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Stimulant Medication Use in College Students: Comparison of Appropriate Users, Misusers, and Nonusers

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While stimulant medication is commonly prescribed to treat Attention-Deficit/Hyperactivity Disorder 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. (PsycINFO Database Record (c) 2013 APA, all rights reserved).
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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 (N1,153) 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 (n2), Rocky Mountain (n1), and Midwest (n1)
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
(n708), (b) nonmedical misusers (i.e., illicitly obtaining and
using stimulant medication without a prescription; n274), (c)
appropriate users (i.e., taking stimulants according to prescription;
n146), and (d) medical misusers (i.e., using higher doses or
more frequently than prescribed; n25). 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
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 (n274); MM Medical misusers (n25); AU Appropriate users (n146). 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).
p.05.
ⴱⴱ
p.01.
ⴱⴱⴱ
p.001 (for pairwise OR).
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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
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
0.60 10.26
Hallucinogens 26.3 12.0 3.11 0.78 26.3 6.9 2.84
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
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
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
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 (n274); MM Medical misusers (n25); AU Appropriate users (n146); NU Nonusers (n708).
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.
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
0.55 28.8 56.0 0.80 0.52 .002
Pain meds 12.0 4.8 3.81
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 (n274); MM Medical misusers (n25); AU Appropriate users (n146). 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).
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
0.06 7.05 (4.16) 8.74 (4.63) 0.99 0.03 7.05 (4.16) 5.38 (3.53) 1.05
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
0.02 17.90
Concern over mistakes 16.15 (7.72) 19.56 (9.55) 0.94
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
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
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
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
0.02 39.15 (10.91) 36.44 (10.50) 1.03
ⴱⴱ
0.01
Note. NMM Nonmedical misusers (n274); MM Medical misusers (n25); AU Appropriate users (n146); NU Nonusers (n708). 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.
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 (n25), 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
0.50 5.81
Gastrointestinal 6.2 12.3 0.40
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
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
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
0.55 52.9 64.0 0.97 0.53 19.31
Note. NMM Nonmedical misusers (n274); MM Medical misusers (n25); AU Appropriate users (n146). 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).
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.
References
Advokat, C. D., Guidry, D., & Martino, L. (2008). Licit and illicit use of
medications for attention-deficit hyperactivity disorder in undergraduate
college students. Journal of American College Health, 56, 601– 606.
doi:10.3200/JACH.56.6.601-606
American College Health Association. (2010). National college health
assessment II: Spring 2010 reference group data report. Retrieved
from http://www.acha-ncha.org/docs/ACHA-NCHA-II_Reference
Group_DataReport_Spring2010.pdf
Arria, A. M., Caldeira, K. M., Vincent, K. B., O’Grady, K. E., & Wish,
E. D. (2008). Perceived harmfulness predicts nonmedical use of pre-
scription drugs among college students: Interactions with sensation-
seeking. Prevention Science, 9, 191–201. doi:10.1007/s11121-008-
0095-8
Arria, A. M., & DuPont, R. L. (2010). Nonmedical prescription stimulant
use among college students: Why we need to do something and what we
need to do. Journal of Addictive Diseases, 29, 417– 426. doi:10.1080/
10550887.2010.509273
Arria, A. M., Wilcox, H. C., Cladeira, K. M., Vincent, K. B., Garnier-
Dykstra, L. M., & O’Grady, K. E. (2013). Dispelling the myth of “smart
drugs”: Cannabis and alcohol use problems predict nonmedical use of
prescription stimulants for studying. Addictive Behaviors, 38, 1643–
1650. doi:10.1016/j.addbeh.2012.10.002
Babcock, Q., & Byrne, T. (2000). Student perceptions of methylphenidate
abuse at a public liberal arts college. Journal of American College
Health, 49, 143–145. doi:10.1080/07448480009596296
Barkley, R. A., Knouse, L. E., & Murphy, K. R. (2011). Correspondence
and disparity in the self- and other ratings of current and childhood
ADHD symptoms and impairment in adults with ADHD. Psychological
Assessment, 23, 437– 446. doi:10.1037/a0022172
Barkley, R. A., & Murphy, K. R. (2006). Attention-deficit hyperactivity
disorder: A clinical workbook (3rd Edition). New York, NY: Guilford
Press.
Barkley, R. A., Murphy, K. R., & Fischer, M. (2008). ADHD in adults:
What the science says. New York, NY: Guilford Press.
Barrett, S. P., Darredeau, C., Bordy, L. E., & Pihl, R. O. (2005). Charac-
teristics of methylphenidate misuse in a university sample. Canadian
Journal of Psychiatry/La Revue Canadienne de Psychiatrie, 50, 457–
461.
Biederman, J. (2003). Pharmacotherapy for attention-deficit/hyperactivity
disorder (ADHD) decreases the risk for substance abuse: Findings from
a longitudinal follow-up of youths with and without ADHD. Journal of
Clinical Psychiatry, 64(Suppl 11), 3– 8.
Bogle, K. E., & Smith, B. H. (2009). Illicit methylphenidate use: A review
of prevalence, availability, pharmacology, and consequences. Current
Drug Abuse Reviews, 2, 157–176. doi:10.2174/1874473710902020157
Byrnes, J. P., Miller, D. C., & Schafer, W. D. (1999). Gender differences
in risk taking: A meta-analysis. Psychological Bulletin, 125, 367–383.
doi:10.1037/0033-2909.125.3.367
Carlson, S. R., Johnson, S. C., & Jacobs, P. C. (2010). Disinhibited
characteristics and binge drinking among university student drinkers.
Addictive Behaviors, 35, 242–251. doi:10.1016/j.addbeh.2009.10.020
DeSantis, A. D., Webb, E. M., & Noar, S. M. (2008). Illicit use of
prescription ADHD medications on a college campus: A multimethod-
ological approach. Journal of American College Health, 57, 315–324.
doi:10.3200/JACH.57.3.315-324
Diller, L. (2010). ADHD in the college student: Is anyone else worried?
Journal of Attention Disorders, 14, 3– 6. doi:10.1177/
1087054710361585
Donohew, L., Zimmerman, R., Cupp, P. S., Novak, S., Colon, S., & Abell,
R. (2000). Sensation seeking, impulsive decision-making, and risky sex:
Implications for risk-taking and design of interventions. Personality and
Individual Differences, 28, 1079 –1091. doi:10.1016/S0191-
8869(99)00158-0
Dunlop, S. M., & Romer, D. (2010). Adolescent and young adult crash
risk: Sensation seeking, substance use propensity and substance use
behaviors. Journal of Adolescent Health, 46, 90 –92. doi:10.1016/j
.jadohealth.2009.06.005
DuPaul, G. J., Weyandt, L. L., O’Dell, S. M., & Varejao, M. (2009).
College students with ADHD: Current status and future directions.
Journal of Attention Disorders, 13, 234 –250. doi:10.1177/
1087054709340650
Faraone, S. V., & Wilens, T. (2003). Does stimulant treatment lead to
substance use disorders? Journal of Clinical Psychiatry, 64, 9 –13.
Fedele, D. A., Hartung, C. M., Canu, W. H., & Wilkowski, B. M. (2010).
Potential symptoms of ADHD for emerging adults. Journal of Psycho-
pathology and Behavioral Assessment, 32, 385–396. doi:10.1007/
s10862-009-9173-x
Flack, W. F., Daubman, K. A., Caron, M. L., Asadorian, J. A., D’Aureli,
N. R., Gigliotti, S. N.,...Stine, E. R. (2007). Risk factors and
consequences of unwanted sex among university students: Hooking up,
alcohol, and stress response. Journal of Interpersonal Violence, 22,
139 –157. doi:10.1177/0886260506295354
Frost, R. O., Marten, P., Lahart, C., & Rosenblate, R. (1990). The dimen-
sions of perfectionism. Cognitive Therapy and Research, 14, 449 468.
doi:10.1007/BF01172967
Graham, J., Banaschewski, T. T., Buitelaar, J. J., Coghill, D. D., Danck-
aerts, M. M., Dittmann, R. W.,...Taylor, E. E. (2011). European
guidelines on managing adverse effects of medication for ADHD. Eu-
ropean Child & Adolescent Psychiatry, 20, 17–37. doi:10.1007/s00787-
010-0140-6
Hall, K. M., Irwin, M. M., Bowman, K. A., Frankenberger, W., & Jewett,
D. C. (2005). Illicit use of prescribed stimulant medication among
college students. Journal of American College Health, 53, 167–174.
doi:10.3200/JACH.53.4.167-174
Hingson, R. W., Edwards, E. M., Heeren, T., & Rosenbloom, D. (2009).
Age of drinking onset and injuries, motor vehicle crashes, and physical
fights after drinking and when not drinking. Alcoholism: Clinical and
Experimental Research, 33, 783–790. doi:10.1111/j.1530-0277.2009
.00896.x
Johnston, L. D., O’Malley, P. M., Bachman, J. G., & Schulenberg, J. E.
(2013). Monitoring the future: National results on drug use; 2012 over-
view: Key findings on adolescent drug use. Ann Arbor, MI: The Uni-
versity of Michigan Institute for Social Research. Retrieved from: http://
www.monitoringthefuture.org/pubs/monographs/mtf-overview2012.pdf
Khawaja, N. G., & Armstrong, K. A. (2005). Factor structure and psycho-
metric properties of the Frost Multidimensional Scale: Developing
shorter version using an Australian sample. Australian Journal of Psy-
chology, 57, 129 –138. doi:10.1080/10519990500048611
Knight, J. R., Wechsler, H., Kuo, M., Seibring, M., Weitzman, E. R., &
Schuckit, M. A. (2002). Alcohol abuse and dependence among U.S.
college students. Journal of Studies on Alcohol, 63, 263–270.
Low, K., & Gendaszek, A. E. (2002). Illicit use of psychostimulants among
college students: A preliminary study. Psychology, Health & Medicine,
7, 283–287. doi:10.1080/13548500220139386
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.
839
STIMULANT MEDICATION IN COLLEGE STUDENTS
Magnusson, P., Smari, J., Siguroardottir, D., Baldursson, G., Sigmundsson,
J., Kristjansson, K.,...Guomundsson, O. O. (2006). Validity of
self-report and informant rating scales of adult ADHD symptoms in
comparison with a semistructured diagnostic interview. Journal of At-
tention Disorders, 9, 494 –503. doi:10.1177/1087054705283650
McCabe, S. E., Knight, J. R., Teter, C. J., & Wechsler, H. (2005).
Non-medical use of prescription stimulants among US college students:
Prevalence and correlates from a national survey. Addiction, 100, 96
106. doi:10.1111/j.1360-0443.2005.00944.x
McCabe, S. E., Teter, C. J., & Boyd, C. J. (2004). The use, misuse and
diversion of prescription stimulants among middle and high school
students. Substance Use & Misuse, 39, 1095–1116. doi:10.1081/JA-
120038031
McCabe, S. E., Teter, C. J., & Boyd, C. J. (2006). Medical use, illicit use
and diversion of prescription stimulant medication. Journal of Psycho-
active Drugs, 38, 43–56. doi:10.1080/02791072.2006.10399827
Merikangas, K. R., He, J., Rapoport, J., Vitiello, B., & Olfson, M. (2013).
Medication use in youth with mental disorders. JAMA Pediatrics, 167,
141–148. doi:10.1001/jamapediatrics.2013.431
Mohler-Kuo, M., Lee, J. E., & Wechsler, H. (2003). Trends in marijuana
and other illicit drug use among college students: Results from 4 Har-
vard School of Public Health College Alcohol Study surveys: 1993–
2001. Journal of American College Health, 52, 17–24. doi:10.1080/
07448480309595719
Pelham, W. E., & Fabiano, G. A. (2008). Evidence-based psychosocial
treatments for attention-deficit/hyperactivity disorder. Journal of Clini-
cal Child & Adolescent Psychology, 37, 184 –214. doi:10.1080/
15374410701818681
Rabiner, D. L., Anastopoulos, A. D., Costello, E. J., Hoyle, R. H., McCabe,
S. E., & Swartzwelder, H. S. (2009). Motives and perceived conse-
quences of nonmedical ADHD medication use by college students: Are
students treating themselves for attention problems? Journal of Attention
Disorders, 13, 259 –270. doi:10.1177/1087054708320399
Rozenbroek, K., & Rothstein, W. G. (2011). Medical and nonmedical users
of prescription drugs among college students. Journal of American
College Health, 59, 358 –363. doi:10.1080/07448481.2010.512044
Safren, S. A., Otto, M. W., Sprich, S., Winett, C. L., Wilens, T. E., &
Biederman, J. (2005). Cognitive-behavior therapy for ADHD in
medication-treated adults with continued symptoms. Behaviour Re-
search & Therapy, 43, 831– 842. doi:10.1016/j.brat.2004.07.001
Smith, M. E., & Farah, M. J. (2011). Are prescription stimulants “smart
pills?”The epidemiology and cognitive neuroscience of prescription
stimulant use by normal healthy individuals. Psychological Bulletin,
137, 717–741. doi:10.1037/a0023825
Staufer, W. B., & Greydanus, D. E. (2005). Attention-deficit/hyperactivity
disorder psychopharmacology for college students. Pediatric Clinics of
North America, 52, 71– 84. doi:10.1016/j.pcl.2004.10.007
Stone, A. M., & Merlo, L. J. (2011). Attitudes of college students toward
mental illness stigma and the misuse of psychiatric medications. Journal
of Clinical Psychiatry, 72, 134 –139. doi:10.4088/JCP.09m05254ecr
Substance Abuse and Mental Health Services Administration (2011). Re-
sults from the 2010 National Survey on Drug Use and Health: Detailed
tables. Retrieved from http://oas.samhsa.gov/NSDUH/2k10NSDUH/
tabs/Sect1peTabs1to46.htm#Tab1.5B
Teter, C. J., McCabe, S., Cranford, J. A., Boyd, C. J., & Guthrie, S. K.
(2005). Prevalence and motives for illicit use of prescription stimulants
in an undergraduate student sample. Journal of American College
Health, 53, 253–262. doi:10.3200/JACH.53.6.253-262
Tucker, J. A., Murphy, J. G., & Kertesz, S. G. (2010). Substance use
disorders. In M. H. Anthony, & D. H. Barlow (Eds.), Handbook of
assessment and treatment planning for psychological disorders (2nd ed.,
pp. 529 –570). New York, NY: Guilford Press.
Weyandt, L. L., Janusis, G., Wilson, K. G., Verdi, G., Paquin, G., Lopes,
J.,...Dussault, C. (2009). Nonmedical prescription stimulant use
among a sample of college students: Relationship with psychological
variables. Journal of Attention Disorders, 13, 284 –296. doi:10.1177/
1087054709342212
White, B. P., Becker-Blease, K. A., & Grace-Bishop, K. (2006). Stimulant
medication use, misuse, and abuse in an undergraduate and graduate
student sample. Journal of American College Health, 54, 261–268.
doi:10.3200/JACH.54.5.261-268
Wilens, T. E. (2004). Impact of ADHD and its treatment on substance
abuse in adults. Journal of Clinical Psychiatry, 65(Suppl 3), 38– 45.
Wilens, T. E., Adler, L. A., Adams, J., Sgambati, S., Rotrosen, J., Sawtelle,
R.,...Fusillo, S. (2008). Misuse and diversion of stimulants prescribed
for ADHD: A systematic review of the literature. Journal of the Amer-
ican Academy of Child & Adolescent Psychiatry, 47, 21–31. doi:
10.1097/chi.0b013e31815a56f1
Wilens, T. E., Faraone, S. V., Biederman, J., & Gunawardene, S. (2003).
Does stimulant therapy of attention-deficit/hyperactivity disorder beget
later substance abuse? A meta-analytic review of the literature. Pediat-
rics, 111, 179 –185. doi:10.1542/peds.111.1.179
Zuckerman, M. (1994). Behavioral expressions and biosocial bases of
sensation seeking. New York, NY: Cambridge University Press.
Zuvekas, S. H., & Vitiello, B. (2012). Stimulant medication use in children:
A 12-year perspective. The American Journal of Psychiatry, 169, 160
166. doi:10.1176/appi.ajp.2011.11030387
Received January 5, 2012
Revision received May 16, 2013
Accepted June 4, 2013
This document is copyrighted by the American Psychological Association or one of its allied publishers.
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840 HARTUNG ET AL.
... In one study, both those who misuse and those who have non-medical use were more likely to experience exaggerated well-being (euphoria) and restlessness than those who have it prescribed and use it as directed. They were also more likely to report changes in sex drive [48]. Specifically, in athletes with ADHD, stimulants may lead to an increased risk of cardiac injury, possibly due to stimulant-induced hyperthermia. ...
... This is concerning due to the potential interaction between stimulants and alcohol. Stimulants may counteract the perceived effect of alcohol intoxication on an individual, leading them to consume more alcohol to have a greater perceived effect, leading to poor decision-making [48]. ...
... This behavioral linkage is thought to be due to the parental pressure to succeed and approach perfection academically, combined with positive attitudes towards prescription stimulants, ultimately leading to the students' perception that taking stimulants is necessary [80]. A similar study by Hartung et al. corroborated these findings-those exhibiting misuse without prescriptions reported feeling higher perceived parental pressure than those with nonuse, further indicating a linkage between parental expectations for academic success and the inappropriate usage of stimulants [48]. ...
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Stimulants are effective in treating attention-deficit/hyperactivity disorder (ADHD). Psychiatrist Charles Bradley first made this discovery in 1937 when he found that children treated with amphetamines showed improvements in school performance and behavior. Between 1995 and 2008, stimulants to treat ADHD increased six-fold among American adults and adolescents at an annual rate of 6.5%. Stimulants without a prescription, known as nonmedical use or misuse, have also increased. The highest rates of nonmedical prescription drug misuse in the United States are seen most notably in young adults between 18 and 25 years, based on data from the Substance Abuse and Mental Health Services Administration in 2021. Aside from undergraduate students, nonmedical prescription stimulant use is prevalent among medical students worldwide. A recent literature review reported the utilization of stimulants without a prescription in 970 out of 11,029 medical students. The percentages of medical students across the country misusing stimulants varied from 5.2% to 47.4%. Academic enhancement, reported in 50% to 89% of college students with stimulant misuse, is the most common reason for nonmedical stimulant use. With the increasing use of stimulants among adolescents and adults, it is unclear what long-term outcomes will be since little data are available that describe differences in how side effects are experienced for prescribed and non-prescribed users. The present narrative review focuses on these adverse effects in this population and the reasonings behind misuse and nonmedical use.
... Some of these reasons for seeking an ADHD evaluation (i.e., accommodations, medication prescription) are "higher stakes;" in other words, there are more consequential outcomes associated with the diagnosis. For instance, there has been growing concern in recent years about stimulant medication misuse (e.g., taking more than prescribed or without a prescription) and diversion on college campuses (e.g., Advokat et al., 2008;Hartung et al., 2013). There are multiple reasons that college students might misuse stimulants (e.g., improving academic performance, dieting, studying all night, partying; Lefler et al., 2016). ...
... There are multiple reasons that college students might misuse stimulants (e.g., improving academic performance, dieting, studying all night, partying; Lefler et al., 2016). Stimulant misusers believe that stimulants will improve their cognitive efficiency including concentration, memory, and productivity (Benson et al., 2015;Hartung et al., 2013). Yet the data are inconclusive regarding the impact on those who do not have ADHD. ...
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Evidence-based practice in psychology (EBPP) has long focused on treatment, but evidence-based psychological assessment (EBPA) is also crucial given the important role of accurate and reliable diagnostic practices in treatment planning. In terms of the diagnosis of attention-deficit/hyperactivity disorder (ADHD), EBPA practices are well-established for children, and more recently for adults, but for college students in particular there are special considerations that warrant attention. College students with symptoms of ADHD have some challenges that are unique, and thus the assessment and diagnosis of ADHD in these students is unique. The aim of this review is not to cover all EBPA strategies for diagnosing ADHD in emerging adult college students; rather, we will focus on the unique considerations at play in college ADHD assessment. These include (a) conceptual matters such as the appropriateness of the DSM-5 criteria for college students, the limitations of our understanding of ADHD this population because of a lack of diversity in research studies, and the issue of late-identified ADHD; and (b) practical matters, such as specific documentation needs, how to gather and interpret self- and other-report of symptoms, how to assess impairment, and alternate explanations for ADHD-like symptoms in college students.
... There are, as noted previously, long-documented reasons that incentivize such intentional faking of ADHD: acquiring academic accommodations that are perceived as desirable, prescription medication for instrumental or recreational use (or diversion), and other disability benefits, among others (Advokat et al., 2010;Hartung et al., 2013;Lefler et al., 2021;Lovett & Harrison, 2021;Rabiner et al., 2009;Tucha et al., 2015). Unsurprisingly, there is evidence suggesting that not an insubstantial minority of adults (estimates ranging to 48% of self-referred college students) make noncredible responses in ADHD assessment (Harrison & Edwards, 2010;Nelson & Lovett, 2019;Suhr et al., 2008;Sullivan et al., 2007). ...
Chapter
While it is widely recognized that attention-deficit/hyperactivity disorder (ADHD) persists into adulthood, methods of assessment for adults are decidedly less well studied than those used with children and adolescents, and this includes the commonly used self-report measures. The assessment of ADHD in adults intrinsically poses certain challenges that are less often present for children and adolescents, one of them being that, in at least some cases, self-report of ADHD symptoms, impairment, and related phenomena may be nearly the only data that are available. Even in cases where collateral informants are available, self-report measures represent an efficient screening tool, at the least, for use in clinical assessment. The current chapter reviews common self-report instruments for ADHD symptoms, related impairments, executive functioning, and quality of life (QoL), including a focus on reliability data for key instruments, which should provide the reader with several good options for use in practice or research. Common challenges to the effective use of self-report measures (e.g., nonvalid responses) are also considered.
... The other most frequently reported reasons were improved concentration, alertness and wakefulness. This could be related to addressing undiagnosed or subthreshold ADHD (Hartung et al. 2013) but also, as previously hypothesised, to reinforcing alertness in social situations and therefore facilitating prolonged social engagement (Rabiner et al. 2009). Even if the potential of abuse of methylphenidate has already been described and discussed (Pauly et al. 2019), the fact that more than a third of the users reported having already felt a sense of dependence could also reflect the need to consume a drug treatment to alleviate symptoms rather than represent misuse itself. ...
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Objectives Students represent a population at risk for substance abuse. That risk may have been exacerbated by the COVID-19 pandemic. We aimed to describe substance abuse among students and to compare consumption according to the university field. Methods A self-administered questionnaire was sent by email to all students at the University of Lille, France, between March and July 2021. This anonymous questionnaire included questions about sociodemographic characteristics, university courses and the use of psychoactive substances (frequency, reasons, routes of administration) since the first university year. Results Among the 4431 students who responded (response rate 6.1%), eighty percent declared having used alcohol since the first university year, 34% cannabis, 15.4% benzodiazepines, 14.7% opioid drugs, 7.5% cocaine, 6.8% nitrous oxide and 6.5% MDMA. More than 20% of the users of cannabis, benzodiazepines, amphetamines and cocaine reported having already felt dependent. Recreational use was described by more than 10% of benzodiazepine or opioid drug users. Nitrous oxide use was significantly more frequent in the health and sport field (p < 0.001). Tobacco, benzodiazepine, cannabis and MDMA uses were significantly more frequent in the humanities and social sciences/art, language and literature fields (p < 0.001). Conclusion Prevention measures focusing on alcohol, cannabis, illicit psychostimulants, nitrous oxide and prescription drugs are required in the student population.
... Prescription stimulant (e.g., Adderall, Ritalin, Concerta) misuse (PSM), which involves use without a prescription or in ways a prescriber did not intend, has received more widespread attention in the past 15 years in college students due to substantial prevalence estimates (17%; Benson et al., 2015) and an increase in prevalence since 2003(McCabe et al., 2014. PSM can lead to adverse physiological and psychological effects ranging from lack of appetite, insomnia, restlessness, rapid heartbeat, and irritability (Hartung et al., 2013;Rabiner et al., 2009) to more severe outcomes such as addiction and emergency room visits, particularly when prescription stimulants are combined with other substances (Chen et al., 2016;Faraone et al., 2020;Schepis et al., 2021). PSM is most often motivated by a desire to increase alertness and concentration, often to complete academic work (Benson et al., 2015). ...
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Prescription stimulant misuse (PSM) is most prevalent among college students and is associated with numerous negative academic and psychosocial outcomes. A large body of literature has identified predictors of PSM in this population, however few studies have utilized a person-centered approach to examine how the sources from which students procure prescription stimulants are associated with substance-related and psychiatric impairment. We used latent class analysis (LCA) to classify a geographically and racially/ethnically diverse sample of U.S. undergraduates (N = 538) who misused prescription stimulants into groups based on their endorsement of nine sources of medication. We selected a five-group classification from the LCA with classes of peer/dealer, given by friend, own prescription, lower multiple sources (i.e., relatively infrequent endorsement of multiple sources), and any source. Compared to the reference group (given by friend), the own prescription class was less likely to report marijuana use, simultaneous alcohol and marijuana use, alcohol or marijuana consequences, and nonoral routes of administration. On the other hand, the own prescription class was more likely to screen positive for anxiety, anger, and suicidality. Similarly, the lower multiple sources group was more likely to screen positive for depression, anxiety, anger, and suicidality. Prevention and intervention efforts focused on PSM may be tailored differently for students who are misusing their own medication and/or endorsing multiple sources. Specifically, these students may need broader assistance with comorbid psychiatric conditions, particularly suicidality, while students who obtain stimulants from peers or a dealer may benefit more from substance-focused interventions. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
... In the current study, participants used substances at the suggestion of peers to overcome the challenges they were experiencing. This is in line with previous research where participants used stimulant medication to concentrate, stay awake for a long time, and improve study skills (Arria & DuPont, 2010;Hartung et al., 2013;Peterkin et al., 2011). It seems there is a common misunderstanding about drug misuse in society. ...
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Attention-deficit/hyperactivity disorder (ADHD) is a relatively prevalent neuropsychiatric and neurodevelopmental condition characterized in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition ( DSM-5 ) as difficulty sustaining attention and maintaining tasks at hand, heightened distractibility, and other deficits in executive functioning. Prescription stimulants—amphetamine (AMP) and methylphenidate (MPH)—are the first-line treatment(s) for ADHD in both pediatric and adult populations and exist in many formulations. Troublingly, the non-medical use (NMU) of amphetamine and methylphenidate is more prevalent in the American population, especially on college and university campuses, than the condition of interest. The neurotoxicological profile and NMU epidemiology of prescription stimulants is of direct relevance to primary care physicians and psychiatrists as they are the providers most frequently tasked with the treatment of ADHD and the surveillance of substance misuse behaviors in the young adult population. As comprehensive literature reviews of the mechanisms and potential adverse sequelae of prescription stimulant-induced neurotoxicity intended for medical clinicians have been quite sparse in the last decade—especially given the gravity of the issue—this article includes a brief primer on ADHD etiology and pathophysiology; considers the current state of NMU epidemiology; reviews the mechanisms of action of AMP and MPH; and, finally, summarizes known molecular and clinical manifestations of AMP and MPH neurotoxicity.
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The safety of ADHD medications is not fully known. Concerns have arisen about both a lack of contemporary-standard information about medications first licensed several decades ago, and signals of possible harm arising from more recently developed medications. These relate to both relatively minor adverse effects and extremely serious issues such as sudden cardiac death and suicidality. A guidelines group of the European Network for Hyperkinetic Disorders (EUNETHYDIS) has therefore reviewed the literature, recruited renowned clinical subspecialists and consulted as a group to examine these concerns. Some of the effects examined appeared to be minimal in impact or difficult to distinguish from risk to untreated populations. However, several areas require further study to allow a more precise understanding of these risks.
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This study tested the hypothesis that college students' substance use problems would predict increases in skipping classes and declining academic performance, and that nonmedical use of prescription stimulants (NPS) for studying would occur in association with this decline. A cohort of 984 students in the College Life Study at a large public university in the US participated in a longitudinal prospective study. Interviewers assessed NPS; Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) cannabis and alcohol use disorders; and frequency of skipping class. Semester grade point average (GPA) was obtained from the university. Control variables were race, sex, family income, high school GPA, and self-reported attention deficit hyperactivity disorder diagnosis. Longitudinal growth curve modeling of four annual data waves estimated the associations among the rates of change of cannabis use disorder, percentage of classes skipped, and semester GPA. The associations between these trajectories and NPS for studying were then evaluated. A second structural model substituted alcohol use disorder for cannabis use disorder. More than one-third (38%) reported NPS for studying at least once by Year 4. Increases in skipping class were associated with both alcohol and cannabis use disorder, which were associated with declining GPA. The hypothesized relationships between these trajectories and NPS for studying were confirmed. These longitudinal findings suggest that escalation of substance use problems during college is related to increases in skipping class and to declining academic performance. NPS for studying is associated with academic difficulties. Although additional research is needed to investigate causal pathways, these results suggest that nonmedical users of prescription stimulants could benefit from a comprehensive drug and alcohol assessment to possibly mitigate future academic declines.
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The authors conducted a meta-analysis of 150 studies in which the risk-taking tendencies of male and female participants were compared. Studies were coded with respect to type of task (e.g., self-reported behaviors vs. observed behaviors), task content (e.g., smoking vs. sex), and 5 age levels. Results showed that the average effects for 14 out of 16 types of risk taking were significantly larger than 0 (indicating greater risk taking in male participants) and that nearly half of the effects were greater than .20. However, certain topics (e.g., intellectual risk taking and physical skills) produced larger gender differences than others (e.g., smoking). In addition, the authors found that (a) there were significant shifts in the size of the gender gap between successive age levels, and (b) the gender gap seems to be growing smaller over time. The discussion focuses on the meaning of the results for theories of risk taking and the need for additional studies to clarify age trends. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
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Aims To examine the prevalence rates and correlates of non-medical use of prescription stimulants (Ritalin, Dexedrine or Adderall) among US college students in terms of student and college characteristics. Design A self-administered mail survey. Setting One hundred and nineteen nationally representative 4-year colleges in the United States. Participants A representative sample of 10 904 randomly selected college students in 2001. Measurements Self-reports of non-medical use of prescription stimulants and other substance use behaviors. Findings The life-time prevalence of non-medical prescription stimulant use was 6.9%, past year prevalence was 4.1% and past month prevalence was 2.1%. Past year rates of non-medical use ranged from zero to 25% at individual colleges. Multivariate regression analyses indicated non-medical use was higher among college students who were male, white, members of fraternities and sororities and earned lower grade point averages. Rates were higher at colleges located in the north-eastern region of the US and colleges with more competitive admission standards. Non-medical prescription stimulant users were more likely to report use of alcohol, cigarettes, marijuana, ecstasy, cocaine and other risky behaviors. Conclusions The findings of the present study provide evidence that nonmedical use of prescription stimulants is more prevalent among particular subgroups of US college students and types of colleges. The non-medical use of prescription stimulants represents a high-risk behavior that should be monitored further and intervention efforts are needed to curb this form of drug use.
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There is little recent research on the illicit use of prescription stimulants such as methylphenidate on college campuses. Given the increasing number of amphetamine prescriptions for attention deficit-hyperactivity disorder in older adolescents, non-medical use seems likely to occur. The present study surveyed undergraduates at a small college in the USA on their use of both legal and illegal stimulants; 35.5% of undergraduates who were convenience-sampled had used prescription amphetamines illicitly (defined as use without a prescription), with men reporting more use than women. Motivations were primarily academic, but 19.3% of students reported using prescription stimulants in combination with alcohol for recreational reasons. In addition, 34% of the sample reported using either cocaine or MDMA in the previous year. Motivations for use of illegal stimulants were primarily recreational. Sensation seeking appears to be a correlate of both types of stimulant use; for abuse of prescription drugs, being both high in sensation seeking and more perfectionistic is associated with greater use. Abuse of prescription and illegal stimulants appears to be widespread in this college sample.
Article
Objective To evaluate the prevalence, demographic and clinical correlates, and specificity of classes of psychotropic medications indicated for mental disorders. Design Cross-sectional survey. Setting Direct household interviews of combined household and school samples representative of the general population of adolescents in the United States. Participants Ten thousand one hundred twenty-three adolescents aged 13 to 18 years who participated in the National Comorbidity Survey Adolescent Supplement. Main Exposures Diagnostic and Statistical Manual of Mental Disorders (Fourth Edition) (DSM-IV) mental disorders and neurodevelopmental disorders. Outcome Measure Psychotropic medication use in the past 12 months. Results Among youth with any DSM-IV mental disorder, 14.2% reported that they had been treated with a psychotropic medication in the past 12 months. Strong associations emerged between specific disorders and classes of medications with evidence for efficacy. Antidepressants were most frequently used among those with primary mood disorders (14.1%); stimulant use was most common among those with attention-deficit/hyperactivity disorder (20.4%); and antipsychotic use was infrequent and mostly seen among those with serious developmental disorders. Less than 2.5% of adolescents without a 12-month mental disorder had been prescribed psychotropic medications, and most had evidence of psychological distress or impairment reflected in a previous mental disorder, subthreshold condition, or developmental disorder. Appropriate medication use was significantly more frequent among those in treatment in the mental health specialty sector than general medicine or other settings. Conclusions These findings challenge recent concerns over widespread overmedication and misuse of psychotropic medications in US youth. In fact, these data highlight the need for greater recognition and appropriate treatment of youth with mental health disorders.