Brief Report: What Diagnostic Observation Can Teach Us About Disruptive Behavior in Young Children with Autism. Issue 1 (January 2021)

• Record Type: Journal Article
• Authors: Hampton, Lauren H.; Roberts, Megan Y.; Anderson, Erica; Hobson, Amanda N.; Kaat, Aaron J.; Bishop, Somer L.; Krogh-Jespersen, Sheila; Wakschlag, Lauren S.; Bevans, Katherine B.
• Is Part Of: Journal of developmental and behavioral pediatrics. Volume 42:Issue 1(2021)
• Publication Date: 2021-01
• Subjects: autism -- disruptive behavior -- assessment -- challenging behavior; Child development -- Periodicals; Developmental disabilities -- Periodicals; Behavior disorders in children -- Periodicals; Learning disabilities -- Periodicals; Child psychiatry -- Periodicals; 618.92805
• DOI: 10.1097/DBP.0000000000000857
• Languages: English
• ISSNs: 0196-206X

Abstract

Abstract : ABSTRACT: Objective: Approximately 50% of children with autism exhibit severe tantrums, defiance, and/or aggression. We propose that the Disruptive Behavior Diagnostic Observation Schedule (DB-DOS)—a standardized clinical observation modeled after, and complementary to, the Autism Diagnostic Observation Schedule (ADOS)—could enhance earlier identification of disruptive behavior (DB) in autism populations and inform treatment planning. Methods: We adapted the DB-DOS for children with autism based on expert input and preliminary feasibility testing to accommodate varying cognitive and social communication capacities and increase the likelihood of observing DB in this population. Thereafter, we concurrently administered the modified DB-DOS and the ADOS to 12 children with autism aged 36 to 50 months. Results: Overall, children exhibited greater DB, especially behavioral regulation challenges, during the DB-DOS than during the ADOS. Conclusion: The use of a developmentally sensitive standardized observation tool that presses for DB to complement standardized observations such as the ADOS shows promise for enabling more precise research on targeted DB interventions. Such a tool holds promise as a reliable and efficient method of identifying comorbid DB disorders in the autism population.