Addictive Behaviors 39 (2014) 1798–1803

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Addictive Behaviors

Alcohol use, impulsivity, and the non-medical use of prescription stimulants among college students Bryan G. Messina a, Mark M. Silvestri a, Andrea R. Diulio a, James G. Murphy b, Kimberly B. Garza c, Christopher J. Correia a,⁎ a b c

Auburn University Department of Psychology, 226 Thach Hall, Auburn, AL 36830, United States University of Memphis, 202 Psychology Building, Memphis, TN 38152, United States Auburn University Harrison School of Pharmacy, 037 James E. Foy Hall, Auburn, AL 36830, United States

H I G H L I G H T S • • • •

25% of our sample reported NMUPS in the past year. 11% of our sample engaged in NMUPS/alcohol co-ingestion within the past year. Alcohol problems, binge drinking, and impulsivity predicted NMUPS. Alcohol consumption also contributed to prediction of NMUPS/alcohol co-ingestion.

a r t i c l e

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Available online 11 July 2014 Keywords: Alcohol Impulsivity Prescription drugs College students

a b s t r a c t The non-medical use of prescription drugs (NMUPD) is a growing public health concern. College students have been identified as a particularly at risk population for engagement in NMUPD. Across all prescription drug classes, stimulants show the highest ratio of illicit to medical use and are thus important to examine within this population. Emerging research has suggested a relationship between the non-medical use of prescription stimulants (NMUPS) and alcohol use within the college student population. Finally, the construct of impulsivity may serve as an additional indicator for students who engage in NMUPS as well as those who engage in NMUPS/alcohol co-ingestion. The purpose of this paper is to expand on previous prevalence data collected for the past year NMUPS and NMUPS/alcohol co-ingestion. Additionally, this paper examines whether those who engage in NMUPS or NMUPS/alcohol co-ingestion differ significantly from their non-using counterparts on measures of alcohol use, alcohol related negative consequences, binge drinking, and impulsivity. Finally, binary logistic regression models indicated that increased alcohol use, alcohol related negative consequences, and impulsivity all significantly increase the odds of an individual engaging in NMUPS or NMUPS/alcohol co-ingestion. © 2014 Published by Elsevier Ltd.

1. Introduction The non-medical use of prescription drugs (NMUPD) is defined as the use of a controlled substance without a prescription, or the use of a prescribed medication in a manner that was not intended by the prescribing medical professional (McCabe, Teter, & Boyd, 2006; McCabe, West, Morales, Cranford, & Boyd, 2007). Illicit psychotherapeutic use is highest among 18–25 year olds, and is the second most abused class of drugs among individuals 12 and older (Substance Abuse and Mental Health Services Administration, 2012). Stimulants are one of the most widely used psychotherapeutics among the college population ⁎ Corresponding author. Tel.: +1 334 844 6480. E-mail addresses: [email protected] (B.G. Messina), [email protected] (C.J. Correia).

http://dx.doi.org/10.1016/j.addbeh.2014.07.012 0306-4603/© 2014 Published by Elsevier Ltd.

(McCabe, Knight, Teter, & Wechsler, 2005). Data from the Monitoring the Future Study (MTF) indicated that 12.1% of college students reported using Adderall or Ritalin in the past year (Johnston, O'Malley, Bachman, & Schulenberg, 2012). Stimulants may be of particular importance to examine as they show the highest ratio of illicit use to medical use across the different drug classes, and are the second most commonly abused psychotherapeutic (McCabe, Teter, et al., 2006) following prescription opiates. The non-medical use of prescription stimulants (NMUPS) has also been associated with negative consequences as a result of use, some of which include engaging in illegal activities to obtain drugs, withdrawal symptoms, cardiovascular risk, and interpersonal consequences (McCabe & Teter, 2007; Substance Abuse and Mental Health Services Administration, Center for Behavioral Health Statistics and Quality, 2013). Data from the Drug Abuse Warning Network (DAWN) report a nearly fourfold

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increase in emergency department visits related to stimulant medications from 2005 to 2010 among those aged 18 to 25 (Substance Abuse and Mental Health Services Administration, Center for Behavioral Health Statistics and Quality, 2013). Finally, despite the perception of academic benefits, misuse of prescription stimulants among college students is associated with missing class as well as earning lower grades (Arria et al., 2008). Emerging research may suggest a potential relationship between NMUPS and alcohol use. Rates of binge drinking (defined as four or more standard drinks for a female and five or more for a male consumed in a single sitting) among college students have remained static despite increased public attention and intervention efforts and it remains a significant public health concern (Hingson & White, 2012). Non-medical use of prescription stimulants has been reported to be highest among students who engage in binge drinking, and those who report alcohol use disorders (Arria et al., 2013; McCabe, West, Morales, et al., 2007; McCabe, West, & Wechsler, 2007). In addition, students who engage in NMUPS are more likely to report risky alcohol consumption as well as other illicit drug use (McCabe, Cranford, Morales, & Young, 2006). Given the mechanisms of action for typical psychostimulants and alcohol, co-ingestion (i.e., consuming psychostimulants and alcohol simultaneously or in close temporal proximity) brings the prospect of a potential negative interaction between these drugs (Julien, Advokat, & Comaty, 2008). Mixing alcohol and engaging in NMUPS can lead to a diminished sense of drunkenness, perhaps leading to excessive intoxication and toxic effects (Simons, Gaher, Wray, & Reed, 2012). The co-ingestion of a psychostimulant with alcohol is not a novel phenomenon. An extensive body of research exists examining the negative consequences of the co-ingestion of alcohol and the psychostimulant caffeine. Compared to those who only consume alcohol, individuals who co-ingest the psychostimulant caffeine and alcohol are twice as likely to experience one or more negative consequences as a result of drinking (Brache & Stockwell, 2011; O'Brien, McCoy, Rhodes, Wagoner, & Wolfson, 2008), three times more likely to leave a bar over the legal drinking limit, and four times more likely to intend on driving (Thombs et al., 2010). However, we are unaware of studies comparing those that engage in NMUPS and alcohol co-injection to those that use NMUPS in the absence of alcohol. The nature of the association between binge drinking and stimulant medication use has largely been unexplored, and variables that impact this association require further elucidation. Impulsivity is one potential variable that may account for some degree of the association. Impulsivity is a broad construct that is commonly operationalized as either a difficulty in inhibiting responses or as a tendency to overvalue immediate relative to delayed rewards (Madden & Bickel, 2010). Measures of impulsivity have been predictive of substance abuse acquisition and maintenance across multiple substances (Anker, Perry, Gliddon, & Carroll, 2009; Carroll, Anker, Mach, Newman, & Perry, 2010; Perry & Carroll, 2008; Stanford et al., 2009) and with specific risky outcomes among college students including 21st birthday drinking and the presence of alcohol related problems (Day-Cameron, Muse, Hauenstein, Simmons, & Correia, 2009; Petry, 2001; Vuchinich & Simpson, 1998). Individuals who report stimulant use also show elevated impulsivity on both personality and behavioral (e.g. delay discounting) measures (Madden & Bickel, 2010; Stanford et al., 2009). However, the relationships between impulsivity, alcohol use, and past year NMUPS have yet to be established. The current study seeks to assess the relationship between weekly alcohol consumption, binge drinking, alcohol related problems, and impulsivity among those who have engaged in NMUPS and NMUPS with alcohol co-ingestion within the past year. Based on previous research, we hypothesize that those who engage in NMUPS and NMUPS with alcohol co-ingestion within the past year will report higher rates of alcohol consumption, more binge drinking episodes, a greater number of alcohol related problems, and higher levels of impulsivity compared to their non-using counterparts. Additionally, we hypothesize that those that engage in NMUPS with alcohol

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co-ingestion will show significantly higher ratings on these variables when compared to individuals that engaged in NMUPS in the past year with no co-ingestion 2. Methods 2.1. Participants Participants were 1016 undergraduate students recruited from a large southeastern public university who were awarded extra credit in their courses for their participation. The participants were at least 19 years old; the average age of the sample was 20.51 (SD = 2.26) years old. The majority of the participants were female (70.5%) and Caucasian (86.1%). Participants from other racial categories were also represented in the sample (African American = 10%, American Indian/ Alaskan Native = 3.4%, Hispanic/Latino = 3%, Asian = 2%, Native Hawaiian or Pacific Islander = 0.3%). 2.2. Measures 2.2.1. Prescription drug use survey The prescription drug use survey is an adaptation of an instrument used by McCabe, Boyd, and Teter (2009) to assess the prevalence and motives for the non-medical use of prescription drugs. This measure is designed to assess prescription drug use within the past year (e.g. “In the past year, how many times have you used stimulants either without the recommendation of a health professional, or for any reason other than a health professional's instructions to do so?”), and the frequency of co-ingestion with alcohol within the past year (e.g. “In the past year, how many times have you mixed prescription stimulants and alcohol?”). The participants responded on a Likerttype scale (1 = Never to 7 = 40 + Occasions). This survey assessed three classes of prescription drugs, though for the purposes of this paper the investigators focused only on the use of prescription stimulants (McCabe et al., 2009; Teter, McCabe, Cranford, Boyd, & Guthrie, 2005). 2.2.2. Daily Drinking Questionnaire The Daily Drinking Questionnaire (DDQ) assesses the quantity of alcohol a participant consumes each day during a typical week over the course of the past month (Collins, Parks, & Marlatt, 1985). Additional open ended questions ascertained participants' heaviest drinking week and frequency of binge drinking episodes within the past 28 days. Binge drinking was defined as four or more standard drinks within a drinking episode for a female, and five or more standard drinks within a drinking episode for a male (Wechsler, Dowdall, Davenport, & Rimm, 1995). 2.2.3. Rutgers Alcohol Problem Index (RAPI) The RAPI is a 23-item questionnaire designed to detect problematic drinking in adolescents and young adults (White & Labouvie, 1989). The measure includes such items as “kept drinking when you promised yourself not to” or “felt physically or psychologically dependent on alcohol.” The participants rate the frequency that each item occurs on a Likert-type scale (0 = Never and 4 = More Than 10 Times). Total scores are then derived by summating the participants' responses across all items. This score can be used to indicate whether adolescent/college age individuals are experiencing negative consequences of alcohol use (White, Labouvie, & Papadaratsakis, 2005). The RAPI was internally consistent in the current sample (α = .90). 2.2.4. Barratt Impulsiveness Scale (BIS) The BIS is a 30-item self-report questionnaire designed to assess the construct of impulsivity (Stanford et al., 2009). Sample items include “I buy things on impulse” and “I make up my mind quickly”. The participant responds on a Likert-type scale (1 = Rarely/Never and 4 = Almost always/Always), and a total score is generated by assigning the

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individual's responses a numeric value based on a rubric provided with the BIS and summating the individual's responses on each item. This total score is used as a general indicator of the overall personality/ behavioral construct of impulsivity. Previous studies have demonstrated that the BIS is psychometrically sound when used with college student samples (Stanford et al., 2009). The measure was internally consistent with the current sample (α = .84). 2.3. Procedure & data analysis plan All study protocols were submitted and approved by the University's Institutional Review Board. The participants for this study were undergraduate students recruited from a large southeastern university. The students were recruited via announcements made in psychology courses, as well as the SONA system (a web-based system to manage research participation), and all measures were self-administered online through the Qualtrics survey website. The participants were compensated with credits they could allot for extra credit within their courses. Data were analyzed using a combination of t-tests, correlations, and binary logistic regressions. Independent sample t-tests were conducted on each of the a priori predictors to compare 1) those who reported past year NMUPS to those that did not, 2) those who reported NMUPS/alcohol co-ingestion to those who did not (i.e., participants who abstained from NMUPS and those who reported NMUPS without alcohol coingestion), and 3) those who reported NMUPS alone to those who reported NMUPS with alcohol co-ingestion. Correlational analyses were conducted between the outcome and predictor variables. Finally, binary logistical regression models were constructed to determine how gender and the predictor variables impacted the odds of NMUPS relative to controls who did not report NMUPS, NMUPS/alcohol co-ingestion relative to controls who did not report NMUPS/alcohol co-ingestion, and NMUPS/alcohol co-ingestion relative to NMUPS without alcohol co-ingestion. 3. Results 3.1. Prevalence Of the 1016 participants 25.4% (n = 258) reported engaging in NMUPS at least once in the past year, and 10.8% (n = 110) reported having co-ingested a combination of prescription stimulants and alcohol at least once over the past year. Males (M = 1.71, SD = 1.47) and females (M = 1.61, SD = 1.26) did not differ significantly with respect to past year stimulant use [t(1004) = 1.02, p = ns], nor did males (M = 1.27, SD = 0.88) or females (M = 1.20, SD = 0.75) differ with respect to co-ingesting alcohol and stimulants [t(1004) = 1.027, p = ns]. Regarding alcohol consumption, 79.6% (n = 814) reported consuming alcohol at least once in the past month while 61% (n = 633) reported engaging in binge drinking at least once in the past month. 3.2. Group comparisons A series of independent sample t-tests were conducted to examine whether or not individuals who engaged in NMUPS within the past year, with or without the co-ingestion of alcohol, differed significantly from controls and from one another on measures of alcohol consumption and impulsivity. More specifically, groups were compared on the RAPI, DDQ for a typical week and heaviest drinking week, the number of binge drinking episodes in the past 28 days, and the BIS. Individuals who engaged in NMUPS within the past year reported a significantly higher scores on the RAPI compared to those that had not engaged in past year NMUPS [M = 7.31 vs. 2.77; t(986) = − 10.49, p b .001], consumed more alcohol in both for a typical week [M = 16.12 vs. 7.46; t(968) = −10.98, p b .001] as well as the heaviest week [M = 30.52 vs. 14.15; t(970) = −9.73, p b .001],

and engaged in significantly more binge drinking episodes [M = 5.76 vs. 2.16; t(980) = −12.48, p b .001]. In addition, those who engaged in past year NMUPS scored significantly higher on the BIS than their non-using counterparts [M = 66.47 vs. 61.28; t(994) = −7.12, p b .001]. Analyses were also conducted to examine if those who co-ingested alcohol with NMUPS differed significantly from those in the sample who did not engage in NMUPS with alcohol co-ingestion on these measures of alcohol and impulsivity. The participants who co-ingested stimulants and alcohol reported significantly higher scores on the RAPI [M = 10.04 vs. 3.19; t(115.83) = −7.20, p b .001], higher average levels of drinks consumed in both a typical week [M = 18.98 vs. 8.56; t(968) = − 9.36, p b .001] and the heaviest week [M = 39.00 vs. 16.14; t(126.88) = −8.35, p b .001], and engaged in a greater number of binge drinking episodes [M = 7.20 vs. 2.58; t(115.39) = − 6.70, p b .001]. In addition, co-ingesters scored significantly higher on the BIS [M = 70.51 vs. 61.64; t(994) = − 8.83, p b .001]. Lastly, t-tests were conducted comparing individuals who had engaged in past year NMUPS with alcohol co-ingestion with those that engaged in NMUPS alone. Individuals who engaged in NMUPS with alcohol co-ingested reported consuming more alcohol for both a typical week [M = 18.98 vs. 13.97; t(254) = − 3.18, p = .002] as well as the heaviest week [M = 39.00 vs. 24.13; t(254) = − 5.10, p b .001]. Co-ingesters also reported engaging in more binge drinking episodes [M = 7.20 vs. 4.66; t(254) = −3.67, p b .001] and significantly higher scores on the RAPI [M = 10.04 vs 4.66; t(253) = − 4.73, p b .001]. Lastly, individuals who co-ingested scored significantly higher on the BIS when compared to those that consumed in the past year with no co-ingestion [M = 70.50 vs. 65.51; t(255) = −5.58, p b .001]. 3.3. Predictors of NMUPS relative to controls We examined the likelihood of past year NMUPS based on the aforementioned measures of alcohol-related problems, alcohol consumption, binge drinking episodes, and impulsivity, using a binary logistic regression. Dichotomous dependent variables were created for both past year NMUPS users and non-users, as well as those who engaged in co-ingestion within the past year compared to those who did not engage in co-ingestion. Correlations indicated a significant relationship between measures of alcohol consumption, alcohol problems, impulsivity, and past year NMUPS (Table 1). Based on this analysis, and to eliminate high covariance between heaviest weekly alcohol consumption in the past month and typical weekly alcohol consumption within the past month, the logistic regression for past year users only used alcohol consumption for a typical week as it was more highly correlated with NMUPS. The overall model was significant in predicting those who engaged in NMUPS within the past year relative to controls who did not report NMUPS [χ2 (6) = 180.32, p b .001]. Those who had higher scores on the RAPI (OR = 1.04, 95% CI = 1.01–1.07, p b .01), consumed more drinks in a typical week (OR = 1.03, 95% CI = 1.01–1.05, p b .01), and engaged in more binge drinking episodes (OR = 1.14, 95% CI = 1.07–1.21, p b .001) were at greater odds of engaging in NMUPS within the past year (Table 2). Additionally, the BIS was also a significant predictor of NMUPS (OR = 1.03, 95% CI = 1.01–1.05, p b .001) indicating that for each point increment increase on the BIS the odds of engaging in NMUPS increased by 3%. Gender was not a significant predictor when entered as a control variable in the first model; however, gender (OR = 1.74, 95% CI = 1.17–2.57) did emerge as significant when the alcohol variables and the BIS were entered in step 2 and suggested that females were are greater risk. 3.4. Predictors of NMUPS and alcohol co-ingestion relative to controls Using a similar model as the one describe above, we examined the likelihood of NMUPS co-ingested with alcohol within the past year. A binary logistic regression was used to calculate the odds of

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Table 1 Correlations among NMUPS during the past year, alcohol use, and impulsivity. Correlations

Gender NMUPS past year Binge drinking BIS total RAPI total DDQ (typical) DDQ (max)

Gender

NMUPS past year

Binge drinking

BIS total

RAPI total

DDQ (typical)

DDQ (max)

– 0.01 −0.19⁎⁎⁎ −0.01 −0.03 −0.28⁎⁎⁎ −0.29⁎⁎⁎

– – 0.37⁎⁎⁎ 0.22⁎⁎⁎ 0.32⁎⁎⁎ 0.33⁎⁎⁎ 0.30⁎⁎⁎

– – – 0.22⁎⁎⁎ 0.51⁎⁎⁎ 0.73⁎⁎⁎ 0.69⁎⁎⁎

– – – – 0.31⁎⁎⁎ 0.22⁎⁎⁎ 0.20⁎⁎⁎

– – – – – 0.49⁎⁎⁎ 0.46⁎⁎⁎

– – – – – – 0.82⁎⁎⁎

– – – – – – –

Notes. NMUPS = Non-medical Use of Prescription Stimulants (past-year users; n = 258); BIS = Barratt Impulsivity Scale; RAPI = Rutgers Alcohol Problem Index; DDQ = Daily Drinking Questionnaire; gender codes as 1 = male and 2 = female. ⁎ p b .05. ⁎⁎ p b .01. ⁎⁎⁎ p b .001.

NMUPS mixed with alcohol using the same predictors as the prior model. Correlations indicated significant positive relationships between alcohol use, impulsivity, and co-ingestion (Table 3). These correlations indicated that heaviest weekly alcohol consumption in the past month exhibited the strongest correlation to our outcome measure. Therefore, the heaviest weekly consumption replaced typical weekly consumption as our predictor variable of alcohol consumption with all other predictors remaining the same. The overall model was significant in predicting NMUPS with alcohol coingestion relative to controls who did not report co-ingestion (χ2 (6) = 152.88, p b .001). Gender did not emerge as a significant predictor in either step. Those who consumed a greater number of standard drinks (OR = 1.01, 95% CI = 1.00–1.02, p b .05), endorsed an

Table 2 Summary of logistic regression analysis for variables predicting past year NMUPS (n = 960) and NMUPS/alcohol co-ingestion (n = 961). Logistic regressions B Past year NMUPS Step one Gender Step two Gender DDQ (typical) RAPI total BIS total Binge drinking Past year NMUPS with alcohol co-ingestion Step one Gender Step two Gender DDQ (max) RAPI total BIS total Binge drinking Alcohol vs. no alcohol co-ingestion Step one Gender Step two Gender DDQ (max) RAPI total BIS total Binge drinking

SE B

OR (95% CI)

0.02

0.16

1.02 (0.74–1.40)

0.55⁎⁎ 0.03⁎⁎ 0.04⁎⁎ 0.03⁎⁎⁎ 0.13⁎⁎⁎

0.20 0.01 0.02 0.01 0.03

1.74 (1.17–2.57) 1.03 (1.01–1.05) 1.04 (1.01–1.07) 1.03 (1.01–1.05) 1.14 (1.07–1.21)

0.22

0.79 (0.52–1.21)

0.27 0.01 0.02 0.01 0.03

1.22 (0.71–2.07) 1.01 (1.00–1.02) 1.06 (1.03–1.10) 1.06 (1.04–1.09) 1.10 (1.03–1.18)

−0.39

0.28

0.68 (0.39–1.17)

−0.16 0.02⁎ 0.05⁎ 0.06⁎⁎⁎

0.33 0.01 0.02 0.02 0.04

0.85 (0.45–1.63) 1.02 (1.01–1.04) 1.05 (1.01–1.10) 1.06 (1.03–1.09) 0.99 (0.92–1.08)

−0.24 0.19 0.01⁎ 0.06⁎⁎⁎ 0.06⁎⁎⁎ 0.10⁎⁎

−0.01

Notes. NMUPS = Non-medical Use of Prescription Stimulants (past-year users n = 258; alcohol co-ingestion n = 110); BIS = Barratt Impulsivity Scale; RAPI = Rutgers Alcohol Problem Index; DDQ = Daily Drinking Questionnaire; gender codes as 1 = male and 2 = female; OR = Odds Ratio. ⁎ p b .05. ⁎⁎ p b .01. ⁎⁎⁎ p b .001.

increased number of problems related to alcohol use (OR = 1.06, 95% CI = 1.03–1.10, p b .001), and engaged in a greater number of binge drinking episodes over the last 28 days (OR = 1.10, 95% CI = 1.03–1.18, p b .01) had greater odds of engaging in NMUPS and alcohol co-ingestion (Table 2). Lastly, the BIS was also a significant predictor of NMUPS and alcohol co-ingestion (OR = 1.06, 95% CI = 1.04–1.09, p b .01). 3.5. Predictors of NMUPS and alcohol co-ingestion relative to NMUPS alone Using a similar model as the one described above, we examined the likelihood of NMUPS co-ingested with alcohol within the past year when compared to those that engaged in past year NMUPS without alcohol co-ingestion. A binary logistic regression was used to calculate the odds of NMUPS mixed with alcohol using the same predictors as the prior model. Correlations indicated significant positive relationships between alcohol use, impulsivity, and co-ingestion. These correlations indicated that heaviest weekly alcohol consumption in the past month exhibited the strongest correlation to our outcome measure. Therefore, the heaviest weekly consumption remained our predictor variable of alcohol consumption. The model also retained all other relevant predictors from the prior models. The overall model was significant in predicting whether or not someone engaged in NMUPS with alcohol co-ingestion (χ 2 (5) = 52.72, p b 0.001). Gender did not emerge as a significant predictor in either step. Those who consumed a greater number of standard drinks (OR = 1.02, 95% CI = 1.01–1.04, p b .05), endorsed an increased number of problems related to alcohol use (OR = 1.05, 95% CI = 1.01–1.10, p b .05), and had higher scores on the BIS (OR = 1.06, 95% CI = 1.03–1.09, p b .001) had greater odds of engaging in NMUPS and alcohol co-ingestion (Table 2). Lastly, engagement in a greater number of binge drinking episodes over the last 28 days was not predictive of NMUPS alcohol co-ingestion when compared to past year users (OR = 0.99, 95% CI = 0.92–1.08, p = ns). 4. Discussion The purpose of this study was to replicate and expand our understanding of the prevalence and correlates of NMUPS. A unique aspect of this study was to look at correlates of NMUPS and alcohol co-ingestion. The current study provides additional evidence that the use of NMUPS and its co-ingestion with alcohol are fairly prevalent behaviors among college students. This study has replicated and expanded previously established relationships between alcohol use and NMUPS by indicating that alcohol use and alcohol related problems are elevated for those who engage in NMUPS and NMUPS/alcohol co-ingestion. In addition, the current study suggests that impulsivity is another key correlate of both NMUPS and NMUPS/alcohol co-ingestion. Those who engaged in NMUPS or NMUPS/alcohol

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Table 3 Correlations among NMUPS with alcohol co-ingestion, alcohol use, and impulsivity. Correlations

Gender NMUPS & alcohol Binge drinking BIS total RAPI total DDQ (typical) DDQ (max)

Gender

NMUPS & alcohol

Binge drinking

BIS total

RAPI total

DDQ (typical)

DDQ (max)

– −0.03 −0.19⁎⁎⁎ −0.01 −0.03 −0.28⁎⁎⁎ −0.29⁎⁎⁎

– – 0.34⁎⁎⁎ 0.27⁎⁎⁎ 0.34⁎⁎⁎ 0.29⁎⁎⁎ 0.31⁎⁎⁎

– – – 0.22⁎⁎⁎ 0.51⁎⁎⁎ 0.73⁎⁎⁎ 0.69⁎⁎⁎

– – – – 0.31⁎⁎⁎ 0.22⁎⁎⁎ 0.20⁎⁎⁎

– – – – – 0.49⁎⁎⁎ 0.46⁎⁎⁎

– – – – – – 0.82⁎⁎⁎

– – – – – – –

Notes. NMUPS = Non-medical Use of Prescription Stimulants (past-year users; n = 258); BIS = Barratt Impulsivity Scale; RAPI = Rutgers Alcohol Problem Index; DDQ = Daily Drinking Questionnaire; gender codes as 1 = male and 2 = female. ⁎ p b .05. ⁎⁎ p b .01. ⁎⁎⁎ p b .001.

co-ingestion indicated significantly higher levels of impulsivity than their non-NMUPS counterparts. In addition, increased alcohol use and higher levels of impulsivity also increased the likelihood of engaging in NMUPS/alcohol co-ingestion relative to NMUPS use without alcohol co-ingestion. The relationships between impulsivity and NMUPS and impulsivity and NMUPS/alcohol co-ingestion were significant even after accounting for the effects of alcohol use. Our results add to a growing literature addressing correlates and risk factors associated with NMUPS. Several studies have linked NMUPS to increased level of alcohol use as well as the use of other substances (Arria et al., 2013; McCabe, Cranford, et al., 2006; McCabe, West, Morales, et al., 2007; McCabe, West, & Wechsler, 2007). There are also several studies linking NMUPS to a range of personality traits. In addition to the relationship with impulsivity reported in the current study, previous studies with college students have reported that NMUPS is related to increased sensation seeking (Arria et al., 2008) and perfectionism (Low & Gendaszek, 2002). Furthermore, Hartung et al. (2013) found that students reporting NMUPS also reported higher levels of inattention and hyperactivity than non-users, suggesting that a subset of users may be attempting to address symptoms of ADHD. Indeed, at least one study has found that 12% of students who engage in NMUPS believe that they have ADHD (Advokat, Guidry, & Martino, 2008). Moreover, it is clear that there are multiple factors that correlate with NMUPS, and additional research is needed to determine which serve as risk factors that would be appropriate targets for intervention. Additional research is also needed to determine similarities and differences in the patterns of risk factors for NMUPS that is motivated by academic versus more social and recreational effects, and for users that use stimulants alone versus co-ingestions of stimulants with alcohol and other substances. Although standard brief motivational interventions do not directly target impulsivity, this construct has been identified as a potential novel intervention target for drug abuse prevention and intervention programs and there are emerging intervention approaches that directly target impulsivity (Bickel, Yi, Landes, Hill, & Baxter, 2011; Dennhardt & Murphy, 2013; Murphy et al., 2012). Our results provide support for the role of impulsivity in NMUPS and therefore suggest the general relevance of interventions targeting impulsivity for students who engage in NMUPS. Additionally, prevention or early intervention programs can use trait or behavioral measures of impulsivity to identify at risk students (Terlecki, Ecker, Copeland, & Buckner, 2012). More generally there is a need to develop and evaluate brief intervention approaches that specifically target illicit drug use (Lee et al., 2013), and to directly target illicit drug use in the context of alcohol focused interventions (White, Mun, Pugh, & Morgan, 2007). The relatively high rate of alcohol and stimulant co-ingestion suggests that alcohol and drug focused interventions should ask about this behavior and provide tailored feedback related to the risks of engaging in this behavior. Interventions

may need to carefully differentiate between NMUPS motivated by academic enhancement versus use that is motivated by a desire to party more or longer and include appropriate matched intervention content. A number of limitations restrict the interpretation and generalizability of these results. First, this study is of correlational nature and therefore lacks evidence of causal relationships among the variables. Second, the study's sample was fairly homogenous in nature as the majority of the participants were White, female, and under the age of 21. The over-representation of females in our sample may have led to a lack of discernable gender difference, a finding that is inconsistent with previous studies that have found that being a male is a prominent risk factor for NMUPS (e.g., McCabe, Cranford, et al., 2006; McCabe, Teter, et al., 2006; Teter et al., 2005). Although gender did emerge as a predictor in the logistic regression predicting NMUPS, the overall pattern of findings (e.g. lack of gender differences in direct comparison, lack of correlation between gender and use, lack of a significant relationship between gender and NMUPS until alcohol use variables are entered into the model) suggests that the finding may be an artifact of the regression model. Additional research is needed to determine the true nature of gender differences, and perhaps to characterize sample-specific circumstances that heighten or mitigate such differences. Third, this study did not examine potential mediators/moderators in the form of motivations for engaging in NMUPS. Recent advances in the literature indicate the importance of motivations for use for those who engage in NMUPS (Hartung et al., 2013). Fourth, we used a single personality based measure of impulsivity and did not control for the potential role of other relevant risk factors such as ADHD. Finally, the large sample led to results that, while statistically significant, were small in terms of effect sizes; future research will be needed to more fully determine the clinical significance of the findings. The findings from this study indicate that 25% of our sample of college students reported NMUPS and 10% reported co-ingestion of NMUPS and alcohol over the past year. This study also implicates alcohol use, alcohol-related problems, and impulsivity as cross-sectional predictors of both NMUPS and NMUPS/alcohol co-ingestion. With the increasing trend for NMUPS on college campuses and the associated negative consequences, it is imperative that researchers and healthcare providers keep in mind the multiple levels of risk associated with NMUPS. Users are likely to have elevated impulsivity, to drink more alcohol and to experience more alcohol-related problems. Future research utilizing longitudinal designs are necessary in order to elucidate the progression of risky behavior among those who engage in NMUPS. Role of funding sources No grant funding was provided for this study.

Contributors Silvestri, Diulio, Murphy, Garza and Correia were responsible for the study design and data collection. Messina was responsible for the data analysis and writing of the first and final drafts of the manuscript, and all authors provided feedback on the drafts.

B.G. Messina et al. / Addictive Behaviors 39 (2014) 1798–1803 Conflict of interest All authors declare that they have no conflicts of interest. Acknowledgments None.

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