Treatment for Tobacco Dependence for Rural, Lower-Income Smokers: Outcomes, Predictors, and Measurement Considerations

Sheffer, Christine E.; Stitzer, Maxine; Payne, Thomas J.; Applegate, Bradford W.; Bourne, David; Wheeler, J. Gary

doi:10.4278/ajhp.06031933

Cited by 24 publications

(45 citation statements)

References 45 publications

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“…They were also comparable to the average U.S. daily smoker who had recently tried to quit in terms of age and race, but the participants were more likely to have graduated high school and be a woman ( Participants also smoked more cigarettes/day and were more dependent smokers than U.S. self-quitters, but appeared to be lighter and less dependent smokers than treatment seekers (Foulds et al, 2006;Sheffer et al, 2009). …”

Section: Participant Characteristicsmentioning

confidence: 69%

Natural History of Attempts to Stop Smoking

Hughes

Solomon

Naud

et al. 2014

Nicotine & Tobacco Research

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Section: Participant Characteristicsmentioning

confidence: 69%

Natural History of Attempts to Stop Smoking

Hughes

Solomon

Naud

et al. 2014

Nicotine & Tobacco Research

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“…The treatment was delivered by certified Tobacco Treatment Specialists (TTSs) with a masters degree in psychology or a bachelors degree in social work and 1–2 years of experience. The approach consisted of six weekly, structured, 60-minute, closed group sessions of content delivered in clinical, community and telephone-based settings and research studies (Payne, Smith, Adams, & Diefenbach, 2006; Schmitz, Rosenfarb, & Payne, 1993; Schmitz & Tate, 1994; Sheffer, et al, 2009; Sheffer, Stitzer, Landes, Brackman, & Munn, 2013). A variety of cognitive-behavioral components were employed, including self-monitoring, stimulus control, problem-solving, conflict management, cigarette refusal training, enhancing social support, goal setting, relapse prevention, and stress management.…”

Section: 0 Materials and Methodsmentioning

confidence: 99%

Delay discounting rates: A strong prognostic indicator of smoking relapse

Sheffer

Christensen

Landes

et al. 2014

Addictive Behaviors

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152

104

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Recent evidence suggests that several dimensions of impulsivity and locus of control are likely to be significant prognostic indicators of relapse. We compared the relative strengths of associations among delay discounting rates, dimensions of trait impulsiveness from the Barratt Impulsiveness Scale - 11, locus of control, nicotine dependence, and stress level with days to relapse among smokers after an intensive multicomponent cognitive-behavioral treatment for tobacco dependence. We used Cox proportional hazard regressions to model days to relapse with each of the following: delay discounting of $100, delay discounting of $1,000, six subscales of the Barratt Impulsiveness Scale (BIS), Rotter’s Locus of Control (RLOC), Fagerstrom Test for Nicotine Dependence (FTND), and the Perceived Stress Scale (PSS). Standardized regression coefficients and hazard ratios (HRs) were used to assess the relative strength and direction of the associations along with a bootstrap method to determine 95% confidence intervals. We then examined the extent to which the measures retained associations with days to relapse while accounting for nicotine dependence and stress level. Our findings indicate that the $100 delay discounting rate had the strongest association with days to relapse, but was only significantly stronger than nicotine dependence. Discounting rates maintained significant associations with days to relapse when combined with the FTND and the PSS, but the BIS subscales and the RLOC did not. These findings indicate that delay discounting is independently associated with relapse and adds to what is already accounted for by nicotine dependence and stress level. These findings signify that delay discounting is a productive new target for enhancing treatment for tobacco dependence. Adding an intervention designed to decrease discounting rates to a comprehensive treatment for tobacco dependence has the potential to decrease relapse rates.

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“…Smoking-related factors associated with positive cessation outcomes include lower baseline craving [1, 2], severity of nicotine dependence [3-8], and number of cigarettes smoked per day [9-14]. Demographic variables shown to be helpful include higher educational level [15-18], older age [19, 20], and being married [21]. Studies have shown mixed results regarding gender as a determinant of successful cessation [9, 12, 20-24].…”

Section: Introductionmentioning

confidence: 99%

“…Studies have shown mixed results regarding gender as a determinant of successful cessation [9, 12, 20-24]. Numerous psychological factors have been associated with a positive response to treatment, including high baseline levels of self-efficacy [17, 18, 25-27], readiness and motivation to quit [16, 24, 28], low stress levels [29], low negative affect [30], no history of depression [31], and low anger [32]. Treatment-related factors include use of behavioral support [13, 33], adherence [34, 35], and absence of lapses during early treatment [36].…”

Section: Introductionmentioning

confidence: 99%

Biomarkers of Response to Smoking Cessation Pharmacotherapies: Progress to Date

et al. 2015

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For the past thirty years, research examining predictors of successful smoking cessation treatment response has focused primarily on clinical variables, such as levels of tobacco dependence, craving, and self-efficacy. Recent research, however, has begun to determine biomarkers (such as genotype, nicotine and metabolite levels, and brain imaging findings) that may have utility in predicting smoking cessation. For genotype, genes associated with nicotinic acetylcholine receptors (nAChRs) and related proteins have been found to predict response to first line medications (e.g., nicotine replacement therapy [NRT], bupropion, or varenicline) or quitting over time without a controlled treatment trial. For nicotine and metabolite levels, function of the CYP 2A6 liver enzyme, which can be assessed with the nicotine metabolite ratio or via genotype, has been found to predict response, with slow nicotine metabolizers having less severe nicotine dependence and a greater likelihood of quitting with NRT than normal metabolizers. And for brain imaging, decreased activation of brain regions associated with emotion regulation and increased connectivity in emotion regulation networks, increased responsiveness to pleasant cues, and altered activation with the Stroop effect have been found in smokers who quit with the first-line medications listed above or counseling. In addition, our group recently demonstrated that lower pre-treatment brain nAChR density is associated with a greater chance of quitting smoking with NRT or placebo. Several of these studies found that specific biomarkers may provide additional information for predicting response beyond subjective symptom or rating scale measures, thereby giving an initial indication that biomarkers may, in the future, be useful for guiding smoking cessation treatment intensity, duration, and type.

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Treatment for Tobacco Dependence for Rural, Lower-Income Smokers: Outcomes, Predictors, and Measurement Considerations

Cited by 24 publications

References 45 publications

Natural History of Attempts to Stop Smoking

Natural History of Attempts to Stop Smoking

Delay discounting rates: A strong prognostic indicator of smoking relapse

Biomarkers of Response to Smoking Cessation Pharmacotherapies: Progress to Date

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