JOURNAL OF AMERICAN COLLEGE HEALTH, VOL. 58, NO. 4
Efficacy of a Web-Based, Tailored, Alcohol Prevention/Intervention Program for College Students: Initial Findings C. Raymond Bingham, PhD; Andrea Ippel Barretto, MHS; Maureen A. Walton, MPH, PhD; Christopher M. Bryant, MS; Jean T. Shope, PhD; Trivellore E. Raghunathan, PhD
Abstract. Objective: Reduce college student at-risk drinking (ARD) using a Web-based brief motivational alcohol prevention/intervention called Michigan Prevention and Alcohol Safety for Students (M-PASS). Participants: Participants included 1,137 randomly sampled first-year college students, including 59% female, 80% white, and averaged age 18.1 years. Methods: Intervention group participants (n = 616) attended 4 online M-PASS sessions, receiving feedback tailored to individual drinking patterns and concepts from 4 behavior change theories. Control group participants (n = 521) completed a mid-phase survey, and both groups were surveyed at baseline and posttest. Results: Evidence of M-PASS’s efficacy was found. The intervention was associated with advanced stage of change, lower tolerance of drinking and drink/driving, fewer reasons to drink, and use of more strategies to avoid ARD. Preliminary evidence of behavioral change was also found. Efficacy was greater for women than men. Conclusions: Web-based programs may be useful in reducing alcohol-related risk among college students. Further evaluation is needed.
drink/driving, vandalism, and student involvement with law enforcement.2–7 Eighty-three percent of college students drink, 41% binge drink regularly,8 and evidence indicates that having more than 5 drinks per occasion is common.9 In response, the US Surgeon General Office’s first Call to Action against underage drinking encouraged increased research to reduce underage drinking and prevent the onset of alcohol use.10 Efficacious and effective brief alcohol interventions that have been developed to reduce college student alcoholrelated risk11 consistently use motivational interviewing (MI)12 techniques. Although in-person interventions have consistently shown effectiveness,13 they are resource intensive, requiring at least 1 intervention provider per session, with only 1 or a small group of students attending, which makes this approach less practical for reaching large college student audiences quickly and efficiently. Mailed alcohol interventions are more efficient and have been improved by incorporating techniques and tools used in effective in-person interventions.11,14 They also provide broader and more rapid dissemination than in-person interventions; however, mailed interventions may be less effective than in-person interventions15 and still require considerable human input. Computerized interventions (eg, PC, CD-ROM, or Webbased) to reduce alcohol misuse are highly automated, resource nonintensive, and have the potential to efficiently reach large audiences in a short time, allowing a public health rather than an individual-based approach to addressing college student alcohol misuse. Modern computerized interventions can simulate in-person interventions by targeting participants with feedback that is specific to their demographics and characteristics related to behavior change.16 This is achieved using 2 tools from in-person interventions. The first
Keywords: alcohol, college, intervention, prevention, Web-based
B
ecause of alcohol misuse (eg, heavy drinking, binge drinking, alcohol-related consequences), many US college students1 experience both long- and shortterm consequences, including assault, unsafe sexual behavior, decreased health, reduced academic performance, Dr Bingham, Ms Barretto, and Dr Shope are with the Transportation Research Institute at the University of Michigan in Ann Arbor, Michigan. Dr Bingham and Dr Shope are with the Department of Health Behavior and Health Education, University of Michigan, Ann Arbor, Michigan. Dr Bingham and Dr Walton are with the Department of Psychiatry, University of Michigan, Ann Arbor, Michigan. Mr Bryant and Dr Raghunathan are with Department of Biostatistics, University of Michigan, Ann Arbor, Michigan. Copyright © 2010 Taylor & Francis Group, LLC 349
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is targeted feedback, which is relevant to participants because the feedback is matched to their general characteristics (eg, sex, membership in fraternities or sororities, race/ethnicity). The second is tailored feedback, which matches feedback to recipients’ personal characteristics related to theoretical processes of behavior change13 (eg, stage of change, selfefficacy to change). The result is feedback that is relevant to participants in terms of their own strengths and weaknesses, motivation to change, and evaluation of health risk behaviors. PC-based computerized interventions range from in-depth CD-ROM programs to simpler approaches using expectancy challenges,17 and computer-generated brief MI.18 These college student interventions vary in efficacy, some showing no or very low levels of efficacy19–21 and/or little improvement over other techniques,17 whereas others show effects on total consumption, heavy consumption, personal alcohol-related problems, and drinking norms.22–24 Web-based interventions have all the advantages of computerized interventions,25 but have the additional advantage of being able to reach very large audiences quickly and efficiently.26 Web-based alcohol interventions that provide targeted feedback, including Alcohol Response-Ability, Electronic Check-Up to Go (e-CHUG), MyStudentBody, AlcoholEdu, and College Alc, vary in their effectiveness.21,27–29 Most existing computerized and Web-based alcohol interventions only use targeted feedback and do not incorporate extensive theory-based tailored content. Also, most computerized and Web-based interventions are intended for high-risk college drinkers, and lack the capacity to provide prevention messages to avert the development of alcoholrelated risk before it develops. Programs that can automatically adapt to deliver either intervention or prevention content are now possible using current tailoring technology. Study Objective This study evaluated the efficacy of Michigan Prevention and Alcohol Safety for Students (M-PASS) in changing attitudes toward and beliefs about alcohol use, and in reducing alcohol-related risk behavior and consequences, immediately following a tailored brief intervention among first year college students. METHODS This study was approved by the University of Michigan Institutional Review Board for Behavioral Sciences. Participants and Recruitment Participants were recruited using an intervention/control design that paired on-campus dormitory houses (ie, blocks of dorm rooms in the same building). In order to minimize cross-group contamination, assignment to intervention and control groups was achieved by dividing all houses into “group 1” or “group 2” in a manner that minimized proximity and accessibility to houses in the opposite group (eg, intervening solid walls, floors, courtyards, etc), and balanced for proximity to substance-free sections; same-sex versus 350
co-ed living; proportion of student athletes; and houses containing Michigan Living/Learning Community students (ie, achievement-oriented students with shared academic majors). After assignment was complete, “group 1” and “group 2” were arbitrarily assigned to a condition. Eligible students were freshmen ages 18 to 20, US citizens/permanent residents, currently living in a dormitory, never married, and not in Living/Learning Communities or substance-free dorm rooms (n = 3000). A sample of eligible students was randomly selected and invited to participate in M-PASS by e-mail invitations sent to student’s university addresses. The invitation described the study and incentives, and included a link to an electronic consent form. Other promotional activities included flyers posted in dorms and campus shuttles, a student testimonial, and an endorsement by the University of Michigan Student Health Services Director. After consenting, students were forwarded to the baseline survey Web page. Following the survey, the intervention group had the option to complete the first intervention session immediately or at a later time. Students not responding to the invitation were sent weekly recruitment e-mails. E-mail contact with nonrespondents ceased after several efforts or upon request. Intervention and Control Group Procedures M-PASS included 4 10- to 15-minute interactive online sessions designed to prevent/reduce alcohol-related risk. The sessions utilized principles of MI and a conceptual model based on 4 complimentary theories of health behavior change (ie, Health Belief Model,30 Theory of Planned Behavior,31 Transtheoretical Model,32 and Precaution Adoption Process Model33). The conceptual model assumes that behavior change results from changes in health-related attitudes, beliefs, and understanding of the individual that together motivate behavior change. The sessions were tailored on each participant’s alcohol-related risk, which determined whether the intervention promoted risk reduction (ie, intervention), risk avoidance (ie, prevention), or postponement of alcohol use (ie, prevention). Tailoring was also guided by the participant’s level on constructs from the conceptual model (eg, stage of change, self-efficacy) to determine the intervention’s intended effect (eg, increase low self-efficacy versus support of already high levels of self-efficacy). Normative feedback was targeted using individual demographic characteristics (eg, sex, Greek fraternity/sorority affiliation). Each session covered a different topic and was designed to sequentially move participants toward changing (ie, at-risk drinkers) or maintaining his/her drinking level (ie, low-risk and nondrinkers). Session 1 raised awareness of alcoholrelated risk, reinforced correct perceptions of alcohol use norms, and explored participants’ priorities related to school, health/appearance, social interaction, and personal finances and how alcohol misuse affected them. Session 2 examined benefits of and barriers to behavior change as they related to the priorities identified in Session 1. Session 3 helped JOURNAL OF AMERICAN COLLEGE HEALTH
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participants identify feasible options/alternatives to avoid alcohol-related risks. Session 4 led participants through their choice of 2 goal-setting exercises: One focusing specifically on alcohol-related goals; the other addressing general life goals in personal priority areas and provided feedback on how alcohol misuse could interfere with goal achievement. Each session contained interactive activities that generated feedback on the sessions’ topics. Interactivity included quizzes about alcohol and its effects, self-assessments of alcohol attitudes and behaviors, and exercises exploring alcohol use norms. Avatars depicting 4 resident advisors (RAs) guided the participants through the sessions using text bubbles. Data for tailoring messages were gathered in the baseline survey or through interactivity during the sessions. Initial alcohol use was measured by the baseline survey and was briefly reassessed at the start of each session to measure changes in use. Participants whose alcohol-related risk increased between sessions received intervention messages appropriate for their higher risk level. Participants decreasing their risk continued receiving messages appropriate for their initial risk level. The intervention interval lasted 9 weeks. Study participants completed a baseline survey, 4 intervention sessions (intervention) or a mid-interval survey (control), and a posttest survey. The mid-interval survey included all the questions asked of the intervention participants during sessions to gather tailoring information, but excluded items that were designed to promote introspection as part of the intervention. The intent of this survey was to control for the effect of these questions so that the only difference between the 2 groups was exposure to intervention content. The survey included 219 individual items with embedded skip logic, and the median completion time was 12 minutes. Participants controlled the timing and spacing of the baseline survey, intervention sessions (intervention) or midinterval survey (control), and posttest, with only the order of completion controlled by the program. For the intervention group, sessions opened sequentially on a weekly basis for the first 4 weeks of the intervention interval. For the control group, the mid-interval survey opened the same day as Session 3 for the intervention group and remained open until the end of the intervention interval. The posttest opened on day 22 of the intervention interval (same day as Session 4 for the intervention group). The baseline survey, Sessions 1 to 4, mid-interval survey, and posttest remained open until the end of the intervention interval, at which time they closed, with the exception of the posttest survey. The posttest remained open 2 additional days, and all participants who consented to the study but had not taken the posttest were invited to complete it. The median time from baseline to posttest was 20 days, with 5 days between sessions, on average. No significant associations were found between session timing and spacing, and the outcome measures. On the day that each survey or intervention session opened, tailored invitation e-mails were sent to the participants announcing the new session/survey and included dynamic text that listed for each participant which sessions/surveys were VOL 58, JANUARY/FEBRUARY 2010
complete, which were available but not completed, and which were not yet available, and the amount of incentive for each survey/session. The incentives were baseline, $10; posttest, $15; Sessions 1–3, $10 each; Session 4, $15; and $10 for the mid-interval survey. Following the initial 4 weeks of the intervention, reminder e-mails were sent on a weekly basis to all participants. Participants who opted out of the reminder were not contacted again until the last 2 days when only the posttest survey remained open. Participants who exited a session/survey without completing it received e-mail reminders to return to the study Web site and complete the incomplete survey/session. A link to the M-PASS login Web page (intervention group only) or survey Web page (control group only) was embedded in the invitation and reminder e-mails. The intervention program content was developed by the authors. Web design and Web page construction was completed by a team of graduate and undergraduate students that was led by a graduate student in the School of Information Science, University of Michigan. A Web survey company constructed the Web site and data collection system. The entire Web program required 19 months to complete, and included over 1100 Web pages and over 700 document pages of intervention content and tailoring logic.34 Sample Description and Retention Of the 2,536 students invited to participate, 1,200 consented and were eligible (10 screened as ineligible), 1,137 completed the baseline survey (616 intervention; 521 control), and 877 completed the posttest survey (476 intervention; 401 control). Overall, retention from baseline to posttest was 77%. The overall retention for the intervention group from Session 1 to Session 4 was 90%. Eighty percent of intervention group participants who completed the baseline survey completed all 4 intervention sessions, and 80% of the control group completed the mid-interval survey. For tailoring (intervention) and analysis purposes, participants were placed into 3 alcohol risk groups: nondrinkers, low-risk drinkers, and high-risk drinkers (see Measures for group definitions). Nondrinkers and female students not intending to join a sorority were more likely to complete all 4 sessions than other alcohol risk or sex groups. The sample is described in Table 1, and was 59% female, 80% white, and averaged age 18.1 (SD = 0.34) years. The intervention and control groups did not differ significantly on any demographic or alcohol use behavior variables listed in Table 1. Logistic regression examining sex, race/ethnicity, age, and US residency as predictors of non-participation and attrition showed that female (odds ratio [OR] = 1.87, 95% confidence interval [CI]: 1.61, 2.17) and white students (OR = 0.52, 95% CI: 0.35, 0.78) were more likely to participate. Men (OR = 1.35, 95% CI: 1.02, 1.78) were more likely to drop out by the posttest, and the rate of dropout by the posttest did not differ by study arm or by sex. However, increased alcohol risk was associated with a higher dropout rate for men, but not women. 351
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TABLE 1. Participant Characteristics by Intervention and Control Condition Intervention Control Total (n = 616) (n = 521) (n = 1137) Race/ethnicity White Hispanic African American Asian Other Mother’s education High school or less Some post high school Bachelors degree Graduate degree Don’t know Father’s education High school or less Some post high school Bachelors degree Graduate degree Don’t know Drank in past 6 months Frequency of drinking Never Once a month or less 2n3 times a month Once a week 2–3 times a week 4 or more times a week Drinks per occasion 0 1–2 3–4 5 6 7–8 9+ Binge drinking Never Less than once a month Once a month 2 to 3 times a month Once a week 2 to 3 times a week At least 4 times a week Alcohol risk group Nondrinkers Low-risk High-risk
79.4% 4.2% 1.7% 12.2% 2.5%
80.3% 4.0% 2.5% 10.2% 3.1%
79.8% 4.1% 2.1% 11.3% 2.7%
11.4% 16.2% 42.9% 26.0% 0.7%
9.6% 16.7% 42.1% 30.4% 1.2%
10.6% 18.0% 42.6% 28.0% 0.9%
10.4% 14.2% 30.4% 43.7% 1.3% 84.3%
7.3% 15.6% 32.7% 43.5% 1.0% 82.5%
9.0% 14.8% 31.5% 43.6% 1.2% 83.5%
15.7% 16.6% 21.4% 19.3% 26.9% 2.8%
17.5% 16.4% 17.7% 18.8% 24.7% 2.3%
16.5% 16.5% 19.7% 19.1% 25.7% 28.2%
18.3% 14.9% 25.6% 13.0% 8.0% 11.0% 9.1%
20.7% 12.1% 25.6% 11.3% 8.6% 10.9% 10.8%
19.4% 13.6% 25.6% 12.2% 8.3% 11.0% 9.9%
15.8% 17.7% 14.8% 12.8% 14.9% 12.0% 12.0%
17.5% 13.6% 16.7% 11.3% 14.2% 10.9% 15.7%
16.5% 15.8% 15.7% 12.1% 14.6% 11.5% 13.7%
17.9% 32.0% 50.1%
20.0% 28.4% 51.6%
18.8% 30.4% 50.8%
Measures Alcohol Risk Groups Based on their baseline drinking (Daily Drinking Questionnaire and 28-day Timeline Follow-Back), participants were placed into alcohol risk groups defined by National Institute for Alcohol Abuse and Alcoholism guidelines.35 Participants in the high-risk group (n = 315, 51%) consumed on 352
average more than 7 (female) or 14 (male) drinks per week or consumed 4 (female) or 5 (male) drinks in a row at least 2 times during the previous 3 months. Participants in the low-risk group (n = 204, 33%) consumed on average 7 or fewer (female) or 14 or fewer (male) drinks, and consumed 4 (female) or 5 (male) drinks in a row once a month or less in the prior 3 months. Nondrinkers had not drunk alcohol in at least the past 6 months (n = 97, 16%). Outcome Measures Stage of Change. The meaning of “stage of change” and the measure used to assess it varied by alcohol risk level. High-risk drinkers completed the 12-item Readiness to Change Questionnaire36 (RTCQ) (Precontemplation α = .61; Contemplation α = .76; Action α = .87). Low-risk drinkers indicated what they thought their drinking level would be in 6 months, with 1 = “My level of alcohol use will decrease” (Action); 2 = “My level of alcohol use will remain the same” (Contemplation); and 3 = “My level of alcohol use will increase” (Precontemplation). Nondrinkers completed 2 items: “Do you anticipate NOT drinking alcohol for at least the next 30 days?” (1 = yes, 0 = no). If yes, they were asked, “Do you anticipate NOT drinking alcohol for at least the next 6 months?” (1 = yes, 0 = no). Combining the items, Action = anticipated not drinking for at least for the next 6 months; Contemplation = anticipated not drinking for at least for the next 30 days; and Precontemplation = anticipated drinking sometime in the next 30 days. Tolerance of Drinking. A measure developed for this study assessed tolerance of college student drinking by evaluating participants’ agreement with 22 items. An example item is “Drinking helps a college student be more attractive to people of the other sex.” Responses were 0 = Strongly disagree, 1 = Disagree, 2 = Neither agree nor disagree, 3 = Agree, 4 = Strongly agree and were recoded to −2 = Not tolerant of drinking, −1 = Less tolerant of drinking, 0 = Neutral, +1 = Tolerant of drinking, +2 = More tolerant of drinking and averaged to obtain a total score (α = .87). Reasons to Drink. Drinking motivations were measured by the 20-item Drinking Motives Questionnaire37 (DMQ). Responses ranged from 1 (almost never or never) to 5 (almost always or always). An example item is “Because it makes social gatherings more fun.” Average scores were calculated overall and for 4 subscales: conformity, coping, enhancement, and social reasons (α = .93). Reasons Not to Drink. A 10-item scale from the Project Northland program38 was modified to measure reasons for not using alcohol. Participants indicated the importance of 17 reason for not drinking (ie, nondrinkers) or on those occasions when they chose not to drink (ie, low- and high-risk drinkers). An example item is “Because my friends don’t drink.” Response options were 0 = Not at all important, 1 = Somewhat important, 2 = Important, 3 = Very important (α = .90). JOURNAL OF AMERICAN COLLEGE HEALTH
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Protective Strategies. The 13-item Campus Alcohol Survey39 (CAS) was modified to measure the frequency of using strategies to avoid alcohol-related risk. An example item was “Limiting the number of drinks.” Responses were given on a 5-point scale, with 0 = never, 1 = rarely, 2 = sometimes, 3 = usually, and 4 = always (α = .89). Tolerance of Drink/Driving. Tolerance of drink/driving was measured by the 12-item Attitudes toward Drinking and Driving Scale40 (ADDS). Participants responded to common rationalizations for drink/driving, such as “It is okay to drink and drive if nobody else is in the car.” Responses were 0 = disagree, 1 = somewhat disagree, 2 = neither agree nor disagree, 3 = somewhat agree, 4 = agree (α = .92). Alcohol Use An interactive version of the Timeline Followback41 (TLFB) was used to measure the number of drinks consumed daily in the prior 28 days. The calendar automatically provided memory prompts by indicating events (eg, first home football game) during the prior 28 days. TLFB data were used to calculate quantity/frequency as the product of the average number of standard drinks per day and drinking days per month, number of binge drinking days, and number of drinks per drinking day. Students who had not drunk alcohol for at least 6 months were given a value of 0 for each TLFB measure. Cross-Group Contamination Because participants were from a single university, crossgroup contamination was a significant concern. Four survey items tailored to study arm measured cross-group contamination at posttest: (1) Approximately how many students do you know who also participated in the M-PASS alcohol study?; (2) Of those, approximately what proportion did NOT participate in similar M-PASS Web sessions as you?; (3) Of those who participated in the alcohol study but did NOT participate in similar M-PASS sessions as you, how often did you talk to them about the M-PASS program? (0 = Never, 1 = 1 to 2 occasions, 2 = 3–5 occasions, 3 = 6–9 occasions, and 4 = 10+ occasions); and (4) Of those who did NOT participate in similar M-PASS sessions as you, which statement best describes the typical conversation with them about the M-PASS program? (0 = I never talked about the M-PASS program, 1 = I barely mentioned the M-PASS program, 2 = I talked about the M-PASS program only in passing, 3 = I only talked briefly about the M-PASS program, or 4 = I talked in depth about the M-PASS program). The measure of cross-group contamination was the product of the fourth and fifth questions. Statistical Analyses The data were analyzed using linear regression models. To properly account for matching, the standard errors were estimated using a Jackknife approach. Also, sampling weights were used to make the estimates generalizable to the entire University of Michigan freshman population. LoVOL 58, JANUARY/FEBRUARY 2010
gistic regression analyses examined sex, race/ethnicity, age, and US residency (ie, US citizen or resident alien) as predictors of nonparticipation and attrition. Sampling weights were calculated by multiplying the inverses of the predicted probabilities. Study group, alcohol risk group (ie, high-risk, low-risk or nondrinkers) and their interactions were primary covariates of interest, along with confounders from the baseline survey. This allowed examination of the intervention effects within each of the alcohol risk groups. Because the number of days from baseline to posttest varied by participant, and was less than 28 days for many participants, the number of days from baseline to posttest was included as a covariate to adjust for differences in intervention exposure times and measurement overlap. RESULTS Differences in Attitudes and Beliefs More significant differences in attitudes and beliefs were observed for women than men, for whom only 1 significant effect emerged. That is, high-risk intervention group men had a more advanced stage of change compared to controls (see Table 2). Compared to the control group, all women showed a more advanced stage of change. High-risk intervention women also had significantly lower tolerance of drinking, and women in the high- and low– alcohol risk groups reported significantly fewer reasons to drink than controls. Finally, women in the low-risk group used significantly more strategies to avoid at-risk drinking than controls, and low-risk and nondrinking women in the intervention group reported significantly less tolerance of drink/driving. Nondrinking intervention women reported nominally more (p = .06) reasons not to drink compared to the control group. These differences represent intervention effects among high-risk drinkers and prevention effects among low-risk and nondrinkers. Preliminary Results for Drinking Behavior High-risk intervention men reported lower quantity of drinking and less frequent binge drinking compared to controls (Table 2). Intervention women in the low-risk group reported fewer drinks per drinking day, and women in the high-risk group reported a lower quantity of drinking per drinking occasion compared to the control group. The same models were tested while adjusting for cross-group contamination, and the results were unchanged for men, and for women stage of change became nonsignificant for the nondrinkers. COMMENT This paper reports the posttest evaluation results of MPASS, a Web-based brief motivational alcohol prevention/intervention for college freshmen. Results provided evidence that M-PASS increased motivation to reduce/avoid alcohol-related risk, and reduced tolerance of alcohol-related risk behavior. In addition, there is preliminary evidence that M-PASS also reduced alcohol-related risk behaviors. Finally, 353
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TABLE 2. Least Squares Means in Attitudes, Beliefs, Alcohol Use, and Consequences Adjusted for Baseline Differences and Time From Baseline to Posttest Men Outcome Stage of change Tolerance of drinking Reasons to drink Reasons not to drink Strategies to reduce drinking Tolerance of drink/driving Drinks per drinking day Quantity frequency Frequency of binge drinking Drinking consequences
Alcohol risk group
Intervention
Control
p
Intervention
Control
p
High-risk Low-risk Nondrinker High-risk Low-risk Nondrinker High-risk Low-risk Nondrinker High-risk Low-risk Nondrinker High-risk Low-risk Nondrinker High-risk Low-risk Nondrinker High-risk Low-risk Nondrinker High-risk Low-risk Nondrinker High-risk Low-risk Nondrinker High-risk Low-risk Nondrinker
1.91 2.18 2.81 −0.72 −0.75 −0.98 1.39 0.87 0.58 1.01 1.17 1.22 2.19 2.60 2.45 0.85 0.92 0.58 4.38 4.10 3.50 9.88 1.13 0.01 3.68 0.39 0.06 0.36 0.08 0.01
1.50 1.82 2.53 −0.79 −0.83 −0.92 1.38 1.06 0.16 0.97 1.08 1.15 2.22 2.41 2.34 0.79 0.81 0.76 4.67 3.66 3.39 13.54 0.60 0.00 4.61 0.16 0.02 0.35 0.09 0.00
.000 .147 .167 .458 .342 .538 .959 .460 .411 .454 .332 .698 .792 .468 .915 .410 .445 .227 .506 .437 .748 .131 .179 .900 .007 .229 .576 .890 .682 .386
1.84 2.22 2.79 −0.88 −0.88 −1.10 1.38 0.75 0.15 1.22 1.27 1.59 2.55 3.03 2.94 0.67 0.62 0.48 3.24 2.83 2.48 6.98 0.77 0.01 4.04 0.27 0.00 0.38 0.11 0.00
1.56 1.85 2.48 −0.96 −0.94 −1.09 1.54 0.98 0.83 1.22 1.24 1.41 2.50 2.79 3.21 0.75 0.78 0.76 3.43 3.21 2.81 12.05 0.82 0.00 4.72 0.45 0.05 0.45 0.11 0.01
.052 .000 .011 .009 .199 .844 .008 .002 .160 .893 .694 .056 .590 .026 .765 .251 .000 .001 .412 .018 .208 .027 .777 .965 .396 .362 .676 .096 .996 .029
evidence suggests that M-PASS had more efficacy for women than men. Like other brief college alcohol interventions,12 M-PASS shows promise of efficacy with high-risk drinkers. Intervention messages are potentially more salient for high-risk drinkers because they address activities that the participants experience and witness in others. High-risk drinkers’ personal awareness of the potential impact of heavy alcohol use may give the intervention greater immediacy that lower risk drinkers perceive less strongly. Hence, it is not surprising college alcohol interventions have been successful with highrisk drinkers. However, the prevalence of high-risk college drinking also highlights the need for prevention messages to avoid the development of high-risk drinking before it begins. M-PASS was unique in its objective of preventing alcoholrelated risk among nondrinking and low-risk drinking students. Because prevention components have not typically been included in computer/Web-based or conventional college alcohol interventions, evidence of such an effect from M-PASS is promising. Prevention effects were evidenced by intervention participants having a more advanced stage of change,42 fewer reasons to drink, lower tolerance of drinking 354
Women
by college students, fewer drinks per drinking day, and more alcohol risk avoidance strategies. These results support the potential efficacy of tailoring an explicitly preventive component to lower risk students in college alcohol interventions. The potential gains of averting problem drinking before it begins, as well as reducing it in individuals already at risk, are considerable. The observed evidence of prevention effects is also encouraging given the short time between the intervention and the posttest. Similarly, it is encouraging that differences were observed in the desired direction on several measures for high-risk participants. Because responding to surveys about drinking behavior can have an intervention-like effect on group outcomes, evaluation studies typically employ a design that controls for potential behavior differences due to survey completion. However, M-PASS employed an extensive tailoring algorithm that relied on responses to items administered during the intervention sessions, which could have spuriously contributed to an intervention effect. For that reason, the control group was administered the items asked of the intervention group during the intervention sessions that were not part of JOURNAL OF AMERICAN COLLEGE HEALTH
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the intervention, per se. As a result, the observed evidence of efficacy is apparent even after controlling for any spurious effects of questions asked during the intervention sessions. Behavioral differences are often difficult to detect at posttest, and this was especially true for M-PASS, which allowed participants to control the spacing and timing of the baseline survey, intervention session or mid-interval survey, and posttest. Differences in attitudes and opinions can emerge over a relatively short interval of time. Therefore, more confidence can be placed in these outcomes; however, behavior change emerges more slowly. As a result, several precautions were taken. First, a measure of the time from baseline to posttest was included as a covariate to adjust the models so that the results assumed the same length interval for all participants. Second, analyses focused primarily on the attitudinal outcomes, while treating the behavioral outcomes as preliminary. Given these precautions, the results suggest that the intervention was efficacious in altering attitudes and beliefs, possibly alcohol-risk behavior as well, though this will require verification at later follow-ups. Most of the significant differences in intervention/control group differences were among the women. It is possible that the intervention itself—its presentation, style, and content—may have been more motivating for women than men. It is also possible that men’s higher rate of noncompletion among high-risk drinkers contributed to a reduction in posttest differences. Finally, it may be that it takes more time for attitudinal and behavioral differences to emerge for men, and a determination on differences in efficacy for men and women will need to wait until later follow-up data are examined. This study has several limitations. As discussed, the results of this evaluation should be interpreted conservatively because of the variation in timing between the baseline and posttest surveys. Although the analyses were adjusted for this variability, there is a limit to what can be achieved by statistically adjusting models. Nevertheless, these results provide reason for optimism. Cross-group contamination is another limitation, even though measures indicated a very low level of contamination. However, if cross-contamination was underestimated, it would have attenuated true differences rather than create false ones between the groups, and would not threaten the validity of the effects that were found. Because of the lack of information regarding intervention session utilization, it is impossible to determine how much of the intervention text was read or the degree to which students interacted with the program. Although, technically, it is possible to measure mouse activity and session length, these are not accurate measures of the amount and quality of interactivity. The generalizability of the results is limited. Although sampling weights used in the analyses yielded estimates that were generalizable to the University of Michigan freshman student population, the results are not necessarily generalizable to other university student populations. Also, blacks were underrepresented in this sample, precluding any specific VOL 58, JANUARY/FEBRUARY 2010
conclusions about efficacy for black students. Like many efficacy studies, it is possible that the intervention participants learned correct posttest responses from the intervention, and that the differences reflect learning and overestimate real changes in attitudes. Also, the use of incentives to promote participation prevents generalization of the results to “real world” conditions where such incentives are lacking. Such a level of generalizabilty can only come from effectiveness and translation studies, which logically follow the identification of efficacious approaches. Finally, it is possible that the results reflect a self-response bias; research supports the validity of self-report of legal and illegal substance use, when confidentiality is protected as it was in this study.43–46 In conclusion, these initial results are promising, suggesting that the intervention was efficacious, and differences observed for the low-risk and nondrinking groups is indicative of prevention effects. Future papers will examine outcomes at later follow-ups. Future research in this area should continue prevention as a goal of computerized college alcohol interventions. ACKNOWLEDGMENT
This research was supported by funding from the National Institutes of Alcohol Abuse and Alcoholism grant RO1 AA14976. NOTE For comments and further information, address correspondence to Dr C. Raymond Bingham, University of Michigan Transportation Research Institute, 2901 Baxter Road, Ann Arbor, MI 48109, USA (e-mail:
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