Factors influencing intervention outcomes of children with ASD

Evidence-based early interventions for autism spectrum disorder (ASD) have been shown to improve child outcomes and quality of life (Lord et al., 2018; Reichow et al., 2018; Smith & Iadarola, 2015). However, response to intervention is variable across both children and implementation contexts. Understanding factors that influence this variability is important for optimizing child outcomes. Individual child characteristics such cognitive ability, adaptive functioning, language, and ASD symptom severity have previously been shown to be associated with variability in response to intervention (Magiati et al., 2011; Reichow et al., 2012; Perry et al., 2011). However, these factors have yet to be examined on a large scale. Additionally, studies suggest that embedding early intervention into "real-world" community settings can be feasible and has the potential to improve child trajectories (e.g., Lawton & Kasari, 20212; Vivanti et al., 2017; Zitter et al., 2023). Yet, interventions employed in these settings often yield less optimal outcomes than in more controlled settings (Nahmias et al., 2019). Notably, our understanding of what contributes to this disparity is still relatively limited. The present study aims to fill these gaps in the literature by examining factors at both the implementation and individual child levels that may contribute to response to intervention. In the first aim of this study, we created and analyzed an aggregate dataset of retrospective data from multiple evidence-based intervention trials (n = 518). Our research question aimed to identify child and intervention factors that predict response to intervention, as measured by changes in adaptive behaviors post-intervention. Six predictor variables that were found to significantly differ between children classified as responders vs. poor-responders were entered into a Stepwise Binary Logistic Regression model. Predictors included measures of baseline cognitive ability, adaptive functioning, language, social communication deficits, as well as treatment duration and type of intervention received. The final regression model revealed that intervention type ([beta] = -0.807, p = .012) and baseline cognitive ability ([beta] = 0.057, p = < .001) were significant predictors of responder status above and beyond the other factors. Children who received classroom-based interventions were more likely to be classified as poor-responders. For the second aim of the present study, we collaborated with a local childcare organization, CORA Services, via a Community-Based Participatory Research approach (Jones & Wells, 2007). We examined perceived acceptability and feasibility of utilizing the Group Early Start Denver Model (G-ESDM; Vivanti et al., 2017) in inclusive classrooms and explored whether implementation factors including completing a modified G-ESDM training, implementation context, and teacher fidelity may be related to changes in children's social communication behaviors and engagement with peers and classroom activities (i.e., response to intervention). Four head teachers and 3 children from two pre-selected inclusive classrooms participated in this study. The study consisted of three phases (Phase I = Baseline, Phase II = Intervention + Teacher Supervision, Phase III = Supervision suspended), and a non-concurrent multiple-baseline approach was used to allow for comparisons within and across classrooms via visual analysis. Teachers rated the G-ESDM as highly acceptable after completing the training and indicated that the intervention would be feasible to employ with certain adaptations to better align with classroom resources and structure. Three out of four teachers' fidelity improved after completing the G-ESDM training and generally continued to improve over time. Children's rate of social communication initiations and engagement with peers and classroom activities mostly increased as fidelity changed over time. Taken together, the findings from this study provide clinically meaningful information about optimizing early intervention treatment targets and implementation to aid in reducing variability in response to intervention.

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