Generalization and Maintenance of Functional Living Skills for Individuals with Autism Spectrum Disorder: a Review and Meta-Analysis

We conducted a meta-analysis of the single-case research design data on the effects of video prompting (VP) on the acquisition of daily living skills (DLS) among individuals with autism spectrum disorder (ASD). An analysis of potential moderators was conducted, and these included VP implemented alone versus VP with additional response prompting or error correction procedures, the effects of VP across participants’ age range, and the effects of VP among participants with ASD versus those with ASD and intellectual disability. There were 54 participants across 17 studies meeting our inclusion criteria. The results from the included studies demonstrated a moderate effect size for VP on the acquisition of DLS among the targeted population. The analysis of potential moderators showed no significant differences. These results and implications for research and practice are discussed.

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Effectiveness of Virtual/Augmented Reality–Based Therapeutic Interventions on Individuals With Autism Spectrum Disorder: A Comprehensive Meta-Analysis

Frontiers in Psychiatry ◽

10.3389/fpsyt.2021.665326 ◽

2021 ◽

Vol 12 ◽

Author(s):

Behnam Karami ◽

Roxana Koushki ◽

Fariba Arabgol ◽

Maryam Rahmani ◽

Abdol-Hossein Vahabie

Keyword(s):

Autism Spectrum Disorder ◽

Augmented Reality ◽

Cognitive Skills ◽

Meta Analysis ◽

Autism Spectrum ◽

Spectrum Disorder ◽

Therapeutic Interventions ◽

Vr Training ◽

Living Skills ◽

And Training

In recent years, the application of virtual reality (VR) for therapeutic purposes has escalated dramatically. Favorable properties of VR for engaging patients with autism, in particular, have motivated an enormous body of investigations targeting autism-related disabilities with this technology. This study aims to provide a comprehensive meta-analysis for evaluating the effectiveness of VR on the rehabilitation and training of individuals diagnosed with an autism spectrum disorder. Accordingly, we conducted a systematic search of related databases and, after screening for inclusion criteria, reviewed 33 studies for more detailed analysis. Results revealed that individuals undergoing VR training have remarkable improvements with a relatively large effect size with Hedges g of 0.74. Furthermore, the results of the analysis of different skills indicated diverse effectiveness. The strongest effect was observed for daily living skills (g = 1.15). This effect was moderate for other skills: g = 0.45 for cognitive skills, g = 0.46 for emotion regulation and recognition skills, and g = 0.69 for social and communication skills. Moreover, five studies that had used augmented reality also showed promising efficacy (g = 0.92) that calls for more research on this tool. In conclusion, the application of VR-based settings in clinical practice is highly encouraged, although their standardization and customization need more research.

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Effectiveness of Virtual Reality-based therapeutic interventions on individuals with autism spectrum disorder: A comprehensive meta-analysis

10.31234/osf.io/s2jvy ◽

2020 ◽

Cited By ~ 1

Author(s):

behnam karami ◽

Roxana Koushki ◽

Fariba Arabgol ◽

Maryam Rahmani ◽

AbdolHossein Vahabie

Keyword(s):

Autism Spectrum Disorder ◽

Virtual Reality ◽

Cognitive Skills ◽

Meta Analysis ◽

Autism Spectrum ◽

Spectrum Disorder ◽

Therapeutic Interventions ◽

Targeted Screening ◽

Vr Training ◽

Living Skills

We conducted a comprehensive meta-analysis for evaluating the effectiveness of Virtual Reality (VR) on the rehabilitation and training of individuals diagnosed with an autism spectrum disorder. After a systematic search and targeted screening, 33 articles were analyzed. Results revealed that individuals undergoing VR training have remarkable improvements with a large effect size (g= 0.76). Furthermore, the results of the analysis of different skills indicated diverse effectiveness. The strongest effect was observed for daily living skills (g=1.23). This effect was moderate for other skills: g=0.45 for Cognitive skills, g=0.47 for Emotion Recognition skills, and g=0.67 for Social and Communication skills. In conclusion, the application of VR-based settings in clinical practice is highly encouraged, though their standardization and customization need more research.

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Supplemental Material for Memory in Autism Spectrum Disorder: A Meta-Analysis of Experimental Studies

Psychological Bulletin ◽

10.1037/bul0000225.supp ◽

2020 ◽

Keyword(s):

Autism Spectrum Disorder ◽

Meta Analysis ◽

Experimental Studies ◽

Autism Spectrum ◽

Spectrum Disorder

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Meta-Analysis of Neurological Foci and Cognitive Dysfunction Correlates in Autism Spectrum Disorder

PsycEXTRA Dataset ◽

10.1037/e659972012-001 ◽

2012 ◽

Author(s):

Peter D. Marle ◽

Alisa J. Estey ◽

Frederick L. Coolidge

Keyword(s):

Autism Spectrum Disorder ◽

Cognitive Dysfunction ◽

Meta Analysis ◽

Autism Spectrum ◽

Spectrum Disorder

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Diagnostic test accuracy for use of machine learning in diagnosis of autism spectrum disorder: A Systematic Review and Meta-Analysis (Preprint)

10.2196/preprints.14108 ◽

2019 ◽

Author(s):

Sun Jae Moon ◽

Jin Seub Hwang ◽

Rajesh Kana ◽

John Torous ◽

Jung Won Kim

Keyword(s):

Machine Learning ◽

Systematic Review ◽

Autism Spectrum Disorder ◽

Meta Analysis ◽

Learning Algorithms ◽

Structural Mri ◽

Autism Spectrum ◽

Machine Learning Algorithms ◽

Spectrum Disorder ◽

Test Accuracy

BACKGROUND Over the recent years, machine learning algorithms have been more widely and increasingly applied in biomedical fields. In particular, its application has been drawing more attention in the field of psychiatry, for instance, as diagnostic tests/tools for autism spectrum disorder. However, given its complexity and potential clinical implications, there is ongoing need for further research on its accuracy. OBJECTIVE The current study aims to summarize the evidence for the accuracy of use of machine learning algorithms in diagnosing autism spectrum disorder (ASD) through systematic review and meta-analysis. METHODS MEDLINE, Embase, CINAHL Complete (with OpenDissertations), PsyINFO and IEEE Xplore Digital Library databases were searched on November 28th, 2018. Studies, which used a machine learning algorithm partially or fully in classifying ASD from controls and provided accuracy measures, were included in our analysis. Bivariate random effects model was applied to the pooled data in meta-analysis. Subgroup analysis was used to investigate and resolve the source of heterogeneity between studies. True-positive, false-positive, false negative and true-negative values from individual studies were used to calculate the pooled sensitivity and specificity values, draw SROC curves, and obtain area under the curve (AUC) and partial AUC. RESULTS A total of 43 studies were included for the final analysis, of which meta-analysis was performed on 40 studies (53 samples with 12,128 participants). A structural MRI subgroup meta-analysis (12 samples with 1,776 participants) showed the sensitivity at 0.83 (95% CI-0.76 to 0.89), specificity at 0.84 (95% CI -0.74 to 0.91), and AUC/pAUC at 0.90/0.83. An fMRI/deep neural network (DNN) subgroup meta-analysis (five samples with 1,345 participants) showed the sensitivity at 0.69 (95% CI- 0.62 to 0.75), the specificity at 0.66 (95% CI -0.61 to 0.70), and AUC/pAUC at 0.71/0.67. CONCLUSIONS Machine learning algorithms that used structural MRI features in diagnosis of ASD were shown to have accuracy that is similar to currently used diagnostic tools.

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