Preliminary Validation and Feasibility of the Autism Detection in Early Childhood-Virtual (ADEC-V) for Autism...

Area of Science:

• Pediatric Psychology
• Developmental Pediatrics
• Telehealth Innovations

Background:

• Early identification of autism spectrum disorder (ASD) is crucial for timely intervention.
• Telehealth offers a scalable solution for accessing diagnostic services, especially in underserved areas.
• The need for validated virtual assessment tools for early childhood ASD is growing.

Purpose of the Study:

• To perform preliminary validation of the Autism Detection in Early Childhood-Virtual (ADEC-V) tool.
• To assess the ADEC-V's utility for telehealth-based autism evaluations in young children.
• To determine the ADEC-V's sensitivity, specificity, and correlation with established ASD measures.

Main Methods:

• The study involved 121 children aged 18-47 months undergoing telehealth evaluations.
• Participants completed the ADEC-V and other standardized ASD symptom measures (CARS-2, ADI-R).
• Data were collected between October 2020 and February 2021 at a Midwestern pediatric hospital.

Main Results:

• The ADEC-V demonstrated good sensitivity (0.82) and specificity (0.78).
• The tool showed significant correlations with the Children's Autism Rating Scale, Second Edition (CARS-2) and Autism Diagnostic Interview-Revised (ADI-R).
• Acceptable internal consistency (Cronbach's alpha = 0.77) was observed.

Conclusions:

• The ADEC-V is a promising measure for telehealth-based autism spectrum disorder assessments in early childhood.
• Further validation in larger, more diverse samples is warranted.
• The ADEC-V can potentially improve access to autism evaluations via virtual platforms.