Real-time coded measures in natural language samples capture change over time in minimally verbal autistic children.
Real-time language sampling spots small speech gains in minimally verbal autistic kids in under five months.
01Research in Context
What this study did
The team watched 42 minimally verbal autistic preschoolers during free play.
They used real-time coding to count every word and every turn the child took.
Four and a half months later they did the same thing again.
What they found
Kids spoke a little more and took more turns in conversation.
The gains were small but real.
The tool caught change in less than five months.
How this fits with other research
Gilchrist et al. (2018) and Lotfizadeh et al. (2020) also used tech to spot behavior fast.
They used wrist sensors to catch hand flapping and self-harm.
La Valle et al. (2024) shows you can do the same with language just by watching and coding.
Allison et al. (2008) and Kim et al. (2023) looked at screening or brain scans.
Those tools tell you if a child has autism.
This new tool tells you if the child is getting better at talking.
Why it matters
You can track tiny language gains every few months without fancy gear.
Just record play, code in real time, and see if your plan is working.
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02At a glance
03Original abstract
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.
Autism research : official journal of the International Society for Autism Research, 2024 · doi:10.1002/aur.3142