Natural language processing of patient in-session speech to predict brief motivational interviewing alcohol intervention response: an exploratory study.
Background Motivational interviewing (MI) is a patient-centred, goal-oriented psychotherapy for alcohol use disorder (AUD) and numerous other conditions. While some language patterns have been linked to MI response, less is known about how broader linguistic features, sentiment, and engagement relat...
| Publicado en: | Alcohol & Alcoholism Vol. 61; no. 4; pp. 1 - 13 |
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| Autores principales: | , , , , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Oxford University Press / USA
Jul2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=195378072&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 195378072 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 07350414 FA3 jtl: Alcohol & Alcoholism issn: 07350414 maglogo: N pubinfo: dt: Jul2026 vid: 61 iid: 4 pid: 622 pub: Oxford University Press / USA artinfo: ui: 195378072 195378072 195378072 10.1093/alcalc/agag033 195378072 ppf: 1 ppct: 12 formats: tig: atl: Natural language processing of patient in-session speech to predict brief motivational interviewing alcohol intervention response: an exploratory study. aug: au: Elsayed, Mahmoud Belisario, Kyla L Blakely, Ashley Syan, Sabrina K Levitt, Emily Garber, Molly MacKillop, Emily Balodis, Iris Sweet, Lawrence H MacKillop, James affil: Department of Psychiatry and Behavioural Neurosciences, McMaster University, 1280 Main Street West, Hamilton, Ontario, L8S 4L8, CanadaPeter Boris Centre for Addictions Research, St. Joseph's Healthcare Hamilton, 100 West 5th Street, L9C 1G2, Hamilton, Canada sug: subj: Natural Language Processing Speech and Language Assessment Linguistics Motivational Interviewing Psychotherapy, Brief Alcohol-Related Disorders Therapy Treatment Outcomes Funding Source Ontario Human Male Female Adult Middle Age Exploratory Research Patient Centered Care Feedback Alcohol Drinking Sentiment Analysis Emotions Linear Regression Prediction Models Descriptive Statistics Motivation Multivariate Analysis Prospective Studies Audiorecording Scales Interviews Algorithms Paired T-Tests Power Analysis Data Analysis Software Multiple Regression Adult: 19-44 years Middle Aged: 45-64 years Male Female ab: Background Motivational interviewing (MI) is a patient-centred, goal-oriented psychotherapy for alcohol use disorder (AUD) and numerous other conditions. While some language patterns have been linked to MI response, less is known about how broader linguistic features, sentiment, and engagement relate to post-intervention drinking. This study used natural language processing to examine these associations and clarify mechanisms through which MI for AUD exerts its effects. Methods Adults with AUD (N = 68) completed a single MI session with structured feedback and discussion of potential drinking changes. Speech transcripts were analysed for change-, emotion-, motivation-, substance-, and health-related words. Sentiment analysis assessed emotional polarity, and engagement was measured by total words spoken. Linear regression models tested associations between linguistic features and drinking outcomes, including drinks/week, percent heavy drinking days (%HDD), and percent drinking days (%DD). Results Participants showed significant reductions in drinks per week and %DD. Consistent with recent reviews of MI predictors, linguistic analyses found that greater use of change talk and health-related language was associated with more drinks per week at follow-up, whereas greater emotional talk and positive sentiment predicted fewer drinks per week. Health- and substance-related language predicted higher %HDD, while social motivation-related language predicted lower %DD. Term-frequency and multivariate analyses supported these patterns. Conclusions Via natural language processing of MI speech, linguistic features such as motivational content and sentiment were linked to drinking outcomes. Findings demonstrate the potential of this approach as a scalable, data-driven complement to traditional coding systems, with applications for real-time feedback, clinician training, and personalized interventions. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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