Development and feasibility of computer based fidelity monitoring for MI

基于计算机的 MI 保真度监控的开发和可行性

• 批准号: 8881136
• 负责人: Zachary E. Imel
• 金额: $ 24.42万
• 依托单位: UNIVERSITY OF UTAH
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-07-01 至 2017-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8881136
• 关键词: Acoustics; Adopted; Agreement; Back; Basic Science; Behavior; Behavior Therapy; Behavioral; Clinical; Code; Cognitive Science; Communities; Compassion; Computers; Data; Development; Discrimination; Distal; Drug Addiction; Drug abuse; Educational workshop; Empathy; Engineering; Evaluation; Evidence based treatment; Feasibility Studies; Feedback; Grant; Health; Human; International; Intervention; Knowledge; Learning; Linguistics; Measures; Methods; Modeling; Monitor; NIH Program Announcements; National Institute of Drug Abuse; Outcome; Patients; Performance; Pharmaceutical Preparations; Positioning Attribute; Privacy; Process; Protocols documentation; Provider; Psychotherapy; Quality Control; Reporting; Research; Running; Semantics; Solutions; Specific qualifier value; Speech; Staging; Supervision; Surveys; System; Technology; Testing; Time; Training; addiction; base; community setting; computer science; cost; effective therapy; evidence base; experience; graduate student; improved; innovation; member; motivational enhancement therapy; predictive modeling; programs; psychologic; response; satisfaction; scale up; signal processing; skills; speech recognition; therapy development; tool; treatment as usual; treatment program

DESCRIPTION (provided by applicant): Why is there such a gap between the evidence base for effective drug treatment and its dissemination in the community? Among behavioral interventions, Motivational Interviewing (MI) should be well positioned for high-impact dissemination: There is an international organization devoted to the training and dissemination of MI (The Motivational Interviewing Network of Trainers [MINT]), and MI developers have been at the forefront of research on dissemination. In addition, MI mechanisms are well specified and many are considered basic building blocks of behavioral interventions (e.g., empathy, use of reflections and open questions).1 Empathy, a central MI component, has a strong research tradition in basic science and is among the most consistent predictors of positive outcome in psychotherapy across treatments and disorders.2 At present, quality control in MI relies on ongoing supervision and feedback to therapists in the community. However, measuring therapist behavior is synonymous with behavioral coding by humans, which is not feasible in community settings3. Accordingly, there is no feasible mechanism for evaluating the practice of evidenced based treatments in clinical settings. Advances in linguistic processing have brought a technology for behavioral coding within reach. Specifically, in the basic science and engineering fields, there are tools that combine acoustic and semantic information into predictive models of human-defined knowledge (e.g., empathy codes).4-8 The current proposal brings together a highly interdisciplinary team to produce a viable technology for generating feedback on therapist empathy without human intervention. This technology may eventually be packaged and disseminated in the community. As an initial step, we will apply a sequence of linguistic processing approaches to develop a process for predicting therapist empathy from audio files obtained from MI sessions (AIM 1).9 Next, we will run a feasibility study to determine the performance of the coding technology with new data obtained from MI sessions conducted by novice and experienced therapists (AIM 2). During this evaluation we will troubleshoot issues related to audio quality and speech recognition as well as the process of feedback. In addition, we will evaluate the agreement of computer-based codes with human coding of new sessions. Drug abuse and dependence represents an incredible societal burden. The current research will develop an innovative process for enhancing the ability disseminate and maintain quality control in the behavioral treatment of addictions.

描述（由申请人提供）：为什么有效药物治疗的证据基础与社区传播之间存在如此差距？在行为干预措施中，应该对高影响力传播的动机访谈（MI）的定位：有一个致力于MI（MINT培训者的动机访谈网络[MINT]）的国际组织，MI开发人员已经成为了传播研究的最前沿。此外，指定了MI机制，许多机制被认为是行为干预措施的基本基础（例如，同理心，反思和开放问题）。1同理心是一种基础科学中的中心研究传统，在基础科学中具有强大的研究传统，并且是治疗界和跨性别范围内质量的跨性别及其质量的跨性别疗法的积极结果的预测指标。但是，测量治疗师的行为是人类行为编码的代名词，这在社区环境中是不可行的3。因此，没有可行的机制来评估临床环境中基于证据的治疗方法的实践。语言处理的进步为行为编码提供了一项技术。具体而言，在基础科学和工程领域中，有一些工具将声学和语义信息结合到人类定义知识的预测模型中（例如，移情代码）。4-8当前的提案将高度跨学科的团队汇集在一起​​，以产生可行的技术，以产生有关治疗疗法同情无人干预的反馈。该技术最终可能会包装并在社区中传播。作为第一步，我们将采用一系列语言处理方法来开发一个过程，以预测从MI会议中获得的音频文件（AIM 1）.9接下来，我们将进行可行性研究，以确定编码技术的性能，并通过从新闻和经验丰富的Thrapists进行的MI会议中获得的新数据（AIM 2）。在此评估中，我们将解决与音频质量和语音识别以及反馈过程有关的问题。此外，我们将评估基于计算机的代码与新课程的人类编码的一致性。滥用药物和依赖代表了令人难以置信的社会负担。当前的研究将开发一个创新的过程，以增强成瘾行为治疗中的能力传播和维持质量控制。