Early Intervention programs for young children with Autism Spectrum Disorder (ASD) demonstrate powerful effects in improving cognitive and social outcomes. Intervention response however is variable, and the factors associated with positive versus suboptimal treatment outcomes remain unknown. Hence the issue of which intervention should be chosen for an individual child remains a common dilemma. As children with ASD vary in their learning abilities and preferences, and different intervention programs vary in their teaching procedures, it is plausible that suboptimal treatment outcomes occur as the consequence of a poor fit between child learning profile and treatment teaching procedures. However, there is currently no established protocol to match children to the program from which they are more likely to benefit from. Three pieces of information are needed to move the field forward: 1) a protocol to obtain a fine-grained characterization of each child?s learning profile, 2) a decision-making algorithm to proactively assign children to a specific program based on the fit between child learning profile and program teaching procedures and 3) evidence that proactive assignment of children to treatment programs congruent to their learning profile will improve outcomes. The proposed project will use a personalized medicine approach to address these gaps in knowledge, through the following research aims: (1) achieving a fine grained characterization of children?s learning profiles through a novel battery testing theoretically- and empirically- motivated putative predictors of treatment response (2) assigning children to different interventions based on a novel child-treatment fit algorithm based on the learning profile of each child, and (3) experimentally testing the hypothesis that assigning children to programs congruent with their learning profile will improve outcomes in this population. Results of this project can foster a major improvement to treatment outcomes as well as cost-effectiveness of early intervention for ASD. Literature indicates that choice of early intervention programs by families and clinicians is currently linked to regional proximity to services, rather than based on knowledge of which approach is likely to result in the best outcomes given the individual characteristics of the child. The costs associated with these gaps in knowledge constitute a major public health issue, as this state of affairs involves the risk of enrolling children in programs from which they will not gain maximal benefit, limiting the efficacy and cost-effectiveness of early intervention programs, ad leading to a profound emotional and economic burden for affected children, their families and the community. If the study hypothesis is supported, the personalized medicine approach adopted in this study will support clinicians and families? decision-making, so that children will be enrolled in programs from which they will benefit the most, thus increasing the rate of optimal treatment outcomes, mitigating later adult disability, reducing societal costs and improving wellbeing and productivity of individuals with ASD.

Public Health Relevance

While several early intervention programs can improve outcomes of young children with autism spectrum disorder, not all children show optimal improvements, plausibly because of a poor fit between child learning style and treatment teaching techniques. The purpose of this project is to provide evidence that assigning children to teaching programs that are based on the match between child and program features will improve outcomes in this population. This personalized medicine approach will reduce the risk of enrolling children in programs from which they will not gain maximal benefit, thus increasing the cost-effectiveness of early intervention programs, and the overall rate of optimal treatment outcomes in young children with autism spectrum disorder.

National Institute of Health (NIH)
Eunice Kennedy Shriver National Institute of Child Health & Human Development (NICHD)
Exploratory/Developmental Grants (R21)
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Child Psychopathology and Developmental Disabilities Study Section (CPDD)
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Kau, Alice S
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Drexel University
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United States
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