Telephone-Based Self-Change Modules Help Stabilize Early Natural Recovery in Problem Drinkers

Abstract

ABSTRACT. Objective: Self-guided attempts to resolve drinking problems are common, but little is known about the processes by which supportive interventions of lower intensity might promote resolution. This study investigated how brief supportive educational modules delivered as part of an interactive voice response self-monitoring (IVR SM) system helped stabilize initial resolution among otherwise untreated problem drinkers. Method: Recently resolved problem drinkers allocated to the intervention group of a randomized controlled trial were offered IVR access for 24 weeks to report daily drinking and hear weekly educational modules designed to support resolution. Using data from the 70 active IVR callers, hierarchical linear models evaluated whether module retrieval reduced subsequent alcohol consumption, including high-risk drinking, and whether module retrieval attenuated the effects on drinking of established proximal risk factors for relapse (e.g., urges, drug use, and weekends). The analyses controlled for initial resolution status (abstinence or low-risk drinking). Results: Urges, drug use, and weekends were associated with increased drinking reports on the next IVR call (all ps < .01), whereas retrieving a module was associated with decreases in next-call drinking reports, including high-risk drinking episodes (p < .05). Module retrieval, however, did not reduce or buffer the effects of urges on drinking. Findings were similar across initially abstinent and low-risk drinkers. Conclusions: IVR-delivered supportive educational modules may help stabilize initial problem-drinking resolutions, but mechanisms of change deserve more study. The study adds to evidence of the co-occurring negative effects of multiple behavioral and environmental risk factors on the temporal patterning of post-recovery alcohol use. (J. Stud. Alcohol Drugs, 74, 902-908, 2013)

The behavioral economics of young adult substance abuse

“…Personalized substance use feedback may help to accomplish this perspective shift; alcohol/drug use decisions are aggregated to form meaningful tallies, like instances of drug use per week, money spent on drug use during a month/year, and rates of drug use relative to peers. However, a key and unique implication of behavioral economic theory is that interventions should encourage individuals to view their day-to-day decisions and activities (both substance-related and substance-free) as cohesive patterns that have implications for long-term substance-free rewards (Schroeder, Tucker, & Simpson, 2013). …”

“…The SFAS may be especially helpful for individuals with psychiatric comorbidity, which is often associated with elevated alcohol reward value (Murphy et al, 2013) and diminished engagement in rewarding alternatives to substance use (Lejuez et al, 2011). Finally, given the goal of developing long term and consistent patterns of substance-free activities, research is needed to develop technology-enhanced elements that can be delivered remotely over time (e.g., Schroder et al, 2013). …”

Utilization, receptivity and reactivity to Interactive Voice Response daily monitoring in risky drinking smokers who are motivated to quit

“…The potential for missing data and lack of compliance with IVR monitoring are two limitations that can reduce statistical power and affect estimation bias 11,12 . Compliance to IVR protocols is important as it has been shown to positively affect treatment outcomes and improve treatment and medication adherence for a variety of health behaviors 13-15 . For studies with drinkers or smokers, compliance rates range significantly from 46% to over 90% 11,15-18 .…”

“…Compliance to IVR protocols is important as it has been shown to positively affect treatment outcomes and improve treatment and medication adherence for a variety of health behaviors 13-15 . For studies with drinkers or smokers, compliance rates range significantly from 46% to over 90% 11,15-18 . Compliance may be related to the timing of assessments, the amount of time between assessments, the number of assessments per day, and the type of behaviors being assessed.…”