Predictive Factors of Stages of Change in Hookah Smoking Cessation Among Iranian Adults Based on the Transtheoretical Model

Document Type : Original Article


1 Department of Health Education and Health Promotion, Faculty of Health, Bushehr University of Medical Sciences, Bushehr, Iran

2 Department of Epidemiology and Biostatistics, Faculty of Health, Bushehr University of Medical Sciences, Bushehr, Iran

3 Department of Health Education and Health Promotion, Bushehr University of Medical Sciences, Bushehr, Iran



Background: Hookah, as a traditional method of smoking, is widely used in Iran, especially in Bushehr province. It is essential to 
identify the most important determinants of modifying hookah smoking behavior. This study aimed to investigate the predictors of the stages of change in quitting hookah smoking in 15-60-year-old individuals in Bushehr province, southern Iran, based on the 
transtheoretical model (TTM).  
Methods: This descriptive-analytical study was conducted on 1173 Hookah smokers in Bushehr province. The samples were selected by two-stage random sampling from 10 cities. Data were collected using a valid and reliable questionnaire consisting of 5 sections (demographic characteristics, stages of change, processes of change, decisional balance, and self-efficacy). Data were analyzed by R version.3.3.1 using analysis of variance and ordinal logistic regression at a significant level of 0.05. 
Findings: The data revealed 82% of the participants were in the preparatory phase (55.3% in pre-contemplation and 26.7% in 
contemplation stages). Marital status, family members smoking hookah, cigarette smoking, level of education, number of family 
members, number of quitting attempts, self-efficacy, self-reevaluation, counter-conditioning, reinforcement management, and 
stimulus control were predictors of quitting hookah smoking.
Conclusion: Given that most study participants were in the inactive stages of quitting hookah smoking, it seems necessary to design and implement behavioral interventions based on the predictive TTM constructs in this population. 


Adel Moqaddas: (Google Scholar) (PubMed)

Mahnoush Reisi: (Google Scholar) (PubMed)

Marzieh Mahmoodi: (Google Scholar) (PubMed)

Homamodin Javadzade: (Google Scholar) (PubMed)


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