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

项目摘要/摘要 帕金森氏病(PD)患者的交流参与受到限制[1，2]，这意味着 他们很难“参与到交流知识、信息、思想或情感的生活情境中”。 [3]。这种有限的参与可能会对一个人的福祉产生重大影响，包括社会孤立， 失业、关系恶化、难以获得服务[3，4]。我们目前没有 有直接和有效提高参与度的行为治疗目标。最基本的形式 交际性参与是参与对话的两个人之间的互动。小小的身体 研究了帕金森氏症患者的互动行为(即相互依赖的言语行为) (和/或构音障碍)使用描述性或案例研究设计来定性地描述对话[5-11]。在……里面 在之前的R21中，我们扩展了这项工作，利用了现有的丰富的对话理论文献 结合语音信号处理技术和高级统计 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 从这一系列研究中可以看出，Pd-NT二联体的相互作用行为与 NT-NT二联体的差异；以及这些差异与限制参与人们的对话有关 和警察在一起。下一步是从这些简单的相关性转移到推导因果关系和机械关系 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 提高帕金森病患者的参与度。我们将使用两个目标来实现这一点。第一个目标，使用中间-内部 设计，将比较PD-NT和NT-NT双联体对话中的相互作用行为，并量化其 在典型的交际情景中的变化。我们将使用PD和NT控制(DYAD)对人进行录音 条件)参与解决问题和建立和谐关系的对话(目标条件) 不熟悉的伴侣(伴侣状况)。由此产生的480个对话将被注释，并自动度量 互动行为，包括每个人的个人互动行为(发音准确、有节奏 可预测性、语言复杂性)和协调交互行为(夹带、会话修复、 话轮转换)，将被提取。线性混合效应模型将被用来评估这些行为的对偶，目标， 以及合伙人的条件。我们的第二个目标是评估每种互动行为对交际的影响 在因果推理框架内参与帕金森病患者对话的结果。鉴于SA1 比较两种类型和不同交际情景下的交互行为，SA2使用交互 行为作为因果推理模型的输入。这将使我们能够量化每个交互行为 促成了该对话的相关参与结果，并严格模拟了 对每种互动行为进行干预。将使用贝叶斯方法对每种行为的影响进行评估 多层次建模。结果将为随后的相互作用靶点的假设驱动的临床试验提供信息。

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.