Article

Behavior Predictors of Language Development Over 2 Years in Children With Autism Spectrum Disorders

The University of British Columbia, 2125 Main Mall, Vancouver, BC, Canada, V6T 1Z4.
Journal of Speech Language and Hearing Research (Impact Factor: 2.07). 10/2009; 52(5):1106-20. DOI: 10.1044/1092-4388(2009/07-0262)
Source: PubMed

ABSTRACT

This exploratory study examined predictive relationships between 5 types of behaviors and the trajectories of vocabulary and language development in young children with autism over 2 years.
Participants were 69 children with autism assessed using standardized measures prior to the initiation of early intervention (T1) and 6 months (T2), 12 months (T3), and 24 months (T4) later. Growth curve modeling examined the extent to which behaviors at T1 and changes in behaviors between T1 and T2 predicted changes in development from T1 to T4.
Regardless of T1 nonverbal IQ and autism severity, high scores for inattentive behaviors at T1 predicted lower rates of change in vocabulary production and language comprehension over 2 years. High scores for social unresponsiveness at T1 predicted lower rates of change in vocabulary comprehension and production and in language comprehension over 2 years. Scores for insistence on sameness behaviors, repetitive stereotypic motor behaviors, and acting-out behaviors at T1 did not predict the rate of change of any child measure over 2 years beyond differences accounted for by T1 autism severity and nonverbal IQ status.
The results are discussed with regard to their implications for early intervention and understanding the complex factors that affect developmental outcomes.

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    • "Nonetheless, it is important to rule out the most obvious global variables that could do so. Severity of autism symptomatology and level of cognitive impairment are among the most salient global child variables that could account for empirical associations among our theoreticallymotivated predictors and language growth (Bopp et al. 2009). Thus, these background variables need to be controlled when considering whether more theoreticallymotivated predictors account for language growth in our sample. "
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    ABSTRACT: Eighty-seven preschoolers with autism spectrum disorders who were initially nonverbal (under 6 words in language sample and under 21 parent-reported words said) were assessed at five time points over 16 months. Statistical models that accounted for the intercorrelation among nine theoretically- and empirically-motivated predictors, as well as two background variables (i.e., cognitive impairment level, autism severity), were applied to identify value-added predictors of expressive and receptive spoken language growth and outcome. The results indicate that responding to joint attention, intentional communication, and parent linguistic responses were value-added predictors of both expressive and receptive spoken language growth. In addition, consonant inventory was a value-added predictor of expressive growth; early receptive vocabulary and autism severity were value-added predictors of receptive growth.
    Full-text · Article · Apr 2015 · Journal of Autism and Developmental Disorders
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    • "For example, Watt et al. (96) reported that prolonged engagement with RBBs was negatively related to social competence across the crucial developmental period from 2 to 3 years. Data on the relevance of RRBs in response to treatment are scant and equivocal (52, 97, 98), so more research is needed to investigate how individual differences in the extent of RBRs affect response to intervention. "
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    ABSTRACT: Response to early intervention programs in autism is variable. However, the factors associated with positive versus poor treatment outcomes remain unknown. Hence the issue of which intervention/s should be chosen for an individual child remains a common dilemma. We argue that lack of knowledge on "what works for whom and why" in autism reflects a number of issues in current approaches to outcomes research, and we provide recommendations to address these limitations. These include: a theory-driven selection of putative predictors; the inclusion of proximal measures that are directly relevant to the learning mechanisms demanded by the specific educational strategies; the consideration of family characteristics. Moreover, all data on associations between predictor and outcome variables should be reported in treatment studies.
    Full-text · Article · Jun 2014 · Frontiers in Pediatrics
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    • "Nonetheless, it is important to rule out the most obvious global variables that could do so. Severity of autism symptomatology and level of cognitive impairment are among the most salient global child variables that could account for empirical associations among our theoreticallymotivated predictors and language growth (Bopp et al. 2009). Thus, these background variables need to be controlled when considering whether more theoreticallymotivated predictors account for language growth in our sample. "
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    ABSTRACT: Background: Between 24 – 30% of children with autism spectrum disorders (ASD) do not use spoken communication by 5 years old. Spoken communication at 5 years predicts later adaptive outcomes in individuals with ASD. Identifying parsimonious predictive models of theory-guided predictors of vocabulary growth moves us towards understanding the variability in learning to communicate via speech within the ASD population. Parsimonious models include only predictors that account for significant variance in the outcome after statistically controlling for other predictors. Objectives: The incremental validity of eight putative predictors of growth curves of parent-reported expressive and receptive vocabulary was tested. Each putative predictor had empirical and theoretical grounds for selection. Methods: Eight-six initially nonverbal preschoolers with ASD were assessed 5 times in 4-month intervals over 16 months. Receptive and expressive vocabulary sizes were estimated using a parent report (i.e., the McArthur-Bates Communicative Development Inventory, Words and Gestures). The Communication and Symbolic Behavior Scale, Early Social Communication Scales, Motor Imitation Scale, Mullen Early Learning Scale, 2 parent-child interaction sessions, the Developmental Play Scale, and an oral motor assessment were used to measure putative predictors. Mixed level modeling was used to quantify individual vocabulary growth curves. The Time 5-centered intercept was selected as the parameter of interest because it is arguably the most interpretable parameter when quadratic or cubic models are needed to model growth (i.e., the best estimate of vocabulary size at Time 5). Putative predictors were measured at Times 1 or Time 2. To afford interpretable effect sizes, ordinary least square estimates of the intercepts for the growth curves were analyzed as the criterion variables in multiple regressions used to identify unique predictors (i.e., after controlling all other predictors in the model). Results: Quadratic models fit the data better than simple linear models. Except for IQ, all putative predictors predicted either expressive or receptive vocabulary. After controlling for other variables and after reducing the model to predictors with incremental validity, 3 predictors remained in each model. The number of parental linguistic responses to child leads at Time 2 (R2 change = .12), number of intentional communication acts at Time 1 and 2 (R2 change = .11), and number of different object play actions at Time 1 (R2 change = .04) added to account for 29% of the variance (adjusted R square) in expressive vocabulary. The number of words understood at Time 1 (R2 change= .27), number of object play actions at Time 1 (R2 change = .10), and number of parental linguistic responses to child leads at Time 2 (R2 change= .04) added to account for 49% of the variance in receptive vocabulary. Putative predictors without incremental validity were oral motor functioning, IQ, motor imitation, responding to joint attention, and consonant inventory. Conclusions: The results support selecting the unique predictors as goals for nonverbal children with ASD. The number of predictors, number of measurement periods, use of growth curves, long interval between predictor measurement and end-point of study, and large sample size make this study particularly important to the field.
    Full-text · Conference Paper · May 2014
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