Numerous clinical trials have shown that Motivational Interviewing (MI;Miller &Rollnick, 2002) is an efficacious treatment for alcohol use disorders (AUD) and related health behavior problems (e.g., Burke, Dunn, Atkins, &Phelps, 2005), but much less is known about the therapy mechanisms of MI (Huebner &Tonigan, 2007). Process research has typically relied on behavioral coding schemes such as the Motivational Interviewing Skills Code (MISC;Miller, Moyers, Ernst, &Amrhein, 2008). Although MI mechanism research with the MISC has produced some of the best data to date (e.g., Moyers et al., 2007), behavioral coding has a number of limitations: 1) it is phenomenally labor intensive, 2) objectivity, reliability, and transportability of coding can be challenging, and 3) it is inflexible (i.e., any new codes require completely new coding). The current proposal brings together a highly interdisciplinary team to develop linguistic processing tools to automate the coding of the MISC and Motivational Interviewing Treatment Integrity (MITI;Moyers, Martin, Manuel, Miller, &Ernst, 2007). The coding of both systems is based on two types of linguistic data: what is said, and how it is said. Our team members in computer science, cognitive science, and electrical engineering are leading researchers in text-mining and speech signal processing, and their methods will be applied to MI transcripts and recordings to automate coding of the MISC/MITI. The core, methodological tool will be topic models (Steyvers &Griffiths, 2007), Bayesian models of semantic knowledge representation. Topic models identify groupings of words that constitute meaning units (or topics), and a recent extension models coded data (e.g., MISC) in which the model learns what specific text is associated with specific tags.
Two specific aims encompass the current proposal: 1) Assess the accuracy of topic models to automatically code the MISC/MITI using transcripts and audiofiles of MI sessions, and 2) Test MI theory (within session and long-term outcome) using approximately 1,167 sessions of MI coded in Aim 1.
These aims will be accomplished using three MI intervention studies: two studies focused on college student drinking and one hospital-based study of drug abuse. The long-term objectives are to use innovative linguistic tools to study therapy mechanisms and develop more efficient systems for collecting psychotherapy process data. Alcohol use disorders continue to represent an incredible societal burden in terms of death, health complications, fractured relationships, and economic costs. The current research will provide innovative tools for studying why therapy works, which in turn can help to ameliorate some of the deleterious effects of AUD.
Research focused on psychotherapy mechanisms of alcohol use disorders (AUD) have often relied upon behavioral observation coding schemes, such as the Motivational Interview Skills Code (MISC), which are time consuming and can present difficulties with reliability. The current, interdisciplinary proposal develops methods for automating behavioral coding through applying recent advances in text-mining and speech signal processing.
|Can, DoÄŸan; MarÃn, Rebeca A; Georgiou, Panayiotis G et al. (2016) "It sounds like...": A natural language processing approach to detecting counselor reflections in motivational interviewing. J Couns Psychol 63:343-50|
|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|
|Holsclaw, Tracy; Hallgren, Kevin A; Steyvers, Mark et al. (2015) Measurement error and outcome distributions: Methodological issues in regression analyses of behavioral coding data. Psychol Addict Behav 29:1031-40|
|Xiao, Bo; Georgiou, Panayiotis; Baucom, Brian et al. (2015) Head Motion Modeling for Human Behavior Analysis in Dyadic Interaction. IEEE Trans Multimedia 17:1107-1119|
|Imel, Zac E; Sheng, Elisa; Baldwin, Scott A et al. (2015) Removing very low-performing therapists: A simulation of performance-based retention in psychotherapy. Psychotherapy (Chic) 52:329-36|
|Mun, Eun-Young; Atkins, David C; Walters, Scott T (2015) Is motivational interviewing effective at reducing alcohol misuse in young adults? A critical review of Foxcroft et al. (2014). Psychol Addict Behav 29:836-46|
|Xiao, Bo; Imel, Zac E; Georgiou, Panayiotis G et al. (2015) "Rate My Therapist": Automated Detection of Empathy in Drug and Alcohol Counseling via Speech and Language Processing. PLoS One 10:e0143055|
|Lord, Sarah Peregrine; Sheng, Elisa; Imel, Zac E et al. (2015) More than reflections: empathy in motivational interviewing includes language style synchrony between therapist and client. Behav Ther 46:296-303|
|Gaut, Garren; Steyvers, Mark; Imel, Zac et al. (2015) Content Coding of Psychotherapy Transcripts Using Labeled Topic Models. IEEE J Biomed Health Inform :|
|Lord, Sarah Peregrine; Can, DoÄŸan; Yi, Michael et al. (2015) Advancing methods for reliably assessing motivational interviewing fidelity using the motivational interviewing skills code. J Subst Abuse Treat 49:50-7|
Showing the most recent 10 out of 21 publications