Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2022 Dec 1.
Published in final edited form as: Int J Ment Health Addict. 2020 May 15;19(6):1963–1979. doi: 10.1007/s11469-020-00292-2

Associations of cannabis- and tobacco-related problem severity with reward and punishment sensitivity and impulsivity in adolescent daily cigarette smokers

Christopher J Hammond 1,2, Suchitra Krishnan-Sarin 3, Linda C Mayes 4, Marc N Potenza 3,4,5, Michael J Crowley 4
PMCID: PMC8735161  NIHMSID: NIHMS1633143  PMID: 35002579

Abstract

Cannabis and tobacco use and related disorders have been separately associated with sensitivity to reward (SR), sensitivity to punishment (SP), and impulsivity. Given the frequent co-occurrence of cannabis and tobacco consumption in adolescents, it is important to understand how SR, SP and impulsivity may relate to both cannabis-use and tobacco-use co-occurrences and problem severities. Presently, sixty-five adolescents (14–21 years, 65% male), including 36 adolescents with daily tobacco smoking and regular cannabis use and 29 non-smoking healthy controls (HCs), completed self-report questionnaires assessing substance use, addiction severity, SR, SP, and impulsivity. Adolescent smokers had decreased SP and increased impulsivity compared to HCs. SR and impulsivity were independent predictors of concurrent cannabis-related problem severity among smokers. These findings suggest that specific approach/avoidance motives and impulsivity warrant further investigation as potential treatment targets in adolescents who consume both tobacco and cannabis.

Keywords: adolescents, cannabis and tobacco co-use, reward sensitivity, punishment sensitivity, impulsivity, substance-specific, transdiagnostic

Introduction

Cannabis and tobacco are two widely used substances among adolescents and young adults worldwide. Twenty-seven and 24% of U.S. high school students report past-30-days use of tobacco and cannabis, respectively, and 3.6% and 6.4% of U.S. high school seniors use cigarettes or cannabis on a daily basis, respectively (Gentzke et al., 2019; Johnston, O’Malley, Miech, Bachman, & Schulenberg, 2015). Combustible tobacco is often co-used with cannabis, either via simultaneous co-administration, with cannabis and tobacco being smoked together in the form of blunts or spliffs, or via sequential co-use, with tobacco use following cannabis use or vise-versa (Agrawal, Budney, & Lynskey, 2012; Delnevo, Gundersen, Hrywna, Echeverria, & Steinberg, 2011; Ramo, Liu, & Prochaska, 2012). More than 5% of American adolescents ages 12–17 years and 21% of young adults ages 18–24 years report combustible cannabis and tobacco co-use within the past month (Cohn, Abudayyeh, Perreras, & Peters, 2018; Schauer & Peters, 2018). Adolescents who smoke cigarettes are 9–15 times more likely to use cannabis than non-smokers, and over half of U.S. users of combustible tobacco aged 12–17 report past-month use of cannabis (Johnston, O’Malley, Bachman, & Schulenberg, 2006; Mathers, Toumbourou, Catalano, Williams, & Patton, 2006). Recent studies suggest that rates of co-use and interrelationships between tobacco and cannabis use may be stronger in youth (Goodwin et al., 2018).

Most existing studies examine use of cannabis and tobacco regardless of administration method (i.e., whether smoked or vaped) separately. The combined effects of cannabis and tobacco on neurodevelopment, psychological functioning, and health outcomes are poorly understood. This represents a major knowledge gap. Compared to single substance use, concurrent use of cannabis and tobacco may interact, both acutely during use episodes and chronically over time, leading to complex immediate-, short-, and long-term interactive effects on brain function and behavior. Some studies suggest that co-use of cannabis and tobacco poses additive risk for neurotoxic effects (Meier & Hatsukami, 2016). For example, co-use of combustible cannabis and tobacco compared to single substance use (combustible cannabis or tobacco mono-use) is associated with higher breath carbon monoxide levels and may result in higher carcinogen exposure (Meier & Hatsukami, 2016). Other studies suggest more complex interactive effects with co-use resulting in different patterns of behavioral and neural effects in single drug users vs. co-users (Karoly et al., 2015). From a clinical perspective, adult combustible cannabis and tobacco co-users have greater addiction severity and poorer treatment outcomes compared to those using single substances (Agrawal et al., 2012). In light of the high prevalence and complicated interactive effects from co-use, additional studies are needed to better understand this high-risk group.

Individual differences in self-reported sensitivity to rewards and punishments and impulsivity have been separately examined in relation to use of combustible cannabis and tobacco in adolescents (Dawe & Loxton, 2004; Eysenck, 1997; Lubman, 2007; Tackett, 2006). Reward and punishment sensitivity are constructs based upon Gray’s theory of behavioral approach and avoidance (Gray, 1972). Sensitivity to reward (SR) reflects appetitive, goal-oriented behaviors and positive affect, while sensitivity to punishment (SP) signifies behavioral inhibition in response to aversive stimuli (Boog, Goudriaan, van de Wetering, Deuss, & Franken, 2013; Dawe & Loxton, 2004; Evenden, 1999). SR and SP may be considered personality features that characterize how attractive (SR) or aversive (SP) an individual finds different experiences. Few studies to date have examined SR as it relates to youth cannabis or tobacco use. Results from an online survey of college students found that combustible use of cannabis was associated with greater SR (Simons & Arens, 2007). Elevated SR among adolescents was linked to alcohol use (Fraken & Muris, 2006; Knyazev, 2004; Lyvers, Duff, Basch, & Edwards, 2012; O’Connor & Colder, 2005; O’Connor, Stewart, & Watt, 2009; van Hemel-Ruiter, de Jong, Ostafin, & Wiers, 2015), alcohol-related problem severity (Franken, Muris, & Georgieva, 2006; Kambouropoulos & Staiger, 2007; Loxton & Dawes, 2001; Zisserson & Palfai, 2007), and risk for alcohol initiation (Lopez-Vergara et al., 2012). Comparatively, SP findings have been mixed. Some studies show increased and others show decreased SP in substance-using adults (Knyazev, 2004; Lopez-Vergara et al., 2012; O’Connor & Colder, 2005). In youth samples, cannabis users (Simons & Arens, 2007) and cigarette smokers (O’Connor et al., 2009) have shown decreased SP compared to non-smokers.

Impulsivity, in contrast to SR, reflects a tendency to act rashly and with diminished consideration of risks or future consequences (Evenden, 1999; Hammond, Potenza, & Mayes, 2011; Lyvers et al., 2012; Moeller, Barratt, Dogherty, Schmidt, & Swan, 2001). Similar to SR, impulsivity in adolescents is linked to substance use and risk-taking behaviors (Kong et al., 2013; Leeman, Hoff, Krishnan-Sarin, Patock-Peckham, & Potenza, 2014). Impulsivity is positively associated with use of tobacco (Kong et al., 2013; Krishnan-Sarin et al., 2007; Leeman et al., 2014) and cannabis (Kong et al., 2013; Leeman et al., 2014; Schepis et al., 2011) in both adolescents and adults (Beaton, Abdi, & Filbey, 2014; Dawe & Loxton, 2004; Moreno et al., 2012; Verdejo-Garcia, Lawrence, & Clark, 2008). Our group has previously shown that greater behavioral impulsivity among adolescent cigarette smokers was associated with poorer treatment outcomes (Krishnan-Sarin et al., 2007).

Differential reward and punishment processing and poor impulse control are observed across psychiatric disorders, including substance-use disorders, and may represent transdiagnostic phenotypes or traits (Goodkind et al., 2015; McTeague et al., 2017; Whitton, Treadway, & Pizzagalli, 2015). Shared genetic risk factors and overlapping patterns in structure and function of neural networks subserving reward processing and attention/cognition have been observed across substance use disorders, supporting a role for common underlying vulnerabilities (Agrawal & Lynskey, 2006; Baskin-Sommers & Foti, 2015). At the same time, other studies have identified distinct genetic and neurocognitive profiles associated with specific substances (including cannabis vs. tobacco), suggesting that substance-specific associations may account for variance in substance-use behaviors (De Pirro, Galati, Pizzamiglio, & Badiani, 2018; Karoly et al., 2015; Vassileva & Conrod, 2019). To date, no published studies have examined whether abnormalities exist in SR, SP, and impulse control in adolescents who co-use cannabis and tobacco, and if present in youth co-users, demonstrate transdiagnostic or substance-specific relationships. To address these knowledge gaps, we examined, in parallel, associations between cannabis-related and separately tobacco-related problem severity with self-report measures of SR, SP, and impulsivity in adolescent daily cigarette smokers with biochemically verified cigarette and cannabis use and a matched group of non-smoking (cigarette or cannabis) healthy control (HC) participants. We hypothesized that adolescent smokers would show greater SR and impulsivity and less SP compared to non-smoking adolescents. Based upon previous studies supporting a transdiagnostic perspective, we hypothesized that SR, SP, and impulsivity would be associated with both cannabis- and tobacco-related problem severity in adolescent smokers.

Method

Participants

Physically healthy adolescents, ages 14–21 years, who were daily cigarette smokers and age-matched, gender-matched, and grade-level-matched non-smoking typically developing adolescent controls (HCs) were recruited from local high schools in the greater New Haven metropolitan area in parallel with an NIH-funded tobacco cessation study and via flyers, peer referrals, and advertisements between July 2012 and June 2014.

Procedures

A comprehensive telephone-screening interview was administered to adolescents and their parents/guardians prior to study entry. Participants who met inclusionary criteria, and whose parents provided consent if under age 18 years, were then scheduled for a single 3-hour study session. In the session, participants completed self-report questionnaires, behavioral assessments, and biochemical measures. For smokers, inclusion criteria included current daily cigarette use and current or past history of smoking 5 or more smoked cigarettes on a daily basis for at least a 6-month period, urine cotinine level above 500ng/ml at study visit, no current illicit substance use and a urine drug screen (UDS) negative for drugs other than cannabis. For HCs, criteria included never smoking daily, no history of regular patterns of smoking, urine cotinine level lower than 100 ng/ml at study visit, no history of illicit substance use (< 5 lifetime experiences with cannabis, no previous use of any other illicit drug, negative UDS for cannabis and other illicit drugs), and not meeting criteria for heavy drinking (Calahan, Cisin, & Crossley, 1969). For all participants, criteria included ages 14–21 years, English language fluency, full scale IQ (FSIQ) > 70, no chronic medical illnesses, no evidence of serious mental illness (psychosis, autism, bipolar disorders), no history of lifetime or current DSM-IV-TR diagnosis of dependence on another psychoactive substance (other than alcohol, cannabis, and tobacco). Additional exclusion criteria included neurologic conditions (e.g. seizures, migraines), head trauma with loss of consciousness > 2 minutes, use of any psychoactive drugs including anxiolytics and antidepressants unless the adolescent had been taking the medication consistently for 3 months, and pregnancy or lactation. All adolescents completed a breathalyzer for alcohol, provided a UDS, and were monitored for signs/symptoms of intoxication (and rescheduled if demonstrating impairment). Participants provided consent/assent, and participants under age 18 years also had a parent/guardian provide consent. This study was approved by the Yale University School of Medicine Human Investigation Committee.

Measures

Sociodemographics

Standard demographics were assessed including age, gender, race/ethnicity, school involvement and grade level. The Wechsler Abbreviated Scale of Intelligence (WASI) (Weschler, 1999) Vocabulary and Matrix subtests were used to estimate a Full Scale IQ based on standard norms.

Sensitivity to Punishment and Sensitivity to Reward

The Sensitivity to Punishment and Sensitivity to Reward Questionnaire-Revised (SPSRQ-R) (Torrubia, Avila, Molto, & Caseras, 2001) is a 48-item self-report scale in yes/no format that yields scores for SP, related to Gray’s behavioral inhibition system, and SR, related to Grays’s behavioral activation system (Gray, 1982). Its 2-factor SPSRQ-R scale reports a SP score examining behavioral inhibition under specific conditions of threat or punishment and a SR score examining approach behavior to specific conditioned and unconditioned rewards (e.g., money, social status, sexual partners), demonstrating good test retest reliability and convergent and discriminant validity (Torrubia et al., 2001).

Impulsivity

The Barratt Impulsiveness Scale (BIS-11) is a 30-item self-report questionnaire (Patton, Stanford, & Barratt, 1995). It includes three subscales which capture: (1) attentional impulsivity, or difficulties focusing (8-items); (2) motor impulsivity, or acting with little forethought (11-items); and, (3) non-planning impulsivity, or difficulties organizing (11-items). The BIS-11 has good divergent and convergent validity and test-retest reliability (Patton et al., 1995).

Substance Use Frequency

Substance-use frequency for cannabis, combustible tobacco, alcohol and other drugs was assessed via calendar method using the Timeline Follow-back (TLFB), characterizing past-90-day patterns of use (Sobell & Sobell, 1992).

Cannabis-related Problem Severity

Cannabis-related problem severity was assessed with the Cannabis Use Disorder Identification Test – Revised (CUDIT-R) (Adamson et al., 2010), an 8-item self-report measure assessing symptoms of DSM-5 cannabis use disorder (CUD) over the past six months. Total scores for the CUDIT-R range from 0 to 32, with a score of 13 used as a suggested cutoff for moderate-to-severe CUD.

Tobacco-related problem severity

Severity of nicotine dependence (termed tobacco-related problem severity throughout this manuscript) was assessed with the modified Fagerström Test for Nicotine Dependence (FTND) (Prokhorov, Pallonen, Fava, Ding, & Niaura, 1996), a 7-item instrument that has been adapted for youth populations. Total scores for the FTND range from 0 to 9, with a score of 3 or more used as a suggested cutoff for moderate-to-severe TUD.

Cronbach’s α values were satisfactory for all scales used in the present study.

Biochemical assays for cannabis, tobacco, and other substances

A urine sample was collected at the beginning of the study visit to provide a biochemical assessment of drug use. Qualitative UDS using an immunoassay-based point of care test kit assessed for five different drugs of abuse (cannabinoids, cocaine, opioids, methamphetamines, benzodiazepines). A semi-quantitative urine cotinine test assessed levels of cotinine in all participants (Acutest NicAlert® urine semi-quantitative cotinine test, Jant Pharmacal Co.). Urine samples of participants whose UDS was positive for cannabinoids were sent to Quest diagnostics laboratory where mass spectroscopy was used to characterize quantitative urine cannabinoid levels (THC-COOH, creatinine-corrected, ng/dL).

Data Analysis

Analyses were conducted using IBM SPSS Statistics Analytic software V25.0 (IBM, Armonk, NY). Group differences between adolescent daily cigarette smokers and controls were calculated with independent sample t-test for continuous variables and Chi-squares for categorical variables. Hierarchical linear regression analyses were used to examine relationships with cannabis-related and tobacco-related problem severity in the daily smoking group. SR, SP, and BIS-11 total scores were entered into separate step-wise linear regressions examining predictors of cannabis- and tobacco-related problem severity, with CUDIT-R and FTND scores serving as the dependent variables. In all models age, gender, FSIQ, and ethnicity/race were entered as covariates in a first step to control for demographic variables. Independent variables of interest (i.e., SR, SP, BIS-11 total scores) were then entered in the second step.

To reduce multicollinearity at the level of predictors (Iacobucci, Schneider, Popovich, & Bakamitsos, 2016), the independent variables were centered (Score centered = Score n – Mean), and centered values were used in the regression analyses. Discrete missing values were replaced using mean substitution methods and subscales were recalculated (Dodeen, 2003). Two participants (2.5%) who did not complete at least one of the questionnaires were excluded in analyses involving those specific questionnaires, but were included the overall sample.

Sensitivity analyses were conducted on all regression models that yielded statistically significant results by re-running the models and covarying in step-wise fashion for past-30-day use of alcohol, cannabis, and tobacco, substance-related problem severity to the other substance of interest (e.g. tobacco-related problem severity in the cannabis analyses and vise-versa), urine semi-quantitative cotinine levels, and urine quantitative cannabinoid levels. This approach helped to ascertain the extent to which significant associations between addiction severity and SP, SR and impulsivity measures identified in analyses were independent of recent or residual substance-use effects, or could be accounted for by use of other drugs.

Results

Demographics and Substance Use

Descriptive statistics and between-group analyses between smokers and HCs are presented in table 1. Sixty-five participants (65% male; 60% Caucasian; Age range = 14–21 years, Mean age = 17.7±1.4; FSIQ = 101.7±12.5) included 36 cigarette smokers and 29 HCs. Adolescent smokers and HCs differed on race (χ2(65) = −2.41, p = 0.02) and FSIQ (t(65)= −3.54, p < 0.001).

Table 1.

Demographics, substance use, reinforcement sensitivity, and impulsivity scores by group

Characteristics Adolescent daily cigarette smokers (n=36) Typically developing controls (n=29)
Demographics
Male, n (%) 24 (67%) 18 (62%)
Age (years) 17.8 (1.15) 17.6 (1.41)
Caucasian, n (%)* 17 (47%) 22 (76%)
WASI Full Scale IQ Score҂*** 98.4 (10.33) 107.9 (11.15)
Substance Use
Cigarettes per day, current† 8.2 (4.99) --
n (%) with over 100 or more lifetime episodes of cannabis useǂ*** 29 (81%) 0 (0%)
Alcohol use days in past 30 days* 2.2 (2.52) 0.8 (2.45)
Binge drinking days in past 30 days 1.5 (2.37) 0.5 (1.84)
Cannabis use days in past 30 days*** 16.5 (12.29) 0.0 (0.14)
Tobacco use days in past 30 days*** 27.2 (5.83) 0.0 (0.00)
CUDIT-R cannabis severity score** 11.6 (7.66) 0.4 (1.32)
FTND tobacco severity score*** 4.1 (1.60) 0.0 (0.00)
Reinforcement and Impulsivity Measures
SPSRQ-R reward sensitivity score 13.2 (4.12) 11.9 (3.80)
SPSRQ-R punishment sensitivity score** 7.5 (4.98) 11.4 (5.71)
BIS-11 Total Score*** 67.9 (11.18) 58.5 (9.76)
 BIS-11 Attentional Impulsiveness 17.5 (3.38) 16.1 (3.89)
 BIS-11 Motor Impulsiveness*** 24.0 (4.93) 20.20 (4.05)
 BIS-11 Non-planning Impulsiveness*** 26.4 (5.54) 22.1 (4.50)
Biochemical Substance Use Assays
Urine toxicology screen, qualitative positivity for cannabinoids, n (%)*** 27 (75%) 0 (0%)
Urine THC-COOH/creatinine level (ng/ml)Δ 154.2 (126.07) --
Carbon Monoxide level, ppm** 4.6 (4.27) 0.9 (0.39)
Breath Alcohol Level 0.00 (0) 0.00 (0)
Open in a new tab
*

= p < 0.05;

**

= p < 0.01;

***

= p < 0.001

Abbreviations: CUDIT-R= Cannabis Use Disorder Identification Test–Revised; FTND= modified Fagerström Test for Nicotine Dependence; BIS-11= Barratt Impulsiveness Scale–11 Total Score; SPSRQ-R= Sensitivity to Punishment Sensitivity to Reward Questionnaire; WASI = Wechsler Abbreviated Scale of Intelligence

ǂ

= Lifetime episodes of cannabis use obtained from Youth Risk Behavior Survey (CDC, 2011)

Δ

= Urine cannabis/creatinine level represents creatinine corrected cannabis metabolite level (ng/ml) obtained during mass spectrometry in 27 participants whose qualitative urine toxicology screening was positive for cannabinoids

Concomitant use of cannabis was common in the daily cigarette smokers, with 80% reporting > 100 lifetime cannabis-use episodes, and 50% reporting cannabis use on a daily basis. None of the daily smokers were cannabis-naïve. Three quarters of smokers has a UDS positive for cannabis and 92% had used cannabis within the past 90 days. As expected, adolescent smokers reported more cannabis-use and tobacco-use days and elevated cannabis-related and tobacco-related problem severity compared to HCs.

Group differences (HCs vs. smokers) in reward and punishment sensitivity and impulsivity

Compared to HCs, smokers had significantly decreased SP scores (t1,62 = −2.95, p= 0.005) and increased BIS-11 total scores (t1,63 = 3.57, p= 0.001). No group differences were observed on SR scores (t1,62 = 1.22, p= 0.22). On examination of BIS-11 subscales, smokers compared to HCs had increased motor (t1,63 = 3.34, p = 0.001) and non-planning (t1,63 = 3.37, p = 0.001) impulsivity, but did not differ on measures of attentional impulsivity (t1,63 = 1.51, p = 0.14).

Multivariate regression analyses statistically predicting cannabis-related and tobacco-related problem severity

In independent cannabis-related problem severity models controlling for age, gender, race/ethnicity, and FSIQ, SR (R2 adj= 0.173, β = 0.39, p = 0.03) and BIS-11 (R2 adj= 0.328, β = 0.57, p= 0.001) but not SP (R2 adj= 0.042, β = 0.09, p = 0.63) were statistical predictors of cannabis-related problem severity in adolescent smokers. Associations between BIS-11 and CUDIT-R scores and between SR and CUDIT-R scores both showed robustness in sensitivity analyses, remaining statistically significant and with modest effect size after separately controlling for past-30-day alcohol, cannabis and tobacco use, tobacco-related problem severity, and urine biochemical quantitative assays for cannabis and cotinine (when covarying for substance use behavioral variables: SR: β’s ~ 0.37 to 0.54, all p’s < 0.05; BIS-11: β’s ~ 0.51 to 0.57, all p’s < 0.001). Results also remained significant when the models were re-run in a sample restricted to smokers with biochemically-verified cannabis use based upon positive cannabis UDS (n=27) and separately in a sample restricted to smokers with past 30 day cannabis use (n=29) (not shown, all p’s < 0.05). In the combined variable step-wise hierarchical cannabis-related-problem-severity model with age, gender, race/ethnicity, FSIQ controlled (R2 adj = 0.392, F1,35 = 6.55 p = 0.002), BIS-11 remained significantly associated with CUDIT-R scores (β = 0.54, p = 0.001) while the association between SR and CUDIT-R scores approached significance (β = 0.29, p = 0.06; Table 2),.

Table 2.

Hierarchical regression model of combined variables predicting cannabis-related problem severity in adolescent daily cigarette smokers

Variable Beta t p-value R2 Change
Step 1 0.172
Age 0.180 1.06 0.30
Genderǂ 0.299 1.77 0.09
FSIQ −0.006 −0.04 0.97
Ethnicity 0.205 1.22 0.23
Constant −0.60 0.002
Step 2 0.392
Age 0.140 1.00 0.32
Gender 0.062 0.39 0.757
FSIQ −0.055 −0.40 0.69
Ethnicity 0.154 1.12 0.27
BIS-11 *** 0.543 3.55 0.001
SRǂ 0.290 1.93 0.06
SP −0.188 −1.19 0.24
Constant −0.26 0.70
Open in a new tab
ǂ

= p < 0.10;

*

= p < 0.05;

**

= p <0.01;

***

=p < 0.001

Note: N = 36. R2 final model = 0.513**, Adjusted R2 = 0.392 based upon age, gender, FSIQ, ethnicity, BIS-11, SR, and SP as predictors. IV’s were centered before analysis.

Abbreviations: FSIQ = Wechsler Abbreviated Scale of Intelligence (WASI) Full Scale IQ Score based upon two subtests, vocabulary and matrix t-scores; Ethnicity = categorical variable (Caucasian = 1, Non-Caucasian = 0); BIS-11 = Barratt Impulsiveness Scale – 11 Total Score; SR = Sensitivity to Reward Score from Sensitivity to Punishment Sensitivity to Reward Questionnaire

To determine how much variance in cannabis-related problem severity was explained by SR and impulsivity, simplified models examining SR and BIS-11 associations were used. The uncorrected model including only SR explained 18% of the variance in CUDIT-R scores among daily smokers (R2 adj = 0.181, β = 0.45 p = 0.006). The uncorrected model including only BIS-11 explained 31% of the variance in CUDIT-R scores among daily smokers (R2 adj = 0.308, β = 0.57, p< 0.001). In exploratory bivariate correlation analyses (Table 3), SR scores (r1,35 = 0.453, p= 0.006) and BIS-11 scores (r1,35 = 0.572, p < 0.001) but not SP scores (r1,35 = 0.012, p = 0.94) significantly correlated with CUDIT-R scores among smokers.

Table 3.

Correlations between reinforcement sensitivity, impulsivity, and cannabis and tobacco severity variables in adolescent daily cigarette smokers

Cannabis severity(1) Tobacco severity(2)
1 CUDIT-R --
2 FTND 0.092 --
3 BIS-11 total score 0.572 *** 0.074
4 SRSPQ-R reward sensitivity 0.453 ** −0.109
5 SRSPQ-R punishment sensitivity 0.012 0.069
Open in a new tab
*

= p < 0.05;

**

= p <0.01;

***

=p < 0.001

Note: N = 36. Numbers represent two-sided Pearson correlation analyses. Cannabis-related problem severity is defined as CUDIT-R total score, tobacco-related problem severity is defined as modified FTND total score.

Abbreviations: CUDIT-R= Cannabis Use Disorder Identification Test–Revised; FTND= modified Fagerström Test for Nicotine Dependence; BIS-11= Barratt Impulsiveness Scale–11 Total Score SPSRQ-R = Sensitivity to Punishment and Sensitivity to Reward Questionnaire-Revised

In tobacco-related-problem-severity analyses, regression models identified no significant associations between tobacco-related problem severity and SR, SP, and impulsivity in smokers. Independent tobacco-related severity models controlling for age, gender, race/ethnicity, and FSIQ found that SR (R2 adj= 0.009, β = 0.04, p = 0.83), SP (R2 adj= 0.008, β = 0.03, p = 0.88), and BIS-11(R2 adj= 0.029, β = 0.14, p = 0.72) scores were all unrelated to FTND scores in smokers.

Discussion

This study used data from a biochemically verified sample of adolescents with daily cigarette smoking who used cannabis and tobacco regularly with matched HCs to examine associations between cannabis-related and tobacco-related problem severities independently and sensitivities to reward and punishment and impulsivity. We found that adolescent smokers with use of both cannabis and tobacco had decreased SP and increased impulsivity compared to HCs, and that SR and impulsivity were associated with cannabis-related problem severity and not tobacco-related problem severity. These results suggest that relationships between SR and self-perceived impulsivity in adolescent smokers may be relevant to target in interventions to reduce severity of cannabis use. Implications are discussed below.

Use of combustible cannabis in adolescents with daily cigarette smoking was common. Half of adolescents with daily cigarette smoking in our sample reported also using combustible cannabis on a daily basis. The frequency of co-use is consistent with those found in adolescents with tobacco use from the Virginia Youth Survey (Cobb, Soule, Rudy, Sutter, & Cohn, 2018). Prior studies in youth report similar clustering of substance-use behaviors in high-risk adolescents (Agrawal et al., 2012). Specifically, prior studies of adolescent smokers report elevated rates of daily cannabis use (approximately 50%) (Goodwin et al., 2018) and of CUDs (10-fold to 14-fold higher than in non-smoking youth) (Weinberger et al., 2018). Our findings lend additional evidence to support that adolescents who smoke cigarettes are more likely than non-smokers to regularly use and experience problems with cannabis. This co-occurrence in adolescents underscores the importance of screening for, diagnosing, and treating comorbid cannabis- and tobacco-use disorders in adolescents, including within tobacco-use and cannabis-use treatment studies (McClure et al., 2018a).

Our hypothesis that adolescent smokers would have increased impulsivity and SR and decreased SP relative to HCs was only partially supported. Consistent with our hypothesis, adolescent smokers had increased impulsivity compared to HCs. This finding is consistent with previous studies of adolescents who use cannabis (Dougherty et al., 2013; Gullo & Dawe, 2008; King, Fleming, Monahan, & Catalano, 2011) or smoke cigarettes (Krishnan-Sarin et al., 2007). With regard to reinforcement sensitivity, we found lower SP in adolescent smokers compared to HCs but no group differences in SR. Our finding of decreased SP among adolescent smokers is consistent with studies of adults who smoke cigarettes (O’Connor et al., 2009) or use cannabis (Simons & Arens, 2007). Other studies report no association (Knyazev, 2004; O’Connor & Colder, 2005) or a positive association between SP and substance use (Lopez-Vergara et al., 2012). Individual differences in SP may represent distinct subgroups (high SP vs. low SP) with different use motives and pathways from cannabis or tobacco initiation to problematic or disordered use. For example, individuals with high SP (i.e., those who subjectively experience a heightened negative emotional reaction to punishments) may have coping-related drug motives, and start using cigarettes or cigarettes in combination with cannabis to cope with negative emotional states. Conversely, individuals with low SP (i.e., those who subjectively experience a blunted negative emotional reaction to punishments) may continue to use cannabis and/or tobacco regularly or struggle to quit despite receiving feedback that their substance use is associated with negative consequences (e.g., unemployment, failing grades, arrest). These possibilities warrant direct testing in longitudinal studies.

We did not observe group differences in SR between adolescent smokers and HCs, consistent with a prior study of SR and substance use in adolescents (Willem et al., 2011). Although positive associations of SR with tobacco and cannabis use have been previously reported, these have involved primarily adult samples (Bijttebier, Beck, Claes, & Vandereycken, 2009; Fraken & Muris, 2006; Franken et al., 2006; Kambouropoulos & Staiger, 2007; O’Connor et al., 2009; Zisserson & Palfai, 2007). Adolescent studies showing relationships between SR and vulnerability for early substance use have typically examined alcohol only or used composite drug-use measures (Knyazev, 2004; Lopez-Vergara et al., 2012; Loxton & Dawes, 2001; O’Connor & Colder, 2005; van Hemel-Ruiter et al., 2015). Differences observed across studies may reflect different methodological approaches or development/age-related differences in the samples, with the latter possibility particularly relevant as adolescence has been considered a ‘sensitive’ period associated with an inflection in SR compared to childhood and adulthood (Casey & Jones, 2010).

A main study objective was to identify addiction-severity associations with SR and impulsivity. The findings partially supported our hypotheses. We identified significant addiction-severity associations with self-report measures of impulsivity and SR in adolescent smokers that were only observed in relation to cannabis but not tobacco. These findings remained significant in analyses that accounted for tobacco-related problem severity and concurrent and recent use of alcohol, cannabis, and tobacco (assessed via self-report measures and biochemical assays). Overall, the findings support a substance-specific association between cannabis-related problems and “approach-based” rather than “avoidance-based” motives and tendencies in adolescent who co-use tobacco and cannabis.

Prior studies have described both transdiagnostic and substance-specific associations between psychoactive substance use and misuse, neurocognitive profiles and self-report features (Agrawal & Lynskey, 2006; Bierut, Schuckit, Hesselbrock, & Reich, 2000; Clark, Gillespie, Adkins, Kendler, & Neale, 2016; Shmulewitz, Greene, & Hasin, 2015; Vanyukov, 2012). Individual differences in susceptibility to cannabis-related problems but not tobacco-related problems in smokers with high self-reported impulsivity and SR may relate to different motivations for use of cannabis versus tobacco, differential sensitivities to their effects, or other factors (Vassileva & Conrod, 2019). Adolescents who score high on sensation-seeking may be more drawn to initiating and using cannabis and alcohol as opposed to tobacco (Castellanos-Ryan et al., 2016; Mahu, Doucet, O’Leary-Barrett, & Conrod, 2015). In adolescents using both tobacco and cannabis, associations may be complex given potential drug interactions, which may result in synergistic or compensatory effects on reward and punishment processing and cognition (Rabin & George, 2015). For example, administration studies suggest that the addition of combustible tobacco to cannabis may amplify subjective ‘highs’ (Valjent, Mitchell, Besson, Caboche, & Maldonado, 2002).

Common underlying vulnerability and substance-specific associations are not mutually exclusive, and both likely contribute variance to addiction vulnerability in youth. Longitudinal data from nearly 4000 adolescents found evidence for both a common vulnerability effect of impulsivity and poor executive functioning related to propensities for cannabis and alcohol use, and cannabis-specific effects on concurrent and later inhibitory control and working memory independent of alcohol use and common addiction vulnerability (Morin et al., 2019). Thus, bidirectional associations reflecting both transdiagnostic and substance-specific phenotypes may be present in different samples, at different developmental stages, and at different levels of cumulative drug exposure to cannabis, tobacco, or both, and may differ based upon state vs. trait effects (Vassileva & Conrod, 2019). Our cross-sectional study represents a ‘snap-shot’ of high-risk adolescents with cannabis and tobacco co-use. Longitudinal studies, such as the Adolescent Brain Cognitive Development (ABCD) study, are needed to clarify how relationships between SR, SP, impulsivity, and substance use emerge and change over time.

Our study is the first to our knowledge to report an association between self-reported SR and cannabis-related problem severity in adolescents who co-use cannabis and tobacco. Prior studies, including a recent meta-analysis, have described associations between sensation-seeking, cannabis use and cannabis-related problem severity in at-risk adolescents (Castellanos-Ryan et al., 2016; Mahu et al., 2015; VanderVeen, Hershberger, & Cyders, 2016). Sensation-seeking, an individual’s tendency to seek out novel and exciting experiences, represents an overlapping construct related to reward sensitivity and approach behaviors (Harden et al., 2018). Our results resonate with these findings and are consistent with reports of positive relationships between cannabis use and impulsivity in adolescent and adult samples (Dougherty et al., 2013; Gullo & Dawe, 2008; King et al., 2011) and between cannabis use and SR in adults (Simons & Arens, 2007). While our study found impulsivity associations with cannabis-use problem severity, impulsivity as a putative transdiagnostic marker has been associated with multiple externalizing disorders including attention deficit/hyperactivity disorder (ADHD), conduct disorder, and substance use and related disorders. Self-reported impulsivity assessed during childhood has been prospectively linked to adolescent-onset substance use and misuse (Iacono, Malone, & McGue, 2008; Ivanov, Schulz, London, & Newcorn, 2008). Within adolescent substance-using samples, those with heavier substance use or substance-use disorders often have higher impulsivity, suggesting that impulsivity may worsen with chronic substance use, especially cannabis (VanderVeen et al., 2016; Vassileva & Conrod, 2019). Interestingly, our current findings do not align clearly with previous work from our group finding that impulsivity was associated with poorer tobacco cessation outcomes in adolescent cigarette smokers and that adding contingency management (CM) to standard behavioral therapy treatments improved tobacco abstinence rates during a cessation attempt in highly impulsive adolescents (Krishnan-Sarin et al., 2007; Morean et al., 2015). Similarly, treatment of adolescent tobacco smokers increased reward-anticipation-related activation in the ventral striatum (Garrison et al., 2017), findings that suggest a normalization of reward-processing differences observed previously in adolescent tobacco smokers (Peters et al., 2011). Differences in findings across studies may reflect different groups of adolescent smokers (e.g., highly impulsive smokers versus smokers who co-use combustible tobacco with cannabis), different sensitivities of outcome measures (CUDIT-R versus FTND) or other factors. That we observed impulsivity associations with cannabis-related but not tobacco-related problem severity may reflect a more robust cannabis association obfuscating a transdiagnostic association that has a smaller effect (see cannabis initiation vs. problem-severity effect sizes in meta-analysis) (VanderVeen et al., 2016). Our findings also parallel results from recent neuroimaging studies showing altered neural activity in brain regions enriched in cannabinoid-1 receptors and involved in networks underlying attention, cognitive/executive control, and reward and punishment processing in youth who use cannabis compared to matched HCs (Nestor, Hester, & Garavan, 2010; van Hell et al., 2010). Such processes warrant further study in adolescents who smoke cannabis.

Collectively, these findings suggest that impulsivity and SR are relevant constructs that may convey risk for cannabis-related problems. While further investigation is required, these measures could be combined with other statistical predictors and used to develop prognostic or diagnostic algorithms for CUD risk in adolescent smokers. Potentially, reinforcement and impulsivity differences may result in divergent motives for using cannabis and tobacco in adolescent smokers (Simons & Arens, 2007). For example, greater SR or impulsivity may lead adolescent smokers to seek cannabis for its rewarding properties (Bonar et al., 2017). Conversely, adolescent daily smokers may use tobacco for other reasons, such as stress reduction or anxiolysis (Patton et al., 1998). Clarifying the diagnostic and prognostic significance of the observed relationships is an important next step, as such information may be leveraged to enhance prevention and early intervention efforts in high-risk youth. For example, a randomized controlled trial involving secondary school students found that interventions administered to adolescents who scored high on sensation-seeking delayed the onset of cannabis use (Mahu et al., 2015).

There are multiple study limitations. Our cross-sectional design precludes causal determination. Differences observed between adolescent smokers and HCs could reflect neuroadaptive/neurotoxic changes related to recent or long-term effects of cannabis or tobacco use or co-use, or could predate cannabis or tobacco exposure and represent risk factors for early initiation and co-use trajectories of cannabis and tobacco. While our study provided detailed characterization of cannabis-use and tobacco-use patterns and addiction severity along with biochemical assays of drug use, we did not specifically assess blunt use or ask about different methods and motivations for co-use. Though our main outcomes utilize psychometrically validated and represent the current ‘gold standard’ for self-report assessment of addiction severity to tobacco and cannabis in youth samples, recent studies suggest that FTND may over emphasize physiological dependence and withdrawal symptoms, and may be less sensitive to lower frequency smoking in youth (Carpenter, Baker, Gray, & Upadhyaya, 2010). Another limitation of our study was that we did not query the use patterns of different types of tobacco products (e-cigarettes, hookah, water pipe, cigarillos) or cannabis products (vaporized, edibles, concentrates). Because of this, our results only characterize combustible tobacco-use and cannabis-use use associations. Cannabis and nicotine vaping have become more common in the U.S., and associations between psychological functioning and other administration methods, such as vaped nicotine or cannabinoids, may differ. At the time of data collection (2012–2014) for this study, vaping was less frequent among youth, exemplified by identification of just one smoker in the sample who endorsed dual e-cigarette and combustible cigarette use. Removal of this participant from analyses had negligible effect on the results. As such, e-cigarette use did not represent confound for the analyses as it may for a sample collected in the current environment. The frequent co-use and high interrelatedness of cannabis and tobacco use made it difficult to examine unique drug effects. Future studies should characterize psychological functioning in separate populations of individuals who use cannabis only, cannabis and tobacco, and tobacco only, with limited history of the other drug if possible. This would require larger samples and may be possible to examine as waves of data from the ABCD study become publicly available.

Despite these limitations, our paper has important strengths. Unlike other published findings relying primarily on self-reported substance use and drawn from national survey data, our study used a combination of self-reported and biochemically verified use of substances. Co-use and use patterns of cannabis and tobacco likely fall along a continuum. Our sample focused on adolescents with frequent use of combustible cannabis and tobacco, exemplified by 50% of smokers using both cannabis and tobacco daily. In contrast, some published studies define co-users as youth who report use of both cannabis and tobacco within the same 30-day period. Adolescents who co-use cannabis and tobacco within the same 30-day period, as compared to those who co-use cannabis and tobacco on a daily basis, may have important differences in their psychological functioning, neurocognitive profiles, and health outcomes. Our study’s characterization of relationships separately between cannabis-related and tobacco-related problem severity in an adolescent sample of co-users enables delineation of relationships that may carry diagnostic and prognostic significance.

Conclusion

Cannabis and tobacco use and co-use are common in adolescents and are associated with alterations in reinforcement sensitivity and impulsivity. Among adolescent daily cigarette smokers who co-use cannabis, self-reported impulsivity and SR were independent predictors of concurrent cannabis-related problems, suggesting specific approach motivational pathways that may underlie an elevated risk for CUDs in co-users. These findings suggest that developing individualized prevention and treatment strategies that build on emerging evidence of substance-specific factors and related drug-use motives in adolescent mono- and co-users of tobacco and cannabis may improve treatment outcomes and reduce relapse to both substances.

Funding:

Support for this study came from an American Academy of Child & Adolescent (AACAP) Pilot Research Award for Junior Investigators sponsored by Lilly, USA (Hammond) and from NIH-grants including K12 DA000357 (Hammond), T32 MH018268 (Crowley), and P50DA009241 (Krishnan-Sarin). Dr. Hammond receives grant support from the National Institute on Drug Abuse, National Institute of Mental Health, the American Academy of Child & Adolescent Psychiatry (AACAP Physician Scientist Career Development Award, K12DA000357, T32MH18268), the National Network of Depression Centers, and the Armstrong Institute at Johns Hopkins Bayview. Dr. Potenza has received support from the National Center on Addiction and Substance Abuse (CASA), the Connecticut Department of Mental Health and Addiction Services and the National Institute on Drug Abuse (K12 DA00167, R01DA039136). Dr. Crowley received support from K01 DA034125.

Footnotes

Compliance with Ethical Standards

The study was approved by the Yale Human Investigation Committee and followed the code of the Declaration of Helsinki.

Conflict of Interest and Financial Disclosures: None of the authors have any conflicts of interest. Dr. Hammond serves as a scientific advisor for the National Courts and Science Institute and as a subject matter expert for the Substance Abuse Mental Health Services Administration (SAMHSA) related to co-occurring substance use disorders and severe emotional disturbance in youth. Dr. Krishnan-Sarin has received investigational medications from Astra Zeneca and Novartis for studies on alcohol drinking behaviors. Dr. Potenza has consulted for Rivermend Health, Opiant Therapeutics, Addiction Policy Forum, Game Day Data and AXA; has received research support (to Yale) from Mohegan Sun Casino and the National Center for Responsible Gaming; has participated in surveys, mailings or telephone consultations related to drug addiction, impulse-control disorders or other health topics; has consulted for and/or advised gambling and legal entities on issues related to impulse-control/addictive disorders; has provided clinical care in a problem gambling services program; has performed grant reviews for research-funding agencies; has edited journals and journal sections; has given academic lectures in grand rounds, CME events and other clinical or scientific venues; and has generated books or book chapters for publishers of mental health texts. Dr. Mayes reports no disclosures. Dr. Crowley received grant funding from the NIH (T32 MH018268).

Informed Consent: All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2000 (5). Informed consent was obtained from all patients for being included in the study.

References

  1. Adamson SJ, Kay-Lambkin FJ, Baker AL, Lewin TJ, Thornton L, Kelly BJ, & Sellman JD (2010). An improved brief measure of cannabis misuse: the Cannabis Use Disorders Identification Test-Revised (CUDIT-R). Drug Alcohol Depend, 110(1–2), 137–143. [DOI] [PubMed] [Google Scholar]
  2. Agrawal A, Budney AJ, & Lynskey MT (2012). The co-occurring use and misuse of cannabis and tobacco: a review. Addiction, 107(7), 1221–1233. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Agrawal A, & Lynskey MT (2006). The genetic epidemiology of cannabis use, abuse and dependence. Addiction, 101(6), 801–812. [DOI] [PubMed] [Google Scholar]
  4. Baskin-Sommers AR, & Foti D (2015). Abnormal reward functioning across substance use disorders and major depressive disorder: Considering reward as a transdiagnostic mechanism. Int J Psychophysiol, 98(2 Pt 2), 227–239. [DOI] [PubMed] [Google Scholar]
  5. Beaton D, Abdi H, & Filbey FM (2014). Unique aspects of impulsive traits in substance use and overeating: specific contributions of common assessments of impulsivity. Am J Drug Alcohol Abuse, 40(6), 463–475. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Bierut LJ, Schuckit MA, Hesselbrock VM, & Reich T (2000). Co-occurring risk factors for alcohol dependence and habitual smoking. Alcohol Research and Health, 24(4), 233–241. [PMC free article] [PubMed] [Google Scholar]
  7. Bijttebier P, Beck I, Claes L, & Vandereycken W (2009). Gray’s Reinforcement Sensitivity Theory as a framework for research on personality-psychopathology associations. Clin Psychol Rev, 29(5), 421–430. [DOI] [PubMed] [Google Scholar]
  8. Bonar EE, Goldstick JE, Collins RL, Cranford JA, Cunningham RM, Chermack ST, … Walton MA (2017). Daily associations between cannabis motives and consumption in emerging adults. Drug Alcohol Depend, 178, 136–142. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Boog M, Goudriaan AE, van de Wetering BJ, Deuss H, & Franken IH (2013). The concepts of rash impulsiveness and reward sensitivity in substance use disorders. Eur Addict Res, 19(5), 261–268. [DOI] [PubMed] [Google Scholar]
  10. Calahan D, Cisin IH, & Crossley HM (1969). American Drinking Practices. New Brunswick, NY: Rutgers Center of Alcohol Studies. [Google Scholar]
  11. Carpenter MJ, Baker NL, Gray KM, & Upadhyaya HP (2010). Assessment of nicotine dependence among adolescent and young adult smokers: a comparison of measures. Addict Behav, 35(11), 977–982. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Casey BJ, & Jones RM (2010). Neurobiology of the adolescent brain and behavior: implications for substance use disorders. J Am Acad Child Adolesc Psychiatry, 49(12), 1189–1201; quiz 1285. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Castellanos-Ryan N, Briere FN, O’Leary-Barrett M, Banaschewski T, Bokde A, Bromberg U, … Consortium, Imagen. (2016). The structure of psychopathology in adolescence and its common personality and cognitive correlates. J Abnorm Psychol, 125(8), 1039–1052. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Clark SL, Gillespie NA, Adkins DE, Kendler KS, & Neale MC (2016). Psychometric modeling of abuse and dependence symptoms across six illicit substances indicates novel dimensions of misuse. Addict Behav, 53, 132–140. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Cobb CO, Soule EK, Rudy AK, Sutter ME, & Cohn AM (2018). Patterns and Correlates of Tobacco and Cannabis co-use by Tobacco Product Type: Findings from the Virginia Youth Survey. Subst Use Misuse, 53(14), 2310–2319. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Cohn AM, Abudayyeh H, Perreras L, & Peters EN (2018). Patterns and correlates of the co-use of marijuana with any tobacco and individual tobacco products in young adults from Wave 2 of the PATH Study. Addict Behav, 92, 122–127. [DOI] [PubMed] [Google Scholar]
  17. Dawe S, & Loxton NJ (2004). The role of impulsivity in the development of substance use and eating disorders. Neurosci Biobehav Rev, 28(3), 343–351. [DOI] [PubMed] [Google Scholar]
  18. De Pirro S, Galati G, Pizzamiglio L, & Badiani A (2018). The Affective and Neural Correlates of Heroin versus Cocaine Use in Addiction Are Influenced by Environmental Setting But in Opposite Directions. J Neurosci, 38(22), 5182–5195. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Delnevo CD, Gundersen DA, Hrywna M, Echeverria SE, & Steinberg MB (2011). Smoking-cessation prevalence among U.S. smokers of menthol versus non-menthol cigarettes. Am J Prev Med, 41(4), 357–365. [DOI] [PubMed] [Google Scholar]
  20. Dodeen HM (2003). Effectiveness of valid mean substitution in treating missing data in attitude assessment. Assessment and Evaluation in Higher Education, 28, 505–513. [Google Scholar]
  21. Dougherty DM, Mathias CW, Dawes MA, Furr RM, Charles NE, Liguori A, … Acheson A (2013). Impulsivity, attention, memory, and decision-making among adolescent marijuana users. Psychopharmacology (Berl), 226(2), 307–319. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Evenden JL (1999). Varieties of impulsivity. Psychopharmacology, 149, 348–361. [DOI] [PubMed] [Google Scholar]
  23. Eysenck HJ (1997). Addiction, personality, and motivation. Human Psychopharmacology, 12, 79–87. [Google Scholar]
  24. Fraken IHA, & Muris P. (2006). BIS/BAS personality characteristics and college student’s substance use. Personality and Individual Differences, 40, 1497–1503. [Google Scholar]
  25. Franken IH, Muris P, & Georgieva I (2006). Gray’s model of personality and addiction. Addict Behav, 31(3), 399–403. [DOI] [PubMed] [Google Scholar]
  26. Garrison KA, Yip SW, Balodis IM, Carroll KM, Potenza MN, & Krishnan-Sarin S (2017). Reward-related frontostriatal activity and smoking behavior among adolescents in treatment for smoking cessation. Drug Alcohol Depend, 177, 268–276. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Gentzke AS, Creamer M, Cullen KA, Ambrose BK, Willis G, Jamal A, & King BA (2019). Vital Signs: Tobacco Product Use Among Middle and High School Students - United States, 2011–2018. MMWR Morb Mortal Wkly Rep, 68(6), 157–164. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Goodkind M, Eickhoff SB, Oathes DJ, Jiang Y, Chang A, Jones-Hagata LB, … Etkin A (2015). Identification of a common neurobiological substrate for mental illness. JAMA Psychiatry, 72(4), 305–315. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Goodwin RD, Pacek LR, Copeland J, Moeller SJ, Dierker L, Weinberger A, … Hasin DS (2018). Trends in Daily Cannabis Use Among Cigarette Smokers: United States, 2002–2014. Am J Public Health, 108(1), 137–142. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Gray. (1972). The psychophysiological nature of introversion-extraversion: A modification of Eysenck’s theory: Academic Press. [Google Scholar]
  31. Gray Jeffrey A. (1982). The neuropsychology of anxiety: An enquiry into the functions of the septo-hippocampal system. New York: Clarendon Press/Oxford University Press. [Google Scholar]
  32. Gullo MJ, & Dawe S (2008). Impulsivity and adolescent substance use: rashly dismissed as “all-bad”? Neurosci Biobehav Rev, 32(8), 1507–1518. [DOI] [PubMed] [Google Scholar]
  33. Hammond CJ, Potenza MN, & Mayes LC. (2011). Development of Impulse Control, Inhibition, and Self-Regulatory Behaviors in Normative Populations across the Lifespan. In Grant J & Potenza M (Eds.), Oxford Handbook of Impulse Control Disorders. New York, NY: Oxford University Press. [Google Scholar]
  34. Harden KP, Mann FD, Grotzinger AD, Patterson MW, Steinberg L, Tackett JL, & Tucker-Drob EM (2018). Developmental differences in reward sensitivity and sensation seeking in adolescence: Testing sex-specific associations with gonadal hormones and pubertal development. J Pers Soc Psychol, 115(1), 161–178. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Iacobucci D, Schneider MJ, Popovich DL, & Bakamitsos GA (2016). Mean centering helps alleviate “micro” but not “macro” multicollinearity. Behavior Research Methods, 48(4), 1308–1317. [DOI] [PubMed] [Google Scholar]
  36. Iacono WG, Malone SM, & McGue M (2008). Behavioral disinhibition and the development of early-onset addiction: common and specific influences. Annu Rev Clin Psychol, 4, 325–348. [DOI] [PubMed] [Google Scholar]
  37. Ivanov I, Schulz KP, London ED, & Newcorn JH (2008). Inhibitory control deficits in childhood and risk for substance use disorders: a review. Am J Drug Alcohol Abuse, 34(3), 239–258. [DOI] [PubMed] [Google Scholar]
  38. Johnston LD, O’Malley PM, Miech RA, Bachman JG, & Schulenberg JE. (2015). Monitoring the Future national survey results on drug use: 1975–2014: Overview, key findings on adolescent drug use. Ann Arbor, MI: Institute for Social Research, The University of Michigan. [Google Scholar]
  39. Johnston LD, O’Malley PM, Bachman JG, & Schulenberg J. (2006). Teen drug use down but progress halts among youngest teens. Retrieved March 30, 2013, from www.monitoringthefuture.org
  40. Kambouropoulos N, & Staiger PK. (2007). Personality, behavioural, and affective characteristics of hazardous drinkers. Personality and Individual Differences, 42, 213–224. [Google Scholar]
  41. Karoly HC, Bryan AD, Weiland BJ, Mayer A, Dodd A, & Feldstein Ewing SW (2015). Does incentive-elicited nucleus accumbens activation differ by substance of abuse? An examination with adolescents. Dev Cogn Neurosci, 16, 5–15. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. King KM, Fleming CB, Monahan KC, & Catalano RF (2011). Changes in self-control problems and attention problems during middle school predict alcohol, tobacco, and marijuana use during high school. Psychol Addict Behav, 25(1), 69–79. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Knyazev GG (2004). Behavioural activation as predictor of substance use: mediating and moderating role of attitudes and social relationships. Drug Alcohol Depend, 75(3), 309–321. [DOI] [PubMed] [Google Scholar]
  44. Kong G, Smith AE, McMahon TJ, Cavallo DA, Schepis TS, Desai RA, … Krishnan-Sarin S (2013). Pubertal status, sensation-seeking, impulsivity, and substance use in high school-aged boys and girls. J Addict Med, 7(2), 116–121. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Krishnan-Sarin S, Reynolds B, Duhig AM, Smith A, Liss T, McFetridge A, … Potenza MN (2007). Behavioral impulsivity predicts treatment outcome in a smoking cessation program for adolescent smokers. Drug Alcohol Depend, 88(1), 79–82. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Leeman RF, Hoff RA, Krishnan-Sarin S, Patock-Peckham JA, & Potenza MN (2014). Impulsivity, sensation-seeking, and part-time job status in relation to substance use and gambling in adolescents. J Adolesc Health, 54(4), 460–466. [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Lopez-Vergara HI, Colder CR, Hawk LW Jr., Wieczorek WF, Eiden RD, Lengua LJ, & Read JP (2012). Reinforcement sensitivity theory and alcohol outcome expectancies in early adolescence. Am J Drug Alcohol Abuse, 38(2), 130–134. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Loxton NJ, & Dawes S. (2001). Alcohol abuse and dysfunctional eating in adolescent girls: the influence of individual differences in sensitivity to reward and punishment. International Journal of Eating Disorders, 29, 455–462. [DOI] [PubMed] [Google Scholar]
  49. Lubman DI (2007). Addiction neuroscience and its relevance to clinical practice. Drug Alcohol Rev, 26(1), 1–2. [DOI] [PubMed] [Google Scholar]
  50. Lyvers M, Duff H, Basch V, & Edwards MS (2012). Rash impulsiveness and reward sensitivity in relation to risky drinking by university students: potential roles of frontal systems. Addict Behav, 37(8), 940–946. [DOI] [PubMed] [Google Scholar]
  51. Mahu IT, Doucet C, O’Leary-Barrett M, & Conrod PJ (2015). Can cannabis use be prevented by targeting personality risk in schools? Twenty-four-month outcome of the adventure trial on cannabis use: a cluster-randomized controlled trial. Addiction, 110(10), 1625–1633. [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Mathers M, Toumbourou JW, Catalano RF, Williams J, & Patton GC (2006). Consequences of youth tobacco use: a review of prospective behavioural studies. Addiction, 101(7), 948–958. [DOI] [PubMed] [Google Scholar]
  53. McClure EA, Baker NL, Sonne SC, Ghitza UE, Tomko RL, Montgomery L, … Gray KM (2018a). Tobacco use during cannabis cessation: Use patterns and impact on abstinence in a National Drug Abuse Treatment Clinical Trials Network study. Drug Alcohol Depend, 192, 59–66. [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. McTeague LM, Huemer J, Carreon DM, Jiang Y, Eickhoff SB, & Etkin A (2017). Identification of Common Neural Circuit Disruptions in Cognitive Control Across Psychiatric Disorders. Am J Psychiatry, 174(7), 676–685. [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. Meier E, & Hatsukami DK (2016). A review of the additive health risk of cannabis and tobacco co-use. Drug Alcohol Depend, 166, 6–12. [DOI] [PubMed] [Google Scholar]
  56. Moeller FG, Barratt ES, Dogherty DM, Schmidt JM, & Swan AC (2001). Psychiatric aspects of impulsivity. Am J Psychiatry, 158, 1783–1793. [DOI] [PubMed] [Google Scholar]
  57. Morean ME, Kong G, Camenga DR, Cavallo DA, Carroll KM, Pittman B, & Krishnan-Sarin S (2015). Contingency management improves smoking cessation treatment outcomes among highly impulsive adolescent smokers relative to cognitive behavioral therapy. Addict Behav, 42, 86–90. [DOI] [PMC free article] [PubMed] [Google Scholar]
  58. Moreno M, Estevez AF, Zaldivar F, Montes JM, Gutierrez-Ferre VE, Esteban L, … Flores P (2012). Impulsivity differences in recreational cannabis users and binge drinkers in a university population. Drug Alcohol Depend, 124(3), 355–362. [DOI] [PubMed] [Google Scholar]
  59. Morin JG, Afzali MH, Bourque J, Stewart SH, Seguin JR, O’Leary-Barrett M, & Conrod PJ (2019). A Population-Based Analysis of the Relationship Between Substance Use and Adolescent Cognitive Development. Am J Psychiatry, 176(2), 98–106. [DOI] [PubMed] [Google Scholar]
  60. Nestor L, Hester R, & Garavan H (2010). Increased ventral striatal BOLD activity during non-drug reward anticipation in cannabis users. Neuroimage, 49(1), 1133–1143. [DOI] [PMC free article] [PubMed] [Google Scholar]
  61. O’Connor RM, & Colder CR (2005). Predicting alcohol patterns in first-year college students through motivational systems and reasons for drinking. Psychol Addict Behav, 19(1), 10–20. [DOI] [PubMed] [Google Scholar]
  62. O’Connor RM, Stewart SH, & Watt MC. (2009). Distinguishing BAS risk for university students’s drinking, smoking, and gambling behaviors. Personality and Individual Differences, 46, 514–519. [Google Scholar]
  63. Patton GC, Carlin JB, Coffey C, Wolfe R, Hibbert M, & Bowes G (1998). Depression, anxiety, and smoking initiation: a prospective study over 3 years. Am J Public Health, 88(10), 1518–1522. [DOI] [PMC free article] [PubMed] [Google Scholar]
  64. Patton JH, Stanford MS, & Barratt ES (1995). Factor structure of the Barratt impulsiveness scale. J Clin Psychol, 51(6), 768–774. [DOI] [PubMed] [Google Scholar]
  65. Peters J, Bromberg U, Schneider S, Brassen S, Menz M, Banaschewski T, … Consortium, Imagen. (2011). Lower ventral striatal activation during reward anticipation in adolescent smokers. Am J Psychiatry, 168(5), 540–549. [DOI] [PubMed] [Google Scholar]
  66. Prokhorov AV, Pallonen UE, Fava JL, Ding L, & Niaura R (1996). Measuring nicotine dependence among high-risk adolescent smokers. Addict Behav, 21(1), 117–127. [DOI] [PubMed] [Google Scholar]
  67. Rabin RA, & George TP (2015). A review of co-morbid tobacco and cannabis use disorders: possible mechanisms to explain high rates of co-use. Am J Addict, 24(2), 105–116. [DOI] [PubMed] [Google Scholar]
  68. Ramo DE, Liu H, & Prochaska JJ (2012). Tobacco and marijuana use among adolescents and young adults: a systematic review of their co-use. Clin Psychol Rev, 32(2), 105–121. [DOI] [PMC free article] [PubMed] [Google Scholar]
  69. Schauer GL, & Peters EN (2018). Correlates and trends in youth co-use of marijuana and tobacco in the United States, 2005–2014. Drug Alcohol Depend, 185, 238–244. [DOI] [PubMed] [Google Scholar]
  70. Schepis TS, Desai RA, Cavallo DA, Smith AE, McFetridge A, Liss TB, … Krishnan-Sarin S (2011). Gender differences in adolescent marijuana use and associated psychosocial characteristics. J Addict Med, 5(1), 65–73. [DOI] [PMC free article] [PubMed] [Google Scholar]
  71. Shmulewitz D, Greene ER, & Hasin D (2015). Commonalities and Differences Across Substance Use Disorders: Phenomenological and Epidemiological Aspects. Alcohol Clin Exp Res, 39(10), 1878–1900. [DOI] [PMC free article] [PubMed] [Google Scholar]
  72. Simons JS, & Arens AM (2007). Moderating effects of sensitivity to punishment and sensitivity to reward on associations between marijuana effect expectancies and use. Psychol Addict Behav, 21(3), 409–414. [DOI] [PubMed] [Google Scholar]
  73. Sobell LC, & Sobell MB. (1992). Timeline follow-back: A Technique for Assessing Self-reported alcohol consumption. In Litten RZ & Allen JP (Eds.), Measuring alcohol consumption: Psychosocial and biochemical methods (pp. 41–72). Totowa, N.J.: Humana Press. [Google Scholar]
  74. Tackett JL (2006). Evaluating models of the personality-psychopathology relationship in children and adolescents. Clin Psychol Rev, 26(5), 584–599. [DOI] [PubMed] [Google Scholar]
  75. Torrubia R, Avila C, Molto J, & Caseras X (2001). The sensitivity to punishment and sensitivity to reward questionnaire (SPSRQ) as a measure of Gray’s anxiety and impulsivity dimensions. Personality and Individual Differences, 31, 837–862. [Google Scholar]
  76. Valjent E, Mitchell JM, Besson MJ, Caboche J, & Maldonado R (2002). Behavioural and biochemcial evidence for interactions between delta-9-tetrahyrocannabinol and nicotine. Br J Pharmacol, 135, 564–578. [DOI] [PMC free article] [PubMed] [Google Scholar]
  77. van Hell HH, Vink M, Ossewaarde L, Jager G, Kahn RS, & Ramsey NF (2010). Chronic effects of cannabis use on the human reward system: an fMRI study. Eur Neuropsychopharmacol, 20(3), 153–163. [DOI] [PubMed] [Google Scholar]
  78. van Hemel-Ruiter ME, de Jong PJ, Ostafin BD, & Wiers RW (2015). Reward sensitivity, attentional bias, and executive control in early adolescent alcohol use. Addict Behav, 40, 84–90. [DOI] [PubMed] [Google Scholar]
  79. VanderVeen JD, Hershberger AR, & Cyders MA (2016). UPPS-P model impulsivity and marijuana use behaviors in adolescents: A meta-analysis. Drug Alcohol Depend, 168, 181–190. [DOI] [PubMed] [Google Scholar]
  80. Vanyukov MM (2012). Substance-specific symptoms and general liability to addiction. Am J Psychiatry, 169(10), 1016–1018. [DOI] [PMC free article] [PubMed] [Google Scholar]
  81. Vassileva J, & Conrod PJ (2019). Impulsivities and addictions: a multidimensional integrative framework informing assessment and interventions for substance use disorders. Philos Trans R Soc Lond B Biol Sci, 374(1766), 20180137. [DOI] [PMC free article] [PubMed] [Google Scholar]
  82. Verdejo-Garcia A, Lawrence AJ, & Clark L (2008). Impulsivity as a vulnerability marker for substance-use disorders: review of findings from high-risk research, problem gamblers and genetic association studies. Neurosci Biobehav Rev, 32(4), 777–810. [DOI] [PubMed] [Google Scholar]
  83. Weinberger AH, Pacek LR, Wall MM, Zvolensky MJ, Copeland J, Galea S, … Goodwin RD. (2018). Trends in cannabis use disorder by cigarette smoking status in the United States, 2002–2016. Drug and Alcohol Dependence, 191(1), 45–51. [DOI] [PMC free article] [PubMed] [Google Scholar]
  84. Weschler D (1999). Wechsler Abbreviated Scale of Intelligence. New York, NY: Harcourt Brace & Co. [Google Scholar]
  85. Whitton AE, Treadway MT, & Pizzagalli DA (2015). Reward processing dysfunction in major depression, bipolar disorder and schizophrenia. Curr Opin Psychiatry, 28(1), 7–12. [DOI] [PMC free article] [PubMed] [Google Scholar]
  86. Willem L, Bijttebier P, Claes L, Sools J, Vandenbussche I, & Nigg JT. (2011). Temperamental characteristics of adolescents with substance abuse and/or dependence: a case-control study. Personality and Individual Differences, 50, 1094–1098. [Google Scholar]
  87. Zisserson RN, & Palfai TP (2007). Behavioral Activation System (BAS) sensitivity and reactivity to alcohol cues among hazardous drinkers. Addict Behav, 32(10), 2178–2186. [DOI] [PMC free article] [PubMed] [Google Scholar]

RESOURCES