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Relationship between quantitative digital behavioral features and clinical profiles in young autistic children
Marika Coffman1,2, J Matias Di Martino3, Rachel Aiello1,2
1Duke Center for Autism and Brain Development, Duke University, Durham, North Carolina, USA.
Summary
Objective app-based measures show promise for early autism screening. Computer vision analysis (CVA) of behaviors like social attention and language responses correlates with autism-related traits in toddlers.
Area of Science:
- Developmental Psychology
- Clinical Psychology
- Digital Health
Background:
- Early behavioral markers for autism spectrum disorder (ASD) include social attention and motor differences.
- Current screening methods can be improved with more efficient, scalable, and objective measures.
Purpose of the Study:
- To evaluate the concurrent validity of objective, quantitative autism-related behaviors measured by a smartphone app (SenseToKnow) using computer vision analysis (CVA).
- To assess correlations between app-elicited behaviors and standardized autism assessments, cognitive, language, and motor abilities.
Main Methods:
- A sample of 485 toddlers, including 43 diagnosed with ASD, participated.
- Objective behavioral data were collected using the SenseToKnow app on smartphones/tablets.
- Computer vision analysis (CVA) quantified behaviors, which were correlated with standardized measures.
Main Results:
- CVA-derived gaze variables linked to social attention correlated with autism-related behaviors.
- App-measured language behaviors (attention to people, responding to name) associated with children's language skills.
- Performance in a bubble-popping game correlated with fine motor skills.
Conclusions:
- Findings support the concurrent validity of the SenseToKnow app for digital phenotyping in autism.
- The app shows potential utility in identifying clinical profiles associated with autism.
- Further research is needed to establish its role as a screening tool and for tracking behavioral changes.
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