Spoken Language Change in Children on the Autism Spectrum Receiving Community-Based Interventions

Trembath, David; Stainer, Matthew J.; Caithness, Teena; Dissanayake, Cheryl; Eapen, Valsamma; Fordyce, Kathryn; Frewer, Veronica; Frost, Grace; Hudry, Kristelle; Iacono, Teresa; Mahler, Nicole; Masi, Anne; Paynter, Jessica; Pye, Katherine; Quan, Shannon; Shellshear, Leanne; Sutherland, Rebecca; Sievers, Stephanie; Thirumanickam, Abirami; Westerveld, Marleen F.; Tucker, Madonna

doi:10.1007/s10803-022-05511-4

Cited by 6 publications

(5 citation statements)

References 42 publications

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“…Our study found a similar pattern when examining the longitudinal relation between child factors at T1 and our NLS‐derived measures at T2. Exploring child factors that predict changes in URate and CTRate aids in tailoring goals to children's individual needs and strengths (Trembath et al, 2023).…”

Section: Discussionmentioning

confidence: 99%

“…Although not characterized by the current study, stakeholder input would motivate future research. Employing a supportive and collaborative framework with multiple stakeholders in research can provide a stronger empirical base with direct relevance to clinical practice and translational applications (Trembath et al, 2023).…”

Section: Discussionmentioning

confidence: 99%

“…Examining child factors that predict changes in URate and CTRate provides explanations for individual differences (Trembath et al, 2023). Numerous studies examined longitudinal predictors of expressive language growth in autistic children (Frazier et al, 2021; Howlin et al, 2000; Smith et al, 2007; Thurm et al, 2007; Weismer & Kover, 2015).…”

Section: Introductionmentioning

confidence: 99%

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Real‐time coded measures in natural language samples capture change over time in minimally verbal autistic children

La Valle,

Shen,

Shih

et al. 2024

Autism Research

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Prior research supports the use of natural language sampling (NLS) to assess the rate of speech utterances (URate) and the rate of conversational turns (CTRate) in minimally verbal (MV) autistic children. Bypassing time‐consuming transcription, previous work demonstrated the ability to derive URate and CTRate using real‐time coding methods and provided support for their strong psychometric properties. (1) Unexplored is how URate and CTRate using real‐time coding methods capture change over time and (2) whether specific child factors predict changes in URate and CTRate in 50 MV autistic children (40 males; M = 75.54, SD = 16.45 (age in months)). A NLS was collected at Time 1 (T1) and Time 2 (T2) (4.5 months between T1 and T2) and coding was conducted in ELAN Linguistic Annotator software using a real‐time coding approach to derive URate and CTRate. Findings from paired samples Wilcoxon tests revealed a significant increase in child URate (not examiner URate) and child and examiner CTRate from T1 to T2. Child chronological age, Mullen expressive language age equivalent scores, and URate and CTRate at T1 were predictive of URate and CTRate at T2. Findings support using NLS‐derived real‐time coded measures of URate and CTRate to efficiently capture change over time in MV autistic children. Identifying child factors that predict changes in URate and CTRate can help in the tailoring of goals to children's individual needs and strengths.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Real‐time coded measures in natural language samples capture change over time in minimally verbal autistic children

La Valle,

Shen,

Shih

et al. 2024

Autism Research

View full text Add to dashboard Cite

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“…We characterized each child's language phase using expressive language benchmarks developed by experts assembled by the National Institute on Deafness and Other Communication Disorders (Tager-Flusberg et al, 2009). These benchmarks use a developmental framework to provide uniform terminology for describing expressive language in children with autism and have proved to be useful for investigating variables related to children's language growth (Ellawadi & Weismer, 2015;Trembath et al, 2022). The guidelines of Tager-Flusberg and colleagues ( 2009) denote that language phases may be determined using multiple sources including language samples collected during administration of semistructured direct assessment measures.…”

Section: Aim 2 Exploring Pvr By Language Phasementioning

confidence: 99%

Verbal Responsiveness in Parents of Toddlers With and Without Autism During a Home Observation

Delehanty

Hooker

Wetherby

2023

J Autism Dev Disord

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This study examined patterns of verbal responsiveness in parents of toddlers (Mage = 20 months) later identified with autism (n = 121), developmental delay (n = 46), or typical development (n = 44) during an hourlong home observation. Parent verbal responsiveness (PVR) was compared using MANOVA across groups and by child expressive language phase. Multiple regression analyses controlling for child age and maternal education were employed to examine the extent to which PVR predicted variance in concurrent child social communication and prospective language skills. Parents provided synchronous responses approximately 90% of the time. Parents of children with autism and developmental delay used smaller proportions of responses that added linguistic information (i.e., expansions and follow-in directives for language) than those of children with typical development. Parents of children in the preverbal phase were more likely, on average, to affirm their children’s acts of intentional communication or provide a follow-in directive for action that did not necessitate a verbal response than to expand or elicit language. Regression results indicated that parental use of expansions and follow-in directives for language made significant contributions to child language outcomes. The patterns we observed may reflect parents’ attunement to their child’s developmental level. Responsiveness to a child’s focus of attention is vital in the earlier stages of language learning; however, results point to the potential importance of parental expansions and follow-in directives for promoting language development across groups in this sample. Directions for intervention research targeting PVR and language skills in toddlers with autism and developmental delays are discussed.

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“…59 As previously identified in the literature, children with ASD may benefit more from interventions if they begin at a younger age, although more needs to be understood regarding the interactions between predictor variables. [60][61][62] The finding of younger age predicting greater improvements following intervention also highlights the importance of continuing research on expressive and receptive communication in ASD in preschool children.…”

mentioning

confidence: 96%

Predicting Communication Skills Outcomes for Preschool Children with Autism Spectrum Disorder Following Early Intervention

Kauley,

John,

Barr

et al. 2024

NDT

Self Cite

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This study aims to assess changes in the receptive and expressive language skills and to determine if the baseline characteristics such as communication, cognitive and motor skills, predict outcomes in preschool children with Autism Spectrum Disorder (ASD) following early intervention. Methods: We recruited 64 children participating in the Early Start Denver Model (ESDM) early intervention program at an Autism Specific Early Learning and Care Center (ASELCC) in Australia. Baseline characteristics across various developmental domains was measured using the Mullen Scales of Early Learning (MSEL), Vineland Adaptive Behaviour Scales, 2nd Edition (VABS-II), and the ESDM Curriculum Checklist. Linear mixed-effects models were used to examine the effects of the intervention on outcomes. Fixedeffects such as time, groups (verbal and minimally verbal), and time-by-group interactions were assessed whilst adjusting for covariates. Further, multiple linear regression models were used to determine if the baseline characteristics were significant predictors of the outcomes following the early intervention. Results: Among the 64 children who participated in this study, 38 children were verbal, whereas 26 were deemed to have minimal verbal skills. The mean age of the sample was 4.1 years with a significant male predilection (83%) and from a culturally and linguistically diverse (CALD) background (64%). Findings of the linear mixed effects model showed significant within and between group differences in the ESDM subscales, indicating higher magnitude of changes in the verbal group compared to the minimally verbal group. Finally, the multiple linear regression models suggested that baseline MSEL visual reception and expressive language scores were predictive of changes in the ESDM receptive and expressive communication scores. Conclusion: Understanding a child's baseline skill levels may provide valuable clues regarding what interventions would work best, or which interventions may be less suitable for individual preschool-aged children with ASD.

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Spoken Language Change in Children on the Autism Spectrum Receiving Community-Based Interventions

Cited by 6 publications

References 42 publications

Real‐time coded measures in natural language samples capture change over time in minimally verbal autistic children

Real‐time coded measures in natural language samples capture change over time in minimally verbal autistic children

Verbal Responsiveness in Parents of Toddlers With and Without Autism During a Home Observation

Predicting Communication Skills Outcomes for Preschool Children with Autism Spectrum Disorder Following Early Intervention

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