Children with autism spectrum disorder (ASD) commonly present with comorbid language impairment, negatively impacting their learning and participation across settings. Addressing these needs requires a detailed understanding of their communication trajectories. In this study, we used the language environment and analysis (LENA) system to examine possible changes in children's (a) vocalizations and (b) ratio of speech to nonspeech vocalizations over a 10-month period. Data for 23 children with ASD (17M, 6F; ages 32–67 months) were analyzed, including monthly 3-hr in-class recordings and standardized measures of language, cognition, and ASD characteristics. Using hierarchical generalized linear models, we found significant time-trends for child vocalizations (P ≤ 0.001) and the vocalization ratio (P = 0.02), reflecting a waxing and waning pattern. Children with higher expressive language scores (Mullen scales of early learning, Vineland adaptive behavior scales – 2nd Ed.) and nonverbal cognition (Mullen scales of early learning), and fewer ASD characteristics (social communication questionnaire) demonstrated greater increases in the vocalization ratio over time (P values 0.04–0.01). Children with greater language and cognition difficulties were the most vocal, but produced a higher proportion of nonspeech vocalizations. The results demonstrate that significant fluctuations, as opposed to linear increases, may be observed in children with ASD receiving intervention, highlighting the value of assessment at multiple time-points. In addition, the findings highlight the need to consider both the quantity (vocalization counts) and quality (ratio of speech to nonspeech vocalizations) when interpreting LENA data, with the latter appearing to provide a more robust measure of communication. Autism Research 2019, 12: 830–842.
Bibliographical noteFunding Information:
This research was supported by a Griffith University Men-zies Health Institute Queensland research grant and a Griffith University School of Allied Health Sciences research grant. David Trembath was supported by a National Health and Medical Research Council Early Career Fellowship (GNT1071811).
This research was supported by a Griffith University Menzies Health Institute Queensland research grant and a Griffith University School of Allied Health Sciences research grant. David Trembath was supported by a National Health and Medical Research Council Early Career Fellowship (GNT1071811). The authors warmly thank the children, parents, and staff who made this study possible.
© 2019 International Society for Autism Research, Wiley Periodicals, Inc.
- individual variability/heterogeneity
- longitudinal data analysis
ASJC Scopus subject areas
- Neuroscience (all)
- Clinical Neurology