Abstract
Measurement of language atypicalities in Autism Spectrum Disorder (ASD) is cumbersome and costly. Better language outcome measures are needed. Using language transcripts, we generated Automated Language Measures (ALMs) and tested their validity. 169 participants (96 ASD, 28 TD, 45 ADHD) ages 7 to 17 were evaluated with the Autism Diagnostic Observation Schedule. Transcripts of one task were analyzed to generate seven ALMs: mean length of utterance in morphemes, number of different word roots (NDWR), um proportion, content maze proportion, unintelligible proportion, c-units per minute, and repetition proportion. With the exception of repetition proportion (p = .07), nonparametric ANOVAs showed significant group differences (p< 0.01). The TD and ADHD groups did not differ from each other in post-hoc analyses. With the exception of NDWR, the ASD group showed significantly (p< 0.01) lower scores than both comparison groups. The ALMs were correlated with standardized clinical and language evaluations of ASD. In age- and IQ-adjusted logistic regression analyses, four ALMs significantly predicted ASD status with satisfactory accuracy (67.9–75.5%). When ALMs were combined together, accuracy improved to 82.4%. These ALMs offer a promising approach for generating novel outcome measures.
Highlights
Many measures of expressive language have been evaluated for their ability to distinguish between Autism Spectrum Disorder (ASD) and a typically developing (TD) control group
In post-hoc tests, we found significant group differences between the TD and ASD group for all Automated Language Measures (ALMs) except Number of Distinct Word Roots (NDWR) ( p = 0.528 )
ALM calculations are blind to participant status unlike most clinical measures, such as the CCC-2 and the Autism Diagnostic Observation Schedule (ADOS), which can be influenced by the parent or professional’s prior knowledge of the child
Summary
Many measures of expressive language have been evaluated for their ability to distinguish between ASD and a typically developing (TD) control group. As ADHD is part of the differential diagnosis of ASD and shows overlap in language domains, it represents a useful comparison group to test how language features in ASD differ from other neurodevelopmental disorders, increasing the ability to examine the specificity of language measures. This study was designed to address a gap in the research: namely the power of automatically calculated measures of expressive language to discriminate between ASD and two non-ASD control groups, and the correlation of these measures with common standardized tests. This is part of a larger research project examining language in already-diagnosed children with ASD. Our specific goals were to: 1. examine language differences (measured by ALMs) between ASD and two non-ASD control groups (ADHD and TD); 2. analyze the convergent validity of these measures with standardized language measures; 3. investigate the discriminant validity of individual ALMs in classifying ASD status; and 4. examine if gains in discriminant validity could be obtained by combining all ALMs together
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