Data analysis
REGRESSION
We conducted the data analysis in three steps. First, we used descriptive statistics to report smoking abstinence rates and used Second, we analysed the change in the scores on the protection motivation theory components, by group, to evaluate the effect of our intervention in strengthening awareness about quitting. We used generalized estimated equations to analyse the data with repeated follow-up times.Third, we analysed the association between participants’ scores on the protection motivation theory components and their continuous abstinence behaviour to explore why the intervention group reported a higher quitting rate. We changed the dependent variable to a dummy variable indicating whether the respondent remained abstinent during the follow-up period (if yes = 1; otherwise = 0). The explanatory variables were the protection motivation theory component scores. We used the second generalized estimated equations to assess which components were associated with sustained abstinence to further explore the reason for the higher sustained abstinence rate in the intervention group. We chose an exchangeable correlation structure for the matrix structure, and binary logistic regression for the model setting. We conducted all statistical analyses using SPSS, version 19.0 (Armonk, IBM, United States of America). A 