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BRIEF REPORT
Stimulant Medication Use in College Students: Comparison of Appropriate
Users, Misusers, and Nonusers
Cynthia M. Hartung
University of Wyoming
Will H. Canu
Appalachian State University
Carolyn S. Cleveland
University of Wyoming
Elizabeth K. Lefler
University of Northern Iowa
Melissa J. Mignogna
Oklahoma State University
David A. Fedele
University of Florida
Christopher J. Correia
Auburn University
Thad R. Leffingwell
Oklahoma State University
Joshua D. Clapp
University of Wyoming
While stimulant medication is commonly prescribed to treat Attention-Deficit/Hyperactivity Disor-
der in children and adolescents (Merikangas, He, Rapoport, Vitiello, & Olfson, 2013;Zuvekas &
Vitiello, 2012) and is considered an empirically supported intervention for those groups (Barkley,
Murphy, & Fischer, 2008;Pelham & Fabiano, 2008;Safren et al., 2005) surprisingly little is known
about the efficacy of stimulants in the slightly older emerging adult population. A focus has
emerged, however, on illicit stimulant use among undergraduates, with studies suggesting such
behavior is not uncommon (e.g., Arria et al., 2013). Unfortunately, details are lacking regarding
outcomes and personal characteristics associated with different patterns of stimulant misuse. The
current study compares the characteristics of four groups of college students, including those with
stimulant prescriptions who use them appropriately (i.e., appropriate users), those who misuse their
prescription stimulants (i.e., medical misusers), those who obtain and use stimulants without a
prescription (i.e., nonmedical misusers), and those who do not use stimulant medications at all (i.e.,
nonusers). Undergraduates (N⫽1,153) from the Southeastern, Midwest, and Rocky Mountain
regions completed online measures evaluating patterns of use, associated motives, side effects,
ADHD symptomatology, and other substance use. Both types of misusers (i.e., students who abused
their prescriptions and those who obtained stimulants illegally) reported concerning patterns of other
and combined substance use, as well as higher prevalence of debilitating side effects such as
insomnia and restlessness. Research and practical implications are discussed.
Keywords: ADHD, psychostimulant misuse, college students, emerging adults
Studies estimate a 4 –14% yearly incidence of nonprescribed
stimulant medication use in college students (American College
Health Association [ACHA], 2010;Hall, Irwin, Bowman, Fran-
kenberger, & Jewett, 2005;McCabe, Teter & Boyd, 2006;Wey-
Cynthia M. Hartung, Department of Psychology, University of Wyo-
ming; Will H. Canu, Department of Psychology, Appalachian State Uni-
versity; Carolyn S. Cleveland, Department of Psychology, University of
Wyoming; Elizabeth K. Lefler, Department of Psychology, University of
Northern Iowa; Melissa J. Mignogna, Department of Psychology, Okla-
homa State University; David A. Fedele, Department of Psychology,
University of Florida; Christopher J. Correia, Department of Psychology,
Auburn University; Thad R. Leffingwell, Department of Psychology, Okla-
homa State University; Joshua D. Clapp, Department of Psychology,
University of Wyoming.
We thank Erica K. Allen and Collin T. Scarince for their contributions
to this project.
Correspondence concerning this article should be addressed to Cynthia
M. Hartung, Department of Psychology, University of Wyoming, 1000
East University Avenue, Department #3415, Laramie, WY 82071. E-mail:
chartung@uwyo.edu
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
Psychology of Addictive Behaviors © 2013 American Psychological Association
2013, Vol. 27, No. 3, 832–840 0893-164X/13/$12.00 DOI: 10.1037/a0033822
832
andt et al., 2009;White, Becker-Blease, & Grace-Bishop, 2006),
which is higher than the national prevalence of cocaine, halluci-
nogen, or inhalant use (SAMHSA, 2011), and approximately dou-
ble the prevalence of prescribed stimulant use (2–3%; Babcock &
Byrne, 2000;Stone & Merlo, 2011) in this age group. In consid-
ering stimulant abuse, however, it is important to note that not all
who use illicitly are qualitatively similar. While motive (e.g.,
getting high vs. increasing concentration) is one way to categorize
stimulant users (Teter, McCabe, Cranford, Boyd & Guthrie, 2005),
means and degree of use differentiate among (a) medical misusers
(i.e., those with a prescription who periodically use excessive
doses), (b) nonmedical misusers (i.e., those who obtain and use
stimulants illegally), and (c) appropriate users (i.e., those who use
prescription according to instructions). The need for closer exam-
ination of these groups is underscored by the somewhat ambiguous
stimulant-related maladjustment (Bogle & Smith, 2009), and in-
frequent and incomplete differentiation among misuser groups in
the literature.
Although prevalence estimates vary widely (e.g., 4%, McCabe,
Knight, Teter, & Wechsler, 2005; 38%, Arria et al., 2013; 43%,
Advokat, Guidry, & Martino, 2008), it seems likely that a sub-
stantial number of college students misuse stimulants (DeSantis,
Webb, & Noar, 2008). In contrast to prescribed use of stimulants
in college students with Attention-Deficit/Hyperactivity Disorder
(ADHD; DuPaul, Weyandt, O’Dell, & Varejao, 2009), which
some have suggested ameliorates maladjustment (Staufer & Grey-
danus, 2005), nonmedical misuse is correlated with lower grades
(McCabe et al., 2005), academic concerns (Rabiner et al., 2009),
risk for polysubstance abuse (Rozenbroek & Rothstein, 2011), and
a desire to improve studying (Stone & Merlo, 2011). However,
unaddressed symptoms of ADHD may be linked to nonmedical
misuse of stimulants too, with one study finding that 12% of
nonmedical misusers believed they had the disorder (Advokat et
al., 2008). It is also possible that students without ADHD use
stimulants to enhance academic performance (Smith & Farah,
2011), as staying awake and increasing studying efficiency are
frequent rationales for misuse (Advokat et al., 2008).
While addressing undiagnosed or undertreated ADHD and re-
lated academic problems is a motive for misuse that parallels the
intended purpose of prescription stimulants, recreation (i.e., eu-
phoric effects; Teter et al., 2005) and socialization (White et al.,
2006) are not uncommonly endorsed as reasons for use. This may
be particularly prevalent in nonmedical misusers, as approximately
one fifth of this group reports using stimulants while drinking
(Low & Gendaszek, 2002), and to prolong intoxication (Rabiner et
al., 2009). Some have suggested that stimulant use may be even
more reinforcing in social situations, as the resulting alertness may
facilitate prolonged social engagement (Hall et al., 2005). How-
ever, recreational motives for stimulant abuse do not outrank
academic motives among nonmedical misusers, and are uncom-
monly the sole motive reported (Rabiner et al., 2009).
Specific personality characteristics have also been related to
stimulant misuse, with both sensation seeking (Arria, Caldeira,
Vincent, O’Grady & Wish, 2008) and perfectionism (Low &
Gendaszek, 2002) positively predicting this behavior in college
populations. Further, men appear more likely than women to
misuse stimulants (Bogle & Smith, 2009;Hall et al., 2005; see
exception in McCabe et al., 2005), which may be due to sex
differences in risk-taking (Byrnes, Miller & Schafer, 1999)or
knowledge about from whom one can illicitly obtain stimulants
(Hall et al., 2005).
Immediate adverse consequences of stimulant use have been
reported in college student nonmedical misuser samples, in-
cluding appetite reduction (63%), sleep problems (60%), irri-
tability (45%), and reduced academic self-efficacy (41%; Ra-
biner et al., 2009). Taken with the potential legal consequences
of illicit use of a Schedule II substance (e.g., methylphenidate)
and increased risk of polysubstance abuse, this suggests illicit
stimulant use is associated with risk across several domains.
However, particularly given some studies suggesting relatively
mild and circumscribed maladjustment in misusing college
students (e.g., Bogle & Smith, 2009), replication and further
detailing of the putative adverse consequences associated with
illicit stimulant use is a valid aim, especially given the potential
downside of overly negative portrayals (e.g., Food and Drug
Administration caps on production).
This study examined four college student groups differentiated
by type of stimulant use (i.e., nonusers, nonmedical and medical
misusers, appropriate users). Given the extant literature, hypothe-
ses were as follows: (a) both misuser groups were expected to
more frequently nominate recreational motives for stimulant use;
(b) misusers, given their nonprescribed drug use, were expected to
endorse high rates of other illicit substance use (i.e., concurrent to
stimulant use or at other times in the past year); (c) nonmedical
misusers would report more ADHD-related symptomatology (i.e.,
inattention, hyperactivity) than nonusers, but less than either ap-
propriate users or medical misusers; (d) nonmedical misusers
would be distinguished by high sensation seeking and perfection-
ism. Finally, other planned analyses examined whether groups
differed on other motives for use, side effects, and methods of
ingestion; however, given a relative dearth of direction from prior
research for these variables, specific hypotheses were not made.
Method
Participants
Participants were 1,153 undergraduates (65.2% female; 88.4%
European American) from four public universities located in the
Southeast (n⫽2), Rocky Mountain (n⫽1), and Midwest (n⫽1)
regions of the United States who were compensated with class
credit. The mean age of these participants was 19.72 years (SD ⫽
1.45; range: 18 –25). Distribution by class standing was 46.2%
freshmen, 24.0% sophomores, 16.8% juniors, and 13.0% seniors.
Based on self-reported stimulant use, groups included (a) nonusers
(n⫽708), (b) nonmedical misusers (i.e., illicitly obtaining and
using stimulant medication without a prescription; n⫽274), (c)
appropriate users (i.e., taking stimulants according to prescription;
n⫽146), and (d) medical misusers (i.e., using higher doses or
more frequently than prescribed; n⫽25). Agreement regarding
group assignment was 100% (consensus of first, second, and
fourth authors). At two of four universities, stimulant users were
overselected via a prescreening questionnaire. Thus, the distribu-
tions across user status do not reflect the true prevalence of use and
misuse on these college campuses.
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833
STIMULANT MEDICATION IN COLLEGE STUDENTS
Measures and Procedure
Participants completed all rating scales online in a fixed order
after providing informed consent. Study procedures were approved
by each university’s Institutional Review Board.
Substance use. Participants reported whether they used a va-
riety of legal and illegal substances in the past year (e.g., alcohol,
cigarettes, marijuana). They also reported whether they used sub-
stances concurrently with prescription stimulants. Previous studies
support the reliability and validity of self-reported substance use
(Tucker, Murphy, & Kertesz, 2010), and endorsement of 12-month
substance use or nonuse is also consistent with prior research in
this area (e.g., Johnston, O’Malley, Bachman, & Schulenberg,
2013;Mohler-Kuo, Lee, & Wechsler, 2003;SAMHSA, 2011).
Stimulant use. Students were asked about: (a) use (e.g., “I
have a prescription and take accordingly”; “I do not have a
prescription but obtain stimulants and use them”; see White et al.,
2006), (b) source for obtaining (e.g., received from my doctor/
pharmacy, given by a friend/family member, or bought or stolen
from someone; based on McCabe, Teter, & Boyd, 2006), (c)
method of ingestion (e.g., oral, intranasal, or intravenous; as per
Teter et al., 2005), (d) reasons for use (e.g., control ADHD
symptoms, suppress appetite, or stay awake; adapted from Low &
Gendaszek, 2002), and (e) side effects experienced while taking
stimulants (e.g., insomnia, loss of appetite, or weight loss).
ADHD symptoms. ADHD symptoms were measured with an
18-item self-report measure of DSM–IV inattention and hyperac-
tivity (Barkley & Murphy, 2006). Participants indicated whether
they never/rarely (0), sometimes (1), often (2), or very often (3)
experienced each symptom. Summary scores were created for
inattention and hyperactivity. Internal consistency has been good
for inattention (␣⫽.80) and adequate for hyperactivity (␣⫽.73)
based on college student self-reports (e.g., Fedele, Hartung, Canu,
& Wilkowski, 2010). In addition, interrater reliability has been
found to be moderately high in adults (e.g., r⫽.67; Barkley,
Knouse, & Murphy, 2011). Convergent and discriminant validity
have also been demonstrated for adult self-reports (e.g., Magnus-
son et al., 2006). Internal consistency in the current sample was
good for inattention (␣⫽.87) and adequate for hyperactivity (␣⫽
.76).
Personality characteristics. Sensation seeking was measured
using a 16-item version (Donohew et al., 2000) of the Sensation
Seeking Scale (Zuckerman, 1994). Responses were disagree a lot
(0), disagree a little (1), don’t agree or disagree (2), agree a little
(3), or agree a lot (4) and were aggregated into a summary score
(range ⫽0 to 64). Previous reports of internal consistency were
adequate (␣⫽.79; Donohew et al., 2000) and internal consistency
was good in the current sample (␣⫽.82). Perfectionism was
measured using a 24-item version (Khawaja & Armstrong, 2005)
of the Frost Multi-Dimensional Perfectionism Scale (Frost, Mar-
ten, Lahart, & Rosenblate, 1990). This version has been reported to
have excellent internal consistency (␣⫽.90) and strong concur-
rent validity with other measures of perfectionism (Khawaja &
Armstrong, 2005). Responses range from strongly disagree (0) to
strongly agree (4). There are four subscales: concern over mistakes
(10 items), organization (4 items), parental expectations (6 items),
and high personal standards (4 items). Internal consistency was
adequate for parental expectations (␣⫽.79), good for organiza-
tion (␣⫽.88) and concern over mistakes (␣⫽.87), but inadequate
for high personal standards (␣⫽.64). Accordingly, the latter was
omitted from analyses.
Results
Multinomial logistic regression analyses were conducted to ex-
amine relations between predictors and user status. For some
analyses, all four user status groups were included. For other
analyses, nonusers were not included because the items were not
relevant (e.g., reasons for use, side effects). For all analyses, sex
and university were entered as covariates due to significant differ-
ences across user status. In keeping with prior findings (e.g., Bogle
& Smith, 2009), men were more likely to engage in nonmedical
misuse than women (p⫽.009). Alpha corrections were conducted
for all analyses and resulting pvalues are noted in each of the
tables. For each regression, likelihood (i.e.,
2
) and pairwise odds
ratios representing the unique relation between predictor and out-
come variable (i.e., user status) are reported.
First, a logistic regression analysis was conducted to examine
the relation between reasons for stimulant use and user status (see
Table 1). We were particularly interested in using “to get high” as
a measure of recreational use. However, we were not able to
include this reason in the regression due to low levels of endorse-
ment. Specifically, 13% of nonmedical misusers and 24% of
medical misusers indicated using stimulants to get high (compared
to none of appropriate users). With regard to other reasons for use,
we conducted planned exploratory analyses. Results showed that
both types of misusers endorsed some reasons significantly more
often than appropriate users. Specifically, nonmedical and medical
misusers were more likely to endorse using to stay awake than
appropriate users. Also, nonmedical misusers were more likely to
report using to study than appropriate users whereas medical
misusers were more likely to endorse using to increase academic
performance than appropriate users. Finally, both appropriate users
and medical misusers were more likely to use “to control ADHD
symptoms” than nonmedical misusers.
Another logistic regression analysis was conducted to examine
the relation between use of other substances and user status (see
Table 2). Across eight substances, nonusers of stimulants were the
least likely to endorse use of other substances, appropriate users
were next in terms of likelihood to endorse, and misusers were the
most likely to endorse. Although alcohol use was surveyed, it
could not be entered in the regression because 100% of medical
misusers endorsed it.
Next, a logistic regression analysis was conducted to examine
the relation between concurrent use of stimulants with other sub-
stances and user status (see Table 3). Appropriate users were
typically the least likely to endorse concurrent use of additional
substances. Medical misusers were significantly more likely to
endorse concurrent marijuana use than appropriate users. Nonmed-
ical misusers were more likely to endorse concurrent marijuana
and pain medication use than appropriate users. Interestingly,
nonmedical misusers were significantly less likely to endorse
concurrent alcohol use than appropriate users.
Next, regressions were conducted to examine how user status
related to ADHD and personality variables (see Table 4). Nonusers
reported significantly lower levels of inattention and hyperactivity
than any other group. In addition, nonmedical misusers reported
lower levels of inattention than appropriate users and lower levels
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834 HARTUNG ET AL.
of hyperactivity than medical misusers. With regard to personality,
nonmedical misusers reported higher parental expectations than
nonusers and appropriate users. Moreover, nonmedical misusers
reported higher levels of sensation seeking than appropriate users
and nonusers.
Exploratory analyses were conducted to examine differences
across user groups for side effects, stimulant source, and ingestion.
An analysis was conducted to examine the relation between side
effects and user status (see Table 5). Overall, misusers appeared to
experience more side effects; both misuser groups were signifi-
cantly more likely to endorse exaggerated well-being and restless-
ness than appropriate users. In addition, nonmedical misusers were
more likely to report insomnia and exaggerated well-being—and
less likely to report weight loss, anxiety, or gastrointestinal prob-
lems—than appropriate users. Finally, medical misusers were
more likely to endorse changes in sex drive than nonmedical
misusers.
Finally, sources for obtaining stimulants and ingestion methods
were examined. No regression analysis could be conducted for
these variables because appropriate users obtained their stimulants
exclusively from prescriptions and participants reported oral in-
gestion as their primary method. Notably, among nonmedical
misusers, 81% got stimulants from a friend, 45% bought them, and
4% stole them. Additionally, nasal ingestion among nonmedical
(17.9%) and medical misusers (20.0%) was much higher than for
appropriate users (0.0%) although the difference between the two
misuser groups was not significant.
Discussion
The purpose of this study was to compare characteristics of
undergraduates who use, misuse, and do not use prescription
stimulants. Overall, those classified as misusers (i.e., medical and
nonmedical) presented relatively more concerning correlates than
those who used stimulants according to prescription. First, al-
though not statistically analyzed due to nonendorsement by all
appropriate users, both medical and nonmedical misusers more
frequently equate stimulant ingestion with recreation (i.e., getting
high). Further, misusers appeared to experience different side
effects. Notably, both misuser groups were more likely to endorse
exaggerated well-being and restlessness than appropriate users.
Nonmedical misusers were more likely to endorse insomnia than
appropriate users, but less likely to have experienced anxiety,
weight loss, or digestive problems. Unfortunately, “desirable” side
effects (e.g., exaggerated well-being) may encourage misuse by
off-setting negative consequences and reinforcing the expectation
of euphoria.
Perhaps not surprisingly, misusers reported the highest rates
of other substance use. Nonmedical misusers were more likely
to report use of marijuana and hallucinogens than nonusers and
appropriate users. Medical misusers were the most likely en-
dorsers for all substances but these differences only reached
statistical significance when compared to nonusers for ciga-
rettes, amphetamines, and anxiety medication. When examining
substances frequently used by college students (e.g., alcohol
and marijuana; ACHA, 2010), appropriate users were more
likely than nonusers to endorse use of these substances. This
finding is consistent with prior research suggesting that ADHD
is associated with increased risk for substance use (Wilens,
2004), but seems to contradict a documented protective effect
of stimulant treatment (Biederman, 2003;Faraone & Wilens,
2003;Wilens, Faraone, Biederman, & Bunawardene, 2003).
However, the current data cannot inform the prospective influ-
ence of stimulant intervention in childhood. Overall, it seems
reasonable to conclude that stimulant misuse is associated with
risk for broader substance use.
With regard to concurrent substance use, misusers were more
likely than appropriate users to report marijuana use in combina-
tion with stimulants. In addition, nonmedical misusers were sig-
nificantly more likely to endorse concurrent pain medication use
than appropriate users. Such recreational use suggests that the
motives of misusers may not be benign (e.g., extra dose for finals).
This is consistent with other studies in which students frequently
endorsed using stimulants while “partying” (e.g., Teter et al., 2005;
White et al., 2006), and those in which short-term positive gain is
Table 1
Multinomial Logistic Regression Analysis for Reasons for Stimulant Use by User Status
Comparisons
Nonmedical misusers vs.
appropriate users
Medical misusers vs. appropriate
users
Nonmedical misusers vs. medical
misusers Omnibus
NMM% AU% OR SE MM% AU% OR SE NMM% MM% OR SE
2
(2, 443)
Stay awake 49.6 18.5 3.77
ⴱⴱⴱ
0.33 60.0 18.5 5.01
ⴱⴱ
0.53 49.6 60.0 0.75 0.53 21.51
Study 81.0 63.7 3.24
ⴱⴱ
0.36 84.0 63.7 0.93 0.71 81.0 84.0 3.48 0.73 11.94
Academics 54.0 61.0 0.73 0.35 96.0 61.0 13.93
ⴱ
1.11 54.0 96.0 0.05
ⴱⴱ
1.10 12.26
Alertness 39.8 44.5 0.70 0.34 64.0 44.5 0.56 0.55 39.8 64.0 1.26 0.55 1.64
Control ADHD 11.3 69.2 0.06
ⴱⴱⴱ
0.30 68.0 69.2 0.71 0.52 11.3 68.0 0.08
ⴱⴱⴱ
0.50 129.78
Weight control 13.5 6.8 1.69 0.51 28.0 6.8 2.54 0.64 13.5 28.0 0.67 0.60 2.30
(Get “high”) 13.1 0.0 — — 24.0 0.0 — — 13.1 24.0 — — —
Note. NMM ⫽Nonmedical misusers (n⫽274); MM ⫽Medical misusers (n⫽25); AU ⫽Appropriate users (n⫽146). OR ⫽Odds ratio, calculated
with the group in BOLD in the subheader (e.g., for NMM vs. AU, NMM) as the criterion and the other as the reference. SE ⫽Standard error of the effect.
Sex and university/site were entered as covariates at the first step of this logistic regression analysis. Get “high” was not included in logistic regression
analyses due to nil endorsement by appropriate users, a violation of logistic regression assumptions. Alpha for each omnibus
2
test was set at .008 to
compensate for family-wise error (6 predictors used in regressions; .05/6 ⫽.008;
2
values in italics are p⬍.008).
ⴱ
p⬍.05.
ⴱⴱ
p⬍.01.
ⴱⴱⴱ
p⬍.001 (for pairwise OR).
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
835
STIMULANT MEDICATION IN COLLEGE STUDENTS
reported with stimulant misuse (Rabiner et al., 2009) despite low
endorsement of long-term academic gain (Hall et al., 2005).
With regard to concurrent alcohol use, the three user groups
reported relatively high rates, which is troubling due to potential
interactions between alcohol and stimulants. Specifically, using
stimulants in combination with alcohol may diminish the experi-
ence of alcohol-related effects. This may in turn lead to underes-
timation of inebriation (Flack et al., 2007;Hingson, Edwards,
Heeren & Rosenbloom, 2009;Knight et al., 2002) and poor deci-
sions (e.g., drunk driving, unsafe sexual activity) that could lead to
Table 2
Multinomial Logistic Regression Analyses for Use of Other Substances
Comparisons
Nonmedical misusers vs. medical
misusers
Nonmedical misusers vs.
appropriate users Nonmedical misusers vs. nonusers Omnibus
NMM% MM% OR SE NMM% AU% OR SE NMM% NU% OR SE
2
(3, 1050)
Cigarettes 62.1 76.0 0.32 0.62 62.1 44.8 0.91 0.27 62.1 24.7 1.30 0.21 7.32
Cigars/chew 40.4 37.5 1.20 0.57 40.4 27.1 1.74 0.30 40.4 18.2 1.84
ⴱ
0.25 6.68
Marijuana 72.2 70.8 1.69 0.57 72.2 45.5 2.90
ⴱⴱ
0.26 72.2 24.3 4.40
ⴱⴱⴱ
0.21 54.26
Amphetamines 18.6 20.0 0.72 0.71 18.6 4.2 2.30 0.54 18.6 0.6 4.77
ⴱ
0.60 10.26
Hallucinogens 26.3 12.0 3.11 0.78 26.3 6.9 2.84
ⴱ
0.45 26.3 1.9 4.36
ⴱⴱⴱ
0.40 17.69
Ecstasy 17.8 16.0 0.96 0.68 17.8 4.9 1.63 0.48 17.8 1.0 2.53 0.49 4.30
Anxiety meds 34.5 44.0 0.71 0.53 34.5 24.7 0.78 0.29 34.5 6.1 2.70
ⴱⴱⴱ
0.27 23.36
Pain meds 34.3 40.0 0.90 0.52 34.3 20.8 1.33 0.30 34.3 12.8 1.25 0.25 1.39
(Alcohol) 97.8 100.0 — — 97.8 90.4 — — 97.8 77.4 — — —
Comparisons
Medical misusers vs. appropriate
users Medical misusers vs. nonusers Appropriate users vs. Nonusers
MM% AU% OR SE MM% NU% OR SE AU% NU% OR SE
Cigarettes 76.0 44.8 2.84 0.63 76.0 24.7 4.02
ⴱ
0.61 44.8 24.7 1.42 0.24
Cigars/chew 37.5 27.1 1.45 0.60 37.5 18.2 1.53 0.58 27.1 18.2 1.06 0.28
Marijuana 70.8 45.5 1.24 0.58 70.8 24.3 2.61 0.56 45.5 24.3 2.10
ⴱⴱ
0.23
Amphetamines 20.0 4.2 3.20 0.83 20.0 0.6 6.64
ⴱ
0.88 4.2 0.6 2.08 0.74
Hallucinogens 12.0 6.9 0.91 0.87 12.0 1.9 1.40 0.85 6.9 1.9 1.54 0.53
Ecstasy 16.0 4.9 1.70 0.77 16.0 1.0 2.63 0.80 4.9 1.0 1.55 0.60
Anxiety meds 44.0 24.7 1.10 0.56 44.0 6.1 3.83
ⴱ
0.55 24.7 6.1 3.48
ⴱⴱⴱ
0.29
Pain meds 40.0 20.8 1.48 0.56 40.0 12.8 1.38 0.54 20.8 12.8 0.93 0.29
(Alcohol) 100.0 90.4 — — 100.0 77.4 — — 90.4 77.4 — —
Note. NMM ⫽Nonmedical misusers (n⫽274); MM ⫽Medical misusers (n⫽25); AU ⫽Appropriate users (n⫽146); NU ⫽Nonusers (n⫽708).
OR ⫽Odds ratio, calculated with group in BOLD in the subheader (e.g., for NMM vs. AU, NMM) as the criterion and the other as the reference. SE ⫽
Standard error of the effect. Sex and university were entered as covariates at the first step. Alcohol was not included in regression because 100% of medical
misusers endorsed using. Alpha was set at .006 to compensate for family-wise error (8 predictors; .05/8 ⫽.006;
2
values in italics are p⬍.006). Some
substances were not included in the analyses because of lack of endorsement by any participant.
ⴱ
p⬍.05.
ⴱⴱ
p⬍.01.
ⴱⴱⴱ
p⬍.001 (for pairwise OR).
Table 3
Multinomial Logistic Regression Analyses for Concurrent Use of Other Substances and Stimulants
Comparisons
Nonmedical misusers vs. appropriate
users
Medical misusers vs. appropriate
users
Nonmedical misusers vs. medical
misusers Omnibus
NMM% AU% OR SE MM% AU% OR SE NMM% MM% OR SE
2
(2, 443)
Alcohol 43.1 52.1 0.41
ⴱⴱ
0.26 80.0 52.1 1.28 0.65 43.1 80.0 0.32 0.64 .001
Tobacco 27.7 26.7 0.99 0.28 60.0 26.7 2.01 0.54 27.7 60.0 0.49 0.52 .348
Marijuana 28.8 17.8 2.71
ⴱⴱ
0.31 56.0 17.8 3.40
ⴱ
0.55 28.8 56.0 0.80 0.52 .002
Pain meds 12.0 4.8 3.81
ⴱ
0.52 24.0 4.8 3.13 0.81 12.0 24.0 1.22 0.71 .023
Anxiety meds 10.2 8.2 0.60 0.46 24.0 8.2 0.85 0.76 8.2 24.0 0.70 0.72 .512
Note. NMM ⫽Nonmedical misusers (n⫽274); MM ⫽Medical misusers (n⫽25); AU ⫽Appropriate users (n⫽146). OR ⫽Odds ratio, calculated
with group in BOLD in the subheader (e.g., for NMM vs. AU, NMM) as the criterion and the other as the reference. SE ⫽Standard error of the effect.
Sex and university/site were entered as covariates at the first step of this logistic regression analysis. Get “high” was not included in logistic regression
analyses due to nil endorsement by appropriate users, a violation of logistic regression assumptions. Alpha for each omnibus
2
test was set at .01 to
compensate for family-wise error (5 predictors used in regressions; .05/5 ⫽.01;
2
values in italics are p⬍.01).
ⴱ
p⬍.05.
ⴱⴱ
p⬍.01.
ⴱⴱⴱ
p⬍.001 (for pairwise OR).
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
836 HARTUNG ET AL.
Table 4
Multinomial Logistic Regression Analyses for (A) Inattention & Hyperactivity and (B) Perfectionism & Sensation Seeking by User Status
Comparisons
Nonmedical misusers vs. medical misusers Nonmedical misusers vs. appropriate users Nonmedical misusers vs. nonusers Omnibus
NMM
M(SD) MM M(SD) OR SE
NMM
M(SD) AU M(SD) OR SE
NMM
M(SD) NU M(SD) OR SE
2
(3, 1050)
Inattention 6.82 (4.87) 11.04 (5.94) 0.92 0.05 6.82 (4.87) 10.07 (5.90) 0.90
ⴱⴱⴱ
0.03 6.82 (4.87) 4.53 (3.74) 1.09
ⴱⴱⴱ
0.02 68.37
Hyperactivity 7.05 (4.16) 10.70 (4.53) 0.89
ⴱ
0.06 7.05 (4.16) 8.74 (4.63) 0.99 0.03 7.05 (4.16) 5.38 (3.53) 1.05
ⴱ
0.02 12.04
Perfectionism
Parental expectations 12.06 (5.00) 12.56 (6.48) 1.03 0.05 12.06 (5.00) 10.87 (4.63) 1.10
ⴱⴱⴱ
0.02 12.06 (5.00) 10.78 (5.38) 1.04
ⴱ
0.02 17.90
Concern over mistakes 16.15 (7.72) 19.56 (9.55) 0.94
ⴱ
0.03 16.15 (7.72) 18.54 (8.19) 0.94
ⴱⴱⴱ
0.02 16.15 (7.72) 15.39 (8.57) 0.99 0.01 27.23
Organization 11.11 (3.82) 10.68 (5.02) 1.05 0.05 11.11 (3.82) 11.04 (3.87) 1.02 0.03 11.11 (3.82) 12.00 (3.53) 0.96
ⴱ
0.02 11.01
Sensation seeking 42.81 (9.69) 41.56 (9.01) 1.01 0.02 42.81 (9.69) 39.15 (10.91) 1.04
ⴱⴱ
0.01 42.81 (9.69) 36.44 (10.50) 1.07
ⴱⴱⴱ
0.01 66.19
Comparisons
Medical misusers vs. appropriate users Medical misusers vs. nonusers Appropriate users vs. nonusers
MM M(SD) AU M(SD) OR SE MMM(SD) NU M(SD) OR SE AU M(SD) NU M(SD) OR SE
Inattention 11.04 (5.94) 10.07 (5.90) 0.97 0.05 11.04 (5.94) 4.53 (3.74) 1.18
ⴱⴱⴱ
0.05 10.07 (5.90) 4.53 (3.74) 1.22
ⴱⴱⴱ
0.03
Hyperactivity 10.70 (4.53) 8.74 (4.63) 1.11 0.06 10.70 (4.53) 5.38 (3.53) 1.18
ⴱⴱ
0.06 8.74 (4.63) 5.38 (3.53) 1.06
ⴱ
0.03
Perfectionism
Parental expectations 12.56 (6.48) 10.87 (4.63) 1.07 0.05 12.56 (6.48) 10.78 (5.38) 1.01 0.05 10.87 (4.63) 10.78 (5.38) 0.95
ⴱ
0.02
Concern over mistakes 19.56 (9.55) 18.54 (8.19) 0.99 0.03 19.56 (9.55) 15.39 (8.57) 1.05 0.03 18.54 (8.19) 15.39 (8.57) 1.06
ⴱⴱⴱ
0.01
Organization 10.68 (5.02) 11.04 (3.87) 0.98 0.06 10.68 (5.02) 12.00 (3.53) 0.91 0.05 11.04 (3.87) 12.00 (3.53) 0.93
ⴱⴱ
0.03
Sensation seeking 41.56 (9.01) 39.15 (10.91) 1.02 0.02 41.56 (9.01) 36.44 (10.50) 1.05
ⴱ
0.02 39.15 (10.91) 36.44 (10.50) 1.03
ⴱⴱ
0.01
Note. NMM ⫽Nonmedical misusers (n⫽274); MM ⫽Medical misusers (n⫽25); AU ⫽Appropriate users (n⫽146); NU ⫽Nonusers (n⫽708). OR ⫽Odds ratio, calculated with group in
BOLD in the subheader (e.g., for NMM vs. AU, NMM) as the criterion and the other as the reference. SE ⫽Standard error of the effect. Sex and university/site were entered as covariates at the first
step of these two logistic regression analyses. Values without a common superscript are statistically significantly different (p⬍.05). One logistic regression analysis was conducted for
Inattention/Hyperactivity; a second logistic regression was conducted for Perfectionism/Sensation Seeking. Alpha was set at .025 to compensate for family-wise error (2 predictors; .05/2 ⫽.025;
2
values in italics are p⬍.025) for the Inattention/Hyperactivity analysis. For the Perfectionism/Sensation Seeking analysis alpha was set at .013 (4 predictors; .05/4 ⫽
2
values in italics are p⬍.013).
Range of scores for Inattention is 0 to 27, Hyperactivity is 0 to 27, Parental expectations is 0 to 24, Concern over mistakes is 0 to 40, Organization is 0 to 16, and Sensation Seeking range is 0 to 64.
ⴱ
p⬍.05.
ⴱⴱ
p⬍.01.
ⴱⴱⴱ
p⬍.001 (for pairwise OR).
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
837
STIMULANT MEDICATION IN COLLEGE STUDENTS
physical harm (e.g., motor vehicle accident, sexually transmitted
disease, unplanned pregnancy).
Regarding inattention, nonmedical misusers reported signifi-
cantly lower levels than appropriate users but higher levels than
nonusers. For hyperactivity, nonmedical misusers reported signif-
icantly lower levels than medical misusers, but higher levels than
nonusers. Thus, nonmedical misusers may be using stimulants to
address subthreshold ADHD, and self-medication may be a viable
explanation for the behavior of some nonmedical misusers (Rabi-
ner et al., 2009). Misusers endorsed levels of sensation seeking that
were significantly higher than nonusers and appropriate users. This
is consistent with research linking sensation seeking to substance
abuse (Carlson, Johnson & Jacobs, 2010;Dunlop & Romer, 2010;
Zuckerman, 1994). Group differences on perfectionism subscales
were also evident. Most notable, perhaps, was that nonmedical
misusers endorsed higher perceived parental pressure relative to
nonusers. Thus, perception of parental expectations for academic
success may moderate the misuse of stimulants among those
without a prescription. When asked about sources for obtaining
stimulants, 81% of nonmedical misusers reported getting them
from friends, closely resembling previous findings (77.8%; Bar-
rett, Darredeau, Bordey, & Pihl, 2005). This suggests that some—
and potentially many— college students with prescription stimu-
lants are taking their medication in smaller doses or less often than
prescribed as there seem to be “leftovers” available to sell or share.
Limitations
First, the medical misuser group was small (n⫽25), and this
limited power to detect differences between this and other groups.
Given that this group reported very high rates of problematic
consequences that were often not statistically significantly differ-
ent from other groups, more research with individuals who misuse
stimulant prescriptions is warranted. Next, our assessments of
substance use and ADHD symptoms were limited to self-report
measures, and future research might use corroborating sources
(e.g., biochemical and parent-report measures, respectively). An-
other limitation was related to reports of type and dose of stimu-
lants. We attempted to gather this information but participant
responses reflected confusion or lack of knowledge. Further, data
regarding frequency of misuse, duration of use, and amount typi-
cally consumed are lacking. Future research should address such
details to extend our appreciation for differences among user
groups. Another limitation was related to the overselection of
stimulant users, which increased power but decreased representa-
tiveness. Further, although the current data were derived from four
universities, the findings may not fully generalize to groups un-
derrepresented in this sample (see McCabe, Teter, & Boyd, 2004).
Finally, while geographic region and Greek affiliation have been
shown to potentially add to risk for illicit stimulant use in college
(McCabe et al., 2005), we did not consider the impact of these
variables in the current study; researchers should include these in
the design of future studies.
Conclusions
These findings reinforce that the misuse of stimulants is asso-
ciated with other risks, such as that for polysubstance misuse.
However, stimulant misuse by itself, even for academic reasons,
may have concerning side effects (Graham et al., 2011). One
university’s decision to change its honor code to include stimulant
misuse as an “improper assistance” violation indirectly supports
the call to proactively address this issue (Arria & DuPont, 2010;
Diller, 2010;Wilens et al., 2008). Additionally, roughly 14% of
Table 5
Logistic Regression Analysis for Various Side Effects of Stimulant Medication by User Status
Comparisons
Nonmedical misusers vs.
appropriate users
Medical misusers vs. appropriate
users
Nonmedical misusers vs. medical
misusers Omnibus
NMM% AU% OR SE MM% AU% OR SE NMM% MM% OR SE
2
(2, 443)
Change in sex drive 14.6 19.2 0.63 0.32 44.0 19.2 1.95 0.53 14.6 44.0 0.32
ⴱ
0.50 5.81
Gastrointestinal 6.2 12.3 0.40
ⴱ
0.44 32.0 12.3 1.26 0.67 6.2 32.0 0.32 0.65 5.94
Depressed mood 13.5 19.9 0.88 0.34 40.0 19.9 1.59 0.64 13.5 40.0 0.55 0.61 0.97
Anxiety 25.5 33.6 0.52
ⴱ
0.29 40.0 33.6 0.34 0.63 25.5 40.0 1.53 0.61 6.60
Well-being 28.5 13.0 3.35
ⴱⴱⴱ
0.32 52.0 13.0 4.99
ⴱⴱ
0.55 28.5 52.0 0.67 0.50 18.67
Dizziness 10.9 10.3 0.77 0.38 20.0 10.3 0.84 0.73 10.9 20.0 0.92 0.69 0.47
Headache 20.1 21.2 0.85 0.31 32.0 21.2 0.83 0.62 20.1 32.0 1.03 0.60 0.30
High blood pressure 8.8 4.8 1.81 0.52 24.0 4.8 1.45 0.78 8.8 24.0 1.25 0.69 1.34
Rapid heartbeat 42.3 34.2 1.19 0.26 64.0 34.2 1.37 0.55 42.3 64.0 0.86 0.54 0.60
Insomnia 49.3 36.3 1.68
ⴱ
0.25 60.0 36.3 1.29 0.55 49.3 60.0 1.30 0.53 4.35
Loss of appetite 63.1 67.8 1.02 0.26 88.0 67.8 1.91 0.72 63.1 88.0 0.53 0.70 0.90
Weight loss 21.5 37.0 0.38
ⴱⴱⴱ
0.28 56.0 37.0 0.93 0.53 21.5 56.0 0.41 0.51 14.03
Tremor/tics 9.5 6.8 1.32 0.45 24.0 6.8 1.64 0.72 9.5 24.0 0.81 0.66 0.62
Dry mouth 38.0 33.6 1.15 0.26 68.0 33.6 2.03 0.53 38.0 68.0 0.57 0.52 1.80
Restlessness 52.9 31.5 2.92
ⴱⴱⴱ
0.26 64.0 31.5 3.00
ⴱ
0.55 52.9 64.0 0.97 0.53 19.31
Note. NMM ⫽Nonmedical misusers (n⫽274); MM ⫽Medical misusers (n⫽25); AU ⫽Appropriate users (n⫽146). OR ⫽Odds ratio, calculated
with group in BOLD in the subheader (e.g., for NMM vs. AU, NMM) as the criterion and the other as the reference. SE ⫽Standard error of the effect.
Sex and university/site were entered as covariates at the first step of this logistic regression analysis. Get “high” was not included in logistic regression
analyses due to nil endorsement by appropriate users, a violation of logistic regression assumptions. Alpha for each omnibus
2
test was set at .003 to
compensate for family-wise error (15 predictors used in regressions; .05/15 ⫽.003;
2
values in italics are p⬍.003).
ⴱ
p⬍.05.
ⴱⴱ
p⬍.01.
ⴱⴱⴱ
p⬍.001 (for pairwise OR).
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
838 HARTUNG ET AL.
students in this sample misused a prescription. Further, 81% of
nonmedical misusers obtained stimulants from a friend. These two
findings emphasize the importance of prescribers closely monitor-
ing consumption and openly discussing consequences of misuse
and diversion with college students. For example, if a student
reports only taking medication on weekdays, then 30 pills might
last 6 weeks rather than 4. Therefore, prescribers may want to
evaluate how often students are taking their medication and pre-
scribe accordingly to reduce the quantity of stimulants available to
be diverted.
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Received January 5, 2012
Revision received May 16, 2013
Accepted June 4, 2013 䡲
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