Women with alcohol dependence: A randomized trial of couple versus individual plus couple therapy.

Abstract

Couple therapy for women with alcohol use disorders (AUDs) yields positive drinking outcomes, but many women prefer individual to conjoint treatment. The present study compared conjoint cognitive behavioral therapy for women with AUDs to a blend of individual and conjoint therapy. Participants were 59 women with AUDs (95% Caucasian, mean age = 46 years) and their male partners randomly assigned to 12 sessions of Alcohol Behavioral Couple Therapy (ABCT) or to a blend of five individual CBT sessions and seven sessions of ABCT (Blended-ABCT). Drinking and relationship satisfaction were assessed during and for one year post-treatment. Treatment conditions did not differ significantly on number of treatment sessions attended, percent of drinking days (PDD), or heavy drinking days (PDH), during or in the 12 months following treatment. However, effect size estimates suggested a small to moderate effect of Blended-ABCT over ABCT in number of treatment sessions attended, d=−.41, and first- and second-half within treatment PDD, d=−.41, d=−.28, and PDH, d=−.46, d=−.38. Moderator analyses found that women lower in baseline sociotropy had lower PDH across treatment weeks 1–8 in Blended-ABCT than ABCT and that women lower in self-efficacy had lower PDH during follow-up in Blended-ABCT than ABCT. The two treatment groups did not differ significantly in within-treatment or post-treatment relationship satisfaction. Results suggest that blending individual and conjoint treatment yields similar or slightly better outcomes than ABCT, is responsive to women’s expressed desire for individual sessions as part of their treatment, and decreases the challenges of scheduling conjoint sessions.

Feasibility of intervention in elder self-neglecters: Setting the stage for future research

“…During the study, the withdrawal rates did not seem to be related to the design or intervention since there were equal dropouts in the control, treatment, and crossover groups. Despite the longevity of the study, overall dropout rates were less than 30%, which is a lower attrition rate when compared with other interventional studies in vulnerable populations such as older adults with psychoses (24) and substance abuse (25). This attrition rate also suggests that oversampling by 30% when conducting similar studies in elder self-neglecters may be needed.…”

Moderators of sudden gains after sessions addressing emotion regulation among women in treatment for alcohol use

“…In one RCT (Study 1, total n =158 women), participants chose either an individual or couple CBT study arm, within which they were then randomized to one of two conditions (McCrady, Epstein, Hallgren, Cook, & Jensen, 2016). For the current secondary analyses, we drew data from women who were randomized to the individual FS-CBT condition ( n =35; other conditions were not female-specific).…”

“…At this baseline assessment, the participants met with a trained research assistant, completing additional self-report questionnaires and interviews to establish baseline measures of the outcome variables. They then were randomized to treatment condition (see parent RCT references for additional details, Epstein et al, under review; McCrady et al, 2016). …”

“…Two randomized clinical trials (RCTs) (McCrady, Epstein, Hallgren, Cook, & Jensen, 2016; Epstein et al, under review) from which data were drawn for the current study provided a unique opportunity to examine moderators of change after particular treatment sessions. In the clinical trials, specific manual-guided interventions were administered consistently in each session, across participants, so that the effect of specific interventions (i.e., identifying and reducing depression/anxiety symptoms in sessions five and six) on drinking in the following days and weeks could be examined (Epstein & McCrady, 2009).…”

Aggregating and Analyzing Daily Drinking Data in Clinical Trials: A Comparison of Type I Errors, Power, and Bias

“…Such methods yield daily drinking data, but there is no consensus on how to aggregate these data into summary measures, such as percentage of drinking days (PDD) and percentage of heavy drinking days (PHDD). For example, TLFB data could be represented as a single PDD or PHDD estimate over the full period of interest (e.g., O'Malley et al, 2015;Ponizovsky et al, 2015), aggregated into repeated measures over weekly or monthly intervals (e.g., Anton et al, 2006;Project MATCH Research Group, 1997), or left completely disaggregated as daily binary indicators of drinking and heavy drinking (e.g., DeSantis et al, 2013;McCrady et al, 2016).…”