Matthew J Worley1, Melodie Isgro2, Jaimee L Heffner3, Soo Yong Lee4, Belinda E Daniel5, Robert M Anthenelli2. 1. Pacific Treatment and Research Center (Pac-TARC), 3350 La Jolla Village Drive, 116A, San Diego, CA 92161, United States; Department of Psychiatry, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0821, United States. Electronic address: mworley@ucsd.edu. 2. Pacific Treatment and Research Center (Pac-TARC), 3350 La Jolla Village Drive, 116A, San Diego, CA 92161, United States; Department of Psychiatry, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0821, United States. 3. Fred Hutchinson Cancer Research Center, 1100 Fairview Ave N., M3-B232, PO Box 19024, Seattle, WA 98109, United States. 4. Department of Psychiatry, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0821, United States. 5. Pacific Treatment and Research Center (Pac-TARC), 3350 La Jolla Village Drive, 116A, San Diego, CA 92161, United States.
Abstract
INTRODUCTION:Adults with alcohol dependence (AD) have exceptionally high smoking rates and poor smoking cessation outcomes. Discovery of factors that predict reduced smoking among AD smokers may help improve treatment. This study examined baseline predictors of smoking quantity among AD smokers in a pharmacotherapy trial for smoking cessation. METHODS: The sample includes male, AD smokers (N = 129) with 1-32 months of alcohol abstinence who participated in a 12-week trial ofmedication (topiramate vs. placebo) and adjunct counseling with 6 months of follow-up. Baseline measures of nicotine dependence, AD severity, psychopathology, motivation to quit smoking, and smoking-related cognitions were used to predict smoking quantity (cigarettes per day) at post-treatment and follow-up. RESULTS: Overall, the sample had statistically significant reductions in smoking quantity. Greater nicotine dependence (Incidence rate ratios (IRRs) = 0.82-0.90), motivation to quit (IRRs = 0.65-0.85), and intrinsic reasons for quitting (IRRs = 0.96-0.98) predicted fewer cigarettes/day. Conversely, greater lifetime AD severity (IRR = 1.02), depression severity (IRRs = 1.05-1.07), impulsivity (IRRs = 1.01-1.03), weight-control expectancies (IRRs = 1.10-1.15), and childhood sexual abuse (IRRs = 1.03-1.07) predicted more cigarettes/day. CONCLUSIONS: Smokers with AD can achieve large reductions in smoking quantity during treatment, and factors that predict smoking outcomes in the general population also predict greater smoking reductions in AD smokers. Treatment providers can use severity of nicotine dependence and AD, motivation to quit, smoking-related cognitions, and severity of depression to guide treatment and improve outcomes among AD smokers. Published by Elsevier Ltd.
RCT Entities:
INTRODUCTION: Adults with alcohol dependence (AD) have exceptionally high smoking rates and poor smoking cessation outcomes. Discovery of factors that predict reduced smoking among AD smokers may help improve treatment. This study examined baseline predictors of smoking quantity among AD smokers in a pharmacotherapy trial for smoking cessation. METHODS: The sample includes male, AD smokers (N = 129) with 1-32 months of alcohol abstinence who participated in a 12-week trial of medication (topiramate vs. placebo) and adjunct counseling with 6 months of follow-up. Baseline measures of nicotine dependence, AD severity, psychopathology, motivation to quit smoking, and smoking-related cognitions were used to predict smoking quantity (cigarettes per day) at post-treatment and follow-up. RESULTS: Overall, the sample had statistically significant reductions in smoking quantity. Greater nicotine dependence (Incidence rate ratios (IRRs) = 0.82-0.90), motivation to quit (IRRs = 0.65-0.85), and intrinsic reasons for quitting (IRRs = 0.96-0.98) predicted fewer cigarettes/day. Conversely, greater lifetime AD severity (IRR = 1.02), depression severity (IRRs = 1.05-1.07), impulsivity (IRRs = 1.01-1.03), weight-control expectancies (IRRs = 1.10-1.15), and childhood sexual abuse (IRRs = 1.03-1.07) predicted more cigarettes/day. CONCLUSIONS: Smokers with AD can achieve large reductions in smoking quantity during treatment, and factors that predict smoking outcomes in the general population also predict greater smoking reductions in AD smokers. Treatment providers can use severity of nicotine dependence and AD, motivation to quit, smoking-related cognitions, and severity of depression to guide treatment and improve outcomes among AD smokers. Published by Elsevier Ltd.
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