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Predicting Participation in and Success of a Concurrent Smoking Cessation Program during Inpatient Treatment for
Ulrich Frischknecht1, Toni Pätz, Iris Reinhard
1German Institute of Addiction and Prevention Research, Catholic University of Applied Sciences NRW, Wörthstraße 10, 50668 Köln, Germany, u.frischknecht@katho-nrw.de.
Background:
Predicting participation in and success of smoking cessation programs in alcohol dependent patients has yielded heterogeneous results. Moreover, these findings have rarely been based on prospective studies from clinical routine settings. Identifying predictors in prospective studies could help to tailor interventions that increase participation and success rates of smoking cessation therapies for these patients at a high risk for alcohol- and smoking-related morbidities and mortalities.
Subjects And Methods:
During inpatient alcohol dependence treatment, 99 nicotine dependent patients were recruited. 73 patients chose to participate in a voluntary smoking cessation program. Interviews and questionnaires were used at baseline and at discharge to assess a large set of variables covering smoking and alcohol related factors, general psychopathology, quality of life and personality traits. Multiple logistic regression models were calculated to predict participation in the smoking cessation program and smoking abstinence at follow-up three months after discharge.
Results:
Participation in the smoking cessation program was predicted by higher stage of change, higher confidence in abstaining from smoking and lower perceived stress. Successful smoking cessation at follow-up was predicted by higher expectations of negative physical feelings due to smoking and lower expectations of temptations to smoke at baseline, and by lower number of daily smoked cigarettes at discharge.
Conclusion:
Despite the small sample size, this prospective study gives a first indication of clinically relevant predictors of participation in and success of a smoking cessation program by exploring many previously reported predictors simultaneously. The findings and their implications for treatment allocation and optimization are discussed.
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