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Updated: Oct 29, 2025

The Motivation for Alcohol Reward: Predictors of Progressive-Ratio Intravenous Alcohol Self-Administration in Humans
Published on: April 28, 2022
Is the motivation to quit smoking a predictor of abstinence maintenance?
José I de Granda-Orive1, José F Pascual-Lledó2, Santos Asensio-Sánchez2
112 of October University Hospital, Complutense University of Madrid, Madrid, Spain.
Introduction:
The aim of this study was to explore the utility of measuring motivation to quit smoking as a predictor of abstinence maintenance among smokers who wanted to quit and who were included in a multicenter study conducted in daily clinical practice.
Methods:
This observational, longitudinal (prospective cohort), multicenter study was conducted in smoking clinics in Spain and the Argentine Republic in daily clinical practice. Motivation was assessed using three quantitative motivation tests and a Visual Analogue Scale. Statistical analysis included descriptive, association measures and logistic regression models.
Results:
Of a total of 404 subjects, 273 were ultimately included for analysis (147 women; 53.8%), mean age 51±11 years). In one year, 53.5% (36.13% by intention to treat) of subjects (146) were successful in quitting smoking [men: 45.2% (66) and women: 54.8% (80)], with no differences between sexes. None of the scales utilized was associated, in an unquestionable or direct way, with long-term abstinence, although three of them, in a very complex model, with additional variables and added interactions, were associated with the 'result' variable, when other variables intervened in certain circumstances.
Conclusions:
None of the analyzed motivational scales alone demonstrated an association with success or failure in quitting smoking; thus, their use in isolation is of no value. Some of the scales analyzed might be related to the maintenance of abstinence but in complex models where other variables intervene, which makes interpretation considerably difficult. Therefore, the predictive capacity of the tests analyzed, based on the models, was low.
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