Toward a technology of treatment individualization for young children with autism spectrum disorders

Abstract

Although the etiology of autism spectrum disorders (ASD) and early development of the ASD are not yet well understood, recent research in the field of autism has heavily emphasized the importance of early intervention (i.e. treatment before the age of 4 years). Currently, several methods have been demonstrated to be efficacious with some children however no treatment completely ameliorates the symptoms of ASD or works for all children with the disorder. The heterogeneity and developmental nature of the disorder make it unlikely that one specific treatment will be best for all children, or will work for any one child throughout his or her educational career. Thus, this paper examines early research validating different technologies for individualizing treatment. A discussion of current research on pre-treatment characteristics associated with differential outcomes in treatment, including child, family, and practitioner variables; and how specific intervention techniques address each of those pre-treatment characteristics is provided. The ultimate goal of this line of research is to enable practitioners to prospectively tailor treatments to specific children and increase the overall rate of positives outcomes for children with autism. Research that furthers understanding of how to match clients with efficacious treatments will decrease the outcome variability that characterizes early intervention research at present, and provide for the most efficient allocation of resources during the critical early intervention time-period. This type of research is in its infancy, but is imperative if we are to determine a priori which treatment method will be most effective for a specific child.

Fig. 1

Strategy Integration at Alexa’s PLAYC. Legend: At Alexa’s PLAYC a structured environmental approach and developmentally appropriate goals are infused throughout all activities. If additional structure is needed teachers add behavioral strategies, beginning with the least structured, and moving to more structure. Supports are faded as quickly as possible.

Fig. 2

Early Start Denver Model (ESDM) Alternative Communication Decision Tree. Legend: In the ESDM model the team (which includes a speech language pathologist) follows the decision tree to alter the teaching approach for children who appear unable to produce speech with the typical intervention model. The decision tree is used in decision making about what alternative or augmentative system to use. From: Early Start Denver Model for Young Children with Autism, Rogers and Dawson, 2009. Copyright Guilford Press. Reprinted with permission of The Guilford Press.

Fig. 3

Integration of teaching techniques from Teaching Social Communication Skills to Children with Autism. Legend: Teaching Social Communication to Children with Autism calls for the use of both Interactive Techniques and the Direct Teaching Techniques together. The figure illustrates that the interactive techniques provide the background for all of the teaching techniques, with more directive techniques being used within the interactive techniques to teach new social-communication skills. From: Teaching Social Communication to Children with Autism, Ingersoll and Dvorszak, 2010. Copyright Guilford Press. Reprinted with permission of The Guilford Press.

Fig. 4

Mean percentage of language use at intake and 6 months for high peer avoiders (HPA), low peer avoiders (LPA) and typical peers (TP). Legend: In the Alexa’s PLAYC program, children who were less avoidant of peers had greater improvement in language skills than children who were avoidant of peers at entry. With kind permission from Springer Science+Business Media: Journal of Autism and Developmental Disorders, Differential treatment outcomes for children with autistic spectrum disorder based on level of peer social avoidance, 31, 2001, 347, Brooke Ingersoll, Aubyn C. Stahmer and Laura Schreibman, Fig. 2.