ABSTRACT
Objective@#This study examined how state and trait anxiety of adolescents with autism spectrum disorders (ASD) are associated with their demographic characteristics, repetitive and restricted behaviors (RRBs), and internalizing and externalizing problem behaviors. @*Methods@#A total of 96 participants with ASD (mean age=14.30 years; 91 males) completed a battery of tests including the State/Trait Anxiety Inventory (STAI), the Autism Diagnostic Interview-Revised, the Social Responsiveness Scale (SRS), and a cognitive test measuring intelligence quotient (IQ). Participants’ parents completed the Child Behavior Checklist (CBCL). Pearson’s correlations among age, IQ, two subscales of the STAI (i.e., STAIS and STAIT, measuring self-reported state and trait anxiety, respectively), and the Anxiety subscale of CBCL (i.e., CBCL-Anxiety, measuring parent-reported trait anxiety) were computed. Subsequently, Pearson’s correlations were computed among the three anxiety measures, RRBs, and problem behaviors, while controlling for participants’ age and IQ. @*Results@#The STAIS and CBCL-Anxiety were both significantly correlated with higher age, sensory sensitivity, depressive symptoms, somatic complaints, and aggressive behaviors. All three anxiety variables were significantly and positively correlated with total SRS RRB scores. Additionally, the STAIS and STAIT were significantly associated with more severe Compulsion/Adherence behaviors, and the CBCL-Anxiety was also significantly associated with more severe Rule-breaking Behaviors. @*Conclusion@#Self-reported state anxiety showed association patterns similar to those of parent-reported trait anxiety. Future studies investigating the precise operationalization of different anxiety instruments are needed to accurately measure the anxiety of adolescents with ASD.
ABSTRACT
Objective@#This study examined how state and trait anxiety of adolescents with autism spectrum disorders (ASD) are associated with their demographic characteristics, repetitive and restricted behaviors (RRBs), and internalizing and externalizing problem behaviors. @*Methods@#A total of 96 participants with ASD (mean age=14.30 years; 91 males) completed a battery of tests including the State/Trait Anxiety Inventory (STAI), the Autism Diagnostic Interview-Revised, the Social Responsiveness Scale (SRS), and a cognitive test measuring intelligence quotient (IQ). Participants’ parents completed the Child Behavior Checklist (CBCL). Pearson’s correlations among age, IQ, two subscales of the STAI (i.e., STAIS and STAIT, measuring self-reported state and trait anxiety, respectively), and the Anxiety subscale of CBCL (i.e., CBCL-Anxiety, measuring parent-reported trait anxiety) were computed. Subsequently, Pearson’s correlations were computed among the three anxiety measures, RRBs, and problem behaviors, while controlling for participants’ age and IQ. @*Results@#The STAIS and CBCL-Anxiety were both significantly correlated with higher age, sensory sensitivity, depressive symptoms, somatic complaints, and aggressive behaviors. All three anxiety variables were significantly and positively correlated with total SRS RRB scores. Additionally, the STAIS and STAIT were significantly associated with more severe Compulsion/Adherence behaviors, and the CBCL-Anxiety was also significantly associated with more severe Rule-breaking Behaviors. @*Conclusion@#Self-reported state anxiety showed association patterns similar to those of parent-reported trait anxiety. Future studies investigating the precise operationalization of different anxiety instruments are needed to accurately measure the anxiety of adolescents with ASD.
ABSTRACT
Objective@#The purpose of this study was to examine the visuospatial processing abilities of children with autism spectrum disorder (ASD) using the Rey Osterrieth Complex Figure (ROCF). @*Methods@#One-hundred thirty-four children with ASD [mean age (MA)=113.56 months], 150 siblings of children with ASD (MA= 111.67 months), and 55 typically developing (TD) children (MA=109.02 months) were included in this study. During their one-time visit, participants completed the ROCF, various autism diagnostic assessments, and the Korean-Leiter International Performance ScaleRevised. Repeated-measures Analysis of Covariance (ANCOVA) and post-hoc Tukey-Kramer comparisons were computed to compare the ROCF scores. Partial correlations and multiple regressions were computed to examine the association between ROCF scores and the severity of autistic symptoms, as measured by the Autism Diagnostic Interview-Revised (ADI-R) among children with ASD. @*Results@#There were significant main effects of the analysis group in Structural and Incidental Accuracy, Error, and Style. More siblings than TD children drew in a part-oriented way, but the performance of the sibling group was comparable or superior to that of the TD group in all parameters. Social Interaction scores of children with ASD were significantly associated with Organization scores in Copy condition. Whether or not a child drew in the Part-Oriented style significantly predicted his/her repetitive and restricted behavior scores. @*Conclusion@#The findings add to the evidence for altered visuospatial processing patterns of ASD as a potential inherent and genetic trait and suggest that this particular cognitive style should not be considered as a deficit. Educational and theoretical implications are discussed.
ABSTRACT
OBJECTIVES: The detection of autism spectrum disorder (ASD) is based on behavioral observations. To build a more objective data-driven method for screening and diagnosing ASD, many studies have attempted to incorporate artificial intelligence (AI) technologies. Therefore, the purpose of this literature review is to summarize the studies that used AI in the assessment process and examine whether other behavioral data could potentially be used to distinguish ASD characteristics. METHODS: Based on our search and exclusion criteria, we reviewed 13 studies. RESULTS: To improve the accuracy of outcomes, AI algorithms have been used to identify items in assessment instruments that are most predictive of ASD. Creating a smaller subset and therefore reducing the lengthy evaluation process, studies have tested the efficiency of identifying individuals with ASD from those without. Other studies have examined the feasibility of using other behavioral observational features as potential supportive data. CONCLUSION: While previous studies have shown high accuracy, sensitivity, and specificity in classifying ASD and non-ASD individuals, there remain many challenges regarding feasibility in the real-world that need to be resolved before AI methods can be fully integrated into the healthcare system as clinical decision support systems.