Psychology of Addictive Behaviors 2015, Vol. 29, No. 2, 444 – 454

© 2014 American Psychological Association 0893-164X/15/$12.00 http://dx.doi.org/10.1037/adb0000039

Alcohol Use Longitudinally Predicts Adjustment and Impairment in College Students With ADHD: The Role of Executive Functions Joshua M. Langberg, Melissa R. Dvorsky, Kristen L. Kipperman, Stephen J. Molitor, and Laura D. Eddy

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Virginia Commonwealth University The primary aim of this study was to evaluate whether alcohol consumption longitudinally predicts the adjustment, overall functioning, and grade point average (GPA) of college students with ADHD and to determine whether self-report of executive functioning (EF) mediates these relationships. Sixty-two college students comprehensively diagnosed with ADHD completed ratings at the beginning and end of the school year. Regression analyses revealed that alcohol consumption rated at the beginning of the year significantly predicted self-report of adjustment and overall impairment at the end of the year, above and beyond ADHD symptoms and baseline levels of adjustment/impairment but did not predict GPA. Exploratory multiple mediator analyses suggest that alcohol use impacts impairment primarily through EF deficits in self-motivation. EF deficits in the motivation to refrain from pursuing immediately rewarding behaviors in order to work toward long-term goals appear to be particularly important in understanding why college students with ADHD who consume alcohol have a higher likelihood of experiencing significant negative outcomes. The implications of these findings for the prevention of the negative functional outcomes often experienced by college students with ADHD are discussed. Keywords: alcohol, college, ADHD, adjustment, impairment, executive function

consistently higher among male college students as compared with female college students and that Caucasian students display higher rates of use in comparison with African American and Hispanic American students. Another subgroup that demonstrates elevated risk for alcohol consumption and related problems is students with mental health issues. Specifically, college students with mental health concerns are significantly more likely to report alcohol-related problems and drinking to the point of intoxication in comparison to their peers (Weitzman, 2004). One subgroup of students at particularly highrisk is students with attention-deficit/hyperactivity disorder (ADHD).

The transition to college is considered a high-risk period for alcohol use and abuse (Johnston et al., 2009; SAMHSA, 2009). Alcohol use is pervasive on college campuses, with upward of 90% of college students reporting that they regularly consume alcohol (McCabe et al., 2006) and 35% reporting binge drinking in the past 30 days (SAMHSA, 2012). Further, college students report alcohol-related problems at a higher rate than their same-age peers who are not enrolled at a college or university; such that enrollment is associated with a 1.32 increase in the likelihood of alcohol-related problems (Slutske, 2005). Of even greater concern, 31% of college students report drinking habits which meet Diagnostic and Statistical Manual (DSM) criteria for alcohol abuse and 6% report habits that meet criteria for alcohol dependence (Knight et al., 2002). Alcohol use in college is associated with a host of problems, including aggression, suicidal ideation, difficulty adapting to academic demands, and interpersonal problems (see Mallett et al., 2013 for a recent review). Certain subgroups of college students may be at particularly high-risk for alcohol use and alcohol-related impairments. In a review of several large studies of alcohol use on college campuses, O’Malley and Johnston (2002) found that rates of alcohol use are

Alcohol Use in College Students With ADHD The number of students with attention-deficit/hyperactivity disorder (ADHD) pursuing higher education has risen significantly in the past 30 years (Wolf, Simkowitz, & Carlson, 2009), with current prevalence estimates ranging from 2% to 8% (DuPaul et al., 2009; Janusis & Weyandt, 2010). Given that ADHD is associated with impulsive and risk-taking behaviors (APA, 2013), there has been a long-standing interest in studying the alcohol use and abuse patterns of college students with ADHD (e.g., Smith, Molina, & Pelham, 2002). Interestingly, the compilation of research completed to date suggests that ADHD is not associated with higher alcohol use or frequency in comparison with peers. In fact, a study by Janusis and Weyandt (2010) found that students with ADHD actually reported less frequent alcohol consumption than non-ADHD peers. However, ADHD does appear to be associated with greater likelihood of experiencing alcohol-related problems and impairment (Molina et al., 2013; Molina et al., 2007) and of developing an alcohol use disorder (Lee et al., 2011). For example, in a sample of 192 college students, Mesman (2013)

This article was published Online First October 27, 2014. Joshua M. Langberg, Melissa R. Dvorsky, Kristen L. Kipperman, Stephen J. Molitor, and Laura D. Eddy, Department of Psychology, Virginia Commonwealth University. None of the authors have any actual or potential conflicts of interest to disclose. Correspondence concerning this article should be addressed to Joshua M. Langberg, VA Commonwealth University, Department of Psychology, 806 West Franklin Street, Richmond, VA 23284. E-mail: [email protected] 444

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found that ADHD symptoms of inattention were significantly associated with alcohol-related problems even after controlling for antisocial behaviors, but were not related to alcohol use. Similar findings were reported in a study reported by Glass and Flory (2012) with larger sample size (n ⫽ 889). Specifically, Glass and Flory (2012) found that ADHD symptoms of inattention predicted problems associated with alcohol even after controlling for conduct disorder (CD) symptoms, but did not predict alcohol use. Similarly, in a sample of 91 college students (n ⫽ 53 with ADHD), Rooney, Chronis-Tuscano, and Yoon (2012) found that ADHD was not associated with alcohol use or frequency but was associated with more dangerous or hazardous drinking and greater frequency of impairments due to alcohol use. Most research to date on the link between alcohol consumption and impairment in individuals with ADHD has focused exclusively on alcohol-related problems, which are typically defined as the experience of blackouts, hangovers, fights with others, unintentional injury, or health problems (see Perkins, 2002 for an in-depth review). Far less research has addressed the impact of alcohol consumption on the overall functioning of college students with ADHD, including such domains as adjustment and grade point average (GPA). This research is needed because relative to their peers, college students with ADHD report significantly poorer adjustment to college, have lower GPAs, are more likely to be placed on academic probation, and are significantly less likely to graduate (e.g., Advokat, Lane, & Luo, 2011; Blasé et al., 2009; Murphy, Barkley, & Bush, 2002; Rabiner et al., 2008). Further, it is remains unclear why individuals with ADHD are experiencing more alcohol-related problems if they are not drinking more or more frequently in comparison to their peers (Glass & Flory, 2012). One prominent hypothesis was that ADHD medication use might play a role; but recent longitudinal work demonstrates that ADHD medication use does not serve as a risk or protective factor for substance use (Molina et al., 2013). Another prominent theory is that deficits in executive function (EF) and self-regulation of behavior might explain why individuals with ADHD experience more alcohol-related impairments (Mullan et al., 2011).

The Role of Executive Function Executive functions (EFs) are higher-order cognitive processes that are involved in self-regulation of decision making and goaldirected behaviors. EF entails the ability to engage in sequences of planned, goal-directed, behaviors over prolonged periods of time by resisting distractions and inhibiting inappropriate responses (Friedman et al., 2006; Naglieri & Das, 2005). Current evidence suggests these abilities may play an important role in an individual’s alcohol use habits. In community samples of adolescents and emerging adults, individual differences in EF have been found to predict risk-taking behaviors, such as alcohol and drug use, with deficits in EF predictive of risk-taking (Pharo et al., 2011). Further, alcohol intoxication has been shown to impair EF in experimental studies (e.g., Montgomery, Ashmore, & Jansari, 2011) and individuals with ADHD appear to have increased sensitivity to the disinhibiting effects of alcohol (Weafer, Fillmore, & Milich, 2009). EFs are especially relevant when discussing college students with ADHD as individuals with the disorder frequently experience deficits in multiple aspects of EF (e.g., Hinshaw et al., 2007;

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Pennington & Ozonoff, 1996; Thorell, 2007). However, EF deficits are not universal or identical in the ADHD population; some individuals with ADHD experience severe deficits and others exhibit EF abilities similar to those of non-ADHD individuals (Willcutt et al., 2005). Research has indicated that college students with ADHD exhibit large and clinically significant EF deficits on ratings scales compared to their non-ADHD peers, but comparisons of performance on EF tasks has been less conclusive (Weyandt et al., 2013). These discrepant findings are not surprising given that a recent review found that correlations between neuropsychological measures of EF and ratings of EF were small to negligible (Toplak, West, & Stanovich, 2013). Overall, laboratorybased tests have been criticized for having poor ecological validity and ratings of EF have been shown to be better predictors of real-life impairment in samples of adults with ADHD (Barkley & Murphy, 2010; Barkley & Murphy, 2011). To date, however, the role of ratings of EF in the alcohol-related impairments of college students with ADHD has received minimal research attention and has largely been discussed theoretically (e.g., Baker, Prevatt, & Proctor, 2012; Pedersen et al., 2014). Only one study has empirically examined the role of EF in predicting the alcohol-related impairments of college students with ADHD. Rooney, Chronis-Tuscano, and Huggins (2012) focused specifically on the potential role of disinhibition in the problematic drinking behaviors of college students with ADHD. In a sample of 100 full-time undergraduate students (n ⫽ 48 with ADHD), the authors found that disinhibition and difficulty stopping drinking mediated the relation between ADHD symptoms and negative consequences of alcohol use. However, the study was crosssectional and only included a single measure of alcohol-related impairment (the negative consequences subscale from the Core Alcohol and Drug Survey). One longitudinal study also found that poor response inhibition was associated with alcohol-related problems, although it focused on a younger population (i.e., adolescents under 18 years of age; Nigg et al., 2006). Further, disinhibition is only one potential aspect of EF that might lead to alcohol-related impairment and the potential role of other aspects of EF has not been studied. Additional research evaluating the role that EF plays in the association between alcohol use and functioning is needed to inform future prevention and intervention efforts. Specifically, if aspects of EF mediate the association between alcohol use and functioning, colleges and universities could assess incoming freshman for deficits in those areas and could target those aspects of EF with intervention when warranted.

Present Study The first aim of this study was to longitudinally evaluate whether alcohol consumption measured at the beginning of the academic year could predict functioning defined broadly at the end of the year above and beyond symptoms of ADHD. Specifically, the association between alcohol consumption and adjustment, overall impairment, and GPA was evaluated. To our knowledge, this association has not been previously been examined longitudinally in a sample of college students comprehensively diagnosed with ADHD. The second aim of the study was to explore what specific aspects of EF are most important in mediating the association between alcohol consumption and functioning. In terms of hypotheses, research in general college samples suggests that there

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is a modest negative effect of alcohol involvement on educational attainment (Thombs et al., 2009; Wood, Sher, & McGowan, 2000) and that the impact on interpersonal relationships and engaging in risky behaviors (e.g., alcohol related driving) is stronger (Cooper, 2002; Fairlie et al., 2010). Accordingly, we predicted that the association between alcohol consumption and adjustment and overall functional impairment would be stronger than the association with GPA. Further, given past work demonstrating the importance of the disinhibition aspects of EF in predicting alcoholrelated impairments (Rooney et al., 2012), we predicted that the self-restraint subscale on the EF rating included in this study would mediate the association between consumption and functioning.

Method Participants Participants were undergraduate students enrolled in a large public university. In total, 139 students called, expressed interest in the study, and completed a phone screen. Of these, 94 were eligible based on the phone screen (prior diagnosis of ADHD or at least four inattentive symptoms endorsed) and completed the inclusion/ exclusion evaluation (described below). Sixty-eight participants met full study criteria and were enrolled. Given the focus on academic (i.e., GPA) and adjustment outcomes, we limited the sample for the current study to those students taking ⱖ 9 credit hours (N ⫽ 62). Participants ranged in age from 17 to 30 years (M ⫽ 19.50, SD ⫽ 2.46) and slightly over half (n ⫽ 35) were male. Forty-four participants (71%) self-identified as Caucasian; the remaining participants were African American (n ⫽ 6), Hispanic (n ⫽ 6), or Multiracial (n ⫽ 6). Approximately half of the participants (n ⫽ 32) were in their first year of college, with remaining participants in their second (n ⫽ 14), third (n ⫽ 10), or fourth (n ⫽ 6) year. Based on procedures described below, 35 participants were diagnosed with DSM–IV ADHD-I and 27 participants were diagnosed with ADHD-C. During the study period, 36 participants were taking stimulant medications for ADHD and four were taking medications for other psychological disorders. Fifty of the participants in the sample (81%) reported having previously been diagnosed with ADHD, prior to receiving a diagnosis through the study evaluation.

Procedure The study was approved by the university IRB and student participants signed informed consent and their parents/guardians provided verbal consent. The inclusionary criteria included attendance at the university where the research was being conducted and meeting full diagnostic criteria for ADHD–I or ADHD-C. Diagnosis was determined through separate administration to both the student and their parent/guardian of both Part 1 and Part 2 of the Conners’ Adult ADHD Diagnostic Interview for the DSM–IV (CAADID; Epstein et al., 2000; Epstein & Kollins, 2006). The CAADID interview assesses both current and childhood symptoms and impairment as well as age of onset and pervasiveness of symptoms across time. Part 1 of the interview provides a detailed patient history and Part II is the ADHD diagnostic interview.

Student participants had to provide consent for the study clinician to contact their parents/guardians so that the CAADID interview could be administered over the phone. A parent/guardian participated for all students included in the study. Strict diagnostic inclusion criteria were adhered to in this study. To be included in the study, participants’ parents/guardians were required to endorse at least six symptoms in one or more ADHD domains as present and impairing during childhood. The emphasis was placed upon parent/guardian report of childhood symptoms due to concerns about individuals with ADHD being able to retrospectively self-report about childhood symptoms/functioning (Sibley et al., 2012). To confirm the presence of current ADHD symptoms, the student and their parents/guardians had to endorse a total of six symptoms in a domain as currently present and impairing on the CAADID. For current ADHD symptoms, we did allow parent interview data to be supplemented with student selfreport of unique ADHD symptoms. However, both the parent and student had to endorse a minimum of four symptoms in a domain as currently present and impairing for supplementation to occur. Flyers describing the study were included in the orientation packets of all incoming freshman, e-mailed to all students currently receiving ADHD accommodations, and posted in the Disability Services Office, at Student Health, and in all university dorms. The flyers stated that students with difficulties with attention and concentration and/or students with a diagnosis of ADHD were eligible to receive a free diagnostic evaluation. Specifically, the results of the evaluation were written-up by a licensed clinical psychologist and an evaluation report was provided to the student. The psychologist had worked with the university Disability Services Office and Student Health to ensure that the evaluation report would be comprehensive enough to serve as documentation of ADHD status for students interested in pursuing university services/accommodations. Students came to a clinical-research lab to complete baseline measures at the beginning of the school year (T1) and returned to the lab to complete follow-up measures at the end of the school year (T2; 9 months postbaseline). The baseline evaluation took an average of 2 hr to complete (range ⫽ 1.5–3 hr) and the postevaluation took an average of 1.5 hr to complete (range ⫽ 1–2 hr) because the CAADID interview was not repeated. Participants were compensated $75 for their time and effort in completing the baseline evaluation and $50 for their time and effort in completing the postevaluation. The same rating scales were completed by participants at the beginning (N ⫽ 62) and end of the year (N ⫽ 59) and are described below. All participants completed the evaluation in the same order, with the CAADID being administered first, followed by participants completing the demographics questionnaire, the ADHD symptoms and comorbidities questionnaires, the alcohol measure, and measures of executive functioning and impairment.

Measures Demographics. Students completed a demographics questionnaire that contained items asking about participant age, employment status, ethnicity, gender, ADHD medication status, and current housing arrangements (i.e., living at home with parents, living off-campus, or living on-campus).

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ADHD symptoms. ADHD symptoms at baseline were assessed using the self-report version of the Barkley Adult ADHD Rating Scale-IV (BAARS-IV; Barkley, 2011a). The BAARS-IV includes the 18 DSM symptoms of ADHD. Each item was rated using a 4-point scale (1 ⫽ never or rarely, 4 ⫽ very often). The BAARS-IV scales demonstrate satisfactory internal consistency (␣ ⫽ .91 for total ADHD score) and 2-week test–retest reliability (r ⫽ .75 for total ADHD score; Barkley, 2011a). It also demonstrates adequate positive and negative predictive power, with positive predictive values ranging from .78 –.91 and negative predictive values ranging from .84 –.98, in distinguishing between those who meet DSM criteria for ADHD and those who do not. The current ADHD total score was used in the current study (␣ ⫽ .84), with higher scores indicating higher ADHD symptom severity. Alcohol use. Alcohol use behaviors at baseline were assessed using the Alcohol Use Disorders Identification Test (AUDIT; Saunders et al., 1993). The AUDIT is a 10-item, self-report rating scale developed by the World Health Organization used to screen for excessive drinking, alcohol related impairment, detect alcohol use disorders and at-risk alcohol consumption (Saunders et al., 1993; Rist, Glockner-Rist & Demmel, 2009). The present study used the AUDIT consumption items (␣ ⫽ .87) as the primary predictor variable, with higher scores representing more consumption (i.e., 5-point Likert scale; 0 ⫽ never, 4 ⫽ daily). The three consumption times include, “How often do you have a drink containing alcohol?,” “How many drinks containing alcohol do you have on a typical day when you are drinking?,” and “How often do you have six or more drinks on one occasion?” The AUDIT has been used in college samples with strong internal consistency for the total score, ␣ ⫽ .81 (Kokotailo et al., 2004). In general population samples, 4-week test–retest reliability has been satisfactory (r ⫽ .84; Park, Sher & Krull, 2009; Selin, 2003). Deficits in executive functioning. The Barkley Deficits in Executive Functioning Scale (BDEFS; Barkley, 2011b) is an 89item rating scale for adults aged 18 – 81 years and used to generate a global composite EF score. The items can also be divided into five scales: self-management to time, self-organization and problem solving, self-restraint (inhibition), self-motivation, and selfregulation of emotion. Items are rated using a 4-point scale identical to the one described for the BAARS-IV. A nationally representative sample of 1,249 adults demonstrated that the BDEFS exhibited adequate internal consistency across each of the five scales (␣s from .91 to .96). Adequate 2-week test–retest reliabilities of the subscale scores have also been reported with ranges from r ⫽ .62 to .90. Internal consistencies in the present study for self-report are as follows: management of time ␣ ⫽ .93, organization ␣ ⫽ .93, restraint ␣ ⫽ .93, motivation ␣ ⫽ .90, regulation of emotion ␣ ⫽ .92. Higher scores indicate a greater degree of EF impairment. Overall functional impairment. Participants completed the Barkley Functional Impairment Scale (BFIS; Barkley, 2011c), which assesses psychosocial impairment in 15 domains of major life activities: interactions with immediate family, chore completion/household management, work/occupation, interactions with acquaintances, relationships with friends, community activities, educational activities, marital/romantic relationships, financial management, driving, sexual activities, daily organization, daily self-care (e.g., hygiene), health, and child rearing. Participants rated impairment in each major life activity on a 10-point Likert

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scale ranging from 0 (not impaired) to 9 (severely impaired). The BFIS is a norm-referenced measure (N ⬎ 1,200) with high internal consistency (.97). Two-week test–retest reliabilities for each domain ranged from .47–.72. BFIS ratings also appear to tap a construct beyond that of general intelligence and academic achievement, as correlations were low between BFIS ratings and performance on subtests of the Shipley Institute of Living Scale and the Wide Range Achievement Test (rs ranged from ⫺.03 to ⫺.15). The total impairment score (sum of all 15 domains of functioning) was examined in the present study (␣ ⫽ .96) and associations with the individual impairment domains were also explored. Grade point average (GPA). Participants’ GPA was used as a measure of academic functioning and was coded based upon a system developed and refined in past work with adolescents and young adults (Molina et al., 2009). Importantly for a college sample, students’ GPA was calculated by taking into account the number of credits attempted and when students withdrew from courses or earned incomplete grades. This was done by taking the sum of the participants’ course grades and multiplying it by the number of credit hours earned then dividing it by the total attempted credit hours. Spring semester grades were examined as an outcome measure; A ⫽ 4.0, B ⫽ 3.0, C ⫽ 2.0, D ⫽ 1.0, and F ⫽ 0. Adjustment. Participants completed the Behavior Assessment System for Children, Second Edition, Self-Report of Personality— College Version (BASC-2: SRP-College Version; Reynolds & Kamphaus, 2004). The BASC-2 has demonstrated high internal consistency for a normative sample of 706 college students (18- to 25-years-old). The measure consists of 185 items, which are rated on either a 4-point rating scale (1 ⫽ never; 2 ⫽ sometimes; 3 ⫽ often; 4 ⫽ almost always) or as true/false. In the present study, the Personal Adjustment Composite score was examined (␣ ⫽ .84). This score is an integration of four scales: relations with parents, interpersonal relations, self-esteem, and self-reliance. Items that contribute to the Personal Adjustment Composite score examine a student’s attitudes and perceptions about his or her ability to be successful as an individual and in his or her interactions with others, such as “I am good at making decisions” and “I feel that nobody likes me.” The Personal Adjustment Composite score has demonstrated satisfactory 5-week test–retest reliability (Spearman’s ␳ ⫽ .83) and discriminant validity, significantly discriminating between a nonclinical and clinically referred sample of college students (Cohen’s d ⫽ .72; Nowinski et al., 2008). Higher scores indicate better adjustment.

Analytic Plan First, correlation analyses were conducted to examine whether any baseline (T1) participant demographics/characteristics (i.e., age, sex, race, medication status, housing status) were significantly associated with follow-up (T2) outcomes and should therefore be included as covariates in the primary analyses. Second, to evaluate the hypothesis that alcohol consumption at the beginning of the year would predict functional outcomes at the end of the year, hierarchical regression analyses were conducted. Specifically, three regressions were run (i.e., one for each outcome; adjustment, impairment, and GPA) to test whether T1 alcohol use significantly

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predicted T2 impairment after controlling for T1 ADHD symptom severity, T1 impairment, and significant covariates. Third, to evaluate the hypothesis that EF mediates the association between alcohol use and impairment, mediation models were conducted, guided by the process modeling strategies described by Hayes and colleagues (Hayes & Preacher, 2013a; b). The MEDIATE macro for SPSS (Hayes & Preacher, 2013a; b) was used to test whether the association between alcohol use and impairment was mediated by any of the T2 EF dimensions separately. All five EF dimensions were entered simultaneously into a multiple mediation model to determine the magnitudes of their relative indirect effects. The MEDIATE macro also tested the interaction effect between EF deficits in order to confirm the independent effects of each mediator. Bootstrapping tests of mediation (10,000 replications in the current study) are preferred over earlier recommendations for tests of mediation (Baron & Kenny, 1986), particularly in smaller samples, because bias-corrected bootstrapped estimates of the confidence intervals for indirect effects (denoted as ab below) do not assume normality of the distribution of sampled indirect effects like the Sobel test does (Preacher et al., 2007). For these analyses, 95% confidence intervals (CIs) are considered significant if they do not encapsulate zero.

Table 3 for results). In sum, T1 alcohol use significantly predicted T2 overall functional impairment (␤ ⫽ .22, p ⫽ .04) and personal adjustment (␤ ⫽ ⫺.18, p ⫽ .04) with ADHD symptoms, baseline levels of impairment, and relevant covariates in the model. Being male was associated with having a lower GPA (␤ ⫽ ⫺.25, p ⫽ .04). Neither ADHD symptoms nor alcohol use at baseline was a significant predictor of spring semester GPA. In addition to examining the relation between alcohol use and overall impairment, similar hierarchal regression analyses were conducted to explore the relation with each of the individual items from the BFIS that were significantly associated with alcohol use. In sum, T1 alcohol use significantly predicted T2 impairment in: relations with friends (␤ ⫽ .23, p ⫽ .03), sexual activities (␤ ⫽ .58, p ⬍ .001), and organization of daily responsibilities ((␤ ⫽ .30, p ⫽ .01), with ADHD symptoms and baseline levels of impairment in the model. Neither ADHD symptoms nor alcohol use at baseline was a significant predictor of impairment in marital relationships or finance management after including baseline levels of impairment in the model. Across all regression analyses, no VIF values were above 10 (values ⬎10 are typically considered problematic) and no tolerance values were below .10 (values ⬍.10 are typically considered problematic; Cohen, West, Aiken, & Cohen (2003) indicating that multicollinearity was not an issue.

Results Mediation Analyses Correlation Analyses Correlations between participant demographic characteristics and study outcome variables are presented in Table 1. Females had higher GPAs than males and gender was included as a covariate in the regression models predicting GPA. Participants who had previously attended college (e.g., transferred to current university) had higher overall impairment at follow-up, and previous college schooling was included as a covariate in the regression and mediation models predicting overall impairment. Participant age, race, employment status, living status, and medication status were not significantly correlated with any of the academic outcome variables and are not considered further (see Table 1). Variable means, standard deviations, and intercorrelations between predictor and outcome variables are displayed in Table 2. As hypothesized, T1 alcohol use was significantly associated with T2 overall impairment, r ⫽ .35, p ⫽ .008, and T2 adjustment, r ⫽ ⫺.36, p ⫽ .004. T1 alcohol use was also significantly positively associated with each of the T2 EF domains (rs ranging from .26 to .41, ps ⬍ .05). In turn, each of the T2 EF domains were significantly positively associated with T2 overall impairment (rs from .58 to .73, ps ⬍ .001) and negatively associated with T2 adjustment (rs from ⫺.36 to ⫺.52, ps ⬍ .01). Of note, neither T1 alcohol use and nor any of the T2 EF domains were significantly associated with GPA (ps ⬎ .05), with the exception of selfmotivation, r ⫽ ⫺.33, p ⫽ .04.

Regression Analyses Next, hierarchical regression analyses were conducted to examine whether T1 alcohol use remained significantly associated with T2 impairment outcomes (i.e., overall impairment, GPA, adjustment) after controlling for T1 ADHD symptom severity,1 T1 impairment scores, and significantly correlated covariatesv (see

Next, exploratory analyses were conducted to examine mediation models using each of five EF dimensions entered simultaneously. Two mediation models were conducted, which included T1 alcohol use as the predictor variable, T2 EF domains as potential mediators, and T2 impairment outcomes (i.e., overall impairment and adjustment) after controlling for both T1 ADHD symptom severity, T1 impairment scores, and significantly correlated covariates. Mediation results using the MEDIATE macro for predicting overall impairment and adjustment are summarized below and results for overall impairment are presented in Figure 1. Although one EF scale, self-motivation, was correlated with GPA (r ⫽ ⫺.33), the mediation model for predicting GPA is not presented given that neither the total, direct or indirect effect paths were significant in the model. Overall impairment. A total effect from T1 alcohol use to overall impairment was present (c ⫽ 3.09, SE ⫽ .2.09, p ⫽ .007), with a nonsignificant direct effect (c’ ⫽ .72, SE ⫽ .73, p ⫽ .33). As shown in Figure 1, the paths from alcohol consumption to each of the EF domains were significant (a paths), as was the path from self-motivation to functional impairment and self-regulation of emotion to functional impairment (b paths). Further, there was a significant indirect effect from alcohol use to overall impairment via self-motivation, ab ⫽ .79, SE ⫽ .49, 95% CI ⫽ [.011, 1.954], over and above transfer student status, ADHD symptom severity and T1 overall impairment. 1 Given concerns about the validity of student self-report of ADHD symptoms, the regression analyses were replicated including both student and parent-rated ADHD symptom severity in the models and the findings were consistent. Specifically, T1 alcohol use significantly predicted T2 overall functional impairment (␤ ⫽ .23, p ⫽ .05) and personal adjustment (␤ ⫽ ⫺.20, p ⫽ .04) with student- and parent-rated ADHD symptoms, baseline levels of adjustment/impairment, and relevant covariates in the model.

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Table 1 Correlations of Participant Demographic Characteristics With Follow-Up Academic Functioning

Variable

GPA (2.30 ⫾ 1.17)

Personal adjustment (50.04 ⫾ 6.08)

Overall impairment (47.58 ⫾ 24.58)

Age Sex Race Year in school Employment status Employment hours Previous college schooling High school GPA College achievement test Housing status Family income Parent education level ADHD medication status Other psychotropic medication

⫺.12 ⫺.36ⴱⴱ ⫺.03 .05 .18 .04 .07 .07 ⫺.08 ⫺.10 .06 .01 .04 .20

.22 ⫺.03 .18 .20 ⫺.06 .01 ⫺.20 .14 ⫺.03 .24 .10 .19 .12 ⫺.19

.22 ⫺.04 ⫺.05 .24 .07 ⫺.12 .27ⴱ ⫺.09 ⫺.02 .00 .19 .01 .07 .10

Note. N ⫽ 62. Age is calculated in years. For sex, female ⫽ 0, male ⫽ 1. For race, Non-Caucasian ⫽ 0, Caucasian ⫽ 1. For employment status, 0 ⫽ participant not employed, 1 ⫽ participant employed. For employment hours, participants estimated the average number of hours worked per week. For previous college schooling, 0 ⫽ participant indicated they did not previously attend any other college, 1 ⫽ participant indicated having transferred to current university after attending another university or community college. For housing status, 0 ⫽ participant not living at home, 1 ⫽ participant living at home. GPA ⫽ grade point average. ADHD medication status, 0 ⫽ not taking medication for ADHD, 1 ⫽ taking medication for ADHD. For other psychotropic medication, 0 ⫽ not taking other psychotropic medication, 1 ⫽ taking psychotropic medication for reasons other than ADHD. ⴱ p ⬍ .05. ⴱⴱ p ⬍ .01.

Adjustment. T1 alcohol use demonstrated a total effect to personal adjustment (c ⫽ ⫺2.03, SE ⫽ .96, p ⫽ .04), with a nonsignificant direct effect (c’ ⫽ ⫺1.71, SE ⫽ 1.28, p ⫽ .19), primarily due to a strong and significant association between T1 and T3 adjustment (b ⫽ .63, SE ⫽ .08, p ⬍ .001). However, examination of the indirect effects from each of the EF domains to adjustment, found that none were significant (ps ⬎ .28) in predicting T2 adjustment. Specifically, the paths from T1 alcohol use to T2 EF deficits in self-organization and self-motivation were significant, but neither dimension in turn predicted T2 adjustment.

Discussion Rates of alcohol consumption are elevated in the college student population, and students with ADHD represent a subgroup that appear to be at increased risk of experiencing impairment associated with alcohol use. To date, most research in ADHD college samples has focused on impairment immediately associated with alcohol consumption (e.g., blackouts, hangovers, alcohol-related injuries), and there has been minimal longitudinal research exploring the relation between alcohol use and functional impairment defined broadly (e.g., adjustment). To our knowledge, the present study is the first to longitudinally evaluate the relation between alcohol use and adjustment and impairment in a sample of college students comprehensively diagnosed with ADHD. Further, this study explored the role that EF deficits may play in understanding why individuals with ADHD experience more alcohol-related problems than their peers, despite the fact that they do not drink more or more frequently. Findings suggest that a significant longitudinal association exists between alcohol use and adjustment and overall impairment above and beyond ADHD symptoms and

baseline levels of functioning. Mediation models revealed that EF deficits were also related to impairment and mediated the relation between alcohol use and overall functioning. Specifically, deficits in the motivation to refrain from engaging in immediately rewarding behaviors in order to pursue long-term goals appears to be particularly important in understanding why college students with ADHD are at high-risk for alcohol-related impairments. These findings are discussed in more detail below. Alcohol consumption rated at the beginning of the school year predicted ratings of impairment and adjustment at the end of the school year but did not predict GPA (see Table 3). The lack of an association with GPA may have been due to relatively small sample size included in this study as prior prospective research in general college samples has demonstrated that the association between alcohol use and academic achievement is modest (Wood, Sher, & McGowan, 2000). Further, research suggests that the impact of alcohol involvement on academic achievement in college is moderated by prior levels of achievement, with alcohol having a smaller impact on those students with lower achievement in high school (Wood et al., 2000). Given that this study focused on college students with ADHD who consistently exhibit poorer secondary school achievement relative to their peers (Langberg et al., 2011), the lack of an association with GPA found in this study is consistent with past research. The longitudinal relation between alcohol use and impairment found in this study is noteworthy because the BFIS measures impairment broadly. Exploratory regression analyses to determine what specific aspects of impairment on the BFIS alcohol consumption was associated with revealed significant

LANGBERG, DVORSKY, KIPPERMAN, MOLITOR, AND EDDY

longitudinal associations with relationships with friends, sexual activities, and organization of daily responsibilities. Thus, these findings are consistent with previous research in general college samples documenting a strong association between alcohol use, interpersonal relationships, and risky behaviors (e.g., Cooper, 2002; Fairlie et al., 2010). The findings from this study suggest that it is important to consider the role that EF deficits play in the relationship between alcohol use and impairment. As shown in Table 2, self-rated EF was strongly associated with ratings of functioning. Further, self-rated EF significantly mediated the association between alcohol use and overall impairment (see Figure 1). These findings are consistent with past cross-sectional work showing that disinhibition mediated the relation between ADHD symptoms and negative consequences of alcohol use in a sample of 48 college students with ADHD (Rooney et al., 2012). The mediation analyses suggest that deficits in EF selfmotivation are driving the overall EF mediation findings. The self-motivation subscale included in this study contains items related to motivation to work toward longer-term or delayed rewards. Increasing evidence suggests that dysfunction in motivation and reward processing plays a significant role in the functional impairments of individuals with ADHD (e.g., Reaser et al., 2007; Volkow et al., 2011). Specifically, individuals with ADHD appear to be particularly sensitive to immediate rewards and have a difficult time with motivation to work toward long-term goals/rewards (Sonuga-Barke, 2003). These motivational deficits have significant implications for the college setting where tasks are often long-term (e.g., papers, projects, and exams) and where there are plenty of immediately available rewards and distractions. Given that alcohol use has been shown to impair EF in community samples (Montgomery et al., 2011), one possibility is that alcohol use is magnifying the EF deficits already present in many college students with ADHD, increasing the likelihood that they will result in significant functional impairment.

52.97 15.58 55.03 12.72 45.97 11.03

36.66 11.48

25.69 8.42

26.29 9.51

196.64 46.24

47.58 24.58

2.29 1.16

⫺.07 ⫺.36ⴱⴱ ⫺.36ⴱⴱ .16 .63ⴱⴱⴱ ⫺.50ⴱⴱⴱ ⫺.51ⴱⴱⴱ ⫺.47ⴱⴱⴱ ⫺.52ⴱⴱⴱ ⫺.36ⴱⴱ ⫺.56ⴱⴱⴱ .03 — 50.04 6.08 p ⬍ .001. ⴱⴱⴱ

p ⬍ .01. ⴱⴱ

p ⬍ .05.

3.30 2.65 46.84 8.53

50.08 23.25

2.43 0.92

Limitations

ⴱ

⫺.08 ⫺.13 .01 .45ⴱⴱⴱ .12 ⫺.24 ⫺.08 ⫺.02 ⫺.33ⴱ .06 ⫺.07 — .37ⴱⴱ .35ⴱⴱ .56ⴱⴱⴱ ⫺.20 ⫺.34ⴱⴱ .73ⴱⴱⴱ .64ⴱⴱⴱ .58ⴱⴱⴱ .69ⴱⴱⴱ .61ⴱⴱⴱ — .52ⴱⴱⴱ .42ⴱⴱⴱ .55ⴱⴱⴱ ⫺.26ⴱ ⫺.36ⴱⴱ .83ⴱⴱⴱ .84ⴱⴱⴱ .82ⴱⴱⴱ .72ⴱⴱⴱ .75ⴱⴱⴱ .32ⴱ .26ⴱ .46ⴱⴱⴱ ⫺.05 ⫺.13 .44ⴱⴱⴱ .56ⴱⴱⴱ .70ⴱⴱⴱ .34ⴱⴱ — .26 .34ⴱⴱ .37ⴱⴱ ⫺.32ⴱ ⫺.38ⴱⴱ .81ⴱⴱⴱ .42ⴱⴱⴱ .43ⴱⴱⴱ — .51ⴱⴱⴱ .37ⴱⴱ .46ⴱⴱⴱ ⫺.23 ⫺.32ⴱ .57ⴱⴱⴱ — .44ⴱⴱⴱ .30ⴱ .40ⴱⴱ ⫺.29ⴱ ⫺.18 — .04 —

1. T1 2. T1 3. T1 4. T1 5. T1 6. T2 7. T2 8. T2 9. T2 10. T2 11. T2 12. T2 13. T2 Mean SD

ADHD total symptoms AUDIT consumption BFIS total Fall semester GPA personal adjustment self-management of time self-organization self-restraint self-motivation self-regulation of emotion BFIS total Spring semester GPA personal adjustment

—

.46ⴱⴱⴱ .32ⴱ —

⫺.15 ⫺.05 ⫺.03 —

⫺.17 ⫺.33ⴱⴱ ⫺.32ⴱ ⫺.15

.47ⴱⴱⴱ .41ⴱⴱⴱ .47ⴱⴱⴱ ⫺.15 ⫺.31ⴱ .48ⴱⴱⴱ .64ⴱⴱⴱ —

11 10 9 8 7 6 5 4 3 2 1

Table 2 Means, Standard Deviations, and Intercorrelations of Predictor, Mediator, and Outcome Variables

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12

13

14

450

There are a number of important limitations that need to be considered. First, the sample size was modest and as such, the multiple mediator analyses should be considered exploratory and need to be replicated. Second, and importantly, this study only included two time points, beginning of the year and end of the year, approximately 9 months apart. In order to better understand the reciprocal relationship between alcohol use and functional impairment, studies with additional time points are needed. Specifically, the relationship between alcohol use, EF, and impairment is likely cyclical and transactional in nature and at least three time points are needed to evaluate this possibility. Third, approximately half of the sample was in their first year of college. As such, participants completed ratings of alcohol use shortly after transitioning to college and alcohol use patterns may change significantly during the first year of college. It is possible that findings would differ had students in all years of college been more evenly represented. Fourth, ADHD symptoms were not significant predictors in the regression models for any of the outcomes (see Table 3). As this was an ADHD diagnosed sample, there was restriction of range for the ADHD

ADHD AND ALCOHOL IN COLLEGE

451

Table 3 Hierarchical Regression Model of T1 Alcohol Use Predicting T2 Academic Outcomes Above and Beyond T1 ADHD Symptoms Step 1 model summary DV: T2 overall impairment

B

Step 2 model summary

␤

SE

t

B

.51 9.27 .25 —

.13 6.74 .37 —

.49 .16 .09 —

3.85ⴱⴱⴱ 1.38 .67 —

.45 7.51 .33 1.96

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Step 1 model summary DV: T2 grade point average

B

SE

.13 6.63 .36 .99

Step 2 model summary

␤

t

B

SE

␤

.15 .27 .02 —

.38 ⫺.26 ⫺.01 —

3.14ⴱⴱ ⫺2.18ⴱ ⫺.08 —

.48 ⫺.58 .01 ⫺.03

Step 1 model summary DV: T2 personal adjustment

B

SE

␤

.15 .27 .02 .05

Step 2 model summary t

B

SE

␤

ⴱ

p ⬍ .05.

ⴱⴱ

p ⬍ .01.

ⴱⴱⴱ

.08 .31 —

.76 ⫺.05 —

t ⴱⴱⴱ

F(3, 56) ⫽ 28.82, R ⫽ .61 ⌬F(1, 56) ⫽ 4.33, ⌬R2 ⫽ .03ⴱ

F(2, 57) ⫽ 38.76, R2 ⫽ .58ⴱⴱⴱ .67 ⫺.16 —

3.09ⴱⴱ ⫺2.10ⴱ ⫺.06 ⫺.63

.38 ⫺.25 ⫺.01 ⫺.08

2

T1 personal adjustment T1 ADHD symptoms T1 AUDIT consumption

t ⴱⴱ

F(4, 55) ⫽ 4.28, R ⫽ .24 ⌬F(1, 56 ⫽ .40, ⌬R2 ⫽ .01

F(3, 56) ⫽ 5.64, R2 ⫽ .23ⴱⴱⴱ .48 ⫺.59 .01 —

3.31ⴱⴱ 1.13 .93 1.97ⴱ

.42 .13 .12 .22

2

T1 grade point average Gender T1 ADHD symptoms T1 AUDIT consumption

t

F(4, 54) ⫽ 8.49, R2 ⫽ .40ⴱⴱⴱ ⌬F(1, 54) ⫽ 3.8, ⌬R2 ⫽ .05ⴱ

F(3, 55) ⫽ 9.52, R2 ⫽ .35ⴱⴱ T1 overall impairment Previous college schooling T1 ADHD symptoms T1 AUDIT consumption

␤

SE

8.76ⴱⴱⴱ ⫺.53 —

.63 ⫺.14 ⫺2.01

.08 .30 .97

.71 ⫺.04 ⫺.18

8.16ⴱⴱⴱ ⫺.48 ⫺2.08ⴱ

p ⬍ .001.

symptoms of inattention, which likely limited the ability to detect effects. Finally, the lack of significant associations between alcohol use and the marital relationships and finances functional domains may because these domains of functioning are less relevant for college students.

Future Directions Future research is needed to evaluate whether EF deficits are also important in predicting alcohol-related problems in adults with ADHD, who are at increased risk for alcohol use disorders (Lee et al., 2011). Research is also needed to explore alternate pathways from alcohol use to functional impairment in samples of college students with ADHD. For example, individuals with ADHD are at significantly increased risk for sleep disturbances, and daytime sleepiness has been shown to negatively impact the functioning of college students with ADHD (Langberg et al., 2014). Accordingly, it may be that alcohol use is exacerbating the sleep problems of college students with ADHD, which in turn, further impairs EF and leads to functional impairment. The inner-relation between alcohol use and sleep in college students with ADHD has not been studied and warrants attention. If future research supports that EF deficits in motivation play a significant role in the relation between alcohol use and impairment in college students with ADHD, it will be important to evaluate whether currently available alcohol and/or ADHD interventions sufficiently address these issues. A recent metaanalysis found that alcohol interventions for first year college

students are effective, but that the impact is modest when compared with control conditions (Scott-Sheldon et al., 2014). Interestingly, interventions that included cognitive strategies such as challenging alcohol-related expectancies resulted in larger effect size improvements. In addition, providing personalized feedback about drinking behaviors and goals (often in the context of motivational interviewing) was associated with larger intervention effects (Scott-Sheldon et al., 2014). These intervention components would appear to be consistent with the needs of college students with ADHD, who show a particular pattern of alcohol expectancies compared to those without ADHD (Pedersen et al., 2014). Specifically, college students with ADHD score below their peers on measures of both negative alcohol expectancies (expectancies about cognitive and behavioral impairments) and positive alcohol expectancies (expectancies about increased sociability, and the “liquid courage” effect). More research is needed to determine whether interventions targeting expectancies are effective for college students with ADHD. Finally, there is some evidence that ADHD medication has a significant impact on EF in college students with ADHD (DuPaul et al., 2011). Medication use was not associated with any of the predictor or outcome variables in this study and this is consistent with past work in college samples (e.g., Advokat et al., 2011; Rabiner et al., 2008). Future research is needed to determine if medication can have an impact on the functioning of college students with ADHD if it is titrated according to

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LANGBERG, DVORSKY, KIPPERMAN, MOLITOR, AND EDDY

Figure 1. Indirect effects model of T1 alcohol consumption predicting T2 functional impairment via T2 executive functioning domains (N ⫽ 59). Unstandardized coefficients shown outside parentheses; standard errors are shown inside parentheses. Dashed paths are nonsignificant (ps ⬎ .05). Analyses controlled for T1 ADHD symptom severity and T1 functional impairment, which in the final model were not significantly associated with any of the mediator or outcome variables. ⴱ p ⬍ .05.

best-practice procedures, similar to those used in experimental medication studies (e.g., DuPaul et al., 2011).

Conclusions The results of this study document that alcohol use in college students with ADHD is longitudinally associated with a range of negative functional outcomes, including interpersonal relationships, risky behaviors, and adjustment. This study contributes to a growing body of evidence suggesting that EF deficits play an important role in understanding why individuals with ADHD are at increased risk for negative outcomes associated with alcohol use (e.g., Rooney et al., 2012). Specifically, deficits in EF self-motivation mediated the association between alcohol use and impairment. This suggests that alcohol use has a negative impact on the ability of college students with ADHD to stay motivated to pursue long-term goals in the face of immediately available reinforcers, which in turn negatively impacts adjustment and leads to greater overall impairment. Although more research is certainly needed, these findings represent a first step toward understanding the pathways between alcohol use and impairment in college students with ADHD.

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Received May 14, 2014 Revision received September 12, 2014 Accepted September 14, 2014 䡲