Accepted for/Published in: JMIR Neurotechnology
Date Submitted: Dec 26, 2024
Date Accepted: Mar 21, 2025
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Effectiveness of AI-Based Platform in Administering Therapies for Children with Autism Spectrum Disorder: A 12-Month Clinical Trial
ABSTRACT
Background:
A 12-month longitudinal observational study was conducted on 43 children aged 2-18 years to evaluate the effectiveness of the Cognitivebotics AI-based platform in conjunction with continuous therapy, in improving therapeutic outcomes for subjects with Autism Spectrum Disorder (ASD).
Objective:
The primary objective was to assess user engagement, assess the software's ability to log daily progress and evaluate efficacy using established clinical parameters across multiple domains.
Methods:
Participants were divided into intervention and control groups and assessed using the Childhood Autism Rating Scale (CARS), Vineland Social Maturity Scale (VSMS), Developmental Screening Test (DST), and Receptive Expressive Emergent Language Test (REEL), at baseline (T1) and at the endpoint (T2).
Results:
Subjects in the intervention group demonstrated statistically significant improvements across multiple scales, with reductions in CARS scores and gains in social, developmental metrics (social age, social quotient, developmental age, and developmental quotient), and language scores.
Conclusions:
Overall, the platform was an effective supplement in enhancing therapeutic outcomes for children with ASD. This platform holds promise as a valuable tool for augmenting ASD therapies across cognitive, social, and developmental domains. Future development should prioritize expanding the product's accessibility across various languages, ensuring cultural sensitivity, and enhancing user-friendliness. Clinical Trial: CTRI/2023/06/054257
Citation
The author of this paper has made a PDF available, but requires the user to login, or create an account.
Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.