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. Author manuscript; available in PMC: 2019 Mar 1.
Published in final edited form as: Transl Issues Psychol Sci. 2018 Mar;4(1):65–75. doi: 10.1037/tps0000144

A Behavioral Economic Analysis of Marijuana and Other Drug Use Among Heavy Drinking Young Adults

Lidia Z Meshesha 1, Bettina Utzelmann 2, Ashley A Dennhardt 1, James G Murphy 1
PMCID: PMC6005693  NIHMSID: NIHMS926782  PMID: 29930973

Abstract

Objective

Behavioral economic models predict that deficits in substance-free reward and future time orientation are associated with greater drug involvement, but this hypothesis has not been systematically investigated among young adult heavy drinkers. This study evaluated the association between drug use levels (heavy drinking (HD) only, HD + marijuana use, and HD + polysubstance use) and substance-free activity engagement, future orientation, and reward deprivation (comprised of reward experience and environmental suppressors of reward) among heavy drinkers.

Method

Participants were 358 college students who reported two or more past-month heavy drinking episodes (5/4 or more drinks in one occasion for a man/woman). The sample was 60% women, 79% Caucasian, and the average age was 18.76 (SD = 1.07) years. Participants completed measures of alcohol and drug use, weekly time allocation to various activities, future time orientation, and reward deprivation.

Results

Overall, any drug use was associated with less time spent engaged in academics and exercise, and lower future time orientation compared to HD only. Any drug use was associated with reward deprivation and HD + polysubstance use was associated with lower reward experience and environmental suppressors.

Conclusion

Drug use among heavy drinkers is associated with lower academic engagement and exercise, future orientation, and reward deprivation. These results provide support for behavioral economic models of drug abuse and suggest that prevention approaches should attempt to increase future orientation and availability of drug-free reward.

Keywords: Behavioral economics, alcohol, marijuana, polysubstance, reward deprivation, future time orientation

National survey data suggest that 58% of college students report drinking alcohol in the past month and 37.9% of those students are episodic heavy drinkers (defined as five/four or more drinks per occasion for men/women; Center for Behavioral Health Statistics and Quality, 2016; Tables 6.88B and 6.89B, respectively). Although past-month heavy drinking among college students has declined from 40.1% in 2005 to 31.9% in 2015, past-month illicit drug use has risen from 19.5% to 23.4% within the same decade (Johnston, O’Malley, Bachman, Schulenberg, & Miech, 2016; Tables 2–4 and 2–3, respectively). This increase is primarily driven by a growth in marijuana use (Johnston et al. 2016).

Table 2.

Correlations of depression, time allocation to academics and exercise, RPI total score and subscales, future time orientation, and future orientation.

1 2 3 4 5 6 7 8
1. Time Academics --
2. Time Exercise 0.01 --
3. Future Orientation .21** 0.09 --
4. RPI Total Score .12* .25** .34** --
5. RPI Environmental Suppression −0.10 −.20** −.27** −.86** --
6. RPI Reward Experience .10* .22** .30** .82** −.40** --
7. Typical Drinks Per Week −.27** −0.03 −0.04 −0.04 0.05 −0.01 --
8. Depression −.16** −.24** −.23** −.65** .58** −.50** −.01 --
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Note: RPI = Reward Probability Index

*

p < .05;

**

p < .01.

Consequences of college drinking and drug use are well documented and range from missing classes to significant health, social, and legal problems (White & Hingson 2014; Buckner, Ecker, & Cohen, 2010; Hall & Degenhardt, 2009). Further, consequences are especially severe for those who combine alcohol and drugs use (McCabe, Cranford, Morales, & Young, 2006). Hospitalizations for alcohol overdoses without other drugs involved increased 25% among 18- to 24-year-olds from 1999 to 2008, hospitalizations for overdoses involving other drugs but not alcohol increased 55% over the same time period, while those involving both alcohol and drugs rose 76% (White, Hingson, Pan, & Yi, 2011). In addition to these acute proximal risks, research is needed to determine whether incremental levels of drug use among young adult heavy drinkers is associated with diminished prosocial activity engagement, access to reward, and ability to value the future.

Behavioral Economic Approach

Behavioral economics is a translational research approach that integrates principles from operant psychology, behavioral pharmacology, and microeconomics to systematically characterize individuals’ environmental context and decision-making. Substance misuse or addiction is viewed as a pathological manifestation of a basic reinforcement process (Bickel, Johnson, Koffarnus, MacKillop, & Murphy, 2014) characterized by a consistent overvaluation of substance-related rewards relative to substance-free rewards. This reinforcer pathology is due in part to the combination of easily accessible alcohol and drugs, deficits in the availability of substance-free rewards, and a tendency to exhibit an excessive preference for immediate versus future rewards despite long-term negative outcomes (Bickel et al., 2014). Low future time orientation has been associated with higher rates of alcohol abuse (Keough, Zimbardo, & Boyd, 1999), craving and alcohol problems (Soltis, McDevitt-Murphy, & Murphy, 2017), and poor response to a brief alcohol intervention (Murphy et al., 2012). Delay discounting, a related but empirically distinct construct that refers to the degree to which a person devalues a reward as a function of time, has also been linked to increased alcohol and drug use in numerous studies (Amlung, Vedelago, Acker, Balodis, & McKillop, 2016; Vuchinich & Simpson, 1998),).

A number of social and bio-developmental factors specific to young adults and college students make this population particularly vulnerable to reinforcer pathologies. Young adults tend to be more sensitive to positive rewarding properties of various drugs, including the social rewards that often accompany alcohol/drug use (Foulkes & Blakemore, 2016), and are less sensitive to the aversive properties of drugs (Casey & Jones, 2010). Moreover, they tend to be more impulsive (steeper delay discounting) and inclined toward risk-taking and reward seeking behavior (Gardner & Steinberg, 2005). These tendencies are further amplified by the transition to a college environment that is characterized by decreased (parental) supervision and increased access to alcohol and peers who drink (Doremus-Fitzwater, Varlinskaya, & Spear, 2010). College students also tend to have more unstructured time and fewer responsibilities that would restrict them from spending time drinking and using drugs (Schulenberg & Maggs, 2002). Frequent alcohol and drug use may lead to a self-perpetuating process in which neurobiological changes diminish the individual’s sensitivity to substance-free natural rewards like sex, food, or exercise, which in turn increases the relative preference for the drug (Volkow & Baler 2014; Koob, 2011). Anhedonia is a common symptom among substance-using populations (Garfield, Lubman, & Yucel, 2014) that has not been carefully studied among young adult substance abusers.

In partial support of the behavioral economic reinforcer pathology model, Meshesha, Dennhardt, and Murphy (2015) found that polysubstance-using college students reported less overall recent substance-free activity participation and enjoyment, and spent less time exercising, studying, and participating in extracurricular activities than heavy drinkers who did not use illicit drugs. Other studies with college students have also found inverse relations between drug use and engagement in prosocial and academic activities (Buckner et al., 2010; Fenzel, 2005), but have not isolated the specific effects of level of drug involvement above and beyond heavy drinking.

Study Aims

This study extends previous research on the behavioral economics of drug use by examining time allocation to academics and exercise, the degree of future orientation, and substance-free reward deprivation in clinically meaningful groups of college student heavy drinkers with varying degrees of drug involvement. Participants were categorized into one of three groups: 1) Heavy Drinking (HD), 2) HD and Marijuana use, 3) HD and Polysubstance use in the past month. Further, exploratory analyses separated marijuana users into light and heavy users to asses for the effects of frequency of use. We hypothesize that drug use, particularly polysubstance use, will be associated with less time allocation to academics and exercise, less future time orientation, and greater reward deprivation compared to HD only.

Method

Participants

Participants were 358 college students from two large public universities who reported heavy drinking in the past month and were enrolled in a brief alcohol intervention trial (Clinical Trials Identifier NCT02834949). On average, participants were 18.76 (SD = 1.07) years old; the sample included 60% women and 79% Caucasian, 8.4% African American, 7.8% Multiracial, 2.8% Latino, and 1.4% Asian students. All data analyzed for this manuscript were collected during the baseline assessment prior to intervention delivery. Participants were recruited from undergraduate courses as well as campus wide research participation solicitation emails. Inclusion criteria were two or more heavy drinking episodes (4/5 drinks per occasion for women/men) in the past month, age between 18–25 years, full-time college status as a first or second year student, and employed less than 21 hours-per week.

Procedure

Both universities’ institutional review boards approved all study procedures. Data were collected between the years 2012–2015 from two states without legalized medical or recreational marijuana. Participants were screened to assess if they meet study inclusion criteria through a paper and pencil measure administered during classroom screenings or a corresponding online measure in email screenings. Eligible participants completed a 50-minute computerized self-report assessment battery individually in a private psychology laboratory and were compensated with a choice of psychology course credit or a $25 cash payment for completing the research appointment.

Measures

Alcohol and drug use

The Daily Drinking Questionnaire (Collins, Parks, & Marlatt, 1985) assessed average drinks consumed on each day of a typical week over the past month. Participants reported the number of days they used street drugs such as marijuana, cocaine, designer drugs (e.g., ecstasy, MDMA, etc.), hallucinogens, heroin, or methamphetamines in the past month.

Weekly time allocation

Participants were asked to report the number of hours they spent engaged in various activity categories in a typical week in the past month, including studying or completing homework assignments, attending classes, extracurricular activities, exercise, employment, alcohol and drug use, family, religious, community, fraternity or sorority, and web browsing (Meshesha et al., 2015). The percent of time allocated to the categories of academic engagement (completing homework, attending classes, and participating in extracurricular activities) and time spent exercising were computed by dividing the time allocated to either activity by all categories and multiplying by 100 (e.g., (time exercise/all other activity categories)*100). We specifically focused on time allocation to academic activities and exercise as they reflect two important substance-free reinforcement domains that have shown inverse relations with substance misuse (Murphy et al., 2005; Stoutenberg, Rethorst, Lawson, & Read, 2016).

Future time orientation

Future time orientation was assessed using the Consideration of Future Consequences – Short Version (CFC-S), an 8-item measure used to examine the extent to which individuals are influenced by the immediate versus distant consequences of their behavior (Strathman, Gleicher, Boninger, & Edwards, 1994; Petrocelli, 2003). Items on the CFC-S were summed to form a single scale that has demonstrated good internal consistency and test-retest reliability (Strathman et al., 1994) as well as convergent and construct validity (Adams & Nettle, 2009). Internal consistency of the CFC-S in this sample was good (α = .82).

Reward deprivation

Reward Deprivation was assessed using the Reward Probability Index (RPI; Carvalho et al., 2011). The Environmental Suppressor subscale of the RPI assesses environmental obstacles to engaging in rewards (environmental suppressors) and the Reward Probability subscale assesses ability to enjoy rewarding activities (reward experience). The items for the Environmental Suppressor subscale are reverse coded and the two subscales are summed to obtain the total score. Carvalho et al., 2011 reported that the RPI has excellent 2-week test-retest reliability, strong convergent validity with self-report measures of activity and avoidance, environmental reward, and an inverse relation with depression. Divergent validity was also demonstrated with weak correlations between the RPI and social support and somatic anxiety (Carvalho et al., 2011). In the current sample, internal consistency for the total score, Environmental Suppressors and Reward Probability subscales were excellent, α’s = .88, .85, .86, respectively.

Depression

Depressive symptoms were measured using the depression scale from the Depression, Anxiety and Stress Scale-21 (Lovibond & Lovibond, 1995). The DASS-21 is a reliable and valid measure of depression in college students (Mahmoud, Hall, & Staten, 2010). Internal consistency in the current sample was excellent (α = .89).

Results

Data Analysis

The depressive symptoms and typical weekly drinks variables were Winsorized using the recommendations of Tabachnick and Fidel (2012). The distribution for both variables were kurtotic and square root transformations were used to obtain normal distributions. A series of analyses of covariance (ANCOVAs) were used to examine drug use group differences on weekly time allocation to academics and exercise, future orientation, and the RPI total scale and two subscales. Bonferroni’s correction was used to adjust for multiple comparisons. Effect size Cohen’s d was computed for each statistically significant pairwise comparison using the equation d = M1 – M2/ spooled where spooled = √[(s12 + s220/2] (Cohen, 1988; Rosenthal & Rosnow, 1991). M1 and s1 denote the mean and standard deviation of one of the two groups in the contrast and M2 and s2 denote the mean and standard deviation of the second group. All ANCOVA models controlled for gender. Race (coded as Caucasian versus Minority) was included as a covariate in the model that assessed RPI Environmental Suppressors subscale as minority students reported greater scores on the scale compared to Caucasian students. Follow-up ANCOVAs were conducted that also included typical drinks per week and depression as covariates and any discrepancies from the primary findings were noted.

All participants reported heavy drinking and were categorized into one of three drug use groups that reflected varying levels of drug use severity: heavy drinking only, (HD, n = 151), heavy drinking plus marijuana use with no other drug use (1 or more days in past-month days; HD + Marijuana, n = 105), and heavy drinking plus two or more different illicit drugs used in the past month (HD + Polysubstance group, n = 102). Among HD + Polysubstance users, 99% also reported use of marijuana, 20.6% cocaine, 19.6% hallucinogen, 15.7% designer drugs, 3% heroin, and non-medical use of prescription drugs with 64.7% stimulant use, 28.4% opioid use, and 27.5% sedative use. Further, a set of exploratory ANCOVA analyses were conducted that assessed marijuana users separated into two groups (HD + Light Marijuana with 1–4 days of past month use, n = 52 and HD + Heavy Marijuana with 4 or more days of past month use, n = 53).

Descriptive Statistics

On average participants reported 16.39 (SD = 11.82) typical drinks per week, and 5.88 (SD = 8.73) past month days of marijuana use (Table 1). Women were less likely to be in the drug using groups compared to men (χ2(2, N = 358) = 6.34, p = .025), and on average men (M = 20.66, SD = 13.56) reported heavier alcohol consumption in a typical week compared to women (M =13.55, SD = 9.53), t(356) = 5.83, p < .001. There were no racial (χ2(2, N = 358) = 2.67, p = .518), Greek life affiliation status (χ2(2, N = 3589) = 1.95, p = .724), site (χ2(2, N = 3589) =.90, p = .954), age (F (2, 350) = 2.48, p = .085), or depression (F (2, 358) = 2.89, p = .057) differences between the five drug use groups. Table 2 presents correlations among study outcome variables, typical weekly drinks, and depression.

Table 1.

Means and standard deviations of typical drinks per week, past month marijuana and other drug use, and depression among the five drug use groups.

Heavy Drinking (n = 151) HD + Marijuana (n = 105) HD + Polysubstance (n = 102) All Participants (n = 358)
Gender Men 52 (14.5%) 40 (11.2%) 51 (14.2%) 143 (39.9%)
Women 99 (27.7%) 65 (18.2%) 51 (14.2%) 215 (60.1%)
Race Caucasian 123 (34.4%) 77 (21.5%) 82 (22.9%) 282 (78.8%)
Minority 28 (7.8%) 28 (7.8%) 20 (5.6%) 76 (21.2%)
Drinks per Week 12.38 (8.67)b,c 16.70 (11.13)a,c 22.01 (14.10)a,b 16.39 (11.82)
Past Month MJ Days 0.00 (0.00) b,c 8.07 (7.86)a,c 12.34 (10.35)a,b 5.88 (8.72)
Past Month Illicit Drug Use Days 0.00 (0.00)e 0.00 (0.00)e 1.25 (2.84)a,b 0.35 (1.61)
Depression 6.31 (8.65) 7.35 (7.29) 8.91 (10.56) 7.36 (8.92)
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Note: HD = Heavy Drinking. MJ = marijuana. Significant differences compared between groups are annotated by respective superscripts aHeavy Drinking, bHD+ Marijuana, and cHD+ Polysubstance.

Weekly Time Allocation

There was a main effect of drug use on weekly time allocation to academics (Table 3). Pairwise comparisons indicated that HD was associated with greater academic time allocation compared to HD + Marijuana (p = .02, d = .34) and HD + Polysubstance use (p < .001, d = .68). In the exploratory analyses, HD was associated with greater academic time allocation compared to HD + Heavy Marijuana (p = .001); HD + Light Marijuana use was associated with greater academic time allocation compared to HD+ Heavy Marijuana use (p = .04) and HD + Polysubstance (p = .006).

Table 3.

Estimated marginal mean scores and standard error (SE) values from analyses of covariance examining academic and exercise time allocation, future time orientation, and Reward Probability Index (RPI) total and subscales controlling for gender. The RPI Environmental Suppression subscale also controlled for race in addition to gender.

HD M (SE) HD + Marijuana
M (SE) 		HD + Polysubstance
M (SE) 		F (df) ηp2 p
Time Academics 41.91 (1.04)b,c 37.49 (1.24)a 33.74 (1.27)a 12.68 (2, 358) .07 <.001
Time Exercise 7.89 (0.45)b,c 6.12 (0.54)a 6.04 (0.55)a 4. 71 (2, 358) .03 .026
Future Time Orientation 28.49 (0.50)c,e 25.58 (0.60)a 25.94 (0.61)a 4.81 (2, 358) .05 <.001
RPI Total 64.87 (0.73)b,c 62.03 (0.87)a 59.43 (0.88)a 11.40 (2, 357) .06 <.001
RPI Environmental Suppression 16.90 (0.45)e 18.53 (0.54)c 20.51 (0.55)a,b 12.89 (2, 358) .07 <.001
RPI Reward Experience 36.69 (0.42)c 35.66 (0.50) 34.91 (0.51)a 3.72 (2, 357) .02 .025
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Note: RPI = Reward Probability Index. Significant differences compared between groups are annotated by respective superscripts aHeavy Drinking (HD), bHD+ Heavy Marijuana, and cHD+ Polysubstance.

There was a main effect of drug use on weekly time allocation to exercise, with the HD group reporting greater time spent exercising than the drug using groups (Table 3). Pairwise comparisons indicated that HD was associated with greater time allocation to exercise compared to HD + Marijuana (p = .03, d = .29) and HD + Polysubstance use (p = .03, d = .27). There were no additional significant results in the exploratory analyses.

In the follow-up model that included depression and drinks per week as covariates there was no main effect of time spent exercising and no significant difference in the academic time allocation pairwise comparison between HD and HD + Marijuana, but all other findings were identical. The depression covariate accounted for 3.8% of the variance in the model assessing the effect of drug use on exercise whereas typical drinks per week accounted for 0.3% of the variance.

Future Time Orientation

There was a main effect of drug use status on the CFC-S (Table 3). Pairwise comparisons indicated that HD was associated with greater future time orientation compared to HD + Marijuana use (p = .001; d = .48) and HD + Polysubstance use (p = .004, d = .43). In the exploratory analyses, HD was associated with greater future time orientation compared to HD + Light Marijuana (p = .001). In the follow-up model that included depression and drinks per week as covariates, the results were identical to our initial analyses.

Reward Probability Index

There was a main effect of drug use status on the total RPI score (Table 3). Pairwise comparisons indicated that HD was associated with greater total RPI compared to HD + Marijuana (p = .037, d = .30) and HD + Polysubstance use (p < .001, d = .55). In the exploratory analyses, HD was associated with greater total RPI compared to HD + Heavy Marijuana (p = .041).

There was a main effect of drug use status on the environmental suppressors subscale (Table 3). Pairwise comparisons indicated HD was associated with lower environmental suppressors to reward compared to HD + Polysubstance use (p < .001, d = −.59), and HD + Marijuana was associated with lower environmental suppressors compared to HD + Polysubstance use (p = .031, d = −.28). There were no additional significant results in the exploratory analyses.

There was a main effect of drug use status on the RPI reward experience subscale (Table 3). Pairwise comparisons indicated HD was associated with greater reward experience compared to HD + Polysubstance use (p = .023, d = .30). There were no additional significant results in the exploratory analyses

In the follow-up analyses that controlled for depression and drinks per week, all findings for RPI total and environmental suppressors remained consistent with initial analyses except that there were no longer significant findings in the RPI total pairwise comparison between HD and HD + Marijuana and the environmental suppressors pairwise comparison between HD + Marijuana and HD + Polysubstance use. Further, the follow-up analyses with RPI reward experience subscale indicated no main effect when controlling for depression and drinks per week. The depression covariate accounted for 23.3% of the variance in the model assessing the effect of drug use on reward experience whereas typical drinks per week accounted for 0% of the variance.

Discussion

The present study examined the associations between drug use and time allocated to academic activities and exercise, future time orientation, and reward deprivation among heavy drinking college students. The overall pattern of results provide support for the behavioral economic assumption that drug use is associated with diminished engagement in constructive future-oriented activities such as academics and exercise, lower levels of future time orientation, and greater reward deprivation. These associations are specific to comorbid heavy drinking and illicit drug use and are independent of gender and race. Most of the results were also significant in models that controlled for drinks per week and drug use. Our results extended previous research by examining the specific role of marijuana use level and polysubstance use among heavy drinkers.

The findings that heavy drinking students who are also marijuana or polysubstance users spend less time in academic activities than heavy drinkers with no other drug use and the findings from our exploratory analyses that heavy marijuana use is associated with less academic time allocation compared to light marijuana use extends the broader literature that suggests that greater substance use is associated with less engagement in constructive substance-fee activities (Buckner et al., 2010, Meshesha et al., 2015). This is consistent with prior research suggesting that drug use is associated with less time spent specifically on academics (Arria, Caldeira, Bugbee, Vincent, & O’Grady, 2015, Meshesha et al., 2015), and that increases in marijuana use are related to poorer academic performance (Arria et al., 2015; Marie & Zölitz, 2015). Further, a recent study found that lower academic engagement at baseline predicted greater alcohol use at one-year follow-up among drug using college students (Meshesha, Pickover, Teeters, & Murphy, 2017).

The main effect for drug use on time spent exercising is consistent with general behavioral economic and other theories of addiction that suggest that drug use is associated with less engagement in natural rewards (Volkow & Baler, 2014). The inverse association between exercise and substance use is a consistent finding in adult samples (Lynch, Peterson, Sanchez, Abel, & Smith, 2013; Smith & Lynch, 2012), yet studies in college populations have generally found that exercise is either positively or not associated with substance use (Meshesha et al., 2015, 2017, Moore & Werch, 2008; Musselmann & Rutledge, 2010). Our results suggest that heavy drug use may be associated with less exercise, and that this effect may be due to the presence of depressive symptoms among heavy drinkers who use drugs.

Consistent with our hypothesis and with previous research, there was a main effect of drug use on future time orientation (Keough et al., 1999; Amlung et al., 2016) and HD was associated with greater future time orientation compared to HD + Marijuana use and HD + Polysubstance use. In general, these results suggest that heavy drinkers who also use drugs are less likely to organize their behavior around future goals and outcomes compared to heavy drinkers who do not use drugs.

The finding that drug use is associated with the RPI reward deprivation total score is consistent with behavioral economic research indicating that substance use is associated with environmental deficits in natural sources of reward (Bickel et al., 2012; Higgins, Heil, & Lussier, 2004). Specifically, we found that HD + Polysubstance use was significantly related to greater environmental restrictions to reward compared to HD and HD + Marijuana use. Further, HD + Polysubstance use was associated with diminished reward experience compared to HD. These results are consistent with previous findings that drug use among young adults is associated with deficits in substance-free reinforcement and lower hedonic responses to natural rewards (Meshesha et al., 2017). However, it should be noted that the finding for lower reward experience was no longer significant when our follow-up analyses controlled for depression. Suggesting that drug use in this college population may be not be implicated in the ability to experience reward above depression. Consistent with this result, a recent study with college students found that environmental suppressors but not reward experience was associated with alcohol use disorder symptoms above and beyond drinking level and depression (Joyner et al., 2016).

Implications

Our results extend the literature on college student substance abuse by indicating that comorbid heavy drinking and drug use is associated with less time engaged in two substance-free activities that are associated with important delayed rewards (academics and exercise), lower future time orientation, and reward deprivation. The finding that students who are heavy drinkers and either use marijuana or engage in polysubstance use spend less time in academic activities such as studying and attending class extends previous research linking substance use and academic impairment (American College Health Association, 2016; Wechsler, Lee, Kuo, & Lee, 2000). Further, our results suggest increased frequency of marijuana use (light versus heavy use) was also associated with lower academic time allocation. Increasing marijuana and other illicit drug use has been linked to discontinuous enrollment in college (Arria et al., 2013), skipping class, poorer GPAs, and eventually a longer time to graduation (Arria et al., 2015). These poor academic outcomes could potentially lead to dropout which is associated with negative social and health outcomes and potentially a more severe pattern of chronic substance abuse (Woolf & Braveman, 2011).

An important implication of these results is that students with these identified risk factors should be specifically targeted for intervention that increase time spent in substance-free rewarding activities. HD + Marijuana and HD + Polysubstance using students in this study had greater reward deprivation overall and more barriers to engaging in drug-free rewards (environmental suppressors) and drug use was associated with lower ability to experience reward. Our findings are consistent with previous behavioral economic research indicating that individuals with less access to substance-free rewards tend to report less reinforcement from substance-free activities and this is in turn associated with greater levels of substance use and related problems (Murphy & Dennhardt, 2016). Studies have shown that substance use may decrease when participation in substance-free activities is increased (Correia, Benson, & Carey, 2005, Murphy et al., 2005) and there are interventions that may be helpful in increasing substance-free activities (Murphy et al., 2012, Daughters, et al., 2008) and future time orientation (Snider, LaConte, & Bickel, 2016).

Strengths, Limitations and Future Directions

This study utilizes multiple measures of substance-free rewards including time allocation, reward probability scales, and future time orientation among heavy drinkers who have comorbid drug use. Given the frequent co-occurrence of heavy drinking and drug use, it is important to examine the impact of different levels of drug use on these behavioral economic risk factors. A limitation of this study was the cross-sectional design. Although behavioral economic theory assumes the observed relations are bidirectional (Bickel et al., 2012), examining the association between different categories of substance users and time spent in academic activities and exercise, future time orientation, and reward deprivation longitudinally would allow us to model the direction of the relations over time. Further, because all participants in the study were heavy drinkers, we could not examine the relations between the behavioral economic variables and drug use among students who were not heavy drinkers. Participants in the drug using groups drank more than those in the heavy drinking only category. This study conducted supplemental analyses that controlled for drinking level, however, future studies should include drug using groups whose drinking does not exceed that of the comparison group. Lastly, this study used self-report, retrospective measures of alcohol and drug use, activity engagement, and reward. Although all measures used are validated, objective measures or real-time reporting of these constructs could provide more accurate data.

Conclusions

Despite these limitations, this study extends the literature by demonstrating an association between comorbid heavy drinking and varying levels of illicit drug use and decreased time spent on academics and exercise, future time orientation, and greater reward deprivation. Students who are heavy-drinkers and use other drugs are likely to under engage in constructive and future oriented activities and experience diminished available rewards in their environment. These risk factors may lead to a more intractable pattern of alcohol and drug use that may be unlikely to respond to standard intervention approaches that only target motivation to reduce drinking (Murphy et al., 2005).

Public Significance Statement.

This study suggests that drug use among college students is related to less academic engagement and exercise, consideration of the future, and substance-free reward deprivation. These results provide support for prevention efforts focused on increasing future orientation and the availability of rewarding alternatives to drug use.

Acknowledgments

Funding:

  1. The National Institute on Alcohol Abuse and Alcoholism (NIAAA) provided funding for this study: 3R01AA020829- 02S1

  2. Lidia Meshesha, M.S. received NIAAA grant support while completing this manuscript: 1F31 AA024381-01

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