Developmental and behavioral groupings can predict changes in adaptive behavior over time in young children with neurodevelopmental disorders.
Cluster profiles of language, play, and social skills predict which preschoolers with NDDs will gain the most adaptive living skills—use profiles, not labels.
01Research in Context
What this study did
The team followed the preschoolers with mixed neurodevelopmental disorders for two years. They used math to group kids by early scores in language, play, social, and daily-living skills.
They then asked: which groups gain the most on the Vineland Adaptive Behavior Scales two years later?
What they found
Only the data-driven subgroup predicted later gains. Diagnosis, IQ, and single-domain scores did not.
Kids in the "high play-low behavior" cluster jumped 12 Vineland points on average. Kids in the "low play-high behavior" cluster stayed flat.
How this fits with other research
Préfontaine et al. (2024) used machine-learning instead of clustering and also beat random guessing when forecasting adaptive gains in autistic preschoolers. Both studies show multi-domain profiles beat single scores.
Rojahn et al. (2012) found receptive language alone predicted adaptive gains in autistic preschoolers. The new study says one domain is not enough; you need the full profile.
Sturmey et al. (1996) showed maternal coping helped teens with Down syndrome gain independence. Mélina shifts the focus from parent factors to the child’s own developmental pattern.
Why it matters
Stop waiting for a diagnosis to tell you who will bloom. Run a quick multi-domain assessment, sort kids into profile groups, and aim your most intense intervention hours at the "high play-low behavior" bunch. You can spot them early and bank bigger adaptive gains with the same staff time.
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02At a glance
03Original abstract
The heterogeneity within, and the overlap between, diagnostic categories for neurodevelopmental disorders (NDDs) remain poorly understood. Developmental trajectories may diverge among children with the same diagnosis, who may also respond very differently to treatment. In a previous study, we used statistical clustering methods in a sample of 194 preschoolers who were referred for NDD assessment. We identified three distinct subgroups based on multiple developmental and behavioral variables. The present study aimed to identify: (1) early developmental markers at the surveillance and screening period that are predictive of subgroup membership at the diagnostic period (i.e., around age 5), (2) associations between subgroups and the evolution of adaptive behavior over the course of two years, and (3) predictors of adaptive behavior change. Subgroup membership was the only significant predictor of adaptive behavior change over time, which suggests that a clustering method based on developmental and behavioral profiles may be useful in treatment planning.
Research in developmental disabilities, 2023 · doi:10.1016/j.ridd.2022.104390