Statistical analysis
RS
REGRESSION, PMT
We based the sample size calculation on the formula for a two-arm RCT. To achieve 80% power with a significance level of 0.05 (two sided), we required a sample size of 280 individuals in each group. Assuming 20% attrition in the follow-up measurements, we set the required sample size at 672, as reported elsewhere [We used descriptive statistics to compare the baseline status between the two groups, after which we conducted an intention-to-treat (ITT) analysis and the Russell Standard (RS) for each group. All randomised subjects were included in the denominator for calculating abstinence rates, except for unavoidable loss to follow-up (died or moved to an untraceable address). Those who declined to be involved in subsequent data collection were counted as smokers [To evaluate the impact on reducing the frequency of smoking (the amount of smoking), we first excluded non-daily smokers at baseline and participants who reached biochemically verified six-month sustained abstinence. Then, we identified participants who changed smoking behaviour from daily to weekly and remained weekly smokers (who had continuously reduced the smoking amount or reduced to a lower level and maintained the lower level) until all the follow-up visits were completed and categorised them as “changers”. We used descriptive statistics and χFinally, we used generalised estimating equations (GEEs) to estimate the psychological determinants of reducing the frequency of smoking and reducing the smoking amount to determine why the intervention group had a more positive change. In the first set of models, we set a dummy variable indicating whether the respondent reduced the frequency of smoking compared to the last follow-up visit (if yes = 1, otherwise = 0) as the dependent and the PMT construct scores as the independent variable, controlling for education level, living area, nicotine dependence, and monthly income. We chose an autocorrelation structure for the matrix structure and logistic regression for the model setting. In the second set of models, we changed the dependent variable to whether the respondent reduced their smoking amount compared to the last follow-up visit (if yes = 1, otherwise = 0). The results are presented as ORs and 95% CIs. We performed all analyses in SPSS, version 11.0 (SPSS, Chicago, Illinois, USA).