Moore & Goodson followed children with ASD from two to four years old and found that overall rates of RRBs reduced, with low level RRBs significantly reducing, yet different RRB subtypes persisted in a more complex form. Whereas Honey, et al. examined preschoolers and found within a year there was a significant decrease in the severity of RRBs observed. However, a more recent study, which utilized observational coding of RRBs in a preschool aged sample, found there was no significant change in any of the RRB subtypes coded across 13 months and three assessment time points . Further, Richler, et al. used the ADI-R to track change in RRBs over a period of 9 years and found that low level sensory motor RRBs actually remained high. Taken together, these findings suggest that the developmental progression and transformation of RRBs overtime within individuals remain unclear, with the biggest influence being the sample population and measurement tools used .The general consensus within the field is that children with more severe adaptive and cognitive impairments exhibit higher frequency, more intense, and more persistent RRBs . More specifically, children with lower cognitive capacity exhibit the most frequent and severe low-level RSM RRBs, whereas children with higher cognitive capacity exhibit significantly less of the RSM behaviors . However, these findings are nuanced, as there is not a singular and linear relationship between IQ and RRBs, and consideration must be given to the types of RRBs being measured. For example, grow bucket in a study examining 830 children with ASD between 15 months and 12 years old with an average age of 5 years old, found that for many RRBs, a significant interaction effect was found between nonverbal IQ and age .
Specifically, in older children, NVIQ was strongly related to low-level RRBs such as hand and finger mannerisms. However, high-level RRBs like circumscribed interests were positively related to NVIQ. Bishop, Richler, & Lord used the ADI-R to examine the total as well as the individual types of RRBs, which included 13 types of behaviors considered to fall under the RRB umbrella. Another interesting finding from this study was the relationship between NVIQ and RRBs actually became stronger with increasing age, where children under the age of 3 showed no relationship between RRBs and NVIQ. Similarly, Kim and Lord found no association between NVIQ and RRBs in toddlers under two years old. Taken together, these findings further evidence the importance of measuring subtypes individually, as there are clear differences across RRB types in the relationship between cognitive ability and RRB presentation. Findings across studies highlight the importance of examining specific subtypes of RRBs and the traits associated with them, as these traits may significantly impact the persistence or the possible reduction of RRBs overtime. For example, Ray-Subramaian and Weismer found that not only were receptive and expressive language skills significantly lower among children with higher rates of RRBs, but they also concluded that higher scores in both language domainsin 2-3 year olds could significantly predict a reduced rate of RRBs. Again, consumers of this area of ASD research must take into account the number of participants and age span included across studies. Most recently, a study examined children at three time points to determine if RRB presentation at 1-2 years old, and/or 3-5 years old can predict cognitive functioning, adaptive skills and ASD symptomology at 8-10 years old . Results showed that increased severity of low-level RRBs were significant predictors of lower cognitive and adaptive skills as well as a greater ASD symptom severity at age 8-10 years. This relationship was not found when examining whether RRBs in the first two years of life could significantly predict the same school-aged outcomes .
Despite the nuanced findings among studies regarding the specific relationship and influence RRBs have on cognitive performance, it is clear that future studies must consider and control for cognitive functioning when examining the relationship between RRBs and other clinical characteristics .Children with ASD exhibit significant impairment in adaptive functioning skills that extend beyond their cognitive deficits . It is important to note that adaptive functioning skills measure the ability of an individual to successfully function within their given environment, and studies have demonstrated greater deficits in adaptive functioning for children with ASD compared to age and IQ matched peers . Several studies have examined the relationship between adaptive skills and RRB presentation in individuals with ASD . Similar to the relationship between RRBs and IQ, results have varied based on the measures used and age of participants; however, it can be deduced that in general, higher rates of RRBs are associated with lower adaptive functioning skills . However, this finding has varied across studies based on the age and IQ of the participant . The relationship between adaptive skills and RRB presentation in ASD is complex and has varied results based on the age and IQ of participants as well as the measures employed. For example, Liss, et al. found that the relationship between adaptive functioning and RRBs was dependent on the severity of adaptive skill impairment. Specifically, there was no significant relationship between RRBs and adaptive functioning in lower functioning children with ASD; yet the high functioning group exhibited a significant correlation between adaptive behaviors and RRBs. Few studies have examined adaptive functioning and RRBs using measures other than parent report. However, in young children, self-regulation has been observed in interactions between parent and child. As shown by Wetherby and Prizant , children with ASD exhibited lower proportions of well-regulated behavior bouts and higher incidences of RRBs during parent child interactions. Theoretically, some have suggested that specific RRBs may be a result of an emotional trigger for children with ASD. However, Militerni, et al. found that most of the low-level RRBs observed in 2-7 year olds were not reactive to a particular emotional trigger. The remaining 29% of RRBs deemed to be reactive in nature consisted of high intensity sensory behaviors, including self-injurious behaviors, motor RRBs and sensory stimulation, which were all more common the younger participants . This notion of an emotional trigger also highlights a theory that RRBs serve as a coping strategy to regulate their state of arousal; however, there are currently not enough results or data to full endorse this theory and further examination is needed .
There are a number of common developmental and neuropsychiatric disorders that overlap in symptom presentation, and in some cases are determined to co-occur in children with ASD. Some of the most common are attention deficit hyperactivity disorder and anxiety disorders . There is limited knowledge about how these ASD-related disorders vary across the population and what impact the co-occurring conditions impact RRB manifestation as well as the impact on overall development, adaptive skills and other child characteristics. Attention deficit hyperactivity disorder . Attention deficit hyperactivity disorder is a neurodevelopmental disorder characterized by symptoms of inattention, impulsivity, and/or hyperactivity exhibited to a degree substantially beyond what is expected for developmental level . ADHD and ASD share overlapping symptoms such as issues with communication problems, issues with attention and the presence of restricted behaviors . Although the last version of the Diagnostic and Statistical Manual of the American Psychiatric Association prohibited a dual diagnosis of ASD and ADHD, dutch bucket for tomatoes preliminary evidence suggests that when these two disorders co-occur, the risk for increased severity of psychosocial issues intensifies . Such findings are in conjunction with a growing number of researchers reporting children who meet criteria for both disorders are evidence to suggest they can co-occur .The need for research to examine the dual presence of clinically significant ADHD symptoms in individuals with ASD has begun to be addressed . This study compared school-aged children 4 to 8 years old that included younger siblings of children with ASD , children with ASD , low-risk controls and children with language delay to include reference points for functioning and skill level across groups. Results indicated that children with comorbid ASD and ADHD diagnoses had lower cognitive functioning, more severe social impairments, and greater delays in adaptive functioning than children with ASD only . There is a great need for continued exploration of the impact of co-occurring ASD and ADHD symptomology on children with ASD; specifically, how elevated levels of hyperactivity influence the presence and severity of RRBs in children with ASD. Anxiety. The role of anxiety for individuals with ASD has been proposed to play a key role in the severity of RRBs, as the function of engaging in specific RRBs has been hypothesized to serve as a coping mechanism to reduce feelings of anxiety . However, it should be noted that there is insufficient evidence currently to support this theory . Scientific evidence illustrating links between anxiety, ASD and RRBs is limited. However, there have been studies that indicate high levels of anxiety in the ASD population and even links to symptom severity increasing . The link between anxiety and ASD symptom severity is logical considering the need for routine, sameness and consistency to a severe degree. Interruption of those may result in increased levels of anxiety and intense stress often accompanied by outbursts when self-control is impaired. The theories accounting for the popular notion that anxiety and arousal states significantly contribute to increased RRB severity for individuals with ASD still needs to be explored .The present study aimed to explore the phenotypic presentation of RRBs and associated characteristics for individuals with ASD between 4 and 18 years old. Previous studies have been limited by measurement tools, limited age included, as well as limited statistical power due to smaller sample sizes; therefore, this study aimed to examine the forms of RRBs across age and IQ, and to examine the impact of hyperactivity, anxiety, coping skills, and ASD severity on RRB presentation in a large, well-characterized sample of individuals with ASD. The first aim was to define the specific RRB subtypes derived from a factor analysis of the Repetitive Behavior Scale- Revised . Secondly, the RRB subtypes derived from the factor analysis were used to cluster participants based on type and severity of co-occurring RRB subtypes into phenotypic profiles. The final aim of this study was to explore the role of clinical , cognitive and adaptive skills in predicting phenotypic profile group membership. Researchers have yet to uncover the specific function/s of RRBs; therefore, examination of the predictive power of individual clinical characteristics on RRB phenotypes contributes to this area of research.The first aim of this study was to examine the factor structure of the Repetitive Behavior Scale- Revised to determine how many unique RRB forms are measured. The first step in determining the factor structure was to run an exploratory factor analysis of the 43 RBS-R items using Mplus Version 7 , an oblique CF-quartimax rotation and a weighted least- squares with mean and variance adjustment to account for the ordinal nature of the data . EFA assumes that each variable, in this case each question on the RBS-R, may be associated with any other factor without an a priori hypothesis about factors or variables . To determine the optimal number of factors, a combination of model fit statistics and examination of factor loadings were used. The chi-square value is typically an informative model fit statistic; however, the chi-square test is sensitive to sample size, such that large samples often result in statistically significant chi-square values . Given the number of participants in the current dataset, the chi-square values were analyzed with caution. Additional model fit statistics included root mean square error of approximation , the Standardized Root Mean Square Residual , the Comparative Fit Index and the Tucker Lewis Index . The RMSEA is a measure of model fit that is not as sensitive to sample size and values below .06 indicate an acceptable model fit . The SRMR is another descriptive model fit statistic in which lower values indicate better model fit, with a suggested cut-off of .08 or below . Lastly, both the CFI and TLI are typically presented together in EFAs and both serve as measures of model fit, ranging from 0 to 1 with higher values indicating better fit and cutoff scores of .90 .Determining Factor Structure. As items were permitted to load on only one factor for the CFA, items that loaded significantly >.30 on more than one factor were evaluated to determine the ideal factor pattern.