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. Author manuscript; available in PMC: 2019 Feb 1.
Published in final edited form as: Addict Behav. 2017 Sep 28;77:152–159. doi: 10.1016/j.addbeh.2017.09.019

Predictors of Short-Term Change after a Brief Alcohol Intervention for Mandated College Drinkers

Kate B Carey 1,2, Jennifer E Merrill 1,2, Jennifer L Walsh 3, Sarah A Lust 4, Seth C Kalichman 5,6, Michael P Carey 2,7,8
PMCID: PMC5701863  NIHMSID: NIHMS912874  PMID: 29032317

Abstract

Objective

Brief motivational interventions (BMIs) reduce problematic drinking for some, but not all, college students. Identifying those students who are less responsive can help to guide intervention refinement. Therefore, we examined demographic, personality, and cognitive factors hypothesized to influence change after a BMI.

Method

Students mandated for intervention following a campus alcohol violation (N = 568; 28% female, 38% freshmen) completed a baseline assessment, then received a BMI, and then completed a 1-month follow-up. At both assessments, alcohol use (i.e., drinks per week, typical BAC, binge frequency) and alcohol-related problems were measured.

Results

Latent change score analyses revealed significant decrease in both alcohol use and problems one month after the BMI. In the final model that predicted change in alcohol use, four factors (male sex, a “fun seeking” disposition, more perceived costs and fewer perceived benefits of change) predicted smaller decreases in alcohol use over time. In the final model that predicted change in alcohol-related problems, three factors (stronger beliefs about the centrality of alcohol to college life, more perceived costs and fewer perceived benefits of change) predicted smaller decreases in problems over time.

Conclusions

Participation in a BMI reduced alcohol use and problems among mandated college students at 1-month follow-up. We identified predictors of these outcomes, which suggest the need to tailor the BMI to improve its efficacy among males and those students expressing motives (pro and cons, and fun seeking) and beliefs about the centrality of drinking in college.

Keywords: brief motivational intervention, college students, drinking, alcohol abuse prevention

College drinking is characterized by high volume consumption that undermines the health, safety, and academic performance of students (White & Hingson, 2014). Research shows that brief preventive interventions reduce alcohol misuse and problems in the general student population, especially those that use motivational interviewing and personalized feedback and those that target descriptive norms (Carey, Scott-Sheldon, Carey, & DeMartini, 2007; Huh et al., 2015; Samson & Tanner-Smith, 2015). A subgroup of students who require special attention are those who violate campus policies. These “mandated” students also respond well to brief alcohol interventions but effect sizes are often heterogeneous (Carey, Scott-Sheldon, Garey, Elliott, & Carey, 2016), such that not all students benefit from exposure to intervention equivalently. Research to clarify who benefits, and how to refine interventions to enhance efficacy, is needed.

One study identified trajectories of change after intervention using data from three samples (including mandated students) who received either a brief face-to-face or a computer-delivered intervention (Henson, Pearson, & Carey, 2015). Fully 82% of students demonstrated a strong initial intervention response, reflected in reductions in measures of alcohol consumption. Initial change was associated with characteristics of the recipients; that is, those who changed the most were female, upper-classmen, with later drinking onset, did not play drinking games, and reported lower peer drinking norms. Conversely, change after intervention was less for males, underclassmen, those reporting higher peer drinking norms and engaging in drinking games.

Building upon this work, we sought to identify predictors of change after a brief motivational intervention (BMI) for students mandated to an alcohol intervention. We drew from theoretical perspectives applied to college drinking interventions such as social cognitive theory (Bandura, 2011) and the transtheoretical model (Prochaska, DiClemente, & Norcross, 1992), and also drew upon the growing empirical literature documenting determinants of young adult drinking. We hypothesized that factors that place students at greater alcohol-related risk may be barriers to change. However, correlates of drinking in the absence of an intervention may differ from predictors of change after intervention. Thus, based on theory, empirical literature, and our intervention experience, we selected promising demographic, personality, and cognitive candidate predictors of change after intervention.

Demographic characteristics have been consistently associated with heavier drinking including male sex, white race (Johnston, O’Malley, Bachman, Schulenberg, & Miech, 2014), affiliation with a Greek organization (A. Park, Sher, & Krull, 2008), and freshman status (Sher & Rutledge, 2007). In addition, both sex and class status have been linked to intervention response (Henson et al., 2015). Demographic variables are often correlated with social and psychological conditions related to establishing and modification of drinking patterns.

Personality constructs have also been linked to drinking. Among these, constructs related to reinforcement sensitivity (Gray, 1970) are promising predictors. Specifically, individuals with a strong Behavioral Activation System (BAS) tend to be impulsive sensation-seekers, reacting to reward-related cues (i.e., alcohol) with increases in positive affect and approach motivation (Hamilton, Sinha, & Potenza, 2012; Voigt et al., 2009). Conversely, the Behavioral Inhibition System (BIS) competes with approach behavior; high-BIS individuals show uncertainty and anxiety in response to mixed reward and punishment cues. It is unclear whether BIS is a risk factor for drinking because anxiety may promote drinking (Wardell, O’Connor, Read, & Colder, 2011) whereas dispositional avoidance of threat may discourage drinking (Keough & O’Connor, 2014). Strong orientations towards reward and/or avoidance of punishment may override the influence of a brief alcohol risk reduction intervention. We also explored multiple dimensions of impulsivity, which has been linked to heavy drinking among students (Diulio, Silvestri, & Correia, 2014; Kazemi, Flowers, Shou, Levine, & Van Horn, 2014). To the extent that impulsivity represents behavioral disinhibition or under-control, it may interfere with alcohol use self-management strategies.

Mental health problems have been associated with heavy episodic drinking (Cranford, Eisenberg, & Serras, 2009) and problems related to alcohol use (Dennhardt & Murphy, 2011; Kenney & LaBrie, 2013; Weitzman, 2004). Thus, the presence of mental health problems, and associated cognitive and motivational impairments, may undermine the ability of students to benefit from a BMI.

Social-cognitive variables also correlate with drinking. Descriptive norms (i.e., perceptions of how other students drink) and injunctive norms (i.e., perceptions of peer approval of drinking) uniquely influence drinking behavior (Larimer, Turner, Mallett, & Geisner, 2004; H. S. Park, Klein, Smith, & Martell, 2009). Henson et al. (2015) reported that stronger descriptive and injunctive norms predicted less change after alcohol interventions. We sought to replicate these findings with mandated students and with a BMI that emphasizes normative correction.

Beliefs about the central role that alcohol consumption plays in the college experience have been associated with both consumption and consequences (Osberg et al., 2010b; Osberg, Insana, Eggert, & Billingsley, 2011), beyond positive alcohol expectancies, injunctive norms for friends, and descriptive norms (Osberg et al., 2011). Students holding stronger centrality beliefs reported less motivation to change drinking after an alcohol sanction (Qi, Pearson, & Hustad, 2014). Because these beliefs may conflict with risk reduction messages, we expect that strong beliefs about the centrality of alcohol to the college experience will predict less change.

According to the transtheoretical model, readiness to change a problem behavior is reflected in perceived costs and benefits of change (Prochaska et al., 1994); typically, individuals who are not ready to change perceive more costs than benefits. In the recovery context, costs of change predict increases in substance use over time, and benefits of change predict reductions (Cunningham, Sobell, Gavin, Sobel, & Breslin, 1997; Korcha, Polcin, Bond, Lapp, & Galloway, 2011). We predict that costs and benefits of changing drinking perceived by mandated students will predict change after a BMI.

To test these hypotheses, we used baseline and 1-month post-intervention data from an intervention trial. In a sample wherein all students receive a BMI, we predict smaller decreases for individuals with certain demographics (male sex, White race, Greek involvement, freshman status); lower behavioral inhibition; higher behavioral activation; higher impulsivity; more psychological distress; more permissive perceived norms (descriptive, injunctive); stronger beliefs about the centrality of alcohol to college life; and more perceived costs and fewer perceived benefits of change.

Method

Participants and Recruitment

Participants were 568 students enrolled in a public university in the northeastern U.S. who were mandated to participate in an alcohol education program following an alcohol-related violation. Students (N = 610) were screened for eligibility (i.e., enrolled in college, ≥ 18 years old, no previous participation in the study). Eligible students viewed a brief presentation outlining their options for satisfying the sanction: (a) pay a fee and participate in the standard sanction (a brief individualized alcohol intervention modeled after BASICS; Dimeff, Baer, Kivlahan, & Marlatt, 1999) or (b) participate in this study (i.e., baseline, BMI, and 1-month assessment). Completion of study activities through the 1-month follow-up was considered equivalent to the standard sanction, and therefore served to satisfy the sanction requirement. Students who selected the study option saved the standard sanction fee and gained the possibility of earning compensation for follow-up assessments after 1-month. Of the 610 mandated students screened, 13 were ineligible, 24 declined, and 5 did not attend the baseline session. Thus, 568 students (93%) consented and completed baseline.

Procedures

The Institutional Review Board approved all procedures, and we obtained a Certificate of Confidentiality. Interested students scheduled an appointment, during which a research assistant (RA) explained the study, reviewed the consent, and answered questions. Consenting students completed the baseline survey and made a BMI appointment within 2 weeks. After the BMI, the RA scheduled a follow-up appointment 1 month later in the research office.

Intervention

We used a BMI shown to be efficacious with mandated students (Carey, Carey, Henson, Maisto, & DeMartini, 2011; Carey, Carey, Maisto, & Henson, 2006; Carey, Henson, Carey, & Maisto, 2009). Nine interventionists (eight females, one male) were trained on the manualized protocol and received weekly supervision from the authors. The BMI used a collaborative, supportive, yet directive style, consistent with motivational interviewing (Miller & Rollnick, 2012). To structure the session, a personalized feedback sheet summarized (a) weekly consumption (compared to sex-specific national and local norms), (b) typical and peak blood alcohol concentration (BAC), and (c) alcohol-related consequences and associated risk behaviors; interventionists also (d) prompted goal setting and (e) provided tips for safer drinking. BMI sessions took 15 to 122 minutes (M = 67; SD = 17).

Measures

All measures were collected at baseline, reflecting past month behavior; alcohol consumption and consequences were also collected at the 1-month follow-up.

Descriptives

Participants reported their sex, age, race/ethnicity, year in college, Greek status, and body weight (for calculation of BAC). A reliable and valid 13-item form of the Marlowe-Crowne Social Desirability scale (Reynolds, 1982) was used as a potential covariate in analyses; responses to the yes/no items are summed (sample α = .67).

Alcohol consumption

We defined a standard drink as 12 oz. of beer, 5 oz. of 12% table wine, 12 oz. of wine cooler, or 1.25 oz. of 80-proof liquor. The Daily Drinking Questionnaire (Collins, Parks, & Marlatt, 1985) was used to assess typical drinks per week (DPW) in the last month. Participants also reported the number of standard drinks consumed and the number of hours spent drinking on a typical day, from which we calculated typical BAC: [(drinks/2) *(GC/weight)] − (0.016 * hours), where drinks = number of standard drinks consumed, hours = number of hours over which the drinks were consumed, weight = weight in pounds and GC = gender constant (9.0 for females, 7.5 for males) (Matthews & Miller, 1979). Participants also reported heavy drinking frequency, the number of times in the past month consuming 4+ (females)/5+ (males) drinks on one drinking occasion (Wechsler, Dowdall, Davenport, & Rimm, 1995).

Alcohol-related consequences

The 24-item Brief Young Adult Alcohol Consequences Questionnaire (B-YAACQ) (Kahler, Strong, & Read, 2005) is a checklist of problems related to drinking in the past month. The B-YAACQ items are summed, reliable (α = .84) and free of gender bias.

Drinking norms

Descriptive norms were assessed using 3 items (α = .82) that asked how many of the participant’s friends (a) drink alcohol, (b) get drunk on a regular basis (at least once a month), and (c) drink primarily to get drunk. Response options ranged from 0 (none) to 4 (nearly all), and items are averaged. Perceived injunctive norms were assessed with 2 items (α = .77) that assessed how most friends’ feel about (a) drinking and (b) getting drunk (Kahler, Read, Wood, & Palfai, 2003). Response options ranged from 0 (strongly disapprove) to 4 (strongly approve); items are averaged to obtain a summary score.

Impulsivity

Impulsivity was measured with the Urgency Premeditation Planning Sensation Seeking Impulsive Behavior Scale (UPPS) (Magid & Colder, 2007). Items, which use a 5-point Likert scale ranging from “not at all” to “very much,” are summed to form four subscales: negative urgency (10 items, α = .82), sensation seeking (12 items, α = .87), (lack of) Premeditation (10 items, α = .85), and (lack of) Perseverance (8 items, α = .85).

Behavioral inhibition and activation

The BIS/BAS Scales (Carver & White, 1994) consist of the 7-item behavioral inhibition scale (BIS; α = .66) and the 13-item behavioral activation (BAS; α = .82). The BAS consists of three sub-factors: Drive (4 items, α = .84), FunSeeking (4 items, α = .84), and Reward Responsiveness (5 items, α = .74). Participants respond on 4-point scales (1 = very false for me to 4 = very true for me); each item set was summed to obtain subscale scores.

Decisional balance

Participants rated the costs and benefits of quitting alcohol use using the Alcohol and Drug Consequences Questionnaire (ADCQ) (Cunningham et al., 1997). Responses ranged from 1 (not important) to 5 (extremely important), and are summarized as mean scores on costs (14 items; α =.96) and benefits (15 items; α =.94).

Distress

Distress was measured with the Patient Health Questionnaire-4 (PHQ-4) (Kroenke, Spitzer, Williams, & Löwe, 2009). Participants report how often (past two weeks) they have been bothered by depression and anxiety on a scale from 0 (not at all) to 3 (nearly every day); the four items are summed. In this sample, α = .84.

Centrality of alcohol

The 15-item College Life Alcohol Salience Scale (CLASS; Osberg et al., 2010a) assesses the extent to which a student believes alcohol is central part of the college experience, from 1 (strongly disagree) to 5 (strongly agree); all items are summed. Alpha in this sample was .87.

Data Analysis Plan

Missing cases were few, with 98% returning for the 1-month follow-up. We used multiple imputation to replace missing values (Schafer, 1999). Outliers were truncated to three times the interquartile range from the 75th percentile (Tabachnick & Fidell, 2007). Using t-tests, we examined differences between baseline and follow-up and sex differences in outcomes. Next, associations between baseline factors and changes in alcohol consumption and alcohol use consequences were examined using latent change score (LCS) analyses (McArdle & Nesselroade, 2013) in Mplus 7.1 (Muthén & Muthén, 1998–2015). LCS models include latent factors representing a construct at two time points as well as a third latent score representing the latent change between these two common-factor scores. Latent change scores are more reliable than simple difference scores (McArdle, 2009).

We created separate LCS models for alcohol consumption and alcohol-related consequences. Consumption was modeled with three indicators: drinks per week, typical BAC, and binge drinking frequency (all log transformed). Consequences were modeled with three parcels of items from the B-YAACQ as indicators (see Little, Cunningham, Shahar, & Widaman, 2002). We allowed correlations between identical indicators at baseline and 1 month to account for shared error variance (Duncan, Duncan, & Strycker, 2006).

Next, we tested for measurement invariance over time. Models were identified by constraining baseline means to 0 and variances to 1, putting scores on a standardized metric with regard to baseline consumption (consequences). Nested models were compared using the Satorra-Bentler scaled χ2 difference test (Satorra & Bentler, 2010). Next, we used standard methods to estimate latent change scores (McArdle & Nesselroade, 2013). These scores represented the change in consumption (consequences) from baseline to 1 month for each participant in standard deviation (SD) units; thus, positive change scores indicate increases and negative change scores indicate decreases in consumption/consequences.

Finally, we regressed change scores on baseline predictors to determine characteristics associated with change following intervention. In preliminary models, predictors were tested in subsets to determine which would be included in final multivariate models. Controlling for significant demographic variables and social desirability, each of six sets (four BIS/BAS, four UPPS, two drinking norms, two decisional balance, CLASS, and PHQ scores) were entered separately into parallel models predicting consumption and consequences. We included paths from predictors associated with change scores (p < .10) to both baseline and change scores. Additionally, change scores were regressed on baseline scores to control for the influence of starting values on change.

Models were fit with an estimator robust to non-normality (the MLR estimator; Muthén & Muthén, 1998–2015), and fit was assessed using the comparative fit index (CFI), TuckerLewis index (TLI), and the root-mean-square error of approximation (RMSEA). Good fit is indicated by CFI and TLI values > .95 and RMSEA values < .05 (Kline, 2011). For models using multiple imputation, we report the average fit across 100 imputed datasets. When discussing predictors of change scores, we report unstandardized coefficients and standard errors; these can be interpreted as the change in consumption or consequences in SD units for each unit change in a predictor variable.

Results

Preliminary Analyses

Participants were mostly male (72%) and freshmen (38%) or sophomores (35%). Mean age was 19.18 years (SD = 1.16). Most identified as White (84%), with 9% multiracial, 4% Black, 6% Asian, and 6% other; 6% were Hispanic/Latino. The majority lived on campus (89%), 9% lived off-campus, and 2% with family. Thirteen percent were members of the Greek system and an additional 4% were pledging. Table 1 displays summary statistics for all predictors.

Table 1.

Baseline values on hypothesized predictors of change among mandated students participating in a brief motivational intervention (N = 568)

Demographic Variables %
Male sex 72%
White race 84%
Class Year
 Freshmen 38%
 Sophomore 35%
 Junior 18%
 Senior 9%
Greek membership 17%
Psychological Variables Mean SD Potential Range
Social Desirability 6.95 2.76 0 – 13
Behavioral Inhibition Scale 20.36 3.35 7 – 28
Reward (BAS) 17.83 2.22 4 – 20
Drive (BAS) 11.27 2.51 4 – 16
Fun (BAS) 12.05 2.43 4 – 16
Sensation Seeking (UPPS) 29.63 9.27 0 – 48
Premeditation (UPPS) 23.12 6.95 0 – 40
Perseverance (UPPS) 20.91 5.48 0 – 32
Negative Urgency (UPPS) 12.61 6.99 0 – 40
Descriptive Norms 3.20 .77 0 – 4
Injunctive Norms 3.12 .64 0 – 4
Costs of Cutting Down 1.47 1.07 1 – 5
Benefits of Cutting Down 2.63 1.28 1 – 5
Psychological Distress (PHQ-4) 1.81 2.30 0 – 12
Centrality of Alcohol (CLASS) 44.26 9.19 15 – 75
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Note. BAS=Behavioral Activation Scale; UPPS=Urgency Premeditation Planning Sensation Seeking Impulsive Behavior Scale; PHQ=Patient Health Questionnaire; CLASS=College Life Alcohol Salience Scale.

All but 8 of the 568 (99%) students who completed baseline received a BMI. Table 2 displays baseline and 1-month values on the outcomes, showing reductions from baseline on all outcomes (ps < 001).

Table 2.

Change in values for primary outcome variables from baseline to 1-month, for entire sample and separately by sex

Entire Sample
N = 568		 Males
N = 407		 Females
N = 161
M (SD) M (SD) M (SD)
Drinks per Week
 Baseline 12.57 (9.85) 14.22 (10.46) 8.33 (6.31)**
 1-month 9.5 (7.82) 10.95 (8.13) 5.76 (5.43)**
Binge Drinking Frequency
 Baseline 4.15 (3.80) 4.49 (3.79) 3.31 (3.69)**
 1-month 2.83 (2.89) 3.20 (3.0) 1.88 (2.33)**
Typical Blood Alcohol Concentration
 Baseline 0.08 (.06) 0.08 (.06) 0.09 (.07)*
 1-month 0.06 (.06) 0.06 (.05) 0.05 (.06)
Alcohol Consequences
 Baseline 5.44 (4.25) 5.34 (4.04) 5.69 (4.74)
 1-month 2.84 (3.51) 2.91 (3.52) 2.64 (3.48)
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Note. All 1-month values differ significantly from baseline values (ps < 001). Asterisks along a row indicate gender differences at that assessment point.

**

females differ from males at p < .001

*

females differ from males at p < .05

Measurement Models and Invariance Testing

The measurement models for both consumption and consequences showed full metric invariance and partial scalar invariance (with two of three items invariant across time), allowing interpretation of both associations of the latent change score with other variables and the mean of the change score (Byrne, Shavelson, & Muthn, 1989). The consumption model fit the data well, χ2(9, N = 568) = 26.74, p < .01, RMSEA = .059, CFI = .99, TLI = .98. The consequences model also fit the data well, χ2(8, N = 568) = 20.67, p < .01, RMSEA = .05, CFI = .99, TLI = .98. All factor loadings in both models were significant, p < .001. Consumption scores at baseline and follow-up were significantly correlated, r = .69, p < .001, as were consequence scores, r = .47, p < .001.

Predictors of Changes in Alcohol Consumption

An initial latent change model without predictors (Figure 1) showed that, relative to baseline, participants decreased their alcohol consumption nearly half a SD (Mchange = −0.42, SE = 0.04, p < .001). The final model fit the data well, χ2(40, N = 568) = 121.55, RMSEA = .06, CFI = .97, TLI = .95. The predictors in Table 3 were retained because they predicted the latent change score in the models described earlier. As expected, male sex, white race, and Greek membership predicted baseline consumption, as did descriptive norms, costs of change, and beliefs about the centrality of drinking to college life (Table 3, top). However, after controlling for baseline consumption, a smaller set of predictors emerged for change in consumption (Table 3, bottom). Those who drank more at baseline reported greater decreases over 1 month, B = − 0.43 (0.05), p < .001. Controlling for this effect, smaller decreases in consumption over time were observed for men, B = 0.32 (0.10), p = .001; those higher in Fun Seeking, B = 0.05 (0.02), p < .01; and those reporting more costs associated with stopping or cutting down alcohol use, B = 0.13 (0.05), p = .01. Those reporting more benefits associated with stopping or cutting down decreased their consumption more, B = −0.07 (0.04), p = .05.

Figure 1. Latent change score model for alcohol consumption at baseline and 1 month follow-up for mandated college drinkers (N = 568) participating in a brief motivational intervention.

Figure 1

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Notes. Model fitted using a MLR estimator in Mplus. DPW = drinks per week (log transformed); TBAC = typical blood alcohol concentration (log transformed); Binge = binge drinking frequency (log transformed). Residual terms for identical indicators are allowed to correlate across time. Standardized factor loadings and correlations are reported.

Table 3.

Predictors of (a) Baseline Alcohol Consumption and (b) Latent Changes in Alcohol Consumption Following a Brief Motivational Intervention (N = 568)

Predictors of Baseline Alcohol Consumption
B SE Est P
Male 0.26 0.11 2.24 0.03
White 0.43 0.12 3.46 0.001
Greek 0.24 0.11 2.08 0.04
Fun Seeking 0.05 0.02 2.25 0.02
Descriptive Norms 0.45 0.07 6.45 < 0.001
Costs of Cutting Down 0.17 0.06 3.18 0.001
Benefits of Cutting Down −0.05 0.05 −1.06 0.29
CLASS 0.04 0.01 6.16 < 0.001
Predictors of Changes in Alcohol Consumption
B SE Est P
Baseline Alcohol Consumption −0.43 10.05 −8.51 < 0.001
Male 0.32 0.10 3.27 0.001
White 0.22 0.13 1.73 0.08
Greek 0.21 0.12 1.77 0.08
Fun Seeking 0.05 0.02 2.94 0.003
Descriptive Norms 0.07 0.07 0.99 0.32
Costs of Cutting Down 0.13 0.05 2.51 0.01
Benefits of Cutting Down −0.07 0.04 −1.96 0.05
CLASS 0.01 0.01 1.10 0.27
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Note. CLASS=College Life Alcohol Salience Scale.

Predictors of Changes in Alcohol-Related Consequences

An initial latent change model without predictors (Figure 2) showed that relative to baseline, participants decreased their consequences significantly (Mchange = −0.71, SE = 0.05, p < .001). The final model fit the data well, χ2(48, N = 568) = 70.87, RMSEA = .03, CFI = .99, TLI = .98. As expected, many of the predictors were associated with baseline consequences (Table 4, top). Participants who reported more alcohol-related consequences at baseline decreased their consequences more over 1 month, B = −0.71 (0.06), p < .001. Controlling for this effect, those reporting more costs associated with stopping or cutting down alcohol use had smaller reductions in consequences, B = 0.16 (0.05), p < .01, while those reporting more benefits had larger reductions in consequences, B = −0.08 (0.04), p < .05. Participants with stronger beliefs about the centrality of alcohol to college life reported smaller decreases in consequences over time, B = 0.01 (0.01), p = .05.

Figure 2. Latent change score model for alcohol-related consequences at baseline and 1 month follow-up for mandated college drinkers (N = 568) participating in a brief motivational intervention.

Figure 2

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Notes. Model fitted using a MLR estimator in Mplus. Indicators are parcels of items from the Brief Young Adult Alcohol Consequences Questionnaire (B-YAACQ). Residual terms for identical indicators are allowed to correlate across time. Standardized factor loadings and correlations are reported.

Table 4.

Predictors of (a) Baseline Alcohol Consequences and (b) Latent Changes in Alcohol-Related Consequences Following a Brief Motivational Intervention

Predictors of Baseline Alcohol-Related Consequences
B SE Est P
Greek 0.07 0.13 0.52 0.60
Social Desirability −0.10 0.02 −4.75 < 0.001
Behavioral Inhibition Scale 0.05 0.02 3.42 0.001
Fun Seeking 0.004 0.02 0.22 0.83
UPPS Premeditation −0.01 0.01 −1.78 0.08
Descriptive Norms 0.20 0.07 2.76 0.006
Injunctive Norms 0.18 0.09 1.98 0.047
Costs of Cutting Down 0.08 0.07 1.19 0.24
Benefits of Cutting Down 0.22 0.05 4.49 < 0.001
CLASS 0.02 0.01 2.94 0.003
Predictors of Changes in Alcohol-Related Consequences
B SE Est P
Baseline Alcohol-Related Consequences −0.71 0.06 −11.46 < 0.001
Greek 0.20 0.13 1.61 0.11
Social Desirability −0.01 0.02 −0.42 0.67
Behavioral Inhibition Scale −0.02 0.01 −1.25 0.21
Fun Seeking 0.03 0.02 1.63 0.10
UPPS Premeditation −0.01 0.01 −1.46 0.15
Descriptive Norms 0.07 0.06 1.23 0.22
Injunctive Norms 0.07 0.08 0.92 0.36
Costs of Cutting Down 0.16 0.05 3.04 0.002
Benefits of Cutting Down −0.08 0.04 −2.09 0.04
CLASS 0.01 0.01 1.96 0.05
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Note. UPPS=Urgency Premeditation Planning Sensation Seeking Impulsive Behavior Scale; CLASS=College Life Alcohol Salience Scale.

Discussion

We sought to identify characteristics of mandated students that predicted reductions in drinking and consequences after an empirically-supported alcohol intervention for college students. Consistent with prior research (Carey et al., 2011; Carey et al., 2009), participants reported less drinking and fewer consequences at the 1-month follow-up. The reductions in consumption and alcohol-related consequences of 0.42 to 0.71 SD units reflect medium-to-large effects, indicative of substantial risk reduction for these at-risk drinkers.

As expected, some participants reported more change than others. Specifically, male sex, Fun Seeking, and more costs and fewer benefits of reducing drinking predicted less change in consumption following the BMI. Similarly, more costs, fewer benefits, and a stronger belief in the centrality of drinking to college life predicted less change in consequences after the BMI. The predictor sets for both outcomes overlapped (costs and benefits of change), but also included unique predictors for changes in consumption (male sex, Fun Seeking) and consequences (beliefs about alcohol’s centrality to college life). Thus, the BMI was less efficacious for fun-seeking individuals who (a) associate college life with drinking, (b) see fewer benefits of cutting down and (c) identify more costs of doing so, and (d) are male. These characteristics clarify the individuals for whom improved interventions are needed.

The current findings extend prior research that identified predictors of change after intervention in a post hoc manner (Henson et al., 2015), or that focused on one or two moderator variables such as sex (Carey et al., 2009) or race (Murphy, Dennhardt, Skidmore, Martens, & McDevitt-Murphy, 2010). Notably, only a subset of the variables identified for their crosssectional associations with outcomes provided unique information about the magnitude of change after participating in a BMI. For example, baseline consumption was predictably associated with being male, White, and a Greek member and having higher descriptive norms, costs of change, and centrality beliefs; once baseline consumption was controlled for, only sex and costs of change uniquely predicted how much participants decreased their drinking following a BMI. Although Fun Seeking and benefits of change were not associated with baseline drinking, they did predict post-intervention changes in drinking. Thus, it appears that person variables associated with concurrent drinking do not always predict the likelihood of change in drinking after intervention, and vice versa.

Our findings suggest ways of improving BMIs. First, because males reduced their drinking less than females after a BMI, it may be necessary to further tailor BMI content (beyond providing sex-specific personalized feedback) to make it more impactful for males. For example, it may be helpful to determine which consequences are evaluated more negatively by males, or identify which aspects of drinking are subject to more normative misperception by males, and provide feedback on those specifically. Because the use of protective behavioral strategies by men is underestimated (Lewis, Rees, & Lee, 2009), providing sex-specific feedback regarding the use of protective strategies may counteract male reluctance to engage in risk reduction. Psychosocial factors such as living in a fraternity house (McCabe, 2002) and endorsing certain masculine norms (e.g., risk taking, winning, being a playboy) raise the risk of heavy drinking among some college men (Iwamoto, Cheng, Lee, Takamatsu, & Gordon, 2011). Such findings are consistent with general observations that, relative to women, men in the United States have more adverse health outcomes, more health risk behaviors and fewer health promotion behaviors (Courtenay, 2000). Thus, interventions might consider challenging the status that alcohol consumption enjoys in male peer groups, and potentially cultivating masculine norms inconsistent with risky drinking (e.g., work orientation; Iwamoto et al., 2011). Clearly, BMIs need to be better tailored to reduce heavy drinking by male students.

Second, fun-seeking individuals who associate college life with drinking changed less after intervention. The Fun Seeking scale describes individuals who do things spontaneously, for fun, excitement, and new sensations (Carver & White, 1994). Although there is overlap between Fun Seeking, as measured by the BAS subscale, and sensation seeking, as measured by the UPPS, the latter focuses more on engaging in risky activities whereas the former emphasizes approaching immediate reward. Interventions designed to make future goals more salient (Hall & Fong, 2003) warrant consideration as well as discussing alternative methods to achieve positive reinforcement (Murphy et al., 2012). Because college life is associated with drinking, partly as a function of advertising and media portrayals (Osberg, Billingsley, Eggert, & Insana, 2012), challenging this association with media literacy training might be fruitful.

Third, students who see fewer benefits to cutting down and more costs of doing so were less likely to change. Subjective utility theory (Bauman, Fisher, Bryan, & Chenoweth, 1985) suggests that they may perceive drinking less to carry social costs without benefits. Correcting exaggerated perceived drinking norms challenges these perceptions (Reid & Carey, 2015) but additional strategies are needed to challenge perceptions of social costs and/or enhance the value of the benefits of moderate drinking (cf LaBrie, Pedersen, Earleywine, & Olsen, 2006). Tailoring decisional balance exercises for male students who endorse drinking as a central part of college life may be warranted.

Strengths of this study include a large sample of mandated students, and delivery of an empirically-supported BMI with well-trained interventionists. The observed short-term reductions were strong, allowing us to identify characteristics associated with lower efficacy. We tested a priori predictions about factors that might affect change after receiving a BMI.

The primary limitation is that we did not include a comparison group, and so cannot address moderators of relative responsiveness to the BMI. We attempted to predict differential change after the BMI to focus attention on ways to improve BMI efficacy. However it is possible that the identified predictors of risk reduction would apply equally to other alcohol-related interventions. Second, the sample contained relatively few women and minority students, characteristic of mandated samples (Merrill, Carey, Lust, Kalichman, & Carey, 2014). Third, our sample consisted of mandated students so generalizability of findings to the broader population of student drinkers remains to be established. Fourth, the design of the parent study only allowed for testing predictors of short-term change associated with a mandated alcohol intervention, so we cannot determine predictors of outcomes later than 1-month post-intervention. Finally, the design of this study does not allow for disentangling the effect of being sanctioned per se versus the effect of receiving the BMI. Prior work has documented self-initiated change immediately after a sanctioned event and prior to intervention (a sanction effect), particularly among women (Carey et al., 2011; Fromme & Corbin, 2004) and those experiencing referrals for more serious legal or medical events (Morgan, Mun, & White, 2008). Even so, exposure to a BMI has been associated with additional reductions in drinking (Carey et al., 2009), and so students who have not reduced their drinking either on their own or after a BMI deserve additional attention.

BMIs reliably produce risk reduction among college drinkers, but efforts to enhance efficacy must continue. We identified demographic, personality, and social cognitive factors associated with change after a BMI. Being male, valuing fun seeking, and perceiving fewer benefits and more costs to drinking less were associated with less change after the BMI. Similarly, associating college life with drinking and seeing fewer benefits and more costs to drinking less predicted smaller reductions in alcohol-related consequences. These predictors suggest psychosocial targets to be addressed in refined BMIs.

Highlights.

  • College drinkers respond differently to brief motivational intervention

  • We examined predictors of change after a mandated intervention

  • Male sex, fun-seeking, more costs, fewer benefits predicted less change in drinking

  • Alcohol beliefs, costs and benefits predicted less change in consequences

  • We identify targets for intervention refinement

Acknowledgments

The authors thank the University of Connecticut SURE team, Donna Korbel, and Catherine Cocks for their assistance with this research.

Role of Funding Sources

Funding for this study was provided by NIAAA Grants R01-AA12518 to Kate B. Carey, T32 AA007459 to Peter M. Monti, K01AA022938 to Jennifer E. Merrill, and NIMH Grant K01-MH099956 to Jennifer L. Walsh. Neither NIAAA or NIMH had any role in the study design, collection, analysis or interpretation of the data, writing the manuscript, or the decision to submit the paper for publication.

Footnotes

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Contributors

Kate B. Carey and Michael P. Carey designed the study, and assisted by Seth C. Kalichman refined the protocol. Kate B. Carey drafted the Introduction and Discussion. Sarah A. Lust supervised data collection and data cleaning, and drafted the Methods. Jennifer L. Walsh and Jennifer E. Merrill conducted the statistical analyses and drafted the Results. All authors have contributed to and have approved the final manuscript.

Conflicts of Interest

All authors declare that they have no conflicts of interest.

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