AI Article Synopsis

Article Abstract

Introduction: The present study aims to investigate the dimensionality of the brief version of the Wisconsin Inventory of Smoking Dependence Motives (B-WISDM) and identify different smoking motivational profiles among young daily smokers (N = 375).

Methods: We tested 3 measurement models of the B-WISDM using confirmatory factor analysis, whereas cluster analysis was used to identify the smokers' motivational profiles. Furthermore, we compared clusters toward dependence level and the number of cigarettes smoked per day using analysis of variance tests.

Results: The results confirmed that the B-WISDM measures 11 first-order intercorrelated factors. The second-order model, originally proposed for the longer version of the questionnaire, showed adequate fit indices but fitted the data significantly worse than the first-order model. Five motivational clusters were identified and differed in terms of tobacco addiction and the number of cigarettes smoked per day. Although each cluster had specific features, 2 main smoker groups were distinguished: Group A (composed of 3 clusters), which was mainly characterized by high levels of secondary dependence motives, and Group B (composed of 2 clusters), in which the primary and secondary dependence motives reached similar levels. In general, the clusters of Group B were more addicted to cigarettes than Group A clusters.

Conclusions: Using the B-WISDM to identify different smoking motivational profiles has important practical implications because they might help characterize addiction, which represents the first step to help an individual quit smoking.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4832969PMC
http://dx.doi.org/10.1093/ntr/ntu143DOI Listing

Publication Analysis

Top Keywords

dependence motives
16
motivational profiles
12
wisconsin inventory
8
inventory smoking
8
smoking dependence
8
young daily
8
daily smokers
8
b-wisdm identify
8
identify smoking
8
smoking motivational
8

Similar Publications

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!