Establishing Intervention Priorities for Nonverbal Communication in Autistic Adults

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Although difficulty with nonverbal communication (NVC) is required for a diagnosis of autism at any age, no evidence-based interventions exist specifically targeting these skills beyond toddlerhood. Thus, older children and adults on the autism spectrum remain unsupported for a core challenge with wide-reaching func- tional impacts. Our long-term goal is to develop and implement targeted interventions to improve NVC expe- riences for autistic adults, yet two gaps in knowledge exist. First, before developing novel NVC interventions, it is critical to evaluate prospective intervention options from the perspectives of autistic adults themselves; what autistic adults want out of intervention remains unknown. Significant resources could be wasted if interventions are developed without consulting the communities who will one day seek to benefit from them. Second, due to the heterogeneity inherent to autism, a “one size fits all” intervention is not a realistic goal; rather, distinct profiles of ability and disability are likely to map onto different intervention priorities, which could facilitate targeted, indi- vidualized intervention development. This grant will address these gaps by establishing foundational knowledge about (1) how autistic adults experience NVC, (2) what they want out of intervention, and (3) how patterns of individual difference map onto patterns of impact and intervention priority. We will use an explanatory-sequential mixed methods design (Quantitative → Qualitative), in consultation with autistic members of a community-aca- demic collaborative, with two specific aims: (1) Discover data-driven behavioral profiles relevant to NVC impact and desire for intervention that will inform how to best match individuals to interventions; and (2) Understand autistic adults’ perspectives on NVC experience and intervention qualitatively. In Aim 1, a diverse sample of autistic adults (n=400) will complete surveys about their communication skills, NVC experiences, and desire for intervention. Machine learning regression and canonical correlation analysis will model individual predictors of NVC impact and desire for intervention and identify combinations of individual differences most predictive of contexts for which NVC intervention is desired. In Aim 2 we will re-recruit n=40 survey participants to complete follow-up semi-structured interviews about their Aim 1 responses. Interviews will elucidate the specific nature of problematic and successful NVC interactions; reasons why intervention is and is not desired; and preferred in- tervention targets and formats. We will analyze interview data using thematic analysis; findings will be used to develop community-drafted guidelines for NVC intervention development. Contributions will be significant be- cause they will provide strong empirical and community-based justification for investing resources in support of the adult autistic community. This research is innovative because it represents a substantive departure from the status quo by starting with the opinions, experiences, and perspectives of the target population. Knowledge generated will parse the heterogeneity of communication skills in autism, and has the potential to guide devel- opment of individualized interventions to meet the greatest areas of need with the greatest chance of success.