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Using Treatment Process Data to Predict Maintained Smoking Abstinence

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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.

Keywords: cigarette smoking; maintained abstinence; signal detection analysis

Document Type: Research Article

DOI: https://doi.org/10.5993/AJHB.34.6.14

Affiliations: 1 Research Associate, Stanford University School of Medicine, Stanford Prevention Research Center, Palo Alto, CA.

Publication date: 2010-11-01

More about this publication?
  • The American Journal of Health Behavior seeks to improve the quality of life through multidisciplinary health efforts in fostering a better understanding of the multidimensional nature of both individuals and social systems as they relate to health behaviors.

    The Journal aims to provide a comprehensive understanding of the impact of personal attributes, personality characteristics, behavior patterns, social structure, and processes on health maintenance, health restoration, and health improvement; to disseminate knowledge of holistic, multidisciplinary approaches to designing and implementing effective health programs; and to showcase health behavior analysis skills that have been proven to affect health improvement and recovery.

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