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.

Public Health Relevance

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.

Agency
National Institute of Health (NIH)
Institute
National Institute on Alcohol Abuse and Alcoholism (NIAAA)
Type
Research Project (R01)
Project #
5R01AA018673-03
Application #
8318917
Study Section
Special Emphasis Panel (ZRG1-BBBP-D (52))
Program Officer
Falk, Daniel
Project Start
2010-09-01
Project End
2015-08-31
Budget Start
2012-09-01
Budget End
2013-08-31
Support Year
3
Fiscal Year
2012
Total Cost
$569,562
Indirect Cost
$42,533
Name
University of Washington
Department
Psychiatry
Type
Schools of Medicine
DUNS #
605799469
City
Seattle
State
WA
Country
United States
Zip Code
98195
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
Gupta, Rahul; Audhkhasi, Kartik; Jacokes, Zach et al. (2018) Modeling multiple time series annotations as noisy distortions of the ground truth: An Expectation-Maximization approach. IEEE Trans Affect Comput 9:76-89
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
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
Gupta, Rahul; Audhkhasi, Kartik; Lee, Sungbok et al. (2016) Detecting paralinguistic events in audio stream using context in features and probabilistic decisions. Comput Speech Lang 36:72-92
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:

Showing the most recent 10 out of 31 publications