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自闭症儿童纵向应用行为分析治疗结果的预测因素:增长曲线分析。

Predictors of longitudinal ABA treatment outcomes for children with autism: A growth curve analysis.

机构信息

University of California, Berkeley, 4511 Tolman Hall, Berkeley, CA, 94720, United States.

University of California, Berkeley, 4511 Tolman Hall, Berkeley, CA, 94720, United States.

出版信息

Res Dev Disabil. 2017 Nov;70:185-197. doi: 10.1016/j.ridd.2017.09.008. Epub 2017 Sep 28.

Abstract

BACKGROUND

Autism spectrum disorder (ASD) is a developmental disorder that causes lifelong disability. Applied Behavior Analysis (ABA) is one of the most empirically studied and validated approaches for treating children diagnosed with ASD. Due to the heterogeneity of ASD, it is important to ascertain who will most benefit from treatment.

METHODS

In this study, 35 participants, with a mean entry age of 3 years, received ABA therapy. Children were assessed at intake and every 6 months thereafter using the Developmental Profile-3 (DP-3) to measure their communication, social-emotional, adaptive behavior, and physical development (2-6 measures per participant). Using a growth curve analysis, we investigated if age, diagnosis severity, cognitive functioning, treatment hours, gender, parent education level, or primary language spoken at home significantly predicted the growth trajectories of ABA treatment outcomes.

RESULTS

Our findings indicated that higher cognitive functioning significantly predicted faster growth across all four developmental domains, age at entry predicted initial status, and other variables only predicted growth rates in one or two domains.

IMPLICATIONS

Knowing the predictors of treatment outcome is important information for customizing treatment and this study demonstrated how longitudinal analyses can illuminate how participant characteristics affect the course of ABA therapy.

摘要

背景

自闭症谱系障碍(ASD)是一种发育障碍,会导致终身残疾。应用行为分析(ABA)是最具经验性研究和验证的治疗自闭症儿童的方法之一。由于 ASD 的异质性,确定谁将从治疗中获益最大非常重要。

方法

在这项研究中,35 名参与者,平均入组年龄为 3 岁,接受了 ABA 治疗。在入组时和之后每 6 个月使用发展概况-3(DP-3)对儿童进行评估,以衡量他们的沟通、社会情感、适应行为和身体发育(每个参与者有 2-6 个测量值)。使用生长曲线分析,我们研究了年龄、诊断严重程度、认知功能、治疗时间、性别、父母教育水平或家庭主要语言是否显著预测 ABA 治疗结果的增长轨迹。

结果

我们的研究结果表明,较高的认知功能显著预测了所有四个发育领域的更快增长,入组年龄预测了初始状态,而其他变量仅在一个或两个领域预测了增长率。

意义

了解治疗结果的预测因素对于定制治疗非常重要,本研究展示了纵向分析如何阐明参与者特征如何影响 ABA 治疗的过程。

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