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自闭症谱系障碍青少年的健康行为、社会心理因素与学业参与:一项潜在类别分析

Health behaviors, psychosocial factors, and academic engagement in youth with autism spectrum disorder: A latent class analysis.

作者信息

Garcia Jeanette M, Hahs-Vaughn Debbie, Shurack Riley

机构信息

Department of Health Sciences, University of Central Florida, Orlando, Florida, USA.

College of Community Innovation and Education, University of Central Florida, Orlando, Florida, USA.

出版信息

Autism Res. 2023 Jan;16(1):143-153. doi: 10.1002/aur.2843. Epub 2022 Nov 5.

Abstract

The purpose of this study was to identify behavioral and health-related profiles of children with autism spectrum disorder (ASD), based on the 2016 National Survey of Children's Health. A sample of 894 children with ASD (weighted sample N = 768,181) were included in the analysis. All data were parent-reported and included measures on current diagnosis of ASD, general child health, weight status, physical activity (PA), screen time (ST), sleep duration, academic engagement, and social engagement. Latent class analysis, estimated with Mplus v. 8.4, was used to identify latent profiles of children with ASD. A three-profile solution was the best fitting model, per model fit criteria. Children in profile 1 had overall more positive attributes (better health and weight, PA, more engaged in school, little difficulty in making friends, and modest ST) relative to children in either profiles 2 or 3. Children in profile 2 had distinctly increased ST and more difficulty in making friends when compared with children in either other profile. A greater proportion of children in profiles 2 and 3 were receiving behavioral treatment compared to profile 1; however, no differences were observed among profiles according to ASD severity, medication status, or additional health conditions. Studies should examine causal mechanisms among health behaviors, academic achievement, and social engagement in youth with ASD.

摘要

本研究旨在根据2016年全国儿童健康调查,确定自闭症谱系障碍(ASD)儿童的行为和健康相关特征。分析纳入了894名ASD儿童样本(加权样本N = 768,181)。所有数据均由家长报告,包括当前ASD诊断、儿童总体健康状况、体重状况、身体活动(PA)、屏幕时间(ST)、睡眠时间、学业参与度和社交参与度等指标。使用Mplus v. 8.4进行估计的潜在类别分析,以确定ASD儿童的潜在特征。根据模型拟合标准,三特征解决方案是最佳拟合模型。与特征2或特征3中的儿童相比,特征1中的儿童总体具有更多积极属性(健康和体重状况更好、身体活动更多、更多参与学校活动、交友困难较小且屏幕时间适度)。与其他任何一个特征中的儿童相比,特征2中的儿童屏幕时间明显增加,交友困难更大。与特征1相比,特征2和特征3中有更大比例的儿童正在接受行为治疗;然而,根据ASD严重程度、用药状况或其他健康状况,各特征之间未观察到差异。研究应探讨ASD青少年健康行为、学业成绩和社交参与之间的因果机制。

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