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其在社区中的传播之间存在如此大的差距？在行为干预措施中，动机性访谈（MI）应该很好地定位于高影响力的传播：有一个国际组织致力于MI的培训和传播（动机性访谈培训师网络[MINT]），MI开发人员一直处于传播研究的最前沿。此外，MI机制被很好地指定，并且许多被认为是行为干预的基本构建块（例如，同理心是MI的核心组成部分，在基础科学中具有强大的研究传统，并且是跨治疗和障碍的心理治疗中积极结果的最一致的预测因素之一。然而，测量治疗师的行为是人类行为编码的同义词，这在社区环境中是不可行的。因此，没有可行的机制来评估临床环境中基于证据的治疗的实践。语言处理的进步使行为编码技术触手可及。具体地，在基础科学和工程领域中，存在将声学和语义信息联合收割机组合到人类定义的知识的预测模型中的工具（例如，4 -8目前的提案汇集了一个高度跨学科的团队，以产生一种可行的技术，用于在没有人为干预的情况下产生对治疗师同情的反馈。这项技术最终可能会被打包并在社区中传播。作为第一步，我们将应用一系列语言处理方法来开发一个过程，用于从MI会话（AIM 1）获得的音频文件中预测治疗师共情。9接下来，我们将进行一项可行性研究，以确定编码技术的性能，这些编码技术从新手和有经验的治疗师进行的MI会话（AIM 2）获得新数据。在此评估期间，我们将解决与音频质量和语音识别以及反馈过程相关的问题。此外，我们将评估新会话的基于计算机的代码与人类编码的一致性。药物滥用和药物依赖是一个令人难以置信的社会负担。目前的研究将开发一个创新的过程，以提高能力，传播和保持成瘾行为治疗的质量控制。