Intensity and Learning Outcomes in the Treatment of Children With Autism Spectrum Disorder

Linstead, Erik; Dixon, Dennis R.; French, Ryan; Granpeesheh, Doreen; Adams, Hilary L.; German, Rene; Powell, Alva; Stevens, Elizabeth; Tarbox, Jonathan; Kornack, Julie

doi:10.1177/0145445516667059

Cited by 54 publications

(37 citation statements)

References 27 publications

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“…314 Children younger than 12 years receiving more hours per week of ABA were found to be more likely to achieve the individualized goals identified in their programs. 315 In retrospective studies, more intense ABA therapy was associated with achieving optimal developmental outcomes. 316 Given the heterogeneity of the ASD phenotype, the service needs of children, youth, and adults need to be individualized by using available clinical data.…”

Section: Section 5: Interventionsmentioning

confidence: 99%

Identification, Evaluation, and Management of Children With Autism Spectrum Disorder

2020

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Autism spectrum disorder (ASD) is a common neurodevelopmental disorder with reported prevalence in the United States of 1 in 59 children (approximately 1.7%). Core deficits are identified in 2 domains: social communication/interaction and restrictive, repetitive patterns of behavior. Children and youth with ASD have service needs in behavioral, educational, health, leisure, family support, and other areas. Standardized screening for ASD at 18 and 24 months of age with ongoing developmental surveillance continues to be recommended in primary care (although it may be performed in other settings), because ASD is common, can be diagnosed as young as 18 months of age, and has evidenced-based interventions that may improve function. More accurate and culturally sensitive screening approaches are needed. Primary care providers should be familiar with the diagnostic criteria for ASD, appropriate etiologic evaluation, and co-occurring medical and behavioral conditions (such as disorders of sleep and feeding, gastrointestinal tract symptoms, obesity, seizures, attention-deficit/hyperactivity disorder, anxiety, and wandering) that affect the child' s function and quality of life. There is an increasing evidence base to support behavioral and other interventions to address specific skills and symptoms. Shared decision making calls for collaboration with families in evaluation and choice of interventions. This single clinical report updates the 2007 American Academy of Pediatrics clinical reports on the evaluation and treatment of ASD in one publication with an online table of contents and section view available through the American Academy of Pediatrics Gateway to help the reader identify topic areas within the report.

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Section: Section 5: Interventionsmentioning

confidence: 99%

Identification, Evaluation, and Management of Children With Autism Spectrum Disorder

2020

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“…Neural networks have also been leveraged by Linstead et al (2015Linstead et al ( , 2017 in a regression task to learn the relationship between treatment intensity and learning outcomes in the context of applied behavior analysis (ABA) treatment for ASD. The authors found that, compared to simple linear regression, neural networks were more accurately able to model the relationship between treatment intensity and learning outcomes, explaining substantially more variance for a sample size of 726 patients.…”

Section: Behavioral and Neuroimaging Datamentioning

confidence: 99%

Applications of Supervised Machine Learning in Autism Spectrum Disorder Research: a Review

Hyde

Novack

LaHaye

et al. 2019

Rev J Autism Dev Disord

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144

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Autism spectrum disorder (ASD) research has yet to leverage Bbig data^on the same scale as other fields; however, advancements in easy, affordable data collection and analysis may soon make this a reality. Indeed, there has been a notable increase in research literature evaluating the effectiveness of machine learning for diagnosing ASD, exploring its genetic underpinnings, and designing effective interventions. This paper provides a comprehensive review of 45 papers utilizing supervised machine learning in ASD, including algorithms for classification and text analysis. The goal of the paper is to identify and describe supervised machine learning trends in ASD literature as well as inform and guide researchers interested in expanding the body of clinically, computationally, and statistically sound approaches for mining ASD data.

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“…These modifications did not change core components of the intervention, nor did they result in an increase in the number of question-asking initiations. The use of additional strategies (e.g., reinforcement, self-management, prompting) are very common in video-based instruction [15][16][17][18][19][20][21][22][23][24] because they do not change the core components of the intervention.…”

Section: Limitationsmentioning

confidence: 99%

Promoting Question-Asking Initiations to Peers in Young Children with Autism Spectrum Disorder (ASD) Using Video-Based Instruction

Kabashi¹,

Epstein²

2017

J Child Dev Disord

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The aim of this study was to promote question-asking initiations of young children with Autism Spectrum Disorder (ASD). Participants in this study were two boys, a first grader and a kindergartner. A multiple baseline single subject study design explored the efficacy of video selfmodeling with video feedback delivered via iPads. New video vignettes of participants displaying the target behavior with peers were created and utilized throughout the implementation of the intervention. Results showed an increase in appropriate question-asking initiations with peers for both participants. One of the participants maintained the skills one month after the intervention was withdrawn. Social validity findings indicated positive results related to question-asking initiations as well as other social skills.

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Intensity and Learning Outcomes in the Treatment of Children With Autism Spectrum Disorder

Cited by 54 publications

References 27 publications

Identification, Evaluation, and Management of Children With Autism Spectrum Disorder

Identification, Evaluation, and Management of Children With Autism Spectrum Disorder

Applications of Supervised Machine Learning in Autism Spectrum Disorder Research: a Review

Promoting Question-Asking Initiations to Peers in Young Children with Autism Spectrum Disorder (ASD) Using Video-Based Instruction

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