A causal framework of communicative participation in people with Parkinson's disease.

项目总结/摘要 帕金森病（PD）患者的沟通参与受到限制[1，2]，这意味着 他们很难“参与交换知识、信息、想法或感情的生活情境” [3]的文件。这种受限制的参与可能对一个人的福祉产生重大影响，包括社会孤立， 失业、人际关系恶化和难以获得服务[3，4]。我们目前不 有直接有效提高参与度的行为治疗目标。的最基本形式 交际参与是两个人之间的一种互动，一种二元体，进行对话。小小的身躯 研究已经检查了交互行为（即，相互依赖的言语行为） (and/或构音障碍）使用描述性或案例研究设计来定性描述对话[5-11]。在 在之前的R21中，我们扩展了这项工作，利用现有的关于对话理论的丰富文献， 与神经型（NT）成人，加上语音信号处理技术和先进的统计 建模，以量化任务控制对话语料库中的交互行为[12-16]。合理的结论 从这些研究中可以看出，PD-NT二联体的相互作用行为与 NT-NT二元体的差异;这些差异与人们对对话的限制参与有关 PD的。下一步是从这些简单的相关性转移到推导出因果的、机械的关系 互动行为和参与结果之间的关系，使我们能够确定候选治疗目标， 提高PD患者的参与度。我们将使用两个目标来做到这一点。第一个目标，使用一个内-间 设计，将比较PD-NT和NT-NT二元对话的交互行为，并量化它们的 在典型的交际情境中的变化。我们将录音的人与PD和NT控制（二分体 条件）参与解决问题和建立融洽关系的对话（目标条件）， 不熟悉的伴侣（伴侣条件）。由此产生的480个对话将被注释， 交互行为，包括每个人的个体交互行为（发音精度、节奏 可预测性，语言复杂性）和协调互动行为（夹带，会话修复， #21453;，将被淘汰。线性混合效应模型将用于评估这些行为的二元体，目标， 和合作伙伴的条件。我们的第二个目标是评估每一种互动行为对交际的影响。 在因果推理框架内，参与PD患者对话的结果。而SA 1 SA 2比较了不同类型和不同交际情境下的互动行为， 行为作为因果推理模型的输入。这将使我们能够量化每种交互行为 为该对话的相关参与成果做出了贡献，并严格模拟了 干预每个交互行为。将使用贝叶斯方法评估每种行为的影响 多级建模结果将告知后续的假设驱动的相互作用靶点的临床试验。

PROJECT SUMMARY/ABSTRACT People with Parkinson's disease (PD) experience restricted communicative participation [1,2], which means that they have difficulty “taking part in life situations where knowledge, information, ideas or feelings are exchanged” [3]. This restricted participation can have significant consequences for one's well-being, including social isolation, loss of employment, deterioration of relationships, and difficulty accessing services [3,4]. We currently do not have behavioral treatment targets that directly and effectively improve participation. The most basic form of communicative participation is an interaction between two people, a dyad, engaged in dialogue. The small body of research that has examined interaction behaviors (i.e., interdependent verbal behaviors) in people with PD (and/or dysarthria) has used descriptive or case study designs to qualitatively describe the dialogues [5-11]. In a prior R21, we extended this work, leveraging the existing rich literature on dialogue theory that was developed with neurotypical (NT) adults, coupled with speech signal processing techniques and advanced statistical modeling, to quantify interaction behaviors in task-controlled dialogue corpora [12-16]. Reasonable conclusions from this collection of studies are that the interaction behaviors of PD-NT dyads are fundamentally different from those of NT-NT dyads; and that these differences correlate to restricted participation in the dialogues of people with PD. The next step is to move from these simple correlations to deriving causal, mechanistic relationships between interaction behaviors and participation outcomes, allowing us to identify candidate treatment targets for improving participation in people with PD. We will use two aims to do this. The first aim, using a between-within design, will compare interaction behavior from the dialogues of PD-NT and NT-NT dyads and quantify their variation across typical communicative situations. We will audio-record people with PD and NT controls (dyad condition) engaged in a problem-solving and a rapport-building dialogue (goal condition) with a familiar and an unfamiliar partner (partner condition). The resulting 480 dialogues will be annotated, and automated metrics of interaction behaviors, including individual interaction behaviors of each person (articulatory precision, rhythmic predictability, language complexity) and coordinative interaction behaviors (entrainment, conversational repair, turn-taking), will be extracted. Linear mixed effects models will be used to assess these behaviors by dyad, goal, and partner condition. Our second aim looks to assess the impact of each interaction behavior on communicative participation outcomes in the dialogues of people with PD within a causal inference framework. Whereas SA1 compares the interaction behaviors across dyad types and communicative situations, SA2 uses the interaction behaviors as input to a causal inference model. This will allow us to quantify how much each interaction behavior contributed to the associated participation outcome for that dialogue and rigorously simulate the effects of intervening on each interaction behavior. Assessment of the impact of each behavior will be done using Bayesian multilevel modeling. Results will inform a subsequent hypothesis-driven clinical trial of interaction targets.