Predictors of Smoking Cessation in a Lifestyle-Focused Text-Message Support Programme Delivered to People with Coronary Heart Disease: An Analysis From the Tobacco Exercise and Diet Messages (TEXTME) Randomised Clinical Trial

Background: Studies have demonstrated the effectiveness of text message-based prevention programs on smoking cessation, including our recently published TEXTME randomised controlled trial. However, little is known about the predictors of smoking cessation in this context and if other clinically important factors interact with the program to lead to quitting. Hence, the objective of this study was to first assess the predictors of smoking cessation in TEXTME and then determine if the effect of texting on quitting was modified by interactions with important clinical variables. This will allow us to better understand how text messaging works and thus help optimise future text-message based prevention programs. Methods This sub-analysis used data collected as part of the TEXTME trial which recruited 710 participants (377 current smokers at baseline) between September 2011 and November 2013 from a large tertiary hospital in Sydney, Australia. Smokers at baseline were analysed at 6 months and grouped into those who quit and those who did not. Univariate analyses were performed to determine associations between the main outcome and clinically important baseline factors selected a priori. A multiple binominal logistic regression analysis was conducted to develop a predictive model for the dependent variable smoking cessation. A test of interaction between the intervention group and baseline variables selected a priori with the outcome smoking cessation was performed. Results Univariate analysis identified receiving text-messages, age, and mean number of cigarettes smoked each day as being associated with quitting smoking. After adjusting for age, receiving the text-messaging program (OR 2.34; 95%CI 1.43-3.86; p<0.01) and mean number of cigarettes smoked per day (OR 1.02; 95%CI 1.00-1.04; p=0.03) were independent predictors for smoking cessation. LDL-C showed a significant interaction effect with the intervention (High LDL*Intervention OR 3.77 (95%CI 2.05-6.94); Low LDL*Intervention OR 1.42 (95%CI 0.77-2.60); P=0.03). Conclusions Smoking quantity at baseline is independently associated with smoking cessation and higher LDL-C may interact with the intervention to result in quitting smoking. Those who have a higher baseline risk maybe more motivated towards beneficial lifestyle change including quitting smoking, and thus more likely to respond to mHealth smoking cessation programs. The effect of text-messages on smoking cessation was independent of age, gender, psychosocial parameters, education, and baseline control of risk factors in a secondary prevention cohort.