Predictors of longitudinal ABA treatment outcomes for children with autism: A growth curve analysis

Abstract

Background: Autism spectrum disorder (ASD) is a developmental disorder that causes lifelong disability. Applied Behavior Analysis (ABA) is one of the most empirically studied and validated approaches for treating children diagnosed with ASD. Due to the heterogeneity of ASD, it is important to ascertain who will most benefit from treatment.

Methods: In this study, 35 participants, with a mean entry age of 3 years, received ABA therapy. Children were assessed at intake and every 6 months thereafter using the Developmental Profile-3 (DP-3) to measure their communication, social-emotional, adaptive behavior, and physical development (2-6 measures per participant). Using a growth curve analysis, we investigated if age, diagnosis severity, cognitive functioning, treatment hours, gender, parent education level, or primary language spoken at home significantly predicted the growth trajectories of ABA treatment outcomes.

Results: Our findings indicated that higher cognitive functioning significantly predicted faster growth across all four developmental domains, age at entry predicted initial status, and other variables only predicted growth rates in one or two domains.

Implications: Knowing the predictors of treatment outcome is important information for customizing treatment and this study demonstrated how longitudinal analyses can illuminate how participant characteristics affect the course of ABA therapy.

Keywords: Applied behavior analysis; Autism spectrum disorder; Growth curve analysis; Longitudinal analysis.