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Ann Behav Med. 2008 Jun;35(3):295-307. doi: 10.1007/s12160-008-9031-1. Epub 2008 Apr 15.

Lifetime characteristics of participants and non-participants in a smoking cessation trial: implications for external validity and public health impact.

Author information

1
Cancer Control Program, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, 3300 Whitehaven Street, NW, Suite 4100, Washington, DC 20007, USA. alg45@georgetown.edu

Abstract

BACKGROUND:

Detailed information about the characteristics of smokers who do and do not participate in smoking cessation treatment is needed to improve efforts to reach, motivate, and treat smokers.

PURPOSE:

The aim of this study is to explore a broad range of characteristics related to participation in a smoking cessation trial.

METHODS:

Eligible smokers were recruited from a longitudinal birth cohort. Participants and non-participants were compared on a broad range of sociodemographics, smoking, psychiatric and substance abuse disorders, personality, and prospective measures from early childhood. Eligible smokers were compared to a matched regional subsample of the Behavioral Risk Factor Surveillance System (BRFSS).

RESULTS:

Few differences were observed, most of which were statistically significant but not clinically meaningful. Compared to non-participants, participants were more likely to be single, have lower income, be more nicotine-dependent, be more motivated to quit, and have higher levels of depressed mood and stress even after covariance of gender, income, and marital status. Sociodemographic differences between participants and the BRFSS sample reflect the skew toward lower socioeconomic status in the original birth cohort.

CONCLUSIONS:

The encouraging conclusion is that smokers who enroll in cessation trials may not differ much from non-participants. Information about treatment participants can inform the development of recruitment strategies, improve the tailoring of treatment to individual smoker profiles, help to estimate potential selection bias, and improve estimates of population impact.

PMID:
18414962
DOI:
10.1007/s12160-008-9031-1
[Indexed for MEDLINE]

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