Mazurek Micah O, Carlson Coleen, Baker-Ericzén Mary, Butter Eric, Norris Megan, Barr Christopher, Kanne Stephen
Department of Human Services, School of Education and Human Development, University of Virginia, Charlottesville, Virginia, USA.
Department of Psychology, Texas Institute for Measurement, Evaluation, and Statistics, University of Houston, Houston, Texas, USA.
Autism Res. 2020 Nov;13(11):1867-1879. doi: 10.1002/aur.2397. Epub 2020 Oct 1.
The Autism Impact Measure (AIM) was designed specifically for treatment-outcome assessment in children with ASD, focusing on treatment-relevant aspects of symptom presentation and efficient detection of short-term improvement. The AIM demonstrated strong reliability and validity in initial psychometric studies. The current study evaluated the AIM's sensitivity to change across well-established treatments. The sample included 471 children with ASD (ages 2-14) participating in one of six treatments. The AIM was administered at baseline and 6-week intervals and a battery of domain-specific concurrent measures was also administered. A longitudinal repeated measures design examined the degree to which: (a) AIM domain scores changed over time in response to treatment and (b) change in AIM domains was associated with change in measures of similar constructs. Results across growth curve models indicated that AIM domains are sensitive to change in symptoms across treatment. Across all models, symptoms decreased over time, with some deceleration in rate of improvement. For all AIM domains except Repetitive Behavior, symptoms improved as a function of treatment group. Correlations of change between AIM and other measures varied across domains (from 0.01-0.43 across measures). This was the first large-scale study to systematically evaluate sensitivity to change in a measure of core ASD symptoms. The results provide support for the AIM's ability to detect short-term improvement across symptom domains and indicate that AIM domains are sensitive to change overall and as a function of different treatment conditions. The brief repeated assessment window also highlights the AIM's utility for detecting improvements across short-term treatments. Autism Res 2020, 13: 1867-1879. © 2020 International Society for Autism Research and Wiley Periodicals LLC LAY SUMMARY: Good measures are important for assessing outcomes in children with autism. However, there are few tools for tracking short-term changes in autism symptoms. This study tested a new measure, the Autism Impact Measure (AIM), in a large group of children with autism. The results showed that the AIM appears to be a valid and accurate tool for measuring autism symptoms. The AIM may be a helpful tool for researchers and clinicians interested in tracking short-term improvements in autism symptoms.
自闭症影响量表（AIM）是专门为评估自闭症谱系障碍（ASD）儿童的治疗效果而设计的，重点关注症状表现中与治疗相关的方面以及对短期改善的有效检测。AIM在最初的心理测量学研究中显示出很强的信度和效度。本研究评估了AIM对各种成熟治疗方法变化的敏感性。样本包括471名年龄在2至14岁之间参与六种治疗方法之一的ASD儿童。在基线和每隔六周时进行AIM评估，同时还进行一系列特定领域的并行测量。采用纵向重复测量设计来检验以下程度：（a）AIM各领域得分随时间因治疗而发生的变化，以及（b）AIM各领域的变化与类似结构测量指标的变化之间的关联。生长曲线模型的结果表明，AIM各领域对不同治疗方法下症状的变化敏感。在所有模型中，症状随时间减少，改善速度有所减缓。除重复行为外，所有AIM领域的症状均随治疗组的不同而有所改善。AIM与其他测量指标之间变化的相关性在不同领域有所不同（各测量指标之间为0.01至0.43）。这是第一项系统评估核心ASD症状测量指标对变化敏感性的大规模研究。结果支持了AIM检测症状领域短期改善的能力，并表明AIM各领域总体上对变化敏感，且因不同治疗条件而敏感。简短的重复评估窗口也凸显了AIM在检测短期治疗改善方面的效用。《自闭症研究》2020年，13卷：1867 - 1879页。© 2020国际自闭症研究协会和威利期刊有限责任公司 内容摘要：好的测量方法对于评估自闭症儿童的治疗效果很重要。然而，用于跟踪自闭症症状短期变化的工具很少。本研究在一大群自闭症儿童中测试了一种新的测量方法——自闭症影响量表（AIM）。结果表明，AIM似乎是一种有效且准确的测量自闭症症状的工具。对于有兴趣跟踪自闭症症状短期改善情况的研究人员和临床医生来说，AIM可能是一个有用的工具。
