Bertamini Giulio, Bentenuto Arianna, Perzolli Silvia, Paolizzi Eleonora, Furlanello Cesare, Venuti Paola
Laboratory of Observation, Diagnosis and Education (ODFLab), Department of Psychology and Cognitive Science, University of Trento, 38122 Trento, TN, Italy.
Data Science for Health (DSH), Bruno Kessler Foundation (FBK), 38123 Povo, TN, Italy.
Brain Sci. 2021 Mar 13;11(3):366. doi: 10.3390/brainsci11030366.
Observational research plays an important part in developmental research due to its noninvasiveness. However, it has been hardly applied to investigate efficacy of the child-therapist interaction in the context of naturalistic developmental behavioral interventions (NDBI). In particular, the characteristics of child-therapist interplay are thought to have a significant impact in NDBIs in children with autism spectrum disorder (ASD). Quantitative approaches may help to identify the key features of interaction during therapy and could be translated as instruments to monitor early interventions.
= 24 children with autism spectrum disorder (ASD) were monitored from the time of the diagnosis (T0) and after about one year of early intervention (T1). A novel observational coding system was applied to video recorded sessions of intervention to extract quantitative behavioral descriptors. We explored the coding scheme reliability together with its convergent and predictive validity. Further, we applied computational techniques to investigate changes and associations between interaction profiles and developmental outcomes.
Significant changes in interaction variables emerged with time, suggesting that a favorable outcome is associated with interactions characterized by increased synchrony, better therapist's strategies to successfully engage the child and scaffold longer, more complex and engaging interchanges. Interestingly, data models linked interaction profiles, outcome measures and response trajectories.
Current research stresses the need for process measures to understand the hows and the whys of ASD early intervention. Combining observational techniques with computational approaches may help in explaining interindividual variability. Further, it could disclose successful features of interaction associated with better response trajectories or to different ASD behavioral phenotypes that could require specific dyadic modalities.
观察性研究因其非侵入性在发育研究中发挥着重要作用。然而，它几乎未被用于在自然主义发育行为干预（NDBI）背景下探究儿童与治疗师互动的效果。特别是，儿童与治疗师互动的特征被认为对自闭症谱系障碍（ASD）儿童的NDBI有重大影响。定量方法可能有助于识别治疗过程中互动的关键特征，并可转化为监测早期干预的工具。
对24名自闭症谱系障碍（ASD）儿童从诊断时（T0）以及在大约一年的早期干预后（T1）进行监测。一种新颖的观察编码系统应用于干预的视频记录片段，以提取定量行为描述符。我们探讨了编码方案的可靠性及其收敛效度和预测效度。此外，我们应用计算技术来研究互动概况与发育结果之间的变化和关联。
随着时间推移，互动变量出现了显著变化，这表明良好的结果与以下特征的互动相关：同步性增加、治疗师成功吸引儿童并进行更长时间、更复杂且更具吸引力的互动的更好策略。有趣的是，数据模型将互动概况、结果测量和反应轨迹联系起来。
当前研究强调需要过程测量来理解ASD早期干预的方式和原因。将观察技术与计算方法相结合可能有助于解释个体间的变异性。此外，它可以揭示与更好的反应轨迹或不同ASD行为表型相关的成功互动特征，而这些表型可能需要特定的二元模式。
