Using treatment process data to predict maintained smoking abstinence

Abstract

Objectives: To identify distinct subgroups of treatment responders and nonresponders to aid in the development of tailored smoking-cessation interventions for long-term maintenance using signal detection analysis (SDA).

Methods: The secondary analyses (n = 301) are based on data obtained in our randomized clinical trial designed to assess the efficacy of extended cognitive behavior therapy for cigarette smoking cessation. Model 1 included only pretreatment factors, demographic characteristics, and treatment assignment. Model 2 included all Model 1 variables, as well as clinical data measured during treatment.

Results: SDA was successfully able to identify smokers with varying probabilities of maintaining abstinence from end-of-treatment to 52-week follow-up; however, the inclusion of clinical data obtained over the course of treatment in Model 2 yielded very different partitioning parameters.

Conclusions: The findings from this study may enable researchers to target underlying factors that may interact to promote maintenance of long-term smoking behavior change.

Figures

Figure 1. Evaluation of an Optimally Efficient Algorithm to Predict Maintained Smoking Abstinence Using Pretreatment Factors

Notes. * P<.05 p>

Figure 2. Evaluation of an Optimally Efficient Algorithm to Predict Maintained Smoking Abstinence Using Pretreatment and Treatment Process Factors

Notes. The decreased number of participants at subsequent stages of testing is due to missing data. *P<.05>