Effectiveness of Telehealth Direct Therapy for Individuals with Autism Spectrum Disorder.

The field of applied behavior analysis (ABA) has utilized telehealth for clinical supervision and caregiver guidance with research supporting the use of both modalities. Research demonstrating effectiveness is crucial, as behavior analysts must ensure the services they provide are effective in order to be ethical. With the increased need for patients to access more services via telehealth, due to the novel coronavirus (COVID-19) pandemic, the current study evaluated the efficacy of telehealth direct therapy to teach new skills to individuals with autism spectrum disorder (ASD). This study examined the utility of natural environment teaching and discrete trial training strategies provided over a videoconferencing platform to teach new skills directly to seven individuals with varying ASD severity levels. The targeted skills were taught solely through telehealth direct therapy with varying levels of caregiver support across participants and included skills in the language, adaptive, and social domains. In a multiple baseline design, all seven participants demonstrated mastery and maintenance for all targets; in addition, generalization to family members was assessed for some targets. The evidence suggests that telehealth is a modality that is effective and can be considered for all patients when assessing the appropriate location of treatment.

Evaluation of an Immersive Virtual Reality Safety Training Used to Teach Pedestrian Skills to Children With Autism Spectrum Disorder.

Children with autism spectrum disorder (ASD) are at an increased risk of injury, making safety skills training essential. Whether such training is conducted in the natural environment or in contrived settings is an important consideration for generalization and safety purposes. Immersive virtual reality (VR) environments may offer the advantages of both contrived and natural environment training settings, providing structure to create repeated learning opportunities in a safe and realistic analogue of the natural environment. The current study evaluated the effectiveness of an immersive VR safety skills training environment in teaching 3 children with ASD to identify whether it is safe to cross the street. After modifications to the VR training environment, all 3 participants reached mastery criteria in both VR and natural environment settings. Findings suggest that immersive VR is a promising medium for the delivery of safety skills training to individuals with ASD.

Identification and analysis of behavioral phenotypes in autism spectrum disorder via unsupervised machine learning.

BACKGROUND AND OBJECTIVE: Autism spectrum disorder (ASD) is a heterogeneous disorder. Research has explored potential ASD subgroups with preliminary evidence supporting the existence of behaviorally and genetically distinct subgroups; however, research has yet to leverage machine learning to identify phenotypes on a scale large enough to robustly examine treatment response across such subgroups. The purpose of the present study was to apply Gaussian Mixture Models and Hierarchical Clustering to identify behavioral phenotypes of ASD and examine treatment response across the learned phenotypes. MATERIALS AND METHODS: The present study included a sample of children with ASD (N = 2400), the largest of its kind to date. Unsupervised machine learning was applied to model ASD subgroups as well as their taxonomic relationships. Retrospective treatment data were available for a portion of the sample (n = 1034). Treatment response was examined within each subgroup via regression. RESULTS: The application of a Gaussian Mixture Model revealed 16 subgroups. Further examination of the subgroups through Hierarchical Agglomerative Clustering suggested 2 overlying behavioral phenotypes with unique deficit profiles each composed of subgroups that differed in severity of those deficits. Furthermore, differentiated response to treatment was found across subtypes, with a substantially higher amount of variance accounted for due to the homogenization effect of the clustering. DISCUSSION: The high amount of variance explained by the regression models indicates that clustering provides a basis for homogenization, and thus an opportunity to tailor treatment based on cluster memberships. These findings have significant implications on prognosis and targeted treatment of ASD, and pave the way for personalized intervention based on unsupervised machine learning.

An Evaluation of a Mobile Application Designed to Teach Receptive Language Skills to Children with Autism Spectrum Disorder.

The current study evaluated the effectiveness of a mobile application, Camp Discovery, designed to teach receptive language skills to children with autism spectrum disorder based on the principles of applied behavior analysis. Participants (N = 28) were randomly assigned to an immediate-treatment or a delayed-treatment control group. The treatment group made significant gains, p < .001, M = 58.1, SE = 7.54, following 4 weeks of interaction with the application as compared to the control group, M = 8.4, SE = 2.13. Secondary analyses revealed significant gains in the control group after using the application and maintenance of acquired skills in the treatment group after application usage was discontinued. Findings suggest that the application effectively teaches the targeted skills.

An Evaluation of the Impact of Supervision Intensity, Supervisor Qualifications, and Caseload on Outcomes in the Treatment of Autism Spectrum Disorder.

Ample research has shown the benefits of intensive applied behavior analysis (ABA) treatment for autism spectrum disorder (ASD); research that investigates the role of treatment supervision, however, is limited. The present study examined the relationship between mastery of learning objectives and supervision hours, supervisor credentials, years of experience, and caseload in a large sample of children with ASD (N = 638). These data were retrieved from a large archival database of children with ASD receiving community-based ABA services. When analyzed together via a multiple linear regression, supervision hours and treatment hours accounted for only slightly more of the observed variance (r2 = 0.34) than treatment hours alone (r2 = 0.32), indicating that increased supervision hours do not dramatically increase the number of mastered learning objectives. In additional regression analyses, supervisor credentials were found to have a significant impact on the number of mastered learning objectives, wherein those receiving supervision from a Board Certified Behavior Analyst (BCBA) mastered significantly more learning objectives. Likewise, the years of experience as a clinical supervisor showed a small but significant impact on the mastery of learning objectives. A supervisor's caseload, however, was not a significant predictor of the number of learning objectives mastered. These findings provide guidance for best practice recommendations.