Project Name: TOPICS
Principal Investigator: David Atkins, PhD
Grant Title: Automating Behavioral Coding via Text-Mining and Speech Signal Processing
Sponsor: National Institute on Alcohol Abuse and Alcoholism
Project Period: 9/1/2010 – 8/31/2015
Grant Number: R01AA018673
Project Coordinator: Rebecca Marín Cordero

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 (Burke, Dunn, Atkins, & Phelps, 2005; Elliott, Carey, & Bolles, 2008; Rubak, Sandbaek, Lauritzen, & Christensen, 2005). However, there is comparatively little information on 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 the related 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 the topic model (Steyvers & Griffiths, 2007), a Bayesian model of linguistic knowledge representation. The topic model identifies groupings of words that constitute meaning units (or topics), and recent extensions have used it with tagged data (e.g., MISC codes) 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 the topic model to automatically code the MISC/MITI using transcripts and voice recordings 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 the therapy mechanisms of MI and in the process, 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.