 1.19, p = .32. 123 J Autism Dev Disord (2014) 44:1959–1971 EF deficits in a more general, ASD population with normal to high intelligence levels. Consistent with other studies, we found that flexibility belongs to the most affected domains of EF (Gioia et al. 2002; Kenworthy et al. 2005). In fact, everyday problems in inhibition, working memory, and planning were only visible in a minority of the children and adolescents with ASD. No age-related differences in proportions of clinically significant scores were observed. This clearly shows that, although group differences exist between groups with ASD and TD (Boyd et al. 2009; Chan et al. 2009; Endedijk et al. 2011; Kalbfleisch and Loughan 2012; Winsler et al. 2007; Yerys et al. 2009; Zandt et al. 2007), not all individuals with ASD encounter EF problems in one or more EF domains. The Contribution of ASD Severity Autism severity, as observed by an objective informant, did not contribute to any of the four EF domains (inhibition, working memory, cognitive flexibility, and planning). To analyse whether the lack of association was due to the relatively mild severity of autistic problems in the sample, we have repeated the analyses with a subsample of participants that scored above the ASD threshold on the ADOS. In this subsample no relation with severity was found either. This is surprising, since the BRIEF is widely used in the clinical practice with regard to ASD, and the ADOS score is based on a broad spectrum of ASD symptoms. Exploratory analyses2 revealed moderate to strong relations between the SRS and the aforementioned BRIEF scores, while no relation between the ADOS score and the SRS index score was found. Relations between the parent report (SRS) and the BRIEF can be explained by informant- and content-overlap. A lack of relations between the BRIEF and the ADOS might be explained by construct validity problems of the BRIEF. However, it could also be the case that the BRIEF is a more general measurement of behavioral disruption (McAuley et al. 2010) instead of EF. Items like ‘‘interrupts others’’, ‘‘gets out of seat at the wrong times, and ‘‘gets out of control more than friends’’ might refer to more generally disruptive and/or inattentive behaviors than to EF. Rather than ASD symptoms, the BRIEF could thus pick up on other symptoms, like comorbid ADHD. This is a question that should be addressed in future research. Next to the already mentioned shortcomings, one shortcoming of this study is that the youngest age group was too small to detect differences with a medium or small 2 Relations ADOS severity score: SRS index score, r = .07; inhibition, r = .49; working memory, r = .39; shift, r = .86; and planning, r = .56. Data can be obtained from the first author. J Autism Dev Disord (2014) 44:1959–1971 effect. It could, therefore, be argued that we had insufficient power to determine age effects. However, we solved this by using more powerful regression analyses with age as a predictor to confirm findings. To conclude, the BRIEF was developed to measure everyday behaviors that are often found disturbed in children and adolescents with ASD and that seem related to EF. Based on the current study, we wonder whether the BRIEF is an appropriate instrument to measure everyday EF from a developmental perspective, however. The perceptions of parents on behaviors take place in a context of environmental demands that vary with age. It is, therefore, complex to determine whether observed differences between age groups point to true developmental changes of an underlying deficit or simply reflect responses to changing environmental demands. The planning scale in particular seems more appropriate to measure problems in adolescents than in children. Therefore, adjustments might be needed in order to increase ecological validity for a broad age range. In a clinical assessment it would be best to study EF with laboratory tasks as well as the BRIEF questionnaire. When both EF skills and environmental demands are taken into account this might help determining whether interventions are needed to aim at actual EF, the social environment, or both. Furthermore, profiles of deficits might predict developmental (Berger et al. 2003) or intervention outcomes (van der Oord, et al. 2008), which could benefit the efficiency and effectiveness in the clinical practice. Nonetheless, likewise to EF studies that take place in the laboratory, the current study highlights that some, but not all domains of everyday EF are impaired in some, but not all children and adolescents with ASD. Since the majority of the children and adolescents does not seem to have clinically significant problems, even within a subsample of children and adolescent who scored above the ASD threshold on the ADOS, and because autism severity is not related to everyday EF problems it is important to focus on individual differences in ASD. Acknowledgments This work was partly funded as part of the research program ‘‘Autism and Aging: A Double Jeopardy’’, which is financed (personal VIDI grant HM Geurts, No. 452-10-003) by NWO, and was partly financially supported by Stichting Nuts Ohra [SNO-T0701-116]. The authors would like to thank all children, adolescents, parents and teachers of the Berg en Boschschool who took part in this study. References Adams, N., & Jarrold, C. (2011). Inhibition in autism: Children with autism have difficulty inhibiting irrelevant distractors but not prepotent responses. 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Journal of Autism and Developmental Disorders, 37(2), 251–259. 123 Copyright of Journal of Autism & Developmental Disorders is the property of Springer Science & Business Media B.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. Journal of Child Psychology and Psychiatry 46:4 (2005), pp 401–408 doi: 10.1111/j.1469-7610.2004.00361.x Continuity and change from early childhood to adolescence in autism Corina W. McGovern and Marian Sigman University of California at Los Angeles, USA Background: This longitudinal study of 48 children diagnosed with autism at 2–5 years of age was designed to test the hypothesis that diagnosis would remain stable for most of the sample but that there would be improvements in symptom severity, adaptive behavior, and emotional responsiveness in adolescence. Methods: A sample of children with autism assessed in both early and middle childhood were observed in late adolescence with the Autism Diagnostic Observation Scale (ADOS) and their parents were administered the Autism Diagnostic Interview-Revised (ADI-R) and the Vineland Adaptive Behavior Scale. Results: All but 2 adolescents (46 of 48) met lifetime criteria for autism according to the ADI-R, and all but 4 adolescents (40 of 44) met criteria for autism spectrum disorder on the ADOS. In contrast to the continuity in diagnosis, parents described improvements in social interactions, repetitive/stereotyped behaviors, adaptive behaviors, and emotional responsiveness to others’ distress in adolescence compared to middle childhood. High-functioning adolescents with autism showed more improvement in these domains than low-functioning adolescents with autism. The extent to which the adolescents were observed to be socially engaged with their peers in school in middle childhood predicted adaptive behavior skills even when intelligence level was statistically constrained. Conclusions: The developmental trajectory of children with autism appears to show both continuity and change. In this sample, most individuals continued to be diagnosed in the autism spectrum but parents reported improvements in adolescence. The results suggest that social involvement with peers improves adaptive behavior skills, and this argues for focusing intervention programs in this area. In addition, it is clear that high-functioning adolescents improve more than low-functioning individuals not only in cognitive abilities but also in social interaction skills. Thus, any early intervention that impacts the cognitive abilities of young children with autism is likely to have a parallel influence on their social skills as they mature into late adolescence and early adulthood. Keywords: Autism, developmental continuity, diagnosis, adaptive behavior, emotional responsiveness. The developmental course from early childhood into adolescence and young adulthood of children with autism has rarely been described. The lack of a clear understanding of the developmental trajectory in autism has several explanations. First, the very few long-term longitudinal studies of children with autism have generally included only one or two measures often administered at only a few time points. In addition, the measures used have tended to be based on parental recollection of general categories of behavior over fairly long time periods. Although parental reports can provide reliable information, this is more likely to be true for reports of current characteristics and behaviors. Finally, it is difficult to generalize findings across studies because of variations in the methods used and sample characteristics since agreement on diagnostic criteria and reliable diagnostic instruments are relatively new. The current study was formulated to contribute to the understanding of developmental trajectories in autism by following a moderate size sample of children with autism with assessments in early childhood, the mid-school years, and late adolescence/ young adulthood. Although the children were diagnosed with a variety of measures at intake, diagnoses were confirmed with a standardized interview (the ADI-R) by middle childhood. While there was some reliance on parental recollections, much of the information about the children came from concurrent parental reports, observations of behavior, and assessments of the children. The study was designed to assess continuity and change in a variety of characteristics important for the adjustment of the individuals over the life course. The factors investigated consist of diagnosis, symptoms, adaptive functioning, and emotional responsiveness. The literature on these factors and our hypotheses concerning change and continuity will be reviewed in the following sections. Diagnosis and symptoms Stability of diagnosis and symptoms across the lifespan is central to understanding any disorder and yet remains a relatively unexplored area in the study of autism. Mesibov and colleagues (Mesibov, Schopler, Schaffer, & Michal, 1989) reported decreases in autistic symptoms in a group of 89 children prior to 10 years of age and again after 13 years of age using the Childhood Autism Rating Scale (CARS) (Schopler, Reichler, & Renner, 1988). The CARS is a 15-item rating scale of behaviors asso-
Association for Child Psychology and Psychiatry, 2004. Published by Blackwell Publishing, 9600 Garsington Road, Oxford OX4 2DQ, UK and 350 Main Street, Malden, MA 02148, USA 402 Corina W. McGovern and Marian Sigman ciated with autism that is generally completed after an observation period. All participants were in North Carolina’s TEACCH (Treatment and Education of Autistic and Related Communication Handicapped Children) program, a series of educational and skills training programs designed to meet the needs of individuals with autism throughout the lifespan. Analysis revealed a significant decrease in mean CARS scores in adolescence, indicating a significant improvement over time in 9 of 15 areas. Decreases in CARS scores and DSM-III criteria from early childhood to early adolescence were also noted among a group of 76 subjects with autism spectrum disorder who had not received any special treatment other than standard services such as early identification, special education, and speech therapy (Eaves & Ho, 1996). Despite the lower scores suggesting an amelioration of autistic symptoms with age, the entire sample continued to receive a diagnosis of autism spectrum disorder at follow-up. In line with these studies, Piven and colleagues (Piven, Harper, Palmer, & Arndt, 1996) compared parental reports of autistic symptom expression in their adolescent and young adult children to retrospective reports when their children were 5 years of age. These comparisons indicated amelioration of social and communicative symptoms but not repetitive behaviors and stereotyped interest. Mapping parental report to DSM-IV criteria, 5 of the 38 participants in their sample no longer met criteria for autism when they reached the adolescent/young adult period. However, all the participants were reported to have persistent autistic characteristics and substantial social impairments in adolescence and young adulthood. Based on the findings from these three studies, we predicted that our sample of adolescents and young adults would show a moderate improvement in autistic symptoms but that the majority would continue to meet diagnostic criteria for autism. ively associated with intelligence. In addition, we hypothesized that children who were more involved with their peers in the mid-school period would make bigger gains in adaptive behavior than children who were less socially engaged. This hypothesis was based on the observation that, during high-level social play, typically developing children are able to learn to interpret and respond to others’ social cues and to acquire skills to initiate and maintain a social interaction (Schuler & Wolfberg, 2000). Emotional responsiveness Young children with autism show deficits in responsiveness to the emotions of others (Sigman, Kasari, Kwon, & Yirmiya, 1992). In a study comparing young children with autism to mental age matched controls, children with autism looked less at an adult showing fear, pleasure, distress, or discomfort. In addition, children with autism were rated as less empathic and showed less inhibition of play during the adult’s distress (Sigman et al., 1992). In the mid-school follow-up the children with autism continued to show less orienting and attending to an adult’s display of distress and appeared less concerned by the adult’s distress than did children with Down syndrome and children with other developmental disabilities (Sigman & Ruskin, 1999). Research suggests that during the period of adolescence and adulthood social interest expands and social skills continue to develop (Ando, Yoshimura, & Wakabayashi, 1980; Mesibov & Handlan, 1997; Mesibov et al., 1989). Given that autistic people tend to become less socially withdrawn as they age, we predicted that there would be more responsiveness to other people’s emotions in adolescence and young adulthood than was seen in the mid-school years. Methods Participants Adaptive behavior Adaptive behavior is generally defined as the individual’s ability to meet the personal and social demands of the environment expected by the culture for someone of his or her age (Nihira, Leland, & Lambert, 1993). Very little information is available about developmental change in the adaptive behavior of individuals with autism or about factors that might predict improvements in adaptive behavior. In one recent study, individuals with autism improved in adaptive behavior with age (Freeman, Del’Homme, Guthrie, & Zhang, 1999). Rates of improvement were related to initial IQ level for two areas of adaptive behavior, communication and daily living skills, but not for socialization skills. Based on the findings of this study, we expected improvements in adaptive behavior from middle childhood to adolescence and young adulthood with rates of improvement posit- The original sample seen in early childhood consisted of 70 children with autism. Because recruitment took place over many years, the location for recruiting subjects varied, although most of the subjects came from either the outpatient or inpatient services of the UCLA Neuropsychiatric Institute or from local elementary or preschools. Fifty-one of the original sample of 70 children (73%) participated in the mid-school follow-up (Sigman & Ruskin, 1999). The children who participated in the mid-school follow-up did not differ from the non-participants in initial age, gender, mental age, DQ/ IQ, or socio-economic status, although the mothers of participants were more highly educated than the mothers of non-participants. At the most recent follow-up, 48 of the original 70 children (68%) were seen. Of the 48 participants from the current follow-up, 45 were also seen during the mid-school follow-up. Six participants from the midschool follow-up did not participate in the most recent follow-up for the following reasons: one participant had Continuity and change from early childhood to adolescence died; one participant who was living in a group home did not have contact with her parents who were needed to provide consent; one participant had re-entered the foster-care system and his original foster mother was unable to locate him; and two families declined to participate. Three families whom we were unable to locate at the mid-school follow-up were located and participated in the current follow-up. The current sample of 48 was composed of 6 females and 42 males. The ethnic composition of the group was as follows: 31 Caucasian, 7 African-American, 7 Asian, 2 Hispanic, and 1 other. The majority of participants (42 of 48) continued to live at home with their family; six lived in residential facilities. Of the 48 participants seen at the most recent follow-up, the mean age of the participants during early childhood, middle childhood/ early adolescence, and later adolescence/young adulthood, respectively, was as follows: 3 years, 11 months of age (SD ¼ 1 year), 12 years, 8 months of age (SD ¼ 3 years, 9 months), and 19 years of age (SD ¼ 3 years, 10 months). The degree to which the group seen in later adolescence/young adulthood was representative of the original sample was analyzed. Repeated measures ANOVA revealed that the current participant group did not differ significantly from the original sample in terms of chronological age, mental age, developmental or intelligence quotient, nor language age at intake. Descriptive information about the sample at intake is provided in Table 1. Procedures Parents were interviewed about their child’s past and current autistic symptoms and adaptive behavior using standardized assessments at two time points: when their children were around 12–13 years of age and approximately seven years later when their children were about 19–20 years of age. During the adolescent/ young adult follow-up, participants’ autistic symptoms were assessed directly using a standardized observation and participants’ cognitive ability was evaluated using standardized assessments. Approximately half the families came to UCLA for testing and the other half of the sample was assessed in their residence based on parental request and/or distance from UCLA. Testing did not begin until parents signed a consent form and participants who had sufficient capacity signed an assent form. Families were paid for their participation. Testing of all participants was conducted by experimenters who were naive as to the scores of any participant on previous testing. At the time when this study was initiated in the late 1970s, there were few specialized intervention programs available to most children with autism. For this 403 reason, no assessment of intervention experiences was carried out at recruitment or at the mid-school followup. However, parents were asked in the current follow-up whether their children had participated in intervention programs other than their school experiences. Fourteen parents stated that their children had been involved in behavioral interventions with a mean duration of 54 months. Seven individuals were involved in speech therapy and two were involved in occupational therapy. Given that these descriptive data were based on parental recollection of their children’s intervention programs over a long period of time, no evaluation of the impact of these interventions was carried out. Diagnostic measures Initial diagnoses. Initial diagnoses of the sample during early childhood changed over time as diagnostic tools and standards evolved. The first half of the 70 children with autism recruited for this study were diagnosed by a team of clinicians based on the standards of the Diagnostic and Statistical Manual of Mental Disorders, third edition (APA, 1980) and third editionrevised (APA, 1987). The team of clinicians who made the diagnoses all had extensive experience with childhood psychopathology. For the second half of the sample, clinical judgment was supplemented by two other diagnostic methods: the Childhood Autism Rating Scale (CARS) (Schopler et al., 1988), a structured observation, and the Autism Behavior Checklist (ABC) (Krug, Arick, & Almond, 1980), used as a parent interview. To be classified as autistic, participants were required to meet diagnostic criteria on two of the three methods. Thus, all participants had a well-established diagnosis of autism during early childhood. Follow-up diagnoses. Parents were interviewed regarding their children’s past and current autistic symptoms using the Autism Diagnostic InterviewRevised (ADI-R) (Lord, Rutter, & Le Couteur, 1994) during the middle childhood and adolescent/young adult follow-ups. The examiners who administered the ADI-R had all attended training workshops and were trained to administer and score the ADI-R reliably. Diagnosis of autism on the ADI-R is primarily based on the child’s history of behaviors associated with autism. To establish a current diagnosis in the adolescent and young adult years, parental report was supplemented with the Autism Diagnostic Observation Schedule-Generic (ADOS-G) (Lord et al., 2000), a diagnostic instrument based on current expressed autistic symptoms displayed during a structured observation. The ADOS-G had not been used at the middle childhood follow-up because it was still under development at the time. Table 1 Descriptive information of sample at intake Early childhood Chronological age (mos.) Language age (mos.) Mental age (mos.) IQ Adolescent/Young adult n M SD n M SD 70 69 70 70 47.21 16.49 23.71 49.31 12.14 7.62 9.81 13.27 48 47 48 48 47.27 17.43 25.04 51.13 12.12 8.62 11.00 12.77 404 Corina W. McGovern and Marian Sigman Adaptive behavior measures Adaptive behavior during the middle childhood/early adolescent follow-up and the adolescent/young adult follow-up was assessed using the Vineland Adaptive Behavior Scales (VABS) (Sparrow, Balla, & Cicchetti, 1984). The validity and usefulness of the VABS has been well established with autistic samples (Perry & Factor, 1989). The VABS raw scores were converted to age equivalents using standardized norms. Percentile rankings were computed using the supplementary norms for individuals with autism (Carter et al., 1998). Emotional responsiveness measure Emotional responsiveness was assessed via parent interview as a question on the ADI-R during the middle school and adolescent/young adult follow-ups. Parents were asked if their child ever tried to comfort them if they were sad, hurt or ill. Parental descriptions of their child’s behavior were coded on a 4-point scale from 0 to 3, with lower scores indicating more comforting behavior. Parents were also asked to report retrospectively on their child’s offering comfort when he/she was 4–5 years of age. Direct observation of peer involvement School assessments were conducted during the middle childhood/early adolescent period to determine the nature of peer interaction of children with autism. Direct observations of the participants were conducted during structured and unstructured periods of two different school days to observe ongoing social interaction. Measures of peer interactions were based on the Peer Play Scale (Howes, 1988) and were coded as nonsocial play, low-level social play, or high-level social play. Peer interactions were coded continuously every 15 seconds, with the highest level of play recorded during every 15-second block. Each observation period lasted at least 30 minutes, ranging from approximately 30 minutes to approximately 60 minutes with an average observation time of 41 minutes. Frequency counts were transformed to proportion scores for the total observation period to account for differences in observation periods (Sigman & Ruskin, 1999). Proportion of time engaged in high-level play was used as the measure of peer involvement. Results Diagnosis and symptoms At the middle childhood/early adolescent follow-up the ADI-R was administered to the parents of 51 of the children originally diagnosed with autism in early childhood. At the adolescent/young adult follow-up, parents of 48 participants originally diagnosed with autism were interviewed with the ADI-R. Of the 48 parents interviewed at the adolescent/young adult follow-up, 45 had also been interviewed at the middle childhood/early adolescent follow-up. Diagnosis. Overall, the results support the ADI-R as a valid and reliable instrument for diagnosing autism in middle childhood, adolescence, and young adulthood. Ninety-eight percent (50/51) of participants originally diagnosed with autism in early childhood met ADI-R lifetime criteria for autism in middle childhood and 96% (46/48) of participants met lifetime criteria during adolescence and young adulthood based on parental report. Of the 48 participants seen at time 3, 44 were administered the ADOS-G to assess current expressed autistic symptoms. Four participants were not administered the ADOS-G because of child noncompliance or parental refusal. Most participants (40 of 44) met ADOS-G criteria for autism spectrum disorder. Thirty-three children met criteria for autism, and seven met criteria for the broader classification of pervasive developmental disorder-not otherwise specified (PDD-NOS). There is little qualitative distinction between autism and PDD-NOS; rather, PDD-NOS is diagnosed as a case of subthreshold autistic symptomatology. By adolescence/young adulthood, four individuals showed sufficient diminution of symptoms to no longer meet criteria for autism or the broader autism spectrum on the ADOS-G. These four participants ranged in age from 15 years, 3 months of age to 18 years, 8 months of age (mean age was 16 years, 7.5 months). All four were verbal and during the ADOS-G used language in a largely correct fashion, were able to maintain a back and forth conversation and showed good use of gestures for non-verbal communication. However, on an independent measure of language, the Clinical Evaluation of Language Fundamental-Revised (Semel, Wiig, & Secord, 1987), all four participants continued to show language delays (2 years, 3 months; 2 years, 10 months; 6 years exactly; and 6 years, 11 months delayed). To consider the general level of functioning of these four individuals we also examined IQ scores. Three of the four participants had IQs within the average range of intelligence (IQs of 101, 106, and 110) and one had an IQ in the borderline range of intelligence (IQ ¼ 77) as measured using the Stanford–Binet Intelligence Scale: Fourth Edition (Thorndike et al., 1986). In terms of functioning in the school system, all four were mainstreamed in regular classes, two did not receive any supports or services and two had a resource class to monitor their progress. During the ADOS-G, all four responded effectively and appropriately to social situations and presses, made clear and appropriate social overtures, and appeared comfortable interacting with the examiner. To obtain a more global sense of social functioning for these four individuals, parental reports of social functioning were considered. According to parental report on the ADI-R and VABS, two of these participants had a group of friends and a best friend and led rather active social lives, showing typical to above average social behavior (70th and 95th percentile on Continuity and change from early childhood to adolescence national percentile ranks), whereas the other two were reported to have just one friend each that they did not socialize with often and showed marked social deficits relative to their age (at or below the 1st percentile on national percentile rankings). Thus, two of the four individuals who did not meet ADOS criteria for autism appear to have relatively good social competence. The other two individuals appear to experience general social difficulties despite not manifesting overt social-communicative impairments during the semi-structured social situations in the ADOS-G. Symptoms. Domain algorithm scores on the ADI-R for socialization, non-verbal communication, verbal communication, and repetitive interests and stereotyped behaviors for current and past expression of autistic symptoms were compared from middle childhood/early adolescence to adolescence/young adulthood. Verbal communication questions were only asked for those participants with phrase speech. For the purpose of comparing current versus past recollection across both interviews, items that were not applicable through adolescence and young adulthood were dropped from the algorithm (a list of items used is available from the authors). Parental reports of autistic symptoms were analyzed for each of the sub-domains of the modified algorithm using single factor repeated measures GLM with four levels (mid-school/current, mid-school/ past, adolescent/current and adolescent/past). Only the data from subjects with all four data points were included in these analyses. All sub-domains showed significant differences in reported autistic symptoms: social, F (3, 129) ¼ 39.55, p < .001; non-verbal communication, F (3, 129) ¼ 21.33, p < .001; verbal communication, F (3, 45) ¼ 32.18, p < .001; and repetitive behaviors/stereotyped interests, F (3, 132) ¼ 18.46, p < .001. Tests of within-subjects contrasts revealed that current symptoms in adolescence were reported as less severe than past symptoms: social, F (1, 43) ¼ 48.83, p < .001; non-verbal communication, F (1, 43) ¼ 36.53, p < .001; verbal communication, F (1, 15) ¼ 59.27, p < .001; and repetitive behaviors/stereotyped interests, F (1, 44) ¼ 49.11, p < .001 (see Table 2). Current symptoms were reported to improve from mid-school to adolescence for two of the subTable 2 Mean recollected and current domain scores on the ADI-R Current Impairments Social* Nonverbal communication* Verbal communication* Repetitive/stereotyped Behaviors* *p < .001. Ever n M SD M SD 44 44 16 45 8.48 3.82 3.56 4.36 4.92 3.17 2.78 2.06 12.93 6.23 8.06 6.02 3.23 2.40 2.79 1.60 405 Table 3 Current domain scores on the ADI-R Mid-school Social* Repetitive/ stereotyped behaviors* Adolescent n M SD M SD 44 45 11.75 5.42 4.10 1.66 8.48 4.36 4.92 2.06 *p < .001. domains: social, F (1, 43) ¼ 45.77, p < .001, and repetitive behaviors/stereotyped interests, F (1, 44) ¼ 14.74, p < .001 (see Table 3). Thus, there was an improvement in the areas of socialization and repetitive behaviors/stereotyped interests based on parents’ current judgments of their children’s symptoms from mid-school to adolescence/young adulthood. There were no significant differences in reporting of past symptoms across the mid-school and adolescent/young adult follow-ups. In order to determine whether the change in symptoms differed for high-functioning and lowfunctioning individuals, participants were grouped into high (IQ P 70) and low (IQ/DQ < 70). Independent two-tailed t-tests were conducted for high (n ¼ 12) and low IQ/DQ (n ¼ 35) groups for change in ADI-R current versus past expression of autistic symptoms for each of the modified algorithm domain totals. High IQ participants were reported to show larger reductions in reported social impairments than low IQ/DQ participants, t (45) ¼ )3.85, p < .001; larger reductions in verbal communicative impairments than low IQ/DQ participants, t (19) ¼ )2.76, p < .05, and larger reductions in repetitive behaviors and stereotyped interests than low IQ/DQ participants, t (45) ¼ )4.05, p < .001. Only the nonverbal communication domain did not show a significant difference in reported change in autistic symptoms for high and low IQ/DQ participants (see Table 4). In terms of improvement in current difficulties, there was no significant difference for the high and low samples in the social domain. However, the highfunctioning sample was reported to have improved more in repetitive and stereotyped interests than the low-functioning sample, t (43) ¼ 2.75, p < .01. The lower scores on the ADI-R for parental report of current behavior vs. their recollections of past behavior at both middle childhood and adolescence/ young adulthood may be interpreted in two ways. First, it may be that parents simply remember their children as more impaired earlier. Because we do not have comparable parent reports in the 3–5-year-old period, this interpretation cannot be ruled out. The second hypothesis is that the characteristics of autism diminish from an early age, particularly for higher-functioning individuals. The strongest evidence for this hypothesis is that parents described fewer current social impairments and less repetitive current behaviors and stereotyped interests in adolescence/young adulthood than in middle childhood. 406 Corina W. McGovern and Marian Sigman Table 4 Mean change in ADI-R symptom domain score and VABS composite score High IQ ‡ 70 Low IQ < 70 Impairments Social** Nonverbal communication Verbal communication* Repetitive/stereotyped behaviors** VABS composite score** n M SD n M SD 35 33 10 35 29 )3.85 )2.15 )2.40 )2.23 3.66 3.56 2.83 2.88 1.94 14.69 12 12 11 12 12 )8.58 )3.50 )5.36 )4.83 46.33 4.19 2.35 2.01 2.04 45.31 *p < .05; **p < .001. Adaptive behavior Adaptive behavior in adolescence and young adulthood was representative of individuals with autism, with average VABS percentile rankings ranging from 62 to 65% compared to a normative sample of adolescents and adults with autism. Individual rankings ranged from the 1st percentile to the 99th percentile (Carter et al., 1998). Comparison of age-equivalent domain scores on the VABS revealed that our sample did not show a particular weakness in socialization relative to other areas of adaptive functioning as is often cited in the literature (Carter et al., 1998; Kraijer, 2000; Rodrigue, Morgan, & Geffken, 1991). Paired samples two-tailed t-tests of VABS adaptive behavior composite age equivalent scores revealed that parents reported that their child’s adaptive behavior improved from the mid-school years to the adolescent and young adult years, t (40) ¼ 3.07, p < .01. Further analysis revealed improvement for 2 of the 3 VABS domain scores: daily living skills improved from the mid-school years to the adolescent and young adult years, t (40) ¼ 3.59, p < .01; socialization also improved from the mid-school years to the adolescent and young adult years, t (40) ¼ 2.39, p < .05. However, scores for the communication domain were not significantly different from the mid-school years to the adolescent/young adult years (see Table 5). In order to determine whether the change in symptoms differed for high-functioning and lowfunctioning individuals, participants were divided into high (IQ P 70) and low (IQ/DQ < 70) groups. Independent samples t-tests revealed that high IQ participants gained significantly more months in adaptive behavior on average than low IQ/DQ participants, t (39) ¼ 4.59, p < .001 (see Table 4). The hypothesis that peer social engagement would contribute to gains in adaptive behaviors was tested with Pearson correlation coefficients. These analyses showed that the percentage of time that the children with autism spent in high-level play with peers in the mid-school years predicted gains in VABS adaptive behavior composite scores, r (32) ¼ .69, p < .001, daily living skills scores, r (32) ¼ .51, p < .01, communication scores, r (32) ¼ .64, p < .001, and socialization scores, r (32) ¼ .70, p < .001 from the mid-school years to the adolescent and young adult years. Even after controlling for mid-school IQ/DQ, percentage of time engaged with peers in high-level play in the mid-school years continued to predict gains in VABS adaptive behavior composite scores, r (30) ¼ .51, p < .01, communication scores, r (30) ¼ .52, p < .01, and socialization scores, r (30) ¼ .56, p < .01, from the mid-school years to the adolescent and young adult years. Thus, the hypothesis that peer social engagement would predict later adaptive behavior was confirmed in this study. Emotional responsiveness The hypothesis that children with autism would manifest increasing emotional responsiveness to others’ distress as they matured was supported by parental report. Paired samples two-tailed t-tests of parental reports of their child’s ‘current’ comforting behavior, assessed during the mid-school years and assessed again in the adolescent and young adult years, revealed significant differences across the two assessment periods, t (44) ¼ 4.957, p < .001. Parents reported that their children offered more comfort to them if they were sad, hurt or ill in the adolescent and young adult follow-up than they did in the mid-school follow-up. Pearson correlations revealed that par- Table 5 Adaptive behavior composite age equivalent score Mid-school Impairments Total score* Daily living* Socialization* Communication *p < .05. Adolescent/Young adult n M SD n M SD 41 41 41 41 57.65 65.95 46.85 60.98 42.76 39.16 37.31 55.96 41 41 41 41 73.59 88.17 62.93 68.83 61.76 62.25 63.99 64.97 Continuity and change from early childhood to adolescence ental reports of their child’s ‘current’ comforting behavior in the mid-school assessment was predictive of parental reports of their child’s ‘current’ comforting behavior in the adolescent and young adult assessment, r (43) ¼ .52, p < .001, even after controlling for participants’ IQ/DQ scores in the mid-school years, r (36) ¼ .37, p < .05. Discussion The results of this study provide evidence of both continuity and change in the developmental trajectory of children with autism from early childhood to late adolescence. Diagnosis of autism spectrum disorder shows very strong stability over time in that almost all the children continue to meet diagnostic criteria as adolescents and young adults even when current behavioral observations were used to establish diagnosis. On the other hand, improvement was reported by parents in terms of symptoms, adaptive behavior, and behavioral responsiveness to the emotions of others. Parents mentioned fewer symptoms in adolescence/early adulthood than they remember when the children were 3–5 years of age. Moreover, they reported fewer symptoms in the areas of socialization and repetitive behaviors/stereotyped interests in adolescence than in middle childhood. They also described stronger adaptive behavior in all areas except communication skills and more empathic behavior in adolescence than in childhood. The behavioral observations largely support the findings from parental reports. It must be pointed out that the improvements in symptoms, adaptive behavior and empathy were mostly confined to the higher-functioning individuals. The parents of the subset of the sample with lower IQs reported much less progress than that reported for children with higher IQs. This finding lends significance to intervention efforts directed toward improving these cognitive and language skills of children with autism in early life. Successful interventions in the cognitive and communicative domains are likely to generalize to improvements in social functioning, even if diagnosis is not altered. Despite the severe limitations of high-functioning adolescents with autism, they still enjoy relatively more adaptive and social skills than low-functioning adolescents with autism. The findings also point to another possible area where interventions may have important pay-offs and that is on the school playground. In the current study, the social involvement of the children was observed and recorded by non-participant observers who did not intervene in the social environment of the children. Elementary school children who were more socially engaged gained more adaptive skills than less engaged elementary school children. Although it is true that social engagement was partly a function of cognitive skills, social engagement predicted improvements in adaptive behavior even when 407 the effects of intelligence were statistically constrained. Thus, it appears that engagement with peers improves the social skills of children with autism as is true for typically developing children. To the extent that peer engagement can be increased in schoolchildren with autism, there may be improvements in their adaptive and social skills. Improvements in social behavior were reported from middle childhood/early adolescence to later adolescence/young adulthood, suggesting the transition from mid-childhood to adolescence may coincide with emerging social interest for individuals with autism. Although it is clear that parents see their children as less symptomatic as the children mature, the interpretation of this finding is not clear. It could be that children actually change in their behavior, as has been suggested by previous research (Eaves & Ho, 1996; Mesi...
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