Jocelyn Cuevas

Does delay discounting play an etiological role in smoking or is it a consequence of smoking

Although higher delay discounting rates have been linked to cigarette smoking, little is known about the stability of delay discounting, whether delay discounting promotes smoking acquisition, whether smoking contributes to impulsive choices, or if different relationships exist in distinct subgroups. This study sought to fill these gaps within a prospective longitudinal cohort study (N=947) spanning mid-adolescence to young adulthood (age 15-21 years old). Smoking and delay discounting were measured across time. Covariates included peer and household smoking, academic performance, depression, novelty seeking, inattention and hyperactivity/impulsivity symptoms, and alcohol and marijuana use. The associated processes latent growth curve modeling (LGCM) with paths from the delay discounting level factor (baseline measure) and the trend factor (slope) to the smoking trend factor (slope) fit the data well, chi(2)((19,n=947)) =15.37, p=.70, CFI=1.00, RMSEA=0, WRMR=.36. The results revealed that delay discounting did not change significantly across time. Baseline delay discounting had a significant positive effect on smoking trend (beta=.08, z=2.16, p=.03). A standard deviation (SD=1.41) increase in baseline delay discounting resulted in an 11% increase (OR=1.11, 95% CI=1.03, 1.23) in the odds of smoking uptake. The alternative path LCGM revealed that smoking did not significantly impact delay discounting (ps>.05). Growth mixture modeling identified three smoking trajectories: nonsmokers, early/fast smoking adopters, and slow smoking progressors. Delay discounting was higher in the smoking versus nonsmoking trajectories, but did not discriminate between the smoking trajectories, despite different acquisition patterns. Delay discounting may provide a variable by which to screen for smoking vulnerability and help identify subgroups to target for more intensive smoking prevention efforts that include novel behavioral components directed toward aspects of impulsivity.

Declining alternative reinforcers link depression to young adult smoking

AIMS Young adulthood represents a period of continued smoking progression and the establishment of regular and long-term smoking practices. Our understanding of the psychological processes that facilitate and solidify regular smoking patterns in this developmental period is limited. We sought to evaluate the role of depression symptoms in young adult smoking uptake and to evaluate whether non-smoking related alternative reinforcers was a mechanism by which depression symptoms influence smoking. PARTICIPANTS The sample was composed of 834 young adults who participated in a longitudinal study of smoking adoption (aged 18-22 years). DESIGN AND MEASUREMENTS In this prospective cohort study, smoking, depression, alternative reinforcers and several covariates were measured annually via telephone from emerging adulthood (age 18) to young adulthood (age 22). FINDINGS Results of a parallel processes latent growth curve model showed that depression symptoms level (baseline age 18) had a significant negative effect on substitute alternative reinforcers trend (β = -0.01, Z = -3.17, P=0.002) and that substitute reinforcers trend had a significant negative effect on smoking trend (β = -0.62, Z = -2.99, P = 0.003). An assessment of indirect effects revealed that depression symptoms level had a significant positive indirect effect on smoking trend through substitute alternative reinforcers trend (β = 0.01, Z = 2.09, P = 0.04, 99% CI = 0.001, 0.02), such that greater depression symptoms at baseline predicted decreases in substitute reinforcers across time which in turn predicted increases in smoking uptake/rate from emerging to young adulthood. CONCLUSIONS Depressive symptoms in emerging adulthood appear to influence smoking uptake and smoking rate through reductions in alternative reinforcers. This suggests that increasing involvement in alternative reinforcers may prevent smoking uptake and escalation in smoking rate.

How Do Psychological Factors Influence Adolescent Smoking Progression? The Evidence for Indirect Effects Through Tobacco Advertising Receptivity

OBJECTIVES. To determine whether novelty seeking and depressive symptoms had mediated or indirect effects on adolescent smoking progression through tobacco advertising receptivity. METHODS. More than 1000 adolescents were monitored from 9th grade to 12th grade and completed annual surveys that measured demographic characteristics, smoking behavior, tobacco advertising receptivity, novelty-seeking personality, depressive symptoms, family and peer smoking, alcohol use, and marijuana use. RESULTS. Latent growth modeling indicated that novelty seeking had a significant indirect effect on smoking progression through baseline tobacco advertising receptivity. For each 1-SD increase in novelty seeking, the odds of being more receptive to tobacco advertising increased by 12% (ie, being in a specific category or higher), which in turn resulted in an 11% increase in the odds of smoking progression from 9th grade to 12th grade. The indirect effect from depressive symptoms to smoking progression did not reach significance. CONCLUSIONS. These findings may inform future research on other factors that influence tobacco advertising receptivity, as well as programs aimed at preventing adolescent smoking initiation and progression.

Young adult smoking: what factors differentiate ex-smokers, smoking cessation treatment seekers and nontreatment seekers?

The present study investigated demographic and psychosocial correlates of smoking status and predictors of smoking cessation among young adults, ages 18-30 years old. Young adults (n=294) completed a self-report survey regarding their health habits and smokers were offered the opportunity to enroll in a smoking cessation program. Substitute reinforcers were greater among ex-smokers compared to nontreatment-seeking smokers, treatment-seeking smokers who did participate in a smoking cessation program and treatment-seeking smokers who did not subsequently participate in a smoking cessation program. Greater complementary reinforcers and delay discounting rates differentiated nontreatment-seeking smokers from ex-smokers and treatment-seeking smokers who subsequently attended a smoking cessation program. Nontreatment seekers were less likely to have higher depression symptoms than ex-smokers. Treatment seekers who did not attend a smoking cessation program tended to live in a household with another smoker, to not be college educated, and to be non-white. Young adult smokers who increased their substitute reinforcers across treatment were almost two times more likely to be quit at treatment end. These results highlight variables that may be important to consider in recruitment strategies and treatment components for smoking cessation interventions for young adult smokers.

Reward and Affective Regulation in Depression-Prone Smokers

BACKGROUND There is a disproportionately high smoking prevalence among individuals who are prone to depression. While depression has been conceptualized as a disorder of dysregulated positive affect and disrupted reward processing, little research has been conducted to determine the role of smoking in these processes among depression-prone smokers. METHODS Depression-prone smokers (DP+; n = 34) and smokers not depression-prone (DP-; n = 49) underwent two laboratory sessions, one while smoking abstinent and one while smoking ad libitum, to assess the relative reinforcing value of smoking and reward sensitivity. Using experience sampling methods, participants completed self-report measures of subjective reward, positive affect, and negative affect across 3 days while smoking as usual and 3 days while smoking abstinent. RESULTS DP+ were two times more likely to work for cigarette puffs versus money in a progressive ratio, choice task (odds ratio 2.05; 95% confidence interval 1.04 to 4.06, p = .039) compared with DP-. Reward sensitivity as measured by the signal detection task did not yield any significant findings. Mixed models regressions revealed a three-way interaction (depression group, smoking phase, and time) for subjective reward, negative affect, and positive affect. For all three of these outcomes, the slopes for DP- and DP+ differed significantly from each other (ps < .05) and the effect of smoking (versus abstinence) over time was greater for DP+ than DP- smokers (ps < .05). CONCLUSIONS These findings indicate that the effects of smoking on reward and positive affect regulation are specific to DP+ smokers and highlight novel targets for smoking cessation treatment in this population.

Reward expectations lead to smoking uptake among depressed adolescents

AIMS While the comorbidity between adolescent depression and smoking has been well documented, less is known about why smoking is disproportionately higher among depressed adolescents. Emerging research suggests that reward-related mechanisms may be important to consider. This study sought to determine whether adolescents with higher depression symptoms have greater smoking reward expectations, which in turn, influence smoking progression. PARTICIPANTS The sample was composed of 1393 adolescents participating in a longitudinal survey study of adolescent health behaviors. DESIGN AND MEASUREMENTS In this prospective cohort study, variables were measured via self-report every six months from age 14 to age 17 resulting in six waves of data. Findings Parallel processes latent growth curve modeling indicated that higher depression symptoms across mid to late adolescence predicted a 17% increase in smoking reward expectations (β=3.50, z=2.85, p=0.004), which in turn predicted a 23% increase in the odds of smoking progression (β=0.206, z=3.29, p=0.001). The indirect effect was significant with delta method (β(indirect)=0.72, z=3.09, p=0.002; 95% CI=0.26, 1.18) and bootstrap (β(indirect)=0.72, z=2.10, p=0.03; 95% CI=0.05, 1.39) standard errors. CONCLUSIONS The study provides novel evidence that expectations of smoking reward facilitate smoking uptake among depressed adolescents. Smoking reward expectations may identify depressed adolescents at risk of smoking. Addressing alternative ways to meet the reward expectations rather than smoking may be an important component to consider in the preventing smoking and promoting smoking cessation among adolescents with elevated depression symptoms.

Longitudinal Variation in Adolescent Physical Activity Patterns and the Emergence of Tobacco Use

OBJECTIVE The objective of this investigation was to examine how variation in adolescent physical activity is related to smoking and alternative tobacco use. METHODS Adolescents (N = 1,384) completed a self-report survey every 6 months from ages 14- to 18-years old in a prospective study of health behaviors. The 8 waves of data were analyzed using General Growth Mixture Modeling (GGMM) RESULTS: GGMM identified five physical activity trajectories including stable higher (SHPA), decreased (DPA), stable regular (SRPA), curvilinear (CPA), and stable low (SLPA). Across 4 years, the likelihood of smoking was greater among adolescents in the DPA, SLPA and SRPA trajectories compared to adolescents belonging to the SHPA trajectory. Alternative tobacco use was greatest among adolescents in the DPA and SRPA trajectories. CONCLUSIONS Adolescents with decreasing physical activity and even adolescents averaging an hour of physical activity a day (SRPA) are important groups to target for tobacco use prevention and intervention efforts.

Initial insight into why physical activity may help prevent adolescent smoking uptake

INTRODUCTION Whereas research supports the importance of regular physical activity to decrease the likelihood of smoking uptake, the mechanisms accounting for this relationship are poorly understood. We sought to determine whether the enjoyment or reward derived from physical activity is one mechanism underlying the relationship between smoking and physical activity. METHODS The sample was composed of 1374 adolescents participating in a prospective longitudinal survey study of health behaviors. Variables were measured via self-report every six months for eight waves of data spanning four years. RESULTS An associative processes latent growth curve model revealed a significant and negative indirect effect of baseline physical activity on baseline smoking through baseline physical activity reward (b(indirect)=-.18, z=-3.11, p=.002; 95% CI=-.29, -.07). Similarly, there was a significant and negative indirect effect of physical activity trend on smoking trend through physical activity reward trend (b(indirect)=-.16, z=-2.09, p=.04; 95% CI=-.30, -.01). The effect of physical activity on smoking at baseline and across time was completely mediated by physical activity reward. There was less support for the idea that smoking progression was associated with reduced physical activity reward and subsequent declines in physical activity. CONCLUSIONS This study provides the first evidence implicating physical activity reward as one mechanism by which physical activity reduces the likelihood of adolescent smoking uptake. Smoking prevention interventions that promote physical activity and target physical activity enjoyment may have an important impact on adolescent smoking initiation and progression.

Cognitive deficits specific to depression-prone smokers during abstinence.

Cigarette smoking is associated with a higher prevalence of depressive symptoms and individuals with elevated symptoms of depression have more difficulty quitting smoking. Depression is accompanied by cognitive deficits similar to those observed during nicotine withdrawal. Depressed smokers may smoke to alleviate these cognitive symptoms, which are exacerbated upon smoking abstinence. We hypothesized that following overnight abstinence, depression-prone smokers (DP+; past history and current depression symptoms; n = 34) would exhibit deficits in short-term and working memory, and experience greater attentional bias for affective stimuli, compared with smokers with no history or current symptoms of depression (DP-; n = 34). All participants underwent two laboratory sessions, once while smoking abstinent and once while smoking ad libitum (order counterbalanced, abstinence biochemically verified). Smokers completed measures of short-term memory (STM; word recognition task), working memory (N-back task), and attentional bias (Emotional Stroop task). The DP+ group showed declines in STM during abstinence compared with smoking, whereas the DP- group did not (interaction p = .02). There were small decrements in working memory accuracy during abstinence (p = .05), but this did not interact with depression status. During the Emotional Stroop task, the DP+ group showed an attentional bias toward positive versus neutral stimuli during abstinence compared with smoking (interaction p = .01). This study provides initial evidence that depressive symptoms may moderate abstinence-induced deficits in STM and shift attentional bias toward emotionally salient stimuli during abstinence. These cognitive changes may prompt relapse and may help identify novel targets for nicotine dependence treatment aimed at attenuating these deficits to improve cessation rates.

The Role of Depression in Adolescent Smoking Trajectories

The public health significance of adolescent smoking and depression has been established, and the co-morbidity has been documented. However, the directionality of the relationship, mediating mechanisms, moderated effects, and the developmental heterogeneity between depression and smoking among adolescents are either unclear or have not been studied. Potential answers and future research needed to address four important questions are highlighted. One, what is the nature of the relationship between smoking and depression? Directionality has implications for distinct etiological and prevention intervention implications. Two, why is depression linked to smoking acquisition, and why does smoking contribute to the development of depression? Information regarding common and unique mechanisms by directional path will pinpoint intervention targets. Three, what modifies the impact of depression on smoking, and what modifies the impact of smoking on depression? This will provide critical information on who to target for an intervention. Four, how do we best capture co-morbidity between depression and smoking? When, how, and for whom is it established? We posit that co-morbidity should be considered within the longitudinal course of each co-occurring behavior. Identifying and characterizing conjoint developmental trajectories may optimize intervention type, timing, target, and content.