The use of freshmen seminar programs to deliver personalized feedback.
Subject: Assisted suicide (Conferences, meetings and seminars)
Assisted suicide (Surveys)
Assisted suicide (Social aspects)
Assisted suicide (Technology application)
Seminars (Conferences, meetings and seminars)
Seminars (Surveys)
Seminars (Social aspects)
Seminars (Technology application)
Political parties (Conferences, meetings and seminars)
Political parties (Surveys)
Political parties (Social aspects)
Political parties (Technology application)
Euthanasia (Conferences, meetings and seminars)
Euthanasia (Surveys)
Euthanasia (Social aspects)
Euthanasia (Technology application)
Alcohol and youth (Conferences, meetings and seminars)
Alcohol and youth (Surveys)
Alcohol and youth (Social aspects)
Alcohol and youth (Technology application)
Authors: Henslee, Amber M.
Correia, Christopher J.
Pub Date: 12/01/2009
Publication: Name: Journal of Alcohol & Drug Education Publisher: American Alcohol & Drug Information Foundation Audience: Academic; Professional Format: Magazine/Journal Subject: Health; Psychology and mental health; Social sciences Copyright: COPYRIGHT 2009 American Alcohol & Drug Information Foundation ISSN: 0090-1482
Issue: Date: Dec, 2009 Source Volume: 53 Source Issue: 3
Topic: Event Code: 290 Public affairs Computer Subject: Technology application
Product: SIC Code: 8651 Political organizations
Accession Number: 218817576
Full Text: ABSTRACT

The current study tested the effectiveness of delivering personalized feedback to first-semester college freshmen in a group lecture format. Participants enrolled in semester-long courses were randomly assigned to receive either personalized feedback or general information about alcohol. Both lecture conditions were delivered during a standard class period. Participants were reassessed after 5 weeks. Participants who received personalized feedback reported more accurate peer perceptions and higher readiness-to-change scores regarding personal alcohol use than participants who received general information. However, the results did not indicate group differences in alcohol use or alcohol-related consequences. These results support the use of freshmen seminar courses as a vehicle to provide personalized feedback to increase awareness of campus norms and increase motivation to change drinking behaviors.

**********

College student alcohol use, in particular high-risk patterns of alcohol use and its associated consequences, is a matter of public concern. Researchers have offered suggestions to prevent or reduce risky college student alcohol use and related consequences from occurring, including modifications in campus policies and community alcohol marketing strategies (e.g., Wechsler, Lee, Kuo, & Lee, 2000). Furthermore, the National Institute of Alcohol Abuse and Alcoholism (NIAAA) identified the use of brief intervention as an effective method to reduce risky alcohol use behaviors among college students (NIAAA, 2002).

One brief intervention is the Brief Alcohol Screening and Intervention for College Students (BASICS; Dimeff, Baer, Kivlahan, & Marlatt, 1999). BASICS incorporates a motivational interviewing approach within a harm reduction framework. One aspect of this intervention includes the provision of personalized feedback. Research supports the use of personalized feedback to reduce risky alcohol use patterns among college students (Walters & Neighbors, 2005; Larimer & Cronce, 2007). Personalized feedback has been delivered in a number of formats, including face-to-face interventions, mailed feedback, and computer-delivered feedback (for a review see Carey, Scott-Sheldon, Carey & DeMartini, 2007).

Another medium in which to deliver personalized feedback is in a group format. In a group-based intervention, McNally & Palfai (2003) found significant reductions in heavy episodic drinking among at-risk students who received information about how their behaviors compared to their peers. Michael, Curtin, Kirkley, Jones, and Harris (2006) randomly assigned students in freshman seminar programs to either a classroom-based motivational interviewing (MI) session, which included a discussion of perceived and normative college student drinking, or an assessment control condition. Students in the MI session reported consuming fewer drinks and also reported fewer days of intoxication at the follow-up assessment. Three recent studies also suggest that group interventions that incorporate elements of MI can be effective in reducing alcohol use among male freshman (LaBrie, Pedersen, Lamb, & Quinlan, 2007), female freshman (LaBrie, et al., 2008), and female adjudicated students (LaBrie, Thompson, Huchting, Lac, and Buckley, 2007). Thus, it appears that group settings may be an efficacious medium to deliver feedback about alcohol use to college students.

The current study sought to investigate the effectiveness of providing personalized feedback, in a group lecture format, to first-semester college freshmen enrolled in a semester-long freshmen seminar course. Participants were assigned to either a personalized feedback lecture or a generic alcohol education lecture and were reassessed at a 5-week follow-up. The hypotheses were that participants assigned to receive personalized feedback would report a greater reduction in alcohol use, fewer alcohol-related consequences, a more accurate perception of their peers' alcohol use, and greater levels of readiness-to-change their own alcohol use behaviors, as compared to participants in a generic alcohol education lecture.

METHODS

Participants

Participants were freshmen enrolled in 14 seminar courses during the fall semester at a large southern university. All students in these courses were invited to participate in the study and given 2 hours of extra credit toward their seminar course as compensation for their participation. Parental consent was obtained for those students under 19 years of age, consistent with Alabama state law. Approximately 350 students were enrolled in the 14 courses and invited into the study; 216 turned in the initial assessment package and 167 of those students (77%) completed the follow-up assessment. The following data analyses were conducted on students who reported drinking on at least one occasion in the previous 28 days at the time of the baseline assessment and who completed the follow-up assessment (n = 112). Participant demographics are as follows: males 36.6%, females 63.4%, mean (SD) age 18.11 (0.40), Greek society members 43.2%, Caucasian 91.1%, African American 8.9%.

There were significant differences between those students who completed the follow-up assessment and those who did not. Participants who completed the follow-up assessment reported significantly lower scores on measures of risky alcohol use (t = 1.98, p=.05) and negative alcohol-related consequences (t = 3.73, p<.001) and reported fewer days spent consuming alcohol in the previous 28 days (t = 2.95, p<.01). These differences should be considered when interpreting the results of this study.

Measures

All participants completed a demographics survey and a set of questionnaires about their alcohol use and related problems, including the Alcohol Use Disorders Identification Test (AUDIT; Babor, de la Fuente, Saunders, & Grant, 1989); the Daily Drinking Questionnaire (DDQ; Collins, Parks, & Marlatt, 1985); and the Rutgers Alcohol Problem Index (RAPI; White & Labouvie, 1989). Participants also completed items on a 5-point Likert scale inquiring about perceived alcohol use among their peers. Perceived frequency was assessed on a scale from 0 (once a month or less) to 5 (nearly every day). Perceived quantity was assessed on a scale from 0 (0-2 drinks per night) to 5 (more than 8 drinks per night). Participants' willingness to change their alcohol-related behaviors was assessed by the Readiness to Change Questionnaire (RTCQ; Rollnick, Heather, Gold, & Hall, 1992).

Procedure

The Principal Investigator visited each classroom during the third week of the semester to explain the study and to distribute copies of the informed consent forms and a packet of baseline measures. Each section of the freshmen seminar course was randomly assigned to one of two lecture groups, a personalized feedback lecture or a generic alcohol education lecture. Both the personalized feedback lecture and the generic alcohol education lecture were delivered during regularly scheduled class meetings during the seventh week of the semester. The personalized feedback and generic alcohol education lectures were equal in length and conformed to the standard 50-minute lecture period.

Personalized feedback forms were modeled after previous studies (Agostinelli et al., 1995; Butler & Correia, in press; Waiters, Bennett, & Miller, 2000) and the BASICS (Dimeff et al., 1999) program. Feedback forms included information about the estimated blood alcohol level (BAL) on typical and peak drinking occasions, self-reported negative consequences, weekly average number of standard drinks, gender-specific normative data, and the amount of time and money allocated to alcohol. Participants in the personalized feedback group also received information on how to reduce risky drinking behaviors. The feedback forms were presented to the students in a sealed envelope with only a participant code number as identifying information.

At the beginning of the lecture, participants were informed that the information in their envelopes contained personalized feedback about their alcohol use based upon the questionnaires they previously completed. The personalized feedback lecture consisted of the Principal Investigator walking the students through the interpretation of their feedback, section by section, and answering any questions. It should be clear that this lecture did not identify any student's personalized feedback, but guided and assisted each student in interpreting their own personalized feedback forms. Students in the class who chose not to participate in the study did not receive personalized feedback forms but were still exposed to the lecture material (e.g., normative information, understanding BAL, binge drinking). These students received a copy of the lecture slides in a sealed envelope, thus preventing their identification as non-participants.

The generic alcohol education lecture included information about alcohol use, the definition of a standard drink, and the prevalence and consequences of alcohol use among college students nationwide. A follow-up assessment session was conducted during the thirteenth week of the semester or approximately 5 weeks after the personalized feedback and generic alcohol education lectures were administered. Participants completed the same packet of self-report measures as they did at the initial assessment, including the AUDIT, the Daily Drinking Questionnaire, the RAPI, perceived frequency and quantity of peer alcohol use, and the Readiness to Change Questionnaire.

Data Analysis Plan

Data analyses include students who reported drinking on at least one occasion during the 28 days preceding the baseline assessment (n = 112). T-test analyses confirmed that the participants in the two lecture groups did not significantly differ from one another on any of the key outcome variables at baseline, including rates of binge drinking, AUDIT scores, RAPI scores, perceived peer frequency and quantity of alcohol use, or self-reported readiness-to-change alcohol-related behaviors. Preliminary analyses also confirmed that participants in the two lecture groups did not significantly differ on demographic variables including race, gender, age, location of residence, Greek society membership, or grade point average. However, males were significantly higher on AUDIT scores (t = 2.94, p<.01), alcohol use in the past 28 days (t = 3.80, p<.001), and perceived peer quantity of alcohol use (t = 5.98, p<.001). Males also reported lower precontemplation scores (t = 2.72, p<.01), which is indicative of an increased recognition of potential problems with alcohol. Thus, gender is included as a covariate in the main analyses.

Each section of UNIV 1000 was randomly assigned to either the personalized feedback lecture (n = 7 sections, 52 students) or a generic alcohol education lecture (n = 7 sections, 60 students). A series of one-way ANOVAs was used to determine if baseline measures of alcohol use and related variables differed across the 14 individual sections. These analyses failed to find significant differences. Therefore, although the participants were nested within sections, the dependent variables did not appear to be significantly associated with section. Given that the dependent variables did not vary by section and that there were very little missing data, we chose to analyze the data using a general linear model repeated measures approach.

A series of repeated measures ANOVAs, with gender as a covariate and lecture group as a between-group variable, was used to examine differences in alcohol use behaviors derived from the DDQ (total drinks consumed and most drinks consumed on a single occasion); alcohol-related consequences (AUDIT and RAPI); perceived peer alcohol use (frequency and quantity); and readiness-to-change (precontemplation, contemplation, and action). We hypothesized that participants assigned to receive personalized feedback would report a greater reduction in alcohol use, fewer alcohol-related consequences, a more accurate perception of their peers' alcohol use, and greater levels of readiness-to-change their own alcohol use behaviors, as compared to participants in a generic alcohol education lecture.

RESULTS

A summary of the results is depicted in Table 1.

Alcohol Use Behaviors and Related Consequences

There was a significant time X gender interaction for AUDIT scores, F (1, 108) = 4.26, p < .05. Mean (SD) AUDIT scores for males decreased from 10.20 (5.19) to 9.59 (5.17), whereas mean (SD) AUDIT scores for females increased from 7.46 (4.63) to 8.44 (4.31). However, the time X group interaction was not significant, indicating that changes in AUDIT scores did not differ across the lecture groups.

There was a significant time X gender interaction for the number of days in the past 28 days that alcohol was consumed [F (1, 109) = 4.10, p<.05)]. Females reported an increase in mean (SD) scores from 6.39 (4.78) days at baseline to 6.79 (4.36) days at follow-up, whereas males reported a decrease in mean (SD) scores from 9.06 (6.55) days at baseline to 7.30 (5.07) days at follow up. However, the time X group interaction was not significant, indicating that the changes in alcohol use within the past 28 days did not differ across lecture groups. There were no significant differences between baseline and follow-up, nor any significant interactions, for any of the other alcohol use variables.

Peer Perception of Alcohol Use

There was a significant time X group interaction for peer perception of the quantity of alcohol consumed, F (1, 108) = 7.15, p < .01. Participants in the personalized feedback lecture reported greater reductions in mean (SD) scores of perceived peer quantity of alcohol use [from 3.10 (1.11) to 2.40 (0.98)] than participants in the generic alcohol education lecture [from 3.19 (1.03) to 3.00 (0.95)]. These results did not differ across male and female participants.

None of the tested effects was significant for the perceived peer frequency of alcohol use. However, at baseline, participants m both groups reported an accurate peer perception of alcohol use (i.e., 1-2 times per week); and, thus perhaps, there was a floor effect in assessing changes in perceived peer frequency of alcohol use.

Stage of Change

There was a significant time X gender interaction for the change in precontemplation stage of change (F (1,109) = 20.74, p < .001). Females reported a decrease in mean (SD) levels of precontemplation from 3.48 (0.71) to 3.23 (0.75). Males reported an increase m mean (SD) levels of precontemplation from 3.07 (0.87) to 3.51 (0.71). However, the differences from baseline to follow-up were not associated with lecture group. A decrease in precontemplation scores indicates a shift towards readiness

to-change one's alcohol-related behaviors, while an increase in precontemplation scores indicates a shift away from readiness-to-change. With regard to the contemplation stage of change, there was neither a main effect of time nor a significant time X gender or time X group interaction.

There was a significant time X group interaction for the action stage of change score, F (1, 108) = 4.60, p < .05). Participants in the generic alcohol education lecture reported similar mean (SD) action scores from baseline [2.59 (0.98)] to follow-up [2.56 (0.85)]. However, participants in the personalized feedback lecture reported an increase in action scores from 2.37 (0.75) at baseline to 2.70 (1.05) at follow-up. These results did not vary as a function of gender. An increase in action scores indicates a shift towards making changes in one's alcohol-related behaviors.

DISCUSSION

The goal of the current study was to test the effectiveness of delivering personalized feedback in a group lecture format to first semester college freshmen. More specifically, the aim was to determine if students enrolled in a semester-long freshmen seminar course and who received personalized feedback during a standard class period would report reductions in alcohol use and related consequences, more accurate perceptions of alcohol use, and more readiness-to-change alcohol use behaviors than participants receiving a generic alcohol education lecture.

The study provides partial support for the use of a group intervention with freshman seminar students. The data do not indicate that providing personalized feedback led to reductions in alcohol use or alcohol-related consequences. These findings are inconsistent with previous studies (McNally & Palfai, 2003; Michael et al., 2006; LaBrie et al., 2007; LaBrie, Pedersen, Lamb, & Quinlan, 2007 & LaBrie et al., 2008) supporting the use of group feedback intervention to reduce alcohol use among freshman. However, it is worth noting that none of the previous researchers reporting on the use of a group intervention with college freshman met with them during a class session of an orientation course. Rather, in these studies they arranged to meet with students during discussion sessions that occurred outside of class (LaBrie, et al., 2007, 2008; Michael, et al., 2006). More research is needed to determine how brief interventions can be efficiently and effectively integrated into courses aimed at helping freshman successfully transition into college.

Despite the lack of evidence that the personalized lecture led to reductions in alcohol use, there were significant changes in variables that have been identified as potential precursors to these reductions. Participants in the personalized feedback group reported more accurate perceptions of the quantity of peer alcohol use at follow-up than did participants who received a generic alcohol education lecture. Previous research suggests that changes in estimations of peer alcohol use mediate changes in personal alcohol use (Neighbors, Larimer & Lewis, 2004), including among freshmen (Waiters, Vader & Harris, 2007). There were also significant changes in readiness-to-change alcohol-related behaviors. Participants in the personalized feedback lecture reported higher action scores than did participants in the generic alcohol education lecture. These results suggest that the participants in the personalized lecture group experienced positive changes in their perceptions of peer alcohol use and their motivation to change their own use, both of which have been identified as appropriate treatment targets for a brief intervention (Dimeff, et al., 1999)

The current study is limited by a relatively brief follow-up period. Whereas the current study used a 5-week follow up, several of the studies showing positive outcomes following a group MI intervention employed 10 or 12 week follow-up periods. It is possible that our study failed to capture potential effects that personalized feedback would have on measures of alcohol consumption. In other words, it is possible that participants exposed to the personalized lecture were assessed after misperceptions of peer alcohol use were corrected and motivations to change were increased, but before any significant behavioral changes were made. Additional research using extended follow-up periods is needed to more fully assess the impact of incorporating a personalized feedback lecture into freshman seminar courses and other structured activities designed to orient students to campus and collegiate life.

Another limitation is the significant differences between those students who completed the follow-up assessment and those who did not. Participants who did not complete the follow-up assessment reported higher baseline levels of risky alcohol use, alcohol-related consequences, and days spent drinking. The differences limit the degree to which the current results can be generalized to other first-year students, and they may also have limited the current study's ability to detect changes in alcohol use, given that several studies have reported that personalized feedback interventions may be more effective for heavier drinkers (e.g., LaBrie, et al., 2007; Murphy, et al., 2001).

To summarize, the current study suggests that a personalized feedback intervention designed to be delivered to first-year students enrolled in a semester-long freshman seminar course can effectively correct normative misperceptions regarding peer alcohol use and increase motivations to make changes in alcohol use behaviors. However, participants exposed to the personalized lecture did not report decreases in alcohol use or alcohol-related consequences. In light of these initial findings, more research is needed to determine how personalized feedback interventions, or other types of alcohol interventions, can be integrated into courses designed to help freshman make the transition from high school to college.

Correspondence concerning this article should be addressed to: Amber M. Henslee, Ph.D., The University of Mississippi Medical Center, Department of Psychiatry & Human Behavior, 2500 N. State Street, Jackson, MS 39216, Phone: 601-984-5957, Fax: 601-984-4489, Email: ahenslee@psychiatry.umsmed.edu

REFERENCES

Agostinelli, G., Brown, J. M., & Miller, W. R. (1995). Effects of normative feedback on consumption among heavy drinking college students. Journal of Drug Education, 25, 31-40.

Babor, T. E, de la Fuente, J. R., Saunders, J., & Grant, M. (1989). AUDIT- The Alcohol-Use Disorders Identification Test." Guidelines for use in primary health care. Geneva, Switzerland: World Health Organization, Division of Mental Health.

Butler, L. & Correia, C. J. (in press). Brief alcohol interventions with college student drinkers: Face-to-face versus computerized feedback. Psychology of Addictive Behaviors.

Carey, K. B., Scott-Sheldon, L. A. J., Carey, M. P., & DeMartini, K.S. (2007). Individual-level interventions to reduce college student drinking: A meta-analytic review. Addictive Behaviors, 32, 2469-2494.

Collins, R. L., Parks, G. A., & Marlatt, G. A. (1985). Social determinants of alcohol consumption: the effects of social interaction and model status on the self-administration of alcohol. Journal of Consulting and Clinical Psychology, 53, 189-200.

Dimeff, L. A., Baer, J. S., Kivlahan, D. R., & Marlatt, G. A. (1999). Brief alcohol screening and interventions for college students: A harm reduction approach. New York: The Guilford Press.

LaBrie, J. W., Huchting, K., Tawalbeh, S., Pedersen, E. R, Thompson, A. D., Shelesky, K., Larimer, M., & Neighbors, C. (2008). A randomized motivational enhancement prevention group reduces drinking and alcohol consequences in first-year college women. Psychology of Addictive Behaviors, 22, 149-155.

LaBrie, J. W., Pedersen, E. R., Lamb, T. F., & Quinlan, T. (2007). A campus-based motivational enhancement group intervention reduces problematic drinking in male college students. Addictive Behaviors, 32, 889-901.

LaBrie, J. W., Thompson, A. D., Huchting, K., Lac, A., & Buckley, K. (2007). A group Motivational Interviewing intervention reduces drinking and alcohol-related negative consequences in adjudicated college women. Addictive Behaviors, 32, 2549-2562.

Larimer, M. E. & Cronce, J. M. (2007). Identification, prevention, and treatment revisited: Individual-focused college drinking prevention strategies 1999-2006. Addictive Behaviors, 32, 2439-2468.

McNally, A. M., & Palfai, T. P. (2003). Brief group alcohol interventions with college students: Examining motivational components. Journal of Drug Education, 33, 159-176.

Michael, K. D., Curtin, L., Kirkley, D. E., Jones, D. L., & Harris Jr., R. (2006). Group-based motivational interviewing for alcohol use among college students: An exploratory study. Professional Psychology. Research and Practice, 37, 629-634.

Murphy, J.G., Duchnick, J.J., Vuchinich, R.E., Davison, J.W., Karg, R.S., Olson, A.M., Smith, A.F., & Coffey, T.T. (2001). Relative efficacy of a brief motivational intervention for college student drinkers. Psychology of Addictive Behaviors, 15, 373-379.

National Institute of Alcohol Abuse and Alcoholism. (2002). A call to action." Changing the culture of drinking at U.S. colleges. Retrieved March 20, 2006 from http://www. collegedrinkingprevention.gov/NIAAACollegeMaterials/ TaskForce/CallToAction_02.aspx

Neighbors, C., Larimer, M. E., & Lewis, M. A. (2004). Targeting misperceptions of descriptive drinking norms: Efficacy of a computer-delivered personalized normative feedback intervention. Journal of Consulting and Clinical Psychology, 72, 434-447.

Rollnick, S., Heather, N., Gold, R., & Hall, W. (1992). Development of a short 'readiness to change' questionnaire for use in brief, opportunistic interventions among excessive drinkers. British Journal of Addiction, 87, 753-754.

Walters, S. T., Bennett, M. E., & Miller, J. E. (2000). Reducing alcohol use in college students: A controlled trial of two brief interventions. Journal of Drug Education, 30, 361-372.

Walters, S. T., & Neighbors, C. (2005). Feedback interventions for college alcohol misuse: What, why and for whom? Addictive Behaviors, 30, 1168-1182.

Waiters, S. T., Vader, A. M., & Harris, T. R. (2007) A controlled trial of web-based feedback for heavy drinking college students. Society for Prevention Research, 8, 83-88.

Wechsler, H., Lee, J. E., Kuo, M., & Lee, H. (2000). College binge drinking in the 1990s: A continuing problem. Results of the Harvard School of Public Health 1999 College Alcohol Study. Journal of American College Health, 48, 199-210.

White, H. R., & Labouvie, E. W. (1989). Towards the assessment of adolescent problem drinking. Journal of Studies on Alcohol, 50, 30-37.

Amber M. Henslee

The University of Mississippi Medical Center

&

Christopher J. Correia

Auburn University
TABLE 1.
Mean and standard deviations for outcome measures at
baseline and follow-up

                               Full Sample (n = 112)

                                      Generic

                               Baseline        Follow-up
Alcohol Use Variables
  AUDIT (a)                  8.18 (4.99)     8.83 (4.90)
  Past 28 Days (b)           8.12 (6.30)     6.97 (4.83)
  Total drinks/week          14.75 (13.48)   14.10 (13.16)
  Most drinks/ccasion        7.30 (5.25)     7.69 (5.60)
  Binge episodes             5.10 (5.83)     5.07 (5.32)
Perceived Peer Alcohol Use
  Frequency of Use           3.48 (0.72)     3.23 (0.56)
  Quantity of Used (c)       3.19 (1.03)     3.00 (0.95)
Stages of Change
  Precontemplation (d)       3.27 (0.78)     3.41 (0.79)
  Contemplation              2.17 (0.80)     2.26 (0.91)
  Action (e)                 2.59 (0.98)     2.56 (0.85)

                                Full Sample (n = 112)

                                    Personalized

                               Baseline       Follow-up
Alcohol Use Variables
  AUDIT (a)                  8.80 (5.04)     8.90 (4.39)
  Past 28 Days (b)           6.50 (4.62)     6.99 (4.40)
  Total drinks/week          14.17 (12.76)   11.95 (8.34)
  Most drinks/ccasion        7.63 (4.64)     7.04 (4.70)
  Binge episodes             4.77 (4.76)     4.92 (4.99)
Perceived Peer Alcohol Use
  Frequency of Use           3.46 (0.80)     3.21 (0.72)
  Quantity of Used (c)       3.10 (1.11)     2.40 (0.98)
Stages of Change
  Precontemplation (d)       3.39 (0.81)     314 (0.68)
  Contemplation              2.15 (0.85)     239 (0.84)
  Action (e)                 2.37 (0.75)     2.70
                                             (1.05)

                                   Males (n = 41)

                                       Generic

                               Baseline        Follow-up
Alcohol Use Variables
  AUDIT (a)                  9.72 (5.02)     9.88 (5.64)
  Past 28 Days (b)           9.78 (720)      7.08 (5.35)
  Total drinks/week          20.18 (16.54)   19.36 (17.59)
  Most drinks/ccasion        9.54 (6.20)     9.92 (7.26)
  Binge episodes             6.11 (6.10)     6.26 (5.89)
Perceived Peer Alcohol Use
  Frequency of Use           3.52 (0.59)     3.28 (0.61)
  Quantity of Used (c)       3.60 (0.96)     3.48 (1.05)
Stages of Change
  Precontemplation (d)       2.96 (0.82)     3.53 (0.87)
  Contemplation              2.29 (0.91)     2.41 (0.84)
  Action (e)                 2.60 (1.00)     2.65 (0.91)

                                   Males (n = 41)

                                   Personalized

                               Baseline        Follow-up
Alcohol Use Variables
  AUDIT (a)                  10.94 (5.53)    9.13 (4.46)
  Past 28 Days (b)           7.94 (5.41)     7.66 (4.74)
  Total drinks/week          22.94 (17.98)   17.44 (10.26)
  Most drinks/ccasion        10.50 (5.90)    10.40 (6.21)
  Binge episodes             6.27 (6.39)     5.73 (6.18)
Perceived Peer Alcohol Use
  Frequency of Use           3.81 (O.83)     3.50 (0.82)
  Quantity of Used (c]       4.19 (0.91)     3.19 (1.05)
Stages of Change
  Precontemplation (d)       3.23 (0.95)     3.47 (0.63)
  Contemplation              2.52 (1.00)     2.47 (0.70)
  Action (e)                 2.61 (0.85)     2.88 (1.11)

                                 Females (n = 71)

                                      Generic

                               Baseline      Follow-up
Alcohol Use Variables
  AUDIT (a)                  7.09 (4.74)    8.09 (4.22)
  Past 28 Days (b)           6.93 (5.36)    6.89 (4.50)
  Total drinks/week          10.87 (9.23)   10.34 (6.88)
  Most drinks/ccasion        9.76 (3.88)    6.17 (3.46)
  Binge episodes             3.75 (4.68)    3.93 (4.78)
Perceived Peer Alcohol Use
  Frequency of Use           3.46 (0.82)    3.20 (0.53)
  Quantity of Used (c]       2.88 (0.98)    2.65 (.069)
Stages of Change
  Precontemplation (d)       3.49 (0.68)    3.33 (0.80)
  Contemplation              2.09 (0.72)    2.16 (0.96)
  Action (e)                 12.59 (0.98)   2.49 (0.81)

                                   Females (n = 71)

                                    Personalized

                               Baseline      Follow-up
Alcohol Use Variables
  AUDIT (a)                  7.83 (4.55)    8.80 (4.42)
  Past 28 Days (b)           5.86 (4.15)    6.69 (4.28)
  Total drinks/week          10.28 (6.92)   9.51 (6.02)
  Most drinks/ccasion        6.37 (3.33)    5.56 (2.91)
  Binge episodes             4.10 (3.80)    4.14 (4.11)
Perceived Peer Alcohol Use
  Frequency of Use           3.31 (0.75)    3.08 (0.65)
  Quantity of Used (c)       2.61 (0.80)    2.06 (0.71)
Stages of Change
  Precontemplation (d)       3.47 (0.74)    3.13 (0.69)
  Contemplation              1.99 (0.74)    2.36 (0.90)
  Action (e)                 12.26 (0.69)   12.56 (0.92)
Note:

(a) There was a significant time X gender interaction for
AUDIT scores between baseline and follow-up. P <.05.

(b) There was a significant time X gender interaction for
the number of days in the past 28 days that alcohol was
consumed. P <.05.

(c) There was a significant time X group interaction for
perceived peer quantity of alcohol use between baseline and
follow-up. p < .01

(d) There was a significant time X gender interaction for
the precontemplation stage of change scores between baseline
and Follow-up. P <.001

(e) There was a significant time X group interaction for the
action stage of change scores between baseline and
follow-up. p < .05
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