USING MIXTURE MODELS TO IDENTIFY SMOKING CESSATION PROFILES BASED ON SELF-EFFICACY, POSITIVE EXPECTANCY, MOTIVATION, AND CESSATION FATIGUE: AN EXPLORATORY LATENT PROFILE AND LATENT TRANSITION ANALYSIS

ABSTRACT

Introduction: The addictive nature of tobacco makes smoking cessation a dynamic and challenging process. Smokers who attempt to quit, transition through different phases based on their withdrawal symptoms, motivation, outcome expectancies, and self-efficacy. Relapse risk is determined collectively by these behavioral constructs, and their effect varies over time. Our study aims to identify underlying classes (or profiles) among smokers participating in a pharmacotherapy cessation program based on these constructs. Our interest further lies in examining changes in class membership over 4 weeks since the target quit day (TQD) and its association with long-term abstinence outcomes.

Methods:  We apply Latent Profile Analysis to classify 1086 participating smokers into subgroups based on their responses to Ecological Momentary Assessment (EMA) prompts indicating latent quitting behavior at four different time points. We then determine the changes in membership over time using Latent Transition Analysis (LTA).

Results: A four latent class model solution is selected through a holistic assessment of model fit-statistics (BIC, VLMR LRT p-value, entropy) and interpretability of profiles. The transition probabilities estimated via the LTA model indicate a moderate to high percentage of neutral/motivated subjects tend to improve on their motivation or stick to the same subgroup, and the demotivated subjects transition to improved subgroups over 4 weeks. In addition, the associations between baseline covariates, smoking profiles, the transition trend, and long-term abstinence outcomes were also examined and identified key predictors of relapse risk.

Conclusions: Change in behavioral patterns due to fluctuating withdrawal tendencies and motivational processes during cessation attempts is useful to identify vulnerable subgroups and target interventions to prevent relapse risk in smoking cessation trials.