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Related Experiment Video

Updated: Jun 11, 2026

A Protocol for Measuring Cue Reactivity in a Rat Model of Cocaine Use Disorder
07:51

A Protocol for Measuring Cue Reactivity in a Rat Model of Cocaine Use Disorder

Published on: June 18, 2018

Using treatment process data to predict maintained smoking abstinence.

Steffani R Bailey1, Sarah A Hammer, Susan W Bryson

  • 1Stanford University School of Medicine, Stanford Prevention Research Center, 1070 Arastradero Road, Palo Alto, CA 94304-1334, USA. stbailey@stanford.edu

American Journal of Health Behavior
|July 8, 2010
PubMed
Summary
This summary is machine-generated.

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Signal detection analysis identified subgroups of smokers who maintained abstinence after treatment. Including clinical data during treatment refined these subgroups, aiding tailored interventions for long-term smoking cessation success.

Area of Science:

  • Behavioral Science
  • Addiction Research
  • Clinical Psychology

Background:

  • Smoking cessation interventions aim for long-term maintenance.
  • Identifying distinct responder subgroups can personalize treatment strategies.

Purpose of the Study:

  • To identify subgroups of treatment responders and nonresponders for tailored smoking cessation interventions.
  • To utilize signal detection analysis (SDA) for long-term maintenance strategies.

Main Methods:

  • Secondary analysis of a randomized clinical trial (n=301) on extended cognitive behavior therapy for smoking cessation.
  • Signal detection analysis (SDA) applied to pretreatment factors (Model 1) and clinical data during treatment (Model 2).

Main Results:

Related Experiment Videos

Last Updated: Jun 11, 2026

A Protocol for Measuring Cue Reactivity in a Rat Model of Cocaine Use Disorder
07:51

A Protocol for Measuring Cue Reactivity in a Rat Model of Cocaine Use Disorder

Published on: June 18, 2018

  • SDA successfully identified varying probabilities of maintaining smoking abstinence from end-of-treatment to 52-week follow-up.
  • Inclusion of clinical data during treatment (Model 2) significantly altered partitioning parameters compared to pretreatment factors alone (Model 1).

Conclusions:

  • Findings enable targeting of underlying factors influencing long-term smoking behavior change.
  • This approach supports the development of personalized smoking cessation interventions for sustained abstinence.