Using treatment process data to predict maintained smoking abstinence.
OBJECTIVE
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.
Bailey
Steffani R
SR
Stanford University School of Medicine, Stanford Prevention Research Center, 1070 Arastradero Road, Palo Alto, CA 94304-1334, USA. stbailey@stanford.edu
Hammer
Sarah A
SA
Bryson
Susan W
SW
Schatzberg
Alan F
AF
Killen
Joel D
JD
D000293:Adolescent; D000328:Adult; D000368:Aged; D015928:Cognitive Behavioral Therapy / Q000379:methods; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D008960:Models, Psychological; D010043:Outcome and Process Assessment, Health Care / Q000379:methods*; D016032:Randomized Controlled Trials as Topic; D055502:Secondary Prevention; D017603:Signal Detection, Psychological; D016540:Smoking Cessation / Q000379:methods*; D014029:Tobacco Use Disorder / Q000145:classification / Q000517:prevention & control* / Q000628:therapy*