Automatic Coding of Therapist and Client Language in Motivational Interviewing to Predict Reductions in Alcohol Use and Problems Using Machine-based Dyadic Multimodal Representation Learning

尽管动机访谈(MI)被广泛使用，但其成功的潜在机制 仍然知之甚少[14]，尤其是客户变更谈话和后续行为之间的联系 更改[14，69]。先前的研究已经确定了两种可能的心肌梗死疗效的活性成分： 涉及治疗师-客户二联体的元素的关系组件，包括表达 移情，以及专注于客户行为的差异化唤起的技术组件，例如 改变谈话或客户对他们承诺改变的看法[57]。目前对这些问题的分析 组件仅限于与语言有关的调查，并受昂贵和 尽管花费了时间和精力来实现可靠性，但艰苦的人工编码可能仍然不会 具有足够的敏感度或针对性，以充分检验 军事理论家。 我们的项目将通过引入一种新的MI编码系统来解决当前MI编码系统的缺陷 计算框架，利用我们在自动语言和非语言行为方面的最新进展 分析以及多模式机器学习。我们的框架旨在联合分析言语(即， 正在说什么)、非语言(即，如何说某事)和二元(即，在什么人际环境中 说了一些话)行为，以更好地识别会话中的变化谈话并维持可预测的谈话 会后饮酒。我们将利用已经从两个NIAAA收集和注释的音频数据- 资助的单期MI随机临床试验以改善饮酒行为(N=91；N=158)。我们 将通过广泛收集的客户和二元行为来传播我们的发现 建议的客户和二元行为数据库。此外，我们还将验证我们的 使用七个额外的NIAAA和联邦资助的RCT的计算框架，这些RCT使用不同的 针对不同目标人群的MI协议。

Despite the widespread use of Motivational Interviewing (MI), the underlying mechanisms of its success are still poorly understood [14], especially the link between client change talk and subsequent behavior change [14,69]. Previous research has identified two possible active components underlying MI efficacy: a relational component involving elements of the therapist-client dyad including the expression of empathy, and a technical component focused on the differential evocation of client behaviors such as change talk or what a client says about their commitment to change [57]. Current analyses of these components are limited to investigations pertaining to language only and restricted by expensive and arduous manual coding which, despite the time and efforts expended to achieve reliability, may still not be sufficiently sensitive or specific to adequately test the complex theoretical propositions espoused by MI theorists. Our project will address shortcomings of current MI coding systems by introducing a novel computational framework that leverages our recent advances in automatic verbal and nonverbal behavior analyses as well as multimodal machine learning. Our framework aims to jointly analyze verbal (i.e., what is being said), nonverbal (i.e., how something is said), and dyadic (i.e., in what interpersonal context something is said) behavior to better identify in-session change talk and sustain talk that is predictive of post-session alcohol use. We will leverage already collected and annotated audio data from two NIAAA- funded single-session MI randomized clinical trials to improve drinking behavior (N=91; N=158). We will disseminate our findings through an extensive collection of client and dyadic behaviors through our proposed Client and Dyadic Behavior Databases. In addition, we will validate the generalizability of our computational framework using seven additional NIAAA- and federally funded RCTs that used different MI protocols for different target populations.