# Automatic Coding of Therapist and Client Language in Motivational Interviewing to Predict Reductions in Alcohol Use and Problems Using Machine-based Dyadic Multimodal Representation Learning

> **NIH NIH R01** · UNIVERSITY OF SOUTHERN CALIFORNIA · 2020 · $554,280

## Abstract

Despite the widespread use of Motivational Interviewing (MI), the underlying mechanisms of its success
are still poorly understood [14], especially the link between client change talk and subsequent behavior
change [14,69]. Previous research has identified two possible active components underlying MI efficacy:
a relational component involving elements of the therapist-client dyad including the expression of
empathy, and a technical component focused on the differential evocation of client behaviors such as
change talk or what a client says about their commitment to change [57]. Current analyses of these
components are limited to investigations pertaining to language only and restricted by expensive and
arduous manual coding which, despite the time and efforts expended to achieve reliability, may still not
be sufficiently sensitive or specific to adequately test the complex theoretical propositions espoused by
MI theorists.
Our project will address shortcomings of current MI coding systems by introducing a novel
computational framework that leverages our recent advances in automatic verbal and nonverbal behavior
analyses as well as multimodal machine learning. Our framework aims to jointly analyze verbal (i.e.,
what is being said), nonverbal (i.e., how something is said), and dyadic (i.e., in what interpersonal context
something is said) behavior to better identify in-session change talk and sustain talk that is predictive of
post-session alcohol use. We will leverage already collected and annotated audio data from two NIAAA-
funded single-session MI randomized clinical trials to improve drinking behavior (N=91; N=158). We
will disseminate our findings through an extensive collection of client and dyadic behaviors through our
proposed Client and Dyadic Behavior Databases. In addition, we will validate the generalizability of our
computational framework using seven additional NIAAA- and federally funded RCTs that used different
MI protocols for different target populations.

## Key facts

- **NIH application ID:** 10001411
- **Project number:** 5R01AA027225-02
- **Recipient organization:** UNIVERSITY OF SOUTHERN CALIFORNIA
- **Principal Investigator:** Stefan Scherer
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $554,280
- **Award type:** 5
- **Project period:** 2019-09-01 → 2023-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10001411

## Citation

> US National Institutes of Health, RePORTER application 10001411, Automatic Coding of Therapist and Client Language in Motivational Interviewing to Predict Reductions in Alcohol Use and Problems Using Machine-based Dyadic Multimodal Representation Learning (5R01AA027225-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10001411. Licensed CC0.

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