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.
Currently, there is no objective/scalable method for evaluating the practice of evidence- based treatments like Motivational Interviewing (MI) in clinical settings Computer-based assessments of MI fidelity have the potential to scale-up in such a manner that the provision of performance-based feedback to therapists in the community is feasible. The current proposal will apply linguistic processing approaches to develop and test the feasibility of an initial technology for coding a central component of MI - provider empathy and its specific behavioral indicators. !
| Hirsch, Tad; Soma, Christina; Merced, Kritzia et al. (2018) ""It's hard to argue with a computer:"" Investigating Psychotherapists' Attitudes towards Automated Evaluation. DIS (Des Interact Syst Conf) 2018:559-571 |
| Caperton, Derek D; Atkins, David C; Imel, Zac E (2018) Rating motivational interviewing fidelity from thin slices. Psychol Addict Behav 32:434-441 |
| Hallgren, Kevin A; Dembe, Aaron; Pace, Brian T et al. (2018) Variability in motivational interviewing adherence across sessions, providers, sites, and research contexts. J Subst Abuse Treat 84:30-41 |
| Hirsch, Tad; Merced, Kritzia; Narayanan, Shrikanth et al. (2017) Designing Contestability: Interaction Design, Machine Learning, and Mental Health. DIS (Des Interact Syst Conf) 2017:95-99 |
| Imel, Zac E; Caperton, Derek D; Tanana, Michael et al. (2017) Technology-enhanced human interaction in psychotherapy. J Couns Psychol 64:385-393 |
| Pace, Brian T; Dembe, Aaron; Soma, Christina S et al. (2017) A multivariate meta-analysis of motivational interviewing process and outcome. Psychol Addict Behav 31:524-533 |
| Gaut, Garren; Steyvers, Mark; Imel, Zac E et al. (2017) Content Coding of Psychotherapy Transcripts Using Labeled Topic Models. IEEE J Biomed Health Inform 21:476-487 |
| Xiao, Bo; Huang, Chewei; Imel, Zac E et al. (2016) A technology prototype system for rating therapist empathy from audio recordings in addiction counseling. PeerJ Comput Sci 2: |
| Xiao, Bo; Imel, Zac E; Georgiou, Panayiotis et al. (2016) Computational Analysis and Simulation of Empathic Behaviors: a Survey of Empathy Modeling with Behavioral Signal Processing Framework. Curr Psychiatry Rep 18:49 |
| Tanana, Michael; Hallgren, Kevin A; Imel, Zac E et al. (2016) A Comparison of Natural Language Processing Methods for Automated Coding of Motivational Interviewing. J Subst Abuse Treat 65:43-50 |
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