Engagement With Motivational Interviewing and Cognitive Behavioral Therapy Components of a Web-Based Alcohol Intervention, Elicitation of Change Talk and Sustain Talk, and Impact on Drinking Outcomes: Secondary Data Analysis

Abstract

Background Down Your Drink (DYD) is a widely used unguided web-based alcohol moderation program for the general public based on cognitive behavioral therapy (CBT) and motivational interviewing (MI); it provides users with many opportunities to enter free-text responses. Objective The aim of this study was to assess participants’ use of key CBT and MI components, the presence of change talk and sustain talk within their responses, and whether these data are associated with drinking outcomes after 3 months. Methods An exploratory secondary data analysis was conducted on data collected in 2008 from the definitive randomized trial of DYD (N=503). Past week alcohol use at baseline and 3-month follow-up were measured with the TOT-AL. Covariates included baseline alcohol use, age, gender, education level, and word count of the responses. Use of MI and CBT components and presence of change talk and sustain talk were coded by two independent coders (Cohen κ range 0.91-1). Linear model regressions on the subsample of active users (n=410) are presented along with a negative binomial regression. Results The most commonly used component was the listing of pros and cons of drinking. The number of listed high-risk situations was associated with lower alcohol use at 3-month follow-up (Badj −2.15, 95% CI −3.92 to −0.38, P=.02). Findings on the effects of the percentage of change talk and the number of listed strategies to deal with high-risk situations were inconsistent. Conclusions An unguided web-based alcohol moderation program can elicit change talk and sustain talk. This secondary analysis suggests that the number of listed high-risk situations can predict alcohol use at 3-month follow-up. Other components show inconsistent findings and should be studied further.

Seeking care where they can: A systematic review of global trends in online alcohol treatment utilization among non-veteran and veteran women

“…Importantly, while the field is indebted to Riper et al. (2018) for the person-level meta-analysis that allowed us to see that overall women do not appear to be benefiting as much as men from online alcohol interventions, overall only 11% of the available studies and only one study published nearly contemporaneously ( Tait et al., 2019 ) have evaluated whether gender interacts with treatment condition to influence outcomes while none of the eight studies published since 2020 have done so ( Augsburger et al., 2022 ; Baumgartner et al., 2021 ; Mujcic et al., 2020 ; Sanatkar et al., 2021 ; Schaub et al., 2021 ; Stapinski et al., 2021 ; Sundström et al., 2020a , 2020b ). Thus, it would be very helpful if future investigations of online alcohol interventions tested gender x time x condition interactions and reported gender-specific baseline and follow-up outcome information as would such secondary analyses of extant trials.…”

A Change Talk Model for Abstinence Based on Web-Based Anonymous Gambler Chat Meeting Data by Using an Automatic Change Talk Classifier: Development Study

“…Despite these limitations, this study developed the change talk model for gambling abstinence in web-based anonymous gambler chat meetings with the change talk classifier. The change talk classifier used in this study increased the data size of psychotherapies [6,12,13,18,[44][45][46][47] and broadened the applicability of language analysis on social media for mental health services [42,43]. The change talk model for abstinence used in this study can provide personalized evaluation scores for abstinence and an ideal proportion of change talks to the gamblers responding to every utterance during the meetings.…”

“…Recent studies have shown that the abstinence process in anonymous self-help group meetings and standardized psychotherapies is similar [ 4 ], and the key factor in both treatments is to motivate participants to cease their addictive behaviors [ 5 ]. Studies have noted that individuals’ motivation can be measured by classifying their utterances during therapy at individual web-based [ 6 ] and offline group settings [ 7 ]. Further, their utterances can be automatically classified using a machine-learned classifier [ 8 ].…”

“…The change and sustain talk classifications were primarily utilized for face-to-face motivational interviewing therapy settings [ 16 , 21 - 23 ]; however, recent studies have applied the classification to other standardized individual therapies [ 24 ], group therapies [ 7 ], and web-based therapies [ 6 ]. These findings indicate that the classifications can be applied to web-based anonymous gambler chat meetings.…”

Predicting Success of a Digital Self-Help Intervention for Alcohol and Substance Use With Machine Learning

“…Cognitive Behavioral Therapy (CBT) and motivational interviewing (MI) based digital self-help interventions have been developed to help treat people suffering from diverse conditions including problem drinking ( Riper et al, 2008 ; Mujcic et al, 2020 ), tobacco smoking ( Mujcic et al, 2018 ), and cannabis ( Olthof et al, 2021 ). Some central elements from CBT self-help interventions are: exploring and exploiting ambiguity regarding behavior change, stimulus control, stress management, social support, goal setting and pursuit through monitoring and exercises ( Foreyt and Poston, 1998 ).…”