Robust findings demonstrate that parents of children with autism spectrum disorder (ASD) experience high levels of parenting stress that are associated with negative outcomes for families. Although the majority of research on parenting stress has focused on parents of children with an existing diagnosis, parents of young children with concerns about ASD‐related behaviors also face many unique challenges before the time of diagnosis. However, no study to date has examined patterns of parenting stress among parents of children with ASD concerns prior to a formal ASD diagnosis. Therefore, the current study investigated longitudinal trajectories of parenting stress among parents of young children with ASD concerns compared to parents of children with non‐ASD developmental concerns (e.g., language delay), and parents of children with no developmental concerns. Known predictors of parenting stress were also examined. Results from multilevel model analyses revealed that parents of children with ASD concerns experienced consistently higher levels of parenting stress across early child development compared to parents of children with non‐ASD developmental concerns and those with no concerns. Additionally, parenting efficacy, psychological functioning, social satisfaction, and child social communication behaviors predicted levels of parenting stress for all parents. Autism Res 2020, 13: 82–92. © 2019 International Society for Autism Research, Wiley Periodicals, Inc.Lay Summary: This study examined parenting stress across time among parents of young children with ASD concerns before receiving a diagnosis. Parents of children with ASD concerns reported consistently higher levels of parenting stress compared to parents of children with other developmental concerns and parents of children with no concerns. Also, ASD concerns predicted parenting stress in addition to other parent and child predictors of parenting stress. These findings highlight the need to better support families before an ASD diagnosis.

Defining the central components of an intervention is critical for balancing fidelity with flexible implementation in both research settings and community practice. Implementation scientists distinguish an intervention’s essential components (thought to cause clinical change) and adaptable periphery (recommended, but not necessary). While implementing core components with fidelity may be essential for effectiveness, requiring fidelity to the adaptable periphery may stifle innovation critical for personalizing care and achieving successful community implementation. No systematic method exists for defining essential components a priori. We present the CORE (COmponents & Rationales for Effectiveness) Fidelity Method—a novel method for defining key components of evidence-based interventions—and apply it to a case example of reciprocal imitation teaching, a parent-implemented social communication intervention. The CORE Fidelity Method involves three steps: (1) gathering information from published and unpublished materials; (2) synthesizing information, including empirical and hypothesized causal explanations of component effectiveness; and (3) drafting a CORE model and ensuring its ongoing use in implementation efforts. Benefits of this method include: (1) ensuring alignment between intervention and fidelity materials; (2) clarifying the scope of the adaptable periphery to optimize implementation; and (3) hypothesizing—and later, empirically validating—the intervention’s active ingredients and their associated mechanisms of change. Lay Interventions that support social communication include several “components,” or parts (e.g. strategies for working with children and families, targeting specific skills). Some of these components may be essential for the intervention to work, while others may be recommended or viewed as helpful but not necessary for the intervention to work. “Recommended” components are often described as “adaptable” because they can be changed to improve fit in different settings where interventions are offered or with different individuals. We need to understand which parts of an intervention are essential (and which are adaptable) when translating interventions from research to community settings, but it is challenging to do this before studying an intervention in the community. This article presents the CORE (COmponents & Rationales for Effectiveness) Fidelity Method—a new method for defining the essential components of evidence-based interventions—and applies it to a case example of Reciprocal Imitation Teaching, an intervention that parents are taught to deliver with their young children with social communication delays. The CORE Fidelity Method involves three steps: (1) gathering information from multiple sources; (2) integrating information from previous research and theory; and (3) drafting a CORE model for ongoing use. The benefits of using the CORE Fidelity Method may include: (1) improving consistency in intervention and research materials to help all providers emphasize the most important skills or strategies; (2) clarifying which parts of the intervention can be adapted; and (3) supporting future research that evaluates which intervention components work and how they work.

Understanding both for whom and how interventions work is a crucial next step in providing personalized care to children with autism spectrum disorder (ASD). Autistic children present with heterogeneity both within core ASD criteria and with respect to co‐occurring mental health challenges, which may affect their ability to benefit from intervention. In a secondary data analysis of a randomized control trial evaluating an executive function (EF) training with 70 7‐ to 11‐year‐old autistic children, we explored: (1) whether co‐occurring attention‐deficit/hyperactivity disorder (ADHD) features or anxiety features at baseline moderated the extent to which children benefited from the EF training. In other words, we asked, “For whom is training effective?” We also explored: (2) the extent to which changes in a brain‐based measure of target engagement predicted the clinical outcomes of the EF training. This is a step towards asking, “How is training effective?” We found that EF training improved behavioral inhibition only for children with clinically significant co‐occurring ADHD features. Anxiety features, while prevalent, did not moderate EF training efficacy. Finally, for the EF training group only, there was a significant correlation between pre‐to‐post change in an EEG‐based measure of target engagement, N2 incongruent amplitude during a flanker task, and change in repetitive behaviors, a behavioral outcome that was reported in the parent RCT to have improved with training compared to waitlist control. This study provides preliminary evidence that EF training may differentially affect subgroups of autistic children and that changes at the neural level may precede changes in behavior.Lay Summary: Understanding both for whom and how interventions work will help us provide personalized care to children with autism spectrum disorder (ASD). Autistic children present with many different strengths and challenges. Co‐occurring mental health challenges may affect how much autistic children benefit from intervention. We analyzed secondary data from a rigorously designed pilot intervention study, a randomized control trial (RCT), that enrolled 70 7‐ to 11‐year‐old autistic children to assess whether a set of computer‐based executive function (EF) training games improved their performance. Executive functions include being able to shift between tasks, inhibit a response, and keep information in working memory. In the current study, we explored: (1) whether children's co‐occurring attention‐deficit/hyperactivity disorder (ADHD) features or anxiety features, measured before the EF training began, affected how much they benefited from the EF training. In other words, we asked, “For whom is training effective?” We also explored: (2) whether children's brain‐based changes in EF predicted their performance in everyday life (e.g., parent report on a survey). This is a step toward asking, “How is training effective?” We found that EF training improved children's inhibition ability, but only for children with clinically significant ADHD features. While many children in our sample also had anxiety features, we found that anxiety levels did not affect how well the EF training worked. Finally, for children who received the EF training, changes in a brain‐based measure of conflict monitoring (i.e., being able to noticing differences in stimuli) predicted changes in children's repetitive behaviors. This study provides early evidence that EF training may be more effective for some autistic children than others, especially those with ADHD features.

The relative difference of resting EEG frontal alpha activation between left and right hemispheres (FAA; i.e., asymmetry) correlates with global approach and avoidance tendencies. FAA may relate to problems with executive and affective functioning in children with neurodevelopmental differences, including autism and ADHD. We (1) characterize relative left vs. right FAA in autistic, ADHD, and neurotypical children (NT) and (2) investigate whether FAA predicts “hot” executive function or emotion dysregulation. Participants were 97 7- to 11-year-old autistic, ADHD, and NT Children. Children with ADHD displayed greater left (relative to right) FAA compared to autistic and neurotypical children. Children with ADHD displayed greater challenges with “hot” EF on a gambling task than autistic children, whereas children with co-occurring autism and ADHD had greater parent-reported emotion dysregulation than NT and autism-only groups. Greater left FAA predicted worse hot EF for all children but was not significantly related to emotion dysregulation. Regardless of clinical diagnosis, relatively greater left FAA relates to hot EF. While hot EF deficits may be specific to ADHD rather than autism, both together confer additive risk for emotion dysregulation. Future research should explore the functional relation between FAA, reward processing, and affect for children with different EF-related neurodevelopmental differences.