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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

Award Number: R01AA027225
ORGANIZATION: NATIONAL INSTITUTE ON ALCOHOL ABUSE AND ALCOHOLISM
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

Group Awards By Issue Date FY or Funding FY:

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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.


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2022 ( Subtotal = $519,211 )
2022	2022	UNIVERSITY OF SOUTHERN CALIFORNIA	3720 S FLOWER ST FL 3	LOS ANGELES	CA	90007	LOS ANGELES	USA	Alcohol Research Programs	000	4	8/19/2022	NON-COMPETING CONTINUATION	$519,211
														Subtotal = $519,211

Issue Date FY: 2021 ( Subtotal = $554,199 )
2021	2021	UNIVERSITY OF SOUTHERN CALIFORNIA	3720 S FLOWER ST FL 3	LOS ANGELES	CA	90007	LOS ANGELES	USA	Alcohol Research Programs	000	3	8/23/2021	NON-COMPETING CONTINUATION	$554,199
														Subtotal = $554,199

Issue Date FY: 2020 ( Subtotal = $554,280 )
2020	2020	UNIVERSITY OF SOUTHERN CALIFORNIA	3720 S FLOWER ST FL 3	LOS ANGELES	CA	90007	LOS ANGELES	USA	Alcohol Research Programs	000	2	8/12/2020	NON-COMPETING CONTINUATION	$554,280
														Subtotal = $554,280

Issue Date FY: 2019 ( Subtotal = $573,135 )
2019	2019	UNIVERSITY OF SOUTHERN CALIFORNIA	3720 S FLOWER ST FL 3	LOS ANGELES	CA	90007	LOS ANGELES	USA	Alcohol Research Programs	000	1	8/23/2019	NEW	$573,135
														Subtotal = $573,135

Grand Total All Awards = $2,200,825

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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

Award Number: R01AA027225

ORGANIZATION: NATIONAL INSTITUTE ON ALCOHOL ABUSE AND ALCOHOLISM

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

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