Growth mixture models of adaptive behavior in adolescents with autism spectrum disorder.


Journal

Autism : the international journal of research and practice
ISSN: 1461-7005
Titre abrégé: Autism
Pays: England
ID NLM: 9713494

Informations de publication

Date de publication:
08 2019
Historique:
pubmed: 12 12 2018
medline: 10 7 2020
entrez: 12 12 2018
Statut: ppublish

Résumé

This study examined growth trajectories of teacher-reported adaptive behavior in a diverse sample of adolescents with autism spectrum disorder. The participants were 244 adolescents between the ages of 14 and 21 years who were assessed at up to four time points across two and a half years of high school. Demographic variables (age, sex, race, maternal education), phenotypic characteristics (intelligence quotient, autism severity) and school factors (location of the school, school quality) were collected. Growth mixture modeling was used to identify distinct classes of growth trajectories in communication, daily living skills, and socialization domains of adaptive behavior. Two distinct classes were identified for each domain. The first class had moderately low adaptive behavior scores and demonstrated growth of adaptive behavior over time and the second class had low adaptive behavior scores and did not demonstrate change over time. Adolescents within the moderately low adaptive behavior classes were younger at enrollment in the study, had higher IQs, and lower autism symptom severity. Logistic regressions were performed, and aspects of school quality predicted the likelihood of being in the moderately low classes above and beyond autism symptoms.

Identifiants

pubmed: 30525941
doi: 10.1177/1362361318815645
pmc: PMC6711369
mid: NIHMS1511888
doi:

Types de publication

Journal Article Research Support, N.I.H., Extramural Research Support, U.S. Gov't, Non-P.H.S.

Langues

eng

Sous-ensembles de citation

IM

Pagination

1472-1484

Subventions

Organisme : NICHD NIH HHS
ID : T32 HD040127
Pays : United States

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Auteurs

Brianne Tomaszewski (B)

1 University of North Carolina at Chapel Hill, USA.

Leann Smith DaWalt (L)

2 University of Wisconsin-Madison, USA.

Samuel L Odom (SL)

1 University of North Carolina at Chapel Hill, USA.

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Classifications MeSH