The proposed research is designed to identify optimal cessation intervention components across three phases of the cessation process: Precessation, Pericessation, and Maintenance. Project 1 comprises two experiments that are based on the Center-wide intervention optimization approach that relies upon factorial designs and sequential experiments to efficiently engineer and evaluate interventions (Collins et al., 2005, 2007).
The specific aim of Experiment 1 a is to determine the optimal intervention strategies to use in the first 2 weeks following the quit day (the Pericessation Phase). Three factors will be tested in a factorial design: varenicline vs. placebo pill;clinician cessation counseling (minimal vs. intensive);and phone counseling (minimal vs. intensive). Experiment 1 a will determine which combination of these components produces optimal effects on measures of mechanism and outcome. The placebo pill condition allows for the demonstration of varenicline efficacy and permits the examination of intervention mechanisms.
The specific aim of Experiment 1b is to examine Precessation and Maintenance Phase (postquit week 3 to 6 months) interventions, when used in combination with the optimal Pericessation strategy identified from Experiment 1a. Four factors will be tested in a factorial design: extended (3 weeks) Precessation varenicline vs. standard 1 week of Precessation varenicline;enhanced Precessation counseling (3 sessions) vs. minimal Precessation counseling (one session);extended Maintenance use of varenicline (25 weeks) vs. standard 13 weeks of varenicline;and extended counseling (5 phone sessions) vs. no additional counseling. Experiment 1 b will determine which of these Precessation and Maintenance intervention components produce optimal effects on measures of mechanism and outcome. The ultimate goal of this research is to engineer an optimal, comprehensive smoking cessation intervention package. The best Precessation, Pericessation and Maintenance intervention components will be selected based upon an appraisal of statistical significance, effect size, cost-effectiveness and other evaluative criteria.

Public Health Relevance

Project 1 will engineer an optimal smoking cessation intervention that is effective, translatable and cost-effective. Using a phase-based model of smoking cessation, investigators will identify efficacious medication and counseling interventions that address the different quitting challenges encountered prior to quitting, early in the quit attempt and in the long-term while individuals attempt to maintain abstinence.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Specialized Center (P50)
Project #
5P50CA143188-15
Application #
8539303
Study Section
Special Emphasis Panel (ZDA1-RXL-E)
Project Start
Project End
Budget Start
2013-09-01
Budget End
2014-08-31
Support Year
15
Fiscal Year
2013
Total Cost
$340,707
Indirect Cost
$77,071
Name
University of Wisconsin Madison
Department
Type
DUNS #
161202122
City
Madison
State
WI
Country
United States
Zip Code
53715
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Cook, Jessica W; Collins, Linda M; Fiore, Michael C et al. (2016) Comparative effectiveness of motivation phase intervention components for use with smokers unwilling to quit: a factorial screening experiment. Addiction 111:117-28
Zhang, Xiao; Martinez-Donate, Ana P; Kuo, Daphne et al. (2016) Beyond cigarette smoking: smoke-free home rules and use of alternative tobacco products. Perspect Public Health 136:30-3
Yoo, Woohyun; Yang, JungHwan; Cho, Eunji (2016) How social media influence college students' smoking attitudes and intentions. Comput Human Behav 64:173-182
Piper, Megan E; Fiore, Michael C; Smith, Stevens S et al. (2016) Identifying effective intervention components for smoking cessation: a factorial screening experiment. Addiction 111:129-41
Piper, Megan E; Schlam, Tanya R; Cook, Jessica W et al. (2016) Toward precision smoking cessation treatment I: Moderator results from a factorial experiment. Drug Alcohol Depend 171:59-65
Loh, Wei-Yin; He, Xu; Man, Michael (2015) A regression tree approach to identifying subgroups with differential treatment effects. Stat Med 34:1818-33

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