Running head RESEARCH PAPER PROPOSAL .docx

Running head: RESEARCH PAPER PROPOSAL
1
Research paper proposal
Two cognitive domain articles:
The first cognitive domain article is Evaluating a Selective
Prevention Programme for Binge
Drinking Among Young Adolescents: Study Protocol of a
Randomized Controlled Trial.
● Study goals: substance abuse is danger for the teenager, but
having a prevention
programme can reduce the danger from substance abuse.
● Subjects: substance abuse prevention programme for alcohol-
related risks to
cognitive functions teenager.
● In the results, I found that teenages who are heavy abuse of
alcohol seems have
the risk development of cognitive abilities; and teenagers who
abuse alcohol will
develop higher damage cognitive functions than in adult.

● A possible campus resources to address the issue is wellness
& health
promotion-Alcohol Consultations. In the campus Alcohol
Consultations, it
provide the individual alcohol consultation to increase the
cognition of alcohol
damage for human development.
The second cognitive domain article is Cognitive Predictors of
Problem Drinking and AUDIT
Scores Among College Students.
● Study goals: within substance abuse, what’s the differences in
cognitive processes.
● Subjects: substance abuse is relate to a range of differences in
cognitive processes.
2
● In the results, I found that alcohol user develop attentional
bias and impulsive decision
than the others who are not a alcohol drinker; And the heavy
alcohol abuser are likely to
develop the cognitive disability over twice than the non alcohol
abuser.

● Overall, in San Jose State University, a possible campus
resources to address the issue is
Student Health Advisory Committee (SHAC). SHAC as an
advisory body has provide
quality advisory services for student to address the health
problems on different domains.
Two social domain articles:
The first social domain article is Prevention and Treatment of
College Student Drug Dse: A
Review of the Literature.
● Study goal: to discover how the individual will get influence
from the peer
interaction through the social.
● Subjects: social influences.
● In the results, I found that peer who abuse alcohol and drug
will influence and
cause the higher rate of the people around them on abuse; And
social norms
divided into two parts, which are descriptive norms and
injunctive norms. These
two norms are shows the individual will get influence from the
misperceive of
others action, thus affecting their own behavior.

● A possible campus resources to address the issue is Health
and Wellness Related
Groups (Peer Health Educators).
The second social domain article is Peer Associations for
Substance Use and Exercise in a
College Student Social Network.
3
● Study goals: find out what is in the university that causes
students to have
negative and unhealthy behaviors.
● Subjects: socialization.
● In the results, I found that college is the critical period to
make student develop
negative behavior, which there are the increase rate on the use
of alcohol and
marijuana; Socialization is a problem that most of the student
are choose to
adhere to norm of behavior with the social group. When there
are some alcohol
abuser appear in their social life, the negative behavior will

subsequent spread.
● In San Jose State University, a possible campus resources to
address the issue is
Health and Wellness Related Groups (Peer Health Educators).
In this program,
students teach their peers on preventive health through
workshops, presentations,
health fairs, and discussions to increase the awareness of
negative social
phenomenon.
Two emotion domain articles:
The first emotion domain article is Personality, Marijuana
Norms, and Marijuana Outcomes
Among College Students.
● Study goals: substance abuse have negative effect on
personality traits.
● Subjects: personality traits.
● In the Results, I found that Marijuana user has relate to the
negative personality
traits on impulsivity, sensation-seeking/high openness to
experience, depression,

4
and anxiety sensitivity; Substance abuse will affect individual
beliefs and
emotion.
● A possible campus resources to address the issue is in Rest &
Relaxation area, it
provide Mindfulness Workshop with Counseling and
Psychological Services
(CAPS) to help student learn about mindfulness meditation, and
self-care
practices to develop positive emotion.
The second emotion domain article is The Influence of Multiple
Ecological Assets on Substance
Use Patterns of Diverse Adolescents.
● Study goals: What are the danger of substance abuse and how
school provide support for
the student to release substance use and make the abuser
develop a positive emotion.
● Subjects is about resilience traits.
● In the results. I found out that substance abuse increase the
damage of teenage psych

health; such as dependence on alcohol or drug, depression, and
anxiety; And student can
develop resilience traits through internal assets and external
assets in school to discourage
risk emotional.
● In San Jose State University, a possible campus resources to
address the issue is A
Thousand Stars Program. In this program, it help student
increase awareness about
suicide and provide valuable knowledge for student to help
them develop positive
emotion.
Two physical domain articles:
5
The first physical domain article is Substance Use, Aggression
Perpetration, and Victimization:
Temporal Co-occurrence in College Males and Females.
● Study goals: To find out what are the negative risk of
substance abuse will cause
physical injury.

● Subjects: physical injury/aggression.
● In the Results, it shows that substance abuse have relate to
involvement in
aggression; and the aggression perpetration includes physical or
sexual coercion.
● A possible campus resources to address the issue is Violence
Prevention
Presentations and Programs, which provide individual violence
prevention
consultations to support student.
The second physical domain article is Romantic Attachment,
Sexual Activity, and Substance
Use: Findings From Substance-Using Runaway Adolescents.
● Study goals: To find out what are the effect after college
student get sexual abuse in
childhood.
● Subjects: sexual abuse in childhood.
● In the Results, it shows that college student who have
experience on childhood abuse
will use substances in order to cope with negative experiences.
● A possible campus resources to address the issue is Student
Health Center. Student

Health Center are include different domains on health
counseling to help student address
the problem they needs.
6
References
Barnett, N., Ott, M., Rogers, M., Loxley, M., Linkletter, C., &
Clark, M. (2014). Peer
associations for substance use and exercise in a college student
social network.
Health Psychology : Official Journal of the Division of Health
Psychology,
American Psychological Association, 33(10), 1134-42.
Dennhardt, & Murphy. (2013). Prevention and treatment of
college student drug use: A
review of the literature. Addictive Behaviors, 38(10), 2607-

2618.
Lammers, Jeroen, Goossens, Ferry, Lokman, Suzanne,
Monshouwer, Karin,
Lemmers, Lex, Conrod, Patricia, Kleinjan, Marloes. (2011).
Evaluating a
selective prevention programme for binge drinking among
young adolescents:
Study protocol of a randomized controlled trial.(Study
protocol)(Clinical report).
BMC Public Health, 11, 126.
Letcher, A., & Slesnick, N. (2013). Romantic attachment,
sexual activity, and substance
use: Findings from substance-using runaway adolescents.
Journal of Applied
Social Psychology, 43(7), 1459-1467.
Margolin, G., Ramos, M. C., Baucom, B. R., Bennett, D. C., &
Guran, E. L. (2013).
Substance Use, Aggression Perpetration, and Victimization:
Temporal
7
Co-occurrence in College Males and Females. Journal of

Interpersonal Violence,
28(14), 2849–2872.
Murphy, & Garavan. (2011). Cognitive predictors of problem
drinking and AUDIT
scores among college students. Drug and Alcohol
Dependence,115(1-2), 94-100.
Pearson, Hustad, Neighbors, Conner, & Bravo. (2018).
Personality, marijuana norms,
and marijuana outcomes among college students. Addictive
Behaviors, 76,
291-297.
Shekhtmeyster, Zhanna, Sharkey, Jill, & You, Sukkyung.
(2011). The influence of
multiple ecological assets on substance use patterns of diverse
adolescents.(Report). School Psychology Review, 40(3), 386-
404.
Addictive Behaviors 38 (2013) 2607–2618
Contents lists available at SciVerse ScienceDirect

Addictive Behaviors
Prevention and treatment of college student drug use: A review
of the literature
Ashley A. Dennhardt, James G. Murphy ⁎
University of Memphis, 202 Psychology Building, Memphis, TN
38152, United States
H I G H L I G H T S
• Approximately 36% of all college students report drug use in
the past year.
• We review the literature on prevention and intervention of
college student drug use.
• Brief counselor-delivered motivational interventions may be
effective.
• The combination of individual and parent-based approaches
may also be effective.
• Prevention should address social/cognitive, personality and
environmental factors.
⁎ Corresponding author. Tel.: +1 901 678 2630.
E-mail addresses: [email protected] (A.A. Den
0306-4603/$ – see front matter © 2013 Elsevier Ltd. All
http://dx.doi.org/10.1016/j.addbeh.2013.06.006
a b s t r a c t
a r t i c l e i n f o
Keywords:
Substance use
Drug use
College
Prevention
Intervention

Drug use during the college years is a significant public health
concern. The primary goal of this paper is to
provide a comprehensive review of prevention and treatment
studies of college student drug use in order
to guide college prevention efforts and to inform and stimulate
new research in this area. First, established
risk factors for drug use were reviewed. High levels of
personality traits including, impulsivity,
sensation-seeking, negative emotionality, emotional
dysregulation, and personality disorder symptoms in-
crease risk for drug use. Drug use has also been linked to
overestimating normative levels of drug use and
experiencing negative life events, and specific motives for drug
use are linked to more problematic patterns.
There have been very few studies examining prevention and
treatment, but parent-based and in-person brief
motivational interventions appear to be promising. Longitudinal
studies of the development and course of
drug use among college students, as well as clinical trials to
evaluate novel theoretically-based intervention
and prevention programs that take into account established risk
factors for drug abuse are needed.
© 2013 Elsevier Ltd. All rights reserved.
Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . 2608
1.1. Epidemiology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . 2608
1.1.1. Prevalence of drug use in college students . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2608
1.1.2. Drug-related consequences in college students . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2609
2. Psychosocial factors related to college student drug use. . . . .

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2609
2.1. Demographic and lifestyle factors. . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2609
2.2. Personality and psychiatric comorbidity. . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2609
2.3. Social, cognitive, peer, and family influence on drug use . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2610
2.3.1. Social influences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . 2610
2.3.2. Drug use norms, motives and expectancies . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2610
3. Drug use prevention studies for college students . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2611
3.1. Uncontrolled studies. . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . 2611
3.2. Controlled studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . 2611
4. Drug use intervention studies for college students . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2613
4.1. Uncontrolled studies. . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . 2613
4.2. Controlled studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . 2613
nhardt), [email protected] (J.G. Murphy).
rights reserved.
http://crossmark.crossref.org/dialog/?doi=10.1016/j.addbeh.201
3.06.006&domain=pdf
mailto:[email protected]
mailto:[email protected]

http://www.sciencedirect.com/science/journal/03064603
2608 A.A. Dennhardt, J.G. Murphy / Addictive Behaviors 38
(2013) 2607–2618
5. Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . 2614
6. Future directions in college drug use prevention and
intervention . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2614
Role of funding sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2615
Contributors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . 2615
Conflict of interest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . 2615
Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . 2615
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . 2615
1. Introduction
Nationwide surveys reveal that rates of illicit drug use peak in
ado-
lescence and young adulthood and that college students account
for ap-
proximately 50% of this high-risk group (SAMHSA, 2010).
Alcohol and
illicit drug use among college students are major public health
prob-
lems. There has been extensive research on alcohol use in
college stu-
dents, including recent review papers (e.g., Carey, Scott-
Sheldon,
Carey, & DeMartini, 2007; Cronce & Larimer, 2011; Ham &
Hope,

2003), but drug use prevention and treatment has received less
atten-
tion, especially for drugs other than marijuana. The primary
goal of
this paper is to provide a comprehensive review of prevention
and
treatment studies of college student illicit drug use in order to
inform
and stimulate new research in this area. Because effective
prevention
and intervention approaches should take into account the factors
that
may contribute to drug use, this paper will begin with a review
of
established risk factors in college students. Although non-
college
students are also at high risk for drug use, these populations
may have
unique risk and protective factors related their distinct
environment
and role-functioning characteristics (Cleveland, Mallett, White,
Turrisi,
& Favero, 2013). Therefore, this review was limited to
prevention and
intervention studies for college students. Due to the paucity of
research
in this area, we included all studies in which an intervention or
preven-
tion program that targeted drugs was implemented and at least
one be-
havioral outcome was measured. Articles were identified using
PsychINFO and PubMed with search terms college, drug, drug
use, and
substance use as well as common and scientific names (where
applica-
ble) for each drug class (e.g. marijuana and cannabis).

Table 1
Prevalence of drug use in college students: data from three
major epidemiological studies.
Monitoring the future (MTF)
Lifetime Annual Current
Any illicit drug 49.2 36.3 21.4
Any illicit drug other than marijuana 24.3 16.8 8.2
Marijuana 46.6 33.2 19.4
Synthetic marijuana – 8.5 –
Inhalants 3.7 0.9 0.3
Hallucinogens 7.4 4.1 1.2
LSD 3.7 2.0 0.5
Other hallucinogens 6.9 3.4 0.8
Ecstasy (MDMA) 6.8 4.2 0.7
Cocaine 5.5 3.3 1.2
Heroin 0.6 0.1 a
Narcotics other than heroin 12.4 6.2 2.1
OxyContin – 2.4 –
Vicodin – 5.8 –
Amphetamines 13.4 9.3 4.5
Methamphetamine-Ice 0.2 0.1 a
Ritalin – 2.3 –
Adderall – 9.8 –
Sedatives 3.6 1.7 –
Tranquilizers 7.1 4.2 1.6
Steroids 1.1 0.2 0.2

All numbers are percentages.
“–” indicates data not available.
a Prevalence rate less than .05%.
1.1. Epidemiology
1.1.1. Prevalence of drug use in college students
There have been several large-scale studies of the prevalence
of drug use among college students in recent years and the most
comprehensive data sets are available from the Core Institute
(CORE), Monitoring the Future (MTF), and National Household
Survey on Drug Abuse (NHSDA). Prevalence rates listed reflect
drug use from 2010 to 2011. Past year prevalence of marijuana
use
was 31.3–33.2% and 11.0–16.8% for illicit drug use other than
mari-
juana (CORE, 2010; Johnston, O'Malley, Bachman, &
Schulenberg,
2012) (see Table 1 for prevalence rates from each study). The
most
commonly used drugs other than marijuana were Adderall
(9.8%),
amphetamines (9.3%), and synthetic marijuana (8.5%) (Johnston
et
al., 2012). Prevalence of current use (past 30 days) was
estimated
to be 21.4–22.0% for any drug use, 18.1–20.3% for marijuana
use
only, and 5.5–8.2% for drug use other than marijuana (CORE,
2010;
Johnston et al., 2012; SAMHSA, 2010). For current use, the
most
commonly used drugs after marijuana were misused
prescriptions
drugs (6.3%) (SAMHSA, 2010) and amphetamines (2.7–4.5%)

(CORE, 2010; Johnston et al., 2012). Among current marijuana
users, 7.2% of students reported using marijuana 3 times a week
or
more frequently, and 4.7% reported using marijuana at least
20 days every month (CORE, 2010; Johnston et al., 2012).
Overall,
although current prevalence rates for each individual drug type
other than marijuana are low, about 1 in 5 college students use
drugs (including marijuana) each month, and approximately 5%
of
students report near daily use.
CORE NHSDA
Lifetime Annual Current Lifetime Annual Current
– – – – – 22.0
– 11.0 5.5 – – 6.4
44.1 31.3 18.1 – – 20.3
– – –
3.1 1.0 0.4 – – 0.6
7.9 4.1 1.2 – – 1.9
– – – – – 0.3
– – – – – –
– – – – – 1.1
7.7 3.7 1.3 – – 1.6
– – – – – 0.2
– – – – – –
– – – – – 0.4
– – – – – –
10.1 5.0 2.7 – – 1.6
– – – – – 0.1
– – – – – –

6.7 3.4 1.5 – – 0.1
– – – – – 1.4
1.0 0.5 .4 – – –
2609A.A. Dennhardt, J.G. Murphy / Addictive Behaviors 38
(2013) 2607–2618
1.1.2. Drug-related consequences in college students
Drug use puts college students at risk for experiencing a range
of
adverse health, behavioral, and social consequences. Although
there
is a lack of representative data on drug use morbidity and
mortality
among college students, based on CDC estimates approximately
1000 college students die from drug overdoses each year
(Hingson
& White, 2010). College drug users are also at risk for
developing a
drug use disorder, characterized by the development of
physiological
and psychological tolerance, use of the drug even in the
presence of
adverse effects, and forgoing social, occupational, or
recreational
activities because of drug use (Budney, 2007). A recent study of
college freshman found that 9.4% of students met criteria for
cannabis
use disorder, and that this number jumped to 24.6% among past-
year
marijuana users (Caldeira, Arria, O'Grady, Vincent, & Wish,
2008).

Even in absence of a diagnosable disorder, the use of marijuana
is
associated with a wide range of consequences including legal
and
health problems (Presley, Meilman, & Cashin, 1996). Students
who
reported using marijuana and alcohol perform more poorly on
tests,
are more likely to miss class, and self-report more memory
problems
than students who reported only using alcohol (Rhodes, Peters,
Perrino, & Bryant, 2008; Shillington & Clapp, 2001). Among
students
who reported using marijuana 5 or more times in the past year,
40.1% reported experiencing concentration problems and 13.9%
reported missing class due to marijuana use (Caldeira et al.,
2008).
Marijuana users also receive lower grades in college and are
more
likely to drop out (Arria et al., 2013; Bell, Wechsler, &
Johnston,
1997; Buckner, Ecker, & Cohen, 2010). A longitudinal study
that
followed students over four years of college found that drug use
over-
all was predictive of college attrition even after controlling for
paren-
tal level of education (Martinez, Sher, Krull, & Wood, 2009).
Researchers have found that nonmedical users of prescription
stimu-
lants and analgesics skipped more classes (21%) than non-users
(9%)
(Arria, Caldeira, O'Grady, Vincent, Fitzelle, et al., 2008; Arria,
Caldeira,
O'Grady, Vincent, Johnson, et al., 2008). Furthermore, after
controlling

for high school GPA, nonmedical use of prescription drugs was
associ-
ated with lower GPAs by the end of the freshman year of
college and
skipping classes partially mediated this relationship.
Drug use has also been linked to a number of risky behaviors.
Caldeira et al. (2008) found of the students who reported using
marijua-
na 5 or more times in the past year, 24.3% reported putting
themselves
at risk for physical injury high. In one study, 47% of current
marijuana
users reporting driving after smoking marijuana (McCarthy,
Lynch, &
Pederson, 2007), and in another 18.6% reported driving while
high
(Caldeira et al., 2008). Drug use has also been linked to risky
sexual
behavior (Simons, Maisto, & Wray, 2010), including not using
condoms
(Caldeira et al., 2009), and STI diagnosis (Vivancos, Abubakar,
& Hunter,
2008). Thus, although most college student drug users use drugs
rela-
tively infrequently, they are nevertheless at significant risk for
a variety
of adverse social, legal, academic, and health-related
consequences.
2. Psychosocial factors related to college student drug use
2.1. Demographic and lifestyle factors
College men have higher annual prevalence rates of marijuana
and

most other drugs than women (Johnston et al., 2012; McCabe,
Cranford, Boyd, & Teter, 2007; McCabe, Morales, et al., 2007);
however,
findings are less consistent for nonmedical use of prescription
drugs
(Matzger & Weisner, 2007; McCabe, 2005; McCabe, Teter, &
Boyd,
2006). Caucasian students tend to have higher rates of drug use
than eth-
nic minority students (McCabe, 2005; McCabe, Knight, Teter, &
Wechsler, 2005; Mohler-Kuo, Lee, & Wechsler, 2003), although
more re-
search is needed to evaluate this and one study found that
Latino stu-
dents reported more drug use than other students (McCabe,
Morales,
et al., 2007). Students who identify as gay, lesbian, or bisexual
(Boyd,
McCabe, & d'Arcy, 2003; Reed, Prado, Matsumoto, & Amaro,
2010) or
who belong to a fraternity or sorority (McCabe, Knight et al.,
2005;
McCabe, Schulenberg, et al., 2005; McCabe, Teter, & Boyd,
2005) are
also more likely to use drugs. Identifying as religious
(Hammermeister,
Flint, Havens, & Peterson, 2001; Helm, Boward, McBride, &
Del Rio,
2002) and participating in college athletics have been shown to
be pro-
tective factors against drug use (Ford, 2007; Yusko, Buckman,
White, &
Pandina, 2008).
2.2. Personality and psychiatric comorbidity

Drug use has been studied in relation to a number of different
per-
sonality traits and models. Neuroticism, the tendency to
experience
strong negative emotions in reaction to stress; psychoticism, a
person-
ality characterized by interpersonal hostility; and, and
sensation-
seeking have been shown to be significantly associated with
meeting
criteria for drug dependence disorder, but interestingly these
traits do
not all predict drug use uniquely after controlling for alcohol
and tobac-
co use disorders (Grekin, Sher, & Wood, 2006; Sher, Bartholow,
& Wood,
2000). Sensation-seeking, however, has been found to predict
drug use
disorders seven years later even after controlling for alcohol-
use disor-
ders (Sher et al., 2000). Overall, both cross-sectional and
longitudinal
studies implicate sensation-seeking as a strong predictor of drug
use
in college students (Arria, Caldeira, O'Grady, Vincent, Fitzelle,
et al.,
2008; Arria, Caldeira, O'Grady, Vincent, Johnson, et al., 2008;
Low &
Gendaszek, 2002). Perceived harmfulness of the drug may
moderate
this relationship (for stimulant and marijuana use but not for
analgesic
use) with those who perceived the drug as being more harmful
being
less likely to report drug use despite high levels of sensation
seeking

(Arria, Caldeira, O'Grady, Vincent, Fitzelle, et al., 2008; Arria,
Caldeira,
O'Grady, Vincent, Johnson, et al., 2008).
Impulsivity refers to a general difficulty in inhibiting responses
and a
tendency to overvalue immediate relative to delayed rewards
(Madden
& Bickel, 2009). Although impulsivity has been consistently
associated
with increased risk for drug use in general adult samples (de
Wit,
2009; Reynolds, 2006), results with college samples have been
less
consistent. One possible explanation for these conflicting
results is
that impulsivity may be a risk factor for the use of certain types
of
drugs, but not others. Significant relationships were most often
found
in studies that examined drug use as a whole, while non-
significant re-
sults in some studies that examined marijuana use (Simons &
Carey,
2002, 2006; Simons, Neal, & Gaher, 2006; Stanford, Greve,
Boudreaux,
& Mathias, 1996). Delay discounting, a behavioral economic
measure
of impulsivity, has been shown to be associated with earlier age
of
first marijuana use and a greater number of illicit drugs used in
a college
sample (Kollins, 2003). It is possible that impulsivity may also
be related
to more general deficits in executive functioning and decision
making

(Hammers & Suhr, 2010), and that impulsivity may be both a
cause
and a consequence of drug use (Perry & Carroll, 2008). One
study
found that polysubstance users did worse on various tests of
executive
functioning than did controls who were matched on personality
charac-
teristics (including impulsivity) which suggests that these
individuals
have decreased decision-making and executive-functioning
capacity
(Hammers & Suhr, 2010). Finally, impulsive individuals may be
less
likely to attend college, which may restrict range on this
variable.
Research regarding the relationship between ADHD, a disorder
cha-
racterized by executive dysfunction and impulsivity, and drug
use has
been equivocal; one study found that students with ADHD are
more
likely to use marijuana and other illicit drugs (Rooney, Chronis-
Tuscano, & Yoon, 2011) while other studies suggest that ADHD
is
associated with elevated risk for marijuana use (Baker, Prevatt,
&
Proctor, 2012). One study demonstrated that reporting
experiencing a
greater number of conduct disorder symptoms prior to age 15
was
associated with more drug dependence symptoms in college
(Grekin
et al., 2006).
Studies have shown marijuana use to be related to negative

affect,
personality disorders, and psychoticism, but unrelated to social
(2013) 2607–2618
anxiety (Buckner, Bonn-Miller, Zvolensky, & Schmidt, 2007;
Buckner
et al., 2010). Students diagnosed with a psychiatric disorder are
at
greater risk for more consequences related to drug use (Dunn,
Larimer, & Neighbors, 2002; Goldstein, Flett, Wekerle, & Wall,
2009;
Rooney et al., 2011). One study found that 17% of the variance
in
drug use could be accounted for by scores on the Beck
Depression In-
ventory (Helm et al., 2002). Higher levels of negative affect
have been
linked to prescription stimulant misuse (both any use and
frequency
of use), and for experiencing more problems related to drug use
gen-
erally (Ford & Schroeder, 2009; James & Taylor, 2007; Teter,
Falone,
Cranford, Boyd, & McCabe, 2010). There is some evidence that
nega-
tive affect is a stronger risk factor for substance use in college
men
compared to women (Helm et al., 2002), and in Caucasian
compared
to African-American students (Mounts, 2004). Students who
report
experiencing panic attacks are more likely to have reported
using

sedatives, but not any other type of drugs (Deacon &
Valentiner,
2000) and students who engage in vomiting, fasting, and
laxative
use are more likely to use stimulants than other drugs
presumably
to aid in weight loss in connection with eating disorders (Dunn,
Neighbors, Fossos, & Larimer, 2009).
Affect dysregulation refers to difficulties in regulating affect
and
the resulting behavioral consequences that lead to impaired
function-
ing (Cole, Michel, & Teti, 1994). Affect lability, defined as
rapid shifts
in emotional expression, and impulsivity are key components of
affect dysregulation, which has been prospectively associated
with
increases in drug-related problems among college students
(Simons
& Carey, 2002, 2006). This is consistent with previous research
that
supports individual temperament characteristics as being more
pre-
dictive of problematic use patterns than social or environmental
factors (Glantz, 1999). Difficulties with negative emotions are
often related to the experience of traumatic or negative life
events
(e.g., the death of a loved one, or abuse) which is another
established
risk factor for drug among college students (Taylor, 2006).
2.3. Social, cognitive, peer, and family influence on drug use
2.3.1. Social influences
A great deal of research has demonstrated that peer attitudes

and
behaviors influence college substance use (Neighbors, Lee,
Lewis,
Fossos, & Larimer, 2007). Students who reported that they had
friends
who used drugs and alcohol regularly reported higher levels of
drug
use related problems themselves (Taylor, 2006), and the
influence
of peer use on drug use has been replicated with Asian
American
(Liu & Iwamoto, 2007) and African American students as well
(Pugh
& Bry, 2007). Although peers are a key influence on substance
use
behavior of college students, parent and sibling drug use can
also
increase risk for drug use (Brook, Brook, & Whiteman, 1999).
2.3.2. Drug use norms, motives and expectancies
Social Norms Theory is based on the idea that individuals
misperceive others' actions and perceptions related to a
behavior
(e.g., drug or alcohol use) and that these misperceptions in turn
influ-
ence the individual's behavior (Martens et al., 2006). Social
norms
have commonly been divided into two categories: descriptive
norms (e.g., perceived frequency of drug use by one's peers) and
injunctive norms (e.g., perceived approval of drug use by one's
peers) (Borsari & Carey, 2003). Research suggests that norms
account
for up to 42% of the variance in marijuana use even after
controlling

for sociodemographic variables (Lewis & Clemens, 2008).
Perceived
frequency of use for close friends (of either gender) is the
normative
reference group that is most predictive of personal use. Students
who
perceive that their friends use marijuana more frequently and
approve of marijuana use also tend to use more marijuana than
stu-
dents with fewer friends who used marijuana and have low
levels
of perceived friend approval of marijuana (Neighbors, Geisner,
&
Lee, 2008; Simons et al., 2006). Although these cross-sectional
studies
suggest a relationship between normative beliefs and use, the
direc-
tion of the relationship is unclear, and longitudinal studies are
neces-
sary to establish causality (Bustamante et al., 2009).
College students report a number of motives for using drugs and
studies have shown that student motives for using can be risk
factors
for greater levels of drug use and problems. A qualitative study
sug-
gested that the most common motives for nonmedical use of
prescrip-
tion drugs were getting high, partying, experimenting,
facilitating
social interactions, and helping to structure free time (Quintero,
2009); however, motives tend to differ for different types of
nonmedical
prescription drug use. The primary motives of nonmedical
prescription
pain medication tend to be to get high or to alleviate pain

(McCabe,
Teter, et al., 2005), and those who used for motives other than
to relieve
pain (e.g., to get high) had more problems related to their drug
use
(McCabe, Cranford, et al., 2007). The most common motives for
nonmedical use of prescription stimulants have been shown to
be to
help with concentration (58%), to increase and sustain alertness
(43%), and to “get high” (43%) (Teter, McCabe, Cranford,
Boyd, &
Guthrie, 2005). Nonmedical use of ADHD medication is
increasingly
common and it appears that the majority of students use
stimulants
in times of increased academic stress, but that some
(approximately
15% in one study) also use these drugs to get high or to enhance
partying (Arria, Caldeira, O'Grady, Vincent, Fitzelle, et al.,
2008; Arria,
Caldeira, O'Grady, Vincent, Johnson, et al., 2008; DeSantis,
Webb, &
Noar, 2008).
Simons, Correia, Carey, and Borsari (1998) found that
expansion
motives (using drugs to increase experiential awareness),
enhance-
ment motives (using drugs to enjoy the feeling of being high),
and
coping motives were associated with marijuana use, with a
stronger
relationship between coping, and use for women. Social and
confor-
mity motives were not significant predictors of marijuana use,
but

social motives were related to a greater number of problems
related
to use (Simons et al., 1998). It appears that enhancement
motives
are important in marijuana use, but more research is necessary
to
examine associations between other motives and marijuana use
and
related problems.
Expectations regarding the positive and negative effects of a
sub-
stance are related to levels of substance use and related
problems
(Gaher & Simons, 2007), with positive expectancies predicting
more use. Studies have shown that the most common
expectancies
associated with marijuana use include social facilitation,
tension
reduction/affect regulation, cognitive impairment and
perceptual
enhancement (Simons & Carey, 2006; Simons, Gaher, Correia,
&
Bush, 2005). Studies have also shown that marijuana users tend
to
associate marijuana with more positive outcomes and fewer
nega-
tive outcomes (Kilmer, Hunt, Lee, & Neighbors, 2007), and to
rate
the consequences as being less negative (Gaher & Simons,
2007)
compared to nonusers. Positive social expectancies are related
to
increased marijuana use and men report higher social
expectancies
for marijuana than do women (Neighbors et al., 2008). Research

on
expectancies and consequences is more equivocal. Students who
perceived driving after using marijuana as less dangerous and
reported greater perceived peer acceptance of driving after use
were more likely to report this behavior (McCarthy et al.,
2007).
Conversely, one study reported that frequency of use was not
related
to perceived likelihood of consequences (Kilmer et al., 2007),
and
another study reported that although greater expected likelihood
of negative outcomes were associated with less use, the
expected
degree of negativity of the outcomes was not related to level of
use
(Gaher & Simons, 2007). Although research on expectancies
and
drugs other than marijuana is sparse, one study found that use
of
prescription stimulants was related to greater expectancies
about
the drug's potential positive effects including staying awake,
study-
ing better, and losing weight (Carroll, McLaughlin, & Blake,
2006).
As is the case with most studies of risk factors included in this
review, the cross-sectional nature doesn't allow for causal
conclusions.
(2013) 2607–2618
Some of the risk factors identified above may be helpful to
consid-
er when planning prevention research and outreach efforts.

Students
who identify as gay, lesbian, or bisexual (Boyd et al., 2003;
Reed et al.,
2010), fraternity or sorority members (McCabe, Knight, et al.,
2005;
McCabe, Schulenberg, et al., 2005; McCabe, Teter, et al.,
2005), and
students who attend university counseling or mental health
clinics
should be prioritized in university drug prevention efforts given
their elevated levels of risk for drug use. In general, there is
evidence
that drug use may exacerbate psychiatric symptoms and that
individ-
uals with more severe symptoms may use more often, perhaps as
a
means of coping with negative affect, and intervention
approaches
should include a focus on developing alternative methods for
coping
with stress (Geisner, Neighbors, Lee, & Larimer, 2007; Murphy,
Dennhardt, et al., 2012; Murphy, Skidmore, et al., 2012).
Intervention
approaches that directly target deficits in impulsivity or
executive
functioning may also be useful (Bickel, Yi, Landes, Hill, &
Baxter,
2011). Normative beliefs have also been implicated as a risk
factor
and interventions that attempt to reduce marijuana use by
providing
corrective feedback on the actual prevalence of use (normative
be-
liefs) may be effective and warrant further research (Lee,
Neighbors,
Kilmer, & Larimer, 2010). Motives and expectancies for drug

use are
also part of established theories of substance abuse and
intervention
approaches are unlikely to be successful if they do not address
com-
mon motives such as stress reduction, social facilitation, and
cogni-
tive/mood enhancement. For example, students who misuse
prescription drugs to cope with pain or to study more
effectively
may benefit from intervention approaches that help them to
develop
alternative means of addressing these goals. The next section
will re-
view prevention and intervention studies for college drug use,
pointing out the relevant risk factor targeted by each
intervention if
applicable. Finally, recommendations for developing further
interven-
tions will be given.
3. Drug use prevention studies for college students
Colleges and universities are especially critical settings for
preven-
tion and early intervention given that it is the gateway to
adulthood
for nearly half of the US population and the period during
which
most young adults initiate or increase drug use (Johnston et al.,
2012). Colleges are also well equipped to implement fairly large
scale prevention and brief intervention programs. Many now do
so
for alcohol abuse but very few colleges have systematic
approaches
to preventing or reducing drug use. The following sections

review
studies that attempted to reduce or prevent drug use in general
student populations (prevention studies) and studies that
attempted
to reduce drug use in samples of students who report using
drugs
(intervention studies). (See Table 2 for characteristics of studies
included in this section).
3.1. Uncontrolled studies
The Fund for the Improvement of Postsecondary Education for
col-
legiate alcohol and other drug prevention efforts awarded funds
for
188 institutions to create drug prevention programs. Rather than
dic-
tate a protocol, these funds allowed institutions to create a
program
that they felt fit their needs. The most common elements used
by pro-
grams were the distribution of flyers and brochures, educational
pre-
sentations, alcohol-free activities, faculty and staff training, and
peer
education programs. The specific intervention elements were
not
specified. CORE survey data from roughly 41,000 students was
com-
pared pre and post program. Surprisingly, results demonstrated
that
prevalence rates (adjusted for 2-year trends using the
Monitoring
the Future data) showed an increase in marijuana and cocaine
use fol-
lowing the implementation of these programs (Licciardone,

2003). It
is difficult to interpret these results in the absence of a control
group
or information regarding the program content, but these findings
likely highlight the ineffectiveness of primarily informational
ap-
proaches to reducing drug use and the importance of developing
and disseminating evidence based approaches to drug use
prevention
(Larimer, Kilmer, & Lee, 2005).
3.2. Controlled studies
The Campus wide Alcohol and Drug Abuse Prevention Program
was implemented from 1988 to 1989 at the University of New
Mexico
(n = 567) (Miller, Toscova, Miller, & Sanchez, 2000) and
compared
to a control campus (n = 457). The program aimed to increase
alco-
hol and drug use risk perception by disseminating information
about
the harms associated with drug use throughout the university
(e.g.,
pamphlets, newspaper stories, lectures, computerized
information
programs, trained peer educators). When compared to the
control
campus, students at the University of New Mexico campus had
signif-
icantly higher levels of perceived risk of substances and had
reduced
levels of marijuana (and alcohol) use at the end of the 1.5 year
inter-
vention period. As previously described, research suggests that
in-

creasing perceived risk may reduce the likelihood of using
drugs for
those high in sensation-seeking (Arria, Caldeira, O'Grady,
Vincent,
Fitzelle, et al., 2008; Arria, Caldeira, O'Grady, Vincent,
Johnson, et al.,
2008). Although this study did not measure sensation-seeking or
whether perceived risk mediated outcomes, this intervention
may
be promising for college drug users with elevated sensation
seeking.
Results from alcohol prevention research studies (reviewed by
Cronce & Larimer, 2011) would suggest that the individualized
com-
puter and peer-administered interventions may have been the
most
effective treatment elements. Reductions in marijuana may also
be a
secondary effect of alcohol interventions (Grossbard et al.,
2010;
Magill, Barnett, Apodaca, Rohsenow, & Monti, 2009).
Marcello, Danish, and Stolberg (1989) developed and tested a
sub-
stance use prevention program for college athletes. Students
were
assigned to an intervention group or to a wait-list control
condition.
The group based intervention was delivered in three 2-hour
compo-
nents and included: (a) Education, (b) Skill Training for
Prevention,
and (c) Skills to Deal with Peer Pressure. The education
component
provided the students with general education about various
drugs,

definitions of use, abuse, and addiction, information about
etiological
factors involved in drug use and the types of treatments
available. The
Skills Training for Prevention aimed to teach the students
decision-
making and coping skills to use to avoid high-risk situations in
which they might use substances. The Skills to Deal with Peer
Pres-
sure component provided the students with the rationale that
increased assertiveness and ability to resist peer pressure would
de-
crease the likelihood they would use drugs. The goal of this
program
was to help students make responsible decisions about drug use
rath-
er than to focus solely on abstinence. Although this intervention
included many components that have demonstrated efficacy in
alco-
hol prevention studies (Cronce & Larimer, 2011), there were no
sig-
nificant differences in drug use between the intervention and
control
group at the 2-month follow-up. Possible mechanisms of change
consis-
tent with the risk factors identified above (coping, anxiety,
attitudes,
adaptive skills, etc.) were also measured pre-intervention,
immediately
post-intervention, and at 2-months post-intervention. There was
a sig-
nificant treatment effect for trait anxiety with the intervention
group
displaying significantly less anxiety immediately post-
intervention;
however, this reduction in anxiety did not lead to less drug use

at
2-month follow-up. Because only 58 of the 110 students
recruited into
the study completed the follow-up the lack of drug use
treatment ef-
fects may be related to poor power and/or attrition. The authors
of
this study also noted that they may have been too ambitious in
trying
to accomplish the goals of the intervention in 6 hours. It is also
possible
that the focus on didactic information, rather than motivational
en-
hancement and skill training, may have undermined student
motiva-
tion and engagement.
Table 2
Prevention and intervention studies for college student drug use.
Study Sample characteristics
(% of eligible sample recruited if available)
Assessments
(% retained from recruited sample)
Intervention conditions Outcomes
Amaro et al.,
(2010)
(49%) Binge drinking or drug using students
recruited through University Health Center

449, Post-intervention and 6-month
follow-up
1. 2-session (45–60 min each) BASICS intervention
with alcohol self-monitoring and personalized feedback
Reduced marijuana and cocaine use for heavy users
Elliot and
Carey (2012)
245 college students who reported no
past-month marijuana use
241, (98.4%) 1-month follow-up 1. e-TOKE (computerized)
2. Assessment-only
Lower descriptive norms, and fewer believed friends
disapproved of abstaining in e-TOKE group.
No difference in marijuana initiation.
Fischer et al.
(2013)
134 (69%)Canadian college students who
had used marijuana for at least 1 year and
12 of past 30 days
113, 3-month follow-up (84%) 1. Marijuana in-person BMI
2. Marijuana written BMI
3. Health in-person BMI
4. Health written BMI
Reduced marijuana use across conditions. Reduction
in specific risky MJ behaviors in the treatment
conditions (1 and 2).

Grossbard et
al. (2010)
1275 (32%) incoming college students who
had participant in high school athletics
1096, 10 month follow-up (86%) 1. BASICS only
2. Parent only
3. BASICS + parent
4. Control
Lower past-month marijuana use in BASICS +
parent condition.
Lee et al.
(2010)
341 incoming college students who used
marijuana in the past 3 months (92.16%)
324, 3-month follow-up (95.01%)
322 6-month follow-up (94.42%)
1. Web-based intervention with personalized feedback
targeting marijuana use
2. Assessment-only control
Students with a positive family history reduced
drug use at both follow-ups
Students with higher motivation to change
reduced at 3 months
Licciardone (2003) 41,567 students who took the CORE survey
39,197 student CORE survey
post-program (ecologic study)

1. Drug prevention programs at 188 colleges created
to meet the needs of that institution
Increase in cocaine and marijuana use in prevention
group (no control group)
Looby et al.
(in press)
106 “at-risk” college students with no
previous Rx stimulant use.
96, 6-month follow-up (91%) 1. Expectancy Challenge
Intervention
2. Assessment only control
Modified expectancies in intervention group, but no
differences in Rx stimulant use at 6-months
Marcello et al.
(1989)
110 college athletes (49%) 58, post-program assessment (64%)
57, 2-month follow-up assessment (63%)
1. Education, skill training, skills to deal with peer
pressure (2 h each)
2. Delayed-intervention/control
No differences in drug outcomes between groups at
2 months
McCambridge
and Strang
(2004)

200 drug-using students enrolled in
vocational colleges in London
179, 3-month follow-up (89.5%) 1. 1-hour motivational
interviewing (MI)
2. Education as usual
Reductions in illicit drug use at 3 months in MI group
Miller et al.
(2000)
1024 students who responded to a mailed
survey (41.2%)
865, 1.5 year follow-up
(quasi-experimental design)
1. Campus-wide alcohol and drug abuse prevention
program (most components focused on alcohol)
2. Control campus (no program)
Lower levels of marijuana use than control campus
Williams
et al.
(1983)
24 Freshman and Junior students at
a junior college
24, 3-month follow-up (100%) 1. Assertiveness training
2. 4 hour group discussion on assertiveness,
drug use (control)

Lower cocaine and amphetamine use in intervention
group
White et al.
(2006)
235 mandated students 222, 3-month follow-up (94%) 1. MI
with personalized feedback
2. Written feedback only
Lower prevalence of MJ use and fewer MJ problems
across conditions
2
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(2013) 2607–2618
A computerized, norm-correcting intervention program has also
been examined to prevent the initiation of marijuana use (Elliot
&
Carey, 2012). The intervention program, Marijuana eCHECKUP
TO
GO (e-TOKE), consists of an assessment phase and provides
feedback

about the students' marijuana use patterns, risk factors, and per-
ceived versus actual marijuana use norms. For this study, 245
college
students who were current abstainers (past month) from
marijuana
were randomly assigned to e-TOKE or an assessment-only
condition.
One month later, all students reported on their marijuana use,
de-
scriptive norms, and injunctive norms. Although students in the
e-TOKE program estimated lower descriptive norms and that
fewer
friends disapproved of their choice to abstain, rates of initiation
did
not differ between the two conditions. Thus, although e-TOKE
ap-
pears to be helpful in correcting perceived norms about
marijuana
use (an established risk factor), this correction does not
translate
into lower marijuana initiation rates one month later. It is
possible
that a longer follow-up period would be necessary for
differences to
be seen. The alcohol intervention counterpart, e-CHUG, has
been
somewhat successful in helping students to reduce their alcohol
consumption (Alfonso, Hall, & Dunn, 2012, but see also
Murphy,
Dennhardt, Skidmore, Martens, & McDevitt-Murphy, 2010), but
more research is necessary to examine the efficacy of e-TOKE
and
the role of norm correction in prevention and intervention
efforts
for marijuana use.

Expectancy challenges (an experiential intervention designed to
modify positive expectancies about a substance) have been
widely
used in the college drinking literature, but until recently have
not
been applied to drug use (Lau-Barraco & Dunn, 2008). Looby,
Young, and Earleywine (in press) designed an expectancy
challenge
intervention to prevent nonmedical prescription stimulant use.
Participants were 96 students who were at-risk for prescription
stim-
ulant use based on having a low grade point average, Greek
involve-
ment, binge drinking, and cannabis use, but reported no
previous
nonmedical use of prescription stimulants. They were
randomized
to an experimental condition in which they orally ingested a
placebo
stimulant or to a control group that did not receive medication.
Researchers found that participants in the experimental group
reported feeling significantly more high and stimulated
compared to
the control subjects. These students were then told that they had
ingested a placebo to illustrate the role of their expectancies for
the
drug. This intervention also included a broad didactic lecture
and dis-
cussion on expectancy effects and the potential negative conse-
quences of nonmedical use of stimulants. The expectancy
challenge
was successful in modifying expectancies, but the intervention
group and a control group showed comparable rates of
nonmedical
prescription use at 6-month follow-up. Across conditions,
negative

expectancies were significant predictors of reduced odds of
future
use. These results provide evidence that expectancies play a
role in
drug effects but do not support the use of expectancy challenge
ap-
proaches as a stand along intervention for drug use.
4. Drug use intervention studies for college students
4.1. Uncontrolled studies
Brief motivational interventions (BMIs) with personalized feed-
back have been shown to be efficacious in reducing alcohol use
in
college students (Cronce & Larimer, 2011), but very few studies
have examined drug use outcomes. One study examined the
efficacy
of a BMI with students recruited through the University Health
and
Counseling Services (Amaro et al., 2010). Participants were
either re-
ferred by the campus health center providers (33%) or self-
referred
(67%). To be eligible for the study, once referred participants
had to
endorse two of six statements on an alcohol and drug use
screening
measure (CRAFFT; Knight, Sherritt, Shrier, Harris, & Chang,
2002).
Eligible participants received a 2-session (45–60 min each)
BASICS
intervention in which the feedback components (e.g., data on
the
student's alcohol consumption, perceptions of other students'
drink-

ing compared to actual usage data, blood alcohol content,
beliefs
about alcohol, consequences, and risk factors) were delivered
using
a motivational interviewing style. Analyses were conducted on
the
449 students who received the intervention and completed a
follow-up assessment 6 months later (56% reported drug use at
base-
line). Results indicated that high frequency drug users (10 times
or
more in the past 6 months) reported reduced (self-reported)
mari-
juana and cocaine use 6 months after the intervention. The
students
experiencing the most drug-related negative consequences (top
tertile) reported reduced consequences at follow-up. Although
the
absence of a control group precludes causal inference, this
study sug-
gests that a BMI targeting alcohol may be effective for helping
college
students reduce their drug use. Although this study did not
target
drug use, a number of risk factors common to alcohol and drug
use
were addressed (normative beliefs, motives and expectancies).
However, these risk factors were addressed in relation to
alcohol
(e.g., corrective norms on alcohol use) so their role in drug use
out-
comes is unclear.
4.2. Controlled studies
One study examined an assertiveness training intervention in a

sample of 24 students who reported low assertiveness and drug
use
(Williams, Hadden, & Marcavage, 1983). Students in the
intervention
condition (n = 12) received instruction on how to respond
assertive-
ly and then were given feedback on responses they gave in a
variety
of hypothetical situations. Participants assigned to the control
group
participated in a four-hour small-group discussion on
assertiveness,
peer pressure, and drug use but did not receive behavioral skills
train-
ing or rehearsal. Students in the intervention condition reported
more assertiveness, more incidents of refusing drug use when
pressured by a peer, and lower use of cocaine, marijuana, and
stimu-
lants at 3-months post intervention. These results are consistent
with
the promising results obtained with similar approaches to
alcohol
prevention (Kivlahan, Marlatt, Fromme, Coppel, & Williams,
1990),
but inconsistent with the findings of Marcello et al. (1989) and
sug-
gest that skills based approaches to reducing drug use may be
espe-
cially effective for less assertive students. These results must be
interpreted cautiously due to the extremely small sample size.
This
intervention targets assertiveness, and although poor
assertiveness
has not been identified as a direct risk factor for drug use it may
con-
fer indirect risk via its association mood or anxiety disorders.

This
study, however, did not examine participants' mental health and
is
therefore unable to determine if the intervention is more
efficacious
for students with a psychiatric disorder.
Several controlled trials have investigated individual single ses-
sion drug use interventions for college students that include
motiva-
tional interviewing and/or personalized feedback components.
McCambridge and Strang (2004) examined the efficacy of a
BMI to re-
duce drug use among students (ages 16–20) at 10 vocational or
junior
colleges in London. Participants endorsed weekly cannabis or
stimu-
lant drug use within the past 3 months and were randomized to
ei-
ther a BMI intervention or an assessment-only condition. The
BMI
included a decisional balance exercise, a discussion of the
actual and
potential consequences of the student's drug use, as well as the
rela-
tion between drug use and the student's values and goals. It did
not
include personalized feedback, which is a key feature of
effective
brief alcohol interventions (Walters, Vader, Harris, Field, &
Jouriles,
2009). At the three month follow-up, students who received the
BMI reduced their use of marijuana and other non-stimulant
illicit
drugs more so than students in the assessment-only group. The
re-

duction in cannabis in the intervention condition was
particularly
compelling as the mean frequency of weekly use decreasing
from
15.7 times per week to 5.4 times per week. There was a 27%
increase
(2013) 2607–2618
in frequency within the control group. Thus, individual
counselor
delivered motivational interviews may show promise for
reducing
marijuana use among college students. Another study examined
the
efficacy of a BMI with a clinician versus written material only
in a
sample of heavy marijuana users at a Canadian university
(Fischer
et al., 2013). The interventions consisted of short, fact-based
and
non-judgmental information on cannabis-related health risks,
strate-
gies to reduce risk, and motivational components. The 20–30
minute
clinician-delivered intervention was presented in an interactive
and
nonjudgmental style. Student could be randomized to one of
these
two intervention conditions or one of two control conditions
which
delivered general health information by a clinician (in the same
for-
mat described above) or written form only. At three months

post-
intervention, there was a decrease in the mean number of
marijuana
use days across the four conditions and a trend-level effect for
greater
reductions in marijuana days for the two intervention
conditions.
Students who received the clinician-delivered intervention
reduced
deep inhalation/breathholding (a risk factor for acute or long-
term
health problems) and those who received the written-only
interven-
tion reduced driving after cannabis use compared with controls
(Fischer et al., 2013). This study suggests that brief
interventions
may be helpful in reducing some of the risky marijuana use
behaviors,
but it is unclear if a clinician-delivered or written-only format
is more
efficacious. More work is needed to identify mechanism of brief
motivational interventions for drug use. Motivational
interviewing
for alcohol use appears to work by altering normative beliefs
about
typical rates of alcohol consumption (Walters et al., 2009).
A study with US college students provided additional support
for
the efficacy of BMIs for college drug use. White et al. (2006)
examined
a BMI with feedback (n = 118) and a feedback-only intervention
(n = 104) with mandated students who received an alcohol
(88.6%) or marijuana violation (11%). Both interventions
provided
feedback (including corrective normative feedback) on students'

sub-
stance use (similar to Amaro et al., 2010 except included
feedback on
illicit drug use in addition to alcohol), but in the BMI condition
the
feedback was presented in the context of a counseling session.
Results
were not examined separately for students who were cited for
mari-
juana use. Students reported significant reductions in marijuana
use
and problems at the 3-month follow-up with no significant
differ-
ences across conditions. This suggests that feedback
interventions
that also include a focus on alcohol may be effective for
reducing mar-
ijuana problems and promoting abstinence, even in the absence
of an
individual counseling session. As noted earlier it is possible
that re-
ductions in alcohol use may have facilitated reductions in
marijuana
use (Grossbard et al., 2010).
Despite these promising results, two other studies have failed to
find effects for computerized feedback targeting marijuana use
among college students. Lee et al. (2010) evaluated a brief,
web-
based intervention for at-risk marijuana users transitioning to
college
(n = 98) compared to an assessment only control condition (n =
88). This study is noteworthy in that it identified participants on
the
basis of drug use rather targeting drug use as a secondary
outcome

in at-risk drinkers. Students viewed computer-delivered
personalized
feedback including information about their marijuana use,
compari-
son of perceived norms to actual norms, perceived pros and
cons of
use, and self-reported consequences of marijuana use. They also
re-
ceived a list of behavioral strategies to reduce their marijuana
use
and problems. In contrast to the overall reduction associated
with
personalized drug use feedback found by White et al. (2006),
Lee et
al. found no overall effect on self-reported marijuana use at
three
and six month follow-ups, but did find that family history of
drug
problems and motivation to change moderated outcomes. Those
who received the intervention and were higher in motivation to
change significantly reduced their drug use at 3-month follow-
up.
Individuals in the intervention condition with a positive family
histo-
ry of drug problems showed a marginally significant reduction
in
marijuana use at 3-months, and a significant reduction at the
6-month follow-up (Lee et al., 2010). Thus, brief computerized
feed-
back on marijuana use may be helpful for students with some
motiva-
tion to change or a family history of drug use. These studies
suggest
that targeting normative beliefs about drug use may be an
important
aspect of drug use interventions, but additional follow-up

studies are
needed to confirm this relationship.
Parent based interventions have shown promise as an adjunct
to brief alcohol interventions with college students (Turrisi et
al.,
2009; Wood, Capone, Laforge, Erickson, & Brand, 2007) and
may
also curtail drug use among college students. Grossbard et al.
(2010) compared an in-person BMI with personalized feedback
(n = 277), a parent-based intervention (n = 316), a combined
BMI
and parent intervention (n = 342), and a control condition (n =
340), for incoming first year students who had participated in
high
school athletics, but did not necessarily report drinking or drug
use.
The intervention focused primarily on reducing current or
preventing
future drinking and did not include any drug use treatment
elements
or target any specific risk factors. Students who were assigned
to the
BMI or the control condition showed significantly higher past-
month
marijuana use than the combined BMI and parent intervention
(Grossbard et al., 2010), which suggests a possible prevention
effect
for the combined condition. The intervention did not impact
other
illicit drug use, but did reduce risky drinking (Turrisi et al.,
2009).
Future research should investigate parent based interventions
that
include a drug use component and with samples of students
selected

on the basis of drug use.
5. Summary
The research thus far suggests that campus wide educational ap-
proaches are unlikely to be successful (Licciardone, 2003), but
that
brief counselor delivered motivational interventions may be an
effec-
tive method for helping college students to reduce their drug
use.
Although several preliminary studies suggest that brief
motivational
and skills-based interventions may be effective in helping
students
reduce their marijuana use, little is known about interventions
that
identify students on the basis of drug use (versus identifying
alcohol
users who also use drugs), and in particular drug use other than
mar-
ijuana. Additionally, although there are conflicting results, two
well
controlled studies suggest that feedback-only BMIs for drug use
are
not generally effective (Elliot & Carey, 2012; Lee et al., 2010),
though
they may be effective for students who are motivated to change
or
have a positive family history of drug use (Lee et al., 2010),
when
they include a focus on alcohol and drug use (White et al.,
2006), or
in helping to reduce risky behavior associated with drug use
(Fischer et al., 2013). The combination of individual BMI and
parent-based approaches is promising and merits further study.

6. Future directions in college drug use prevention
and intervention
Almost 50% of college students report lifetime use of drugs and
many experience negative consequences associated with their
drug
use such as lower academic performance, risky behaviors such
as
unprotected sex or driving after using drugs, and legal and
health prob-
lems. Men, European Americans or Latinos, students involved
in Greek
organizations, GLB students, and non-religious students are
more likely
to use drugs. Personality factors such as high levels of
neuroticism,
psychoticism, impulsivity, and novelty or sensation-seeking
increase
risk for drug use, as does negative emotionality, emotional dys-
regulation and the presence of personality disorders. Drug use
has
also been linked to socio-environmental factors such as
overestimating
normative levels of drug use and experiencing negative life
events.
College student drug users are also at risk for developing a drug
use
disorder or accidental overdose, particularly when they engage
in
(2013) 2607–2618
mixing sedatives or analgesics with alcohol (Budney, 2007;

Caldeira
et al., 2008; Johnston et al., 2012; Presley et al., 1996).
Longitudinal studies are essential to better understand the
trajec-
tory of drug use during the college years and beyond. Although
the
proportion of students who drink and use drugs increases during
each year of college (Arria, Caldeira, O'Grady, Vincent,
Fitzelle, et al.,
2008; Arria, Caldeira, O'Grady, Vincent, Johnson, et al., 2008),
many
students who are heavy drinkers “mature out” of this pattern
(Demb & Campbell, 2009), but there is little research examining
this
process in college student drug users. Current research
demonstrates
that there are different trajectories of marijuana use, but does
not
provide information regarding possible individual-level risk
factors
for more severe trajectories in use (Caldeira, O'Grady, Vincent,
&
Arria, 2012).
Although factors that predict escalating use are unknown, infor-
mation about trajectories of drug use that predict long-term
harm
can be helpful in informing prevention and intervention.
Preliminary
evidence suggests that differing trajectories of marijuana use
predicts
long-term mental and physical health outcomes (Caldeira et al.,
2012). Despite no differences in health indicators during the
first
year of college, students who used marijuana chronically and

those
who increased their use mid-college utilized health care more
often
and had higher levels of depressive and anxiety symptoms seven
years post-college (Caldeira et al., 2012). This study suggests
that
some of the individuals that are among the most at risk for
long-term negative health outcomes may not be heavy marijuana
users or may be nonusers in the beginning of college. It may be
im-
portant for colleges and universities to continue to screen
throughout
college for marijuana use as to not overlook students that do not
exhibit drug use until later in college. This information can also
be
useful at the individual level. Students who use marijuana are at
risk for health consequences and are likely at greater risk if
they
increase their use late in college or maintain a pattern of
frequent
use. These individuals should be targeted for intervention.
Studies have shown that impulsivity and difficulties with mood
are robust predictors of drug use among college students and
should
be targeted in the content of prevention and intervention
programs
(Conrod, Castellanos-Ryan, & Mackie, 2011). Conrod et al.
(2011)
developed an intervention program that is designed to target the
dif-
ferent motivational processes linked to these four personality
traits
that have been shown to predict alcohol or drug use in
adolescents.
Individuals who show elevations on one of the four personality

risk
profiles (hopelessness, anxiety sensitivity, impulsivity and
sensation-
seeking) go through a tailored two-session coping skills
intervention
that aims to target the relevant personality and mood factors
that may
contribute to substance use. Similar interventions could be
developed
for college student drug users, perhaps as an adjunct to standard
BMIs. For example, because the perceived harmfulness of drugs
moder-
ates the risk of sensation-seeking, interventions that increase
aware-
ness of the potentially harmful effects of drugs may be a useful
intervention component for drug users high in sensation
seeking. Sen-
sation seekers might also benefit from intervention approaches
that
help them find alternative experiences to rival or replace drug
use.
The strong link between mood difficulties and drug use suggests
that
interventions that incorporate mood regulating strategies may be
useful
(Geisner et al., 2007, 2004). Murphy, Dennhardt, et al. (2012)
and
Murphy, Skidmore, et al. (2012) developed a one-session
behavioral
economic supplement to brief alcohol interventions that
attempts to in-
crease engagement in constructive goal-directed alternatives to
sub-
stance use (e.g., academic and campus/community activities).
The
Substance-Free Activity Session uses motivational interviewing

and
personalized feedback to enhance the salience of delayed
rewards asso-
ciated with academic success and includes tailored information
on
mood management and substance-free social leisure activities
(Murphy, Dennhardt, et al., 2012; Murphy, Skidmore, et al.,
2012).
This approach resulted in significant improvements in drinking
outcomes relative to standard alcohol BMIs, in particular for
students
with symptoms of depression and low levels of substance-free
activi-
ties, and may also show promise in the treatment of college
drug
abuse. There have also been promising results with a group
intervention
that focused on assertiveness skills (Williams et al., 1983)
similar to
those included in successful relapse prevention treatments for
sub-
stance abuse (Carroll & Rawson, 2005).
Another significant challenge for this area of research is to
develop in-
tervention components that are tailored to drug users, providing
both
cross-cutting drug treatment elements as well as elements
tailored to
specific drug types. For example, if a student reports
nonmedical use of
prescription stimulants with a motive to enhance concentration,
a useful
intervention component might include training in study skills,
whereas if
a student reports using marijuana to relieve stress, a component

that
targets coping skills might prove beneficial. Another challenge
is that
many students may lack motivation to change marijuana due to
the
widely held notion that marijuana is relatively benign, and may
be un-
able to generate “cons” of their marijuana use in the decisional
balance
exercises often included in BMIs. Elliot, Carey, and Scott-
Sheldon
(2011) compiled a list of common pros and cons of marijuana
use that
might be useful to use as a stimulus to prompt students to
consider
adverse outcomes related to their drug use in the context of a
BMI. In-
terventions could also combat this perception of marijuana as
benign
by providing personally tailored and credible information about
the
specific health, legal, and social risks and consequences
associated
with the student's pattern of use (e.g., cardiovascular health
effects, im-
pact on concentration, legal ramifications, driving risk, and
academic
risk). In particular, the highest risk behaviors should be targeted
with
harm reduction techniques – even with students who are
unwilling to
reduce their drug use – including discussing specific strategies
to avoid
drugged driving, risky sexual behaviors, sharing needles, and
accidental
overdose (McCambridge & Strang, 2004).

Existing research suggests that marijuana users may benefit
from
a brief motivational intervention, but more research is necessary
to
replicate these findings, to determine the relative efficacy of
various
brief intervention formats and treatment elements, and to
identify
possible mediators and moderators of treatment outcomes.
Studies
that examine novel intervention elements that target established
risk factors for drug use and drugs other than marijuana may be
particularly helpful.
Role of funding sources
No grant funding was provided for this study.
Contributors
Author AD conducted literature searches and wrote the first
draft of the
manuscript. Author JM contributed substantially to subsequent
drafts. Both authors
have approved the final manuscript.
Conflict of interest
All authors declare that they have no conflicts of interest.
Acknowledgements
The authors wish to thank Ms. Lindsey Gilbert who assisted in
conducting litera-
ture searches for this article.
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