Quitting smoking, cognitive behavioral therapy and differential profiles with decision trees

Abstract

The aim of this study is to analyse if gender, nicotine dependence, and emotional variables (anxiety, depression, and anger) help to describe a patient profile that could benefit from a cognitive behavioral therapy (CDT) to quit tobacco addiction. The sample consisted of 120 adult smokers who voluntarily received the CBT. Decision trees were used to assess patients' treatment adherence and program success. Data showed that just programme adherence implied a high success probability (86.4%), increasing to 95.6% when participants showed a high anger response. Besides, treatment adherence was 100% when anxiety in an evaluative context, physiologic anxiety, and motivation were high. Finding these differential profiles would help to determine the patient profile that would benefit most from treatment, and would increase their effectiveness.