Proportion and Profile of Autistic Children Not Acquiring Spoken Language Despite Receiving Evidence-Based Early Interventions - PubMed (original) (raw)

doi: 10.1080/15374416.2025.2579286. Online ahead of print.

Michael V Lombardo 2, Ashley Zitter 3, Brian Boyd 4, Cheryl Dissanayake 5, Sarah Dufek 6, Helen E Flanagan 7, Suzannah Iadarola 8, Ann Kaiser 9, So Hyun Kim 10 11, Lynne Levato 8, Catherine Lord 12, Joshua Plavnick 13, Diana L Robins 1, Sally J Rogers 6, Isabel M Smith 7 14, Tristram Smith 8, Aubyn Stahmer 6, Linda Watson 4

Affiliations

Proportion and Profile of Autistic Children Not Acquiring Spoken Language Despite Receiving Evidence-Based Early Interventions

Giacomo Vivanti et al. J Clin Child Adolesc Psychol. 2025.

Abstract

Objective: To determine the proportion and profile of preschoolers on the autism spectrum who do not acquire spoken language despite receiving evidence-supported interventions that target spoken language.

Methods: We examined an aggregate dataset comprising 707 preschoolers on the autism spectrum who had received evidence-supported interventions to determine the proportion and profile of those who experienced limited progress in spoken language. Interventions were delivered through programs affiliated with university research settings and ranged in duration from 6 to 24 months. Spoken language outcomes were determined from parent-report measures, which were validated against direct assessments and natural language samples.

Results: Approximately two-thirds of children who were non-speaking at baseline were using single words or more complex spoken language by intervention exit. Those who remained non-speaking had lower baseline motor imitation scores, derived mainly from parent reports. Approximately half of the children who were minimally speaking (i.e. had single words or no words) at baseline were combining words by intervention exit. Those who did not acquire word combinations had lower baseline scores in cognitive, social, adaptive and motor imitation measures, and shorter intervention duration. Age at intervention start influenced spoken language advancement differently depending on the initial spoken language level. The odds of acquiring spoken language did not differ based on the intervention received.

Conclusions: Approximately one-third of children who had limited or no spoken language at baseline did not advance to spoken language stages following intervention. Development of spoken language was associated with modifiable factors at the child and intervention level.

PubMed Disclaimer

Figures

Figure 1.

Figure 1.

Factors differentiating initially minimally speaking children (based on APPL Phase 2 or 1 classification) who do/do not advance to acquisition of phrase speech by the end of early intervention. Panel a shows a correlation matrix of all continuous predictor variables utilized in the statistical model predicting advancer status. Due to the highly correlated nature of these predictors, principal components analysis (PCA) was used to decorrelate predictors into a set of orthogonal latent variables (panel b). How each variable contributes to the resulting latent principal component (PC) variables can be seen in panel B showing a heatmap of PCA loadings. PC and PC2 were statistically significant in predicting advancer status and specific variables with loadings >0.2 are highlighted in green outlines as the primary variables contributed most to those PCs. Panel C shows sample sizes and relative proportions of individuals in each intervention type that were either advancers (orange; total n = 233) or non-advancers (light blue; total n = 237). Panel D shows scores on each predictor variable when only the variance from statistically significant PCs (PC1, PC2) are retained. Standardized effect sizes are shown for the difference between advancers versus non-advancers on each of these variables.

Figure 2.

Figure 2.

Factors differentiating initially non-speaking children (based on APPL Phase 1 classification) who do/do not advance to acquire single words. Panel a shows a correlation matrix of all continuous predictor variables utilized in the statistical model predicting advancer status. Due to the highly correlated nature of these predictors, principal components analysis (PCA) was used to decorrelate predictors into a set of orthogonal latent variables (panel b). How each variable contributes to the resulting latent principal component (PC) variables can be seen in panel B showing a heatmap of PCA loadings. PC2 was statistically significant in predicting advancer status and specific variables with loadings >0.2 are highlighted in green outlines as the primary variables contributed most to those PCs. Panel C shows sample sizes and relative proportions of individuals in each intervention type that were either advancers (orange; total n = 195) or non-advancers (light blue; total n = 98). Panel D shows scores on the predictor variables contributing to PC2 when only the variance from the statistically significant PC is retained. Effect size is d = 0.69 for the variables in panel D.

References

    1. Arick JR, Loos L, Falco RA, & Krug DA (2004). The STAR program: Strategies for teaching based on autism research. Level 2. Pro-ed.
    1. Bacon EC, Osuna S, Courchesne E, & Pierce K (2019). Naturalistic language sampling to characterize the language abilities of 3-year-olds with autism spectrum disorder. Autism, 23(3), 699–712. 10.1177/1362361318766241 -DOI -PMC -PubMed
    1. Bak MYS, Chung S, Avendaño SM, Plavnick JB, Brehmer JS, & Reilly AM (2024). Using the LENA® system for children with autism in educational settings: A comparison with human coders. Research in Autism Spectrum Disorders, 111, 102312. 10.1016/j.rasd.2023.102312 -DOI
    1. Bal VH, Fok M, Lord C, Smith IM, Mirenda P, Szatmari P, Vaillancourt T, Volden J, Waddell C, Zwaigenbaum L, Bennett T, Duku E, Elsabbagh M, Georgiades S, Ungar WJ, & Zaidman-Zait A (2020). Predictors of longer-term development of expressive language in two independent longitudinal cohorts of language-delayed preschoolers with autism spectrum disorder. Journal of Child Psychology and Psychiatry, and Allied Disciplines, 61(7), 826–835. 10.1111/jcpp.13117 -DOI -PMC -PubMed
    1. Bal V, Katz T, Bishop SL, & Krasileva K (2016). Understanding definitions of minimally verbal across instruments: Evidence for subgroups within minimally verbal children and adolescents with autism spectrum disorder. Journal of Child Psychology and Psychiatry, 57(12), 1424–1433. 10.1111/jcpp.12609 -DOI -PubMed

LinkOut - more resources