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. Author manuscript; available in PMC: 2021 May 1.
Published in final edited form as: Addict Behav. 2019 Dec 17;104:106256. doi: 10.1016/j.addbeh.2019.106256

Influencing college students’ normative perceptions of protective behavioral strategies: A pilot randomized trial

Eleanor L S Leavens 1, Mary Beth Miller 2, Emma I Brett 1, Amanda Baraldi 1, Thad R Leffingwell 1
PMCID: PMC7286103  NIHMSID: NIHMS1597248  PMID: 31927219

Abstract

Introduction

Personalized feedback interventions (PFIs) are associated with small but reliable decreases in alcohol consumption among college students. While they often include information regarding protective behavioral strategies (PBS), PFIs do not typically include feedback aimed to modify normative perceptions of PBS. This study aimed to enhance the efficacy of existing PFIs among college students by incorporating normative feedback on participants’ use of PBS.

Methods

Students enrolled in undergraduate courses (N = 268) completed baseline and 1-month follow-up assessments of past-month use of PBS, normative perceptions of PBS use, alcohol consumption, and alcohol-related consequences. Participants were randomized to one of three conditions: typical feedback on PBS (typical strategies PFI), descriptive normative feedback on PBS (strategy norms PFI), or assessment-only control (AOC). Participants in the typical strategies PFI and strategy norms PFI conditions received web-based personalized feedback profiles.

Results

Compared to AOC, both the strategy norms PFI and typical strategies PFI were effective in correcting participants’ perceptions of other students’ engagement in PBS (p = .01) but did not differ significantly from one another. No statistically significant differences were observed between conditions in terms of actual PBS use, alcohol consumption, or alcohol-related consequences at 1-month follow-up (p > .05).

Discussion

The strategy norms and typical strategies PFIs were both successful in increasing normative perceptions of PBS use, indicating that general (rather than normative) feedback regarding PBS use may be sufficient for changing perceptions of PBS use.

Keywords: alcohol, descriptive norms, college drinking, intervention, feedback

Introduction

Risky drinking behavior remains a problem throughout the U.S., with approximately one in three college students engaging in heavy episodic drinking (HED) in the past two weeks (Johnson, O’Malley, Bachman, Schulenberg, & Miech, 2016). College students who misuse alcohol are at increased risk for a range of negative consequences, including poor academic performance, physical injury, and legal trouble (Hingson, Zha, & Weitzman, 2009; Perkins, 2002). Furthermore, individuals who drink heavily in adolescence/early adulthood are more likely to continue to drink heavily later in life (Zucker, 2008) and experience negative long-term consequences of excessive use, including neurocognitive deficits (Stavro, Pelletier, & Potvin, 2013). Given the variety of negative outcomes and societal costs associated with risky drinking, interventions for alcohol use among college students remain an important area for continued research.

Personalized feedback interventions (PFIs) for alcohol use assess an individual’s drinking patterns and related behaviors and then provide individual-specific feedback. PFIs are low-cost and effective interventions for college student drinking (Cronce & Larimer, 2011; M. B. Miller et al., 2013). Web-based PFIs are particularly promising, given their potential to reach a large number of people without extensive financial and personnel resources. However, computerized PFIs also tend to have small effects that disappear within 3–4 months (Carey, Scott-Sheldon, Elliott, Garey, & Carey, 2012). Though extensive research has been conducted examining the effectiveness of PFIs, mechanisms of change and optimal content for enhancing the effects of PFIs, especially computerized PFIs, is largely unknown.

Virtually all PFIs include a descriptive normative feedback component (M. B. Miller et al., 2013). According to Social Norms Theory, students are less likely to be concerned about their own drinking behavior when they simultaneously overestimate the prevalence of their peers’ drinking behaviors and approval of those behaviors (Perkins, 2002). Descriptive normative feedback on drinking quantity/frequency, which contrasts an individual’s own and perceived peer drinking with peers’ actual drinking to elicit discrepancy, has shown efficacy as a stand-alone intervention (Dotson, Dunn, & Bowers, 2015). Though descriptive normative feedback components have focused primarily on drinking quantity and frequency, other types of normative content may potentially elicit discrepancies and motivate behavior change. For example, research indicates that students also overestimate the acceptability of consequences (Brett, Leavens, Miller, Lombardi, & Leffingwell, 2016), and underestimate (a) the number of alcohol-related consequences other students experience (Brett et al., 2016), (b) alcohol-related risky sexual behaviors (Lewis et al., 2014), and (c) use of PBS while drinking (Benton, Downey, Glider, & Benton, 2008; Lewis, Rees, & Lee, 2009).

In addition to descriptive norms, many PFIs for alcohol use incorporate information on protective behavioral strategies (PBS) that can reduce the potential for experiencing negative consequences when drinking alcohol (M. B. Miller et al., 2013). The use of PBS has been associated repeatedly with decreased experience of alcohol-related consequences (Barnett, Murphy, Colby, & Monti, 2007; Larimer et al., 2007; Martens et al., 2005). Such strategies follow a harm-reduction approach, as they are specific behaviors used while drinking alcohol instead of recommending decreased frequency of drinking or abstinence, likely making such strategies more acceptable to college student drinkers. PBS include strategies directly and indirectly related to alcohol consumption, such as alternating between alcoholic drinks and water and enlisting a designated driver (Martens et al., 2005). Though PFIs to date have focused primarily on descriptive norms for drinking quantity and frequency, research indicates that students also underestimate other students’ use of protective behavioral strategies (PBS) while drinking (Benton et al., 2008; Lewis et al., 2009). Theoretically (Perkins, 2002), students who view PBS use as more normative are more likely to use PBS themselves. Thus, normative feedback on PBS use may also be expected to influence young adults’ drinking behaviors.

Many existing PFIs for alcohol use include a list of potential PBS that students could use to decrease alcohol-related risk, such as alternating drinks with water or using a designated driver (Mary Beth Miller et al., 2013). Normative perceptions of peers’ PBS use predicts personal use of PBS (Benton et al., 2008; Lewis et al., 2009), and use of PBS is associated with decreased alcohol-related consequences (Barnett et al., 2007; Larimer et al., 2007; Martens et al., 2005). Therefore, correcting normative perceptions of PBS use has the potential to increase PBS use, which is expected to decrease alcohol consumption and related consequences. However, the literature remains mixed, with some studies failing to find significant effects (Martens, Smith, & Murphy, 2013) and others finding effects only for interventions focused on indirect PBS (e.g., looking out for friends, planning a ride home; Leeman et al., 2016). However, no research has incorporated normative feedback specific to PBS use. Given the importance of descriptive normative feedback within PFIs for alcohol use, PBS norms may be an important target for intervention.

This study aimed to enhance the efficacy of existing PFIs by evaluating the relative efficacy of normative versus traditional PBS feedback via an exploratory randomized trial. It was hypothesized that descriptive normative feedback on PBS (strategy norms PFI) would outperform traditional PBS feedback (typical strategies PFI) and assessment only control (AOC) in correcting normative perceptions of other students’ PBS use, increasing PBS use, reducing alcohol consumption, and reducing experience of alcohol-related consequences at 1-month follow-up.

Method

Participants and Procedure

Students in undergraduate courses were recruited from a large, four-year, Midwestern university from January 2015 to March 2016. Participants were enrolled in introductory psychology and speech courses and enlisted in the university’s online recruitment pool. Potential participants completed a pre-screening questionnaire. If eligible based on this pre-screening, they were invited to read a brief description of the study and consider participation. Those who screened eligible completed the baseline survey online from remote locations. They were then randomly assigned by computer algorithm to one of three conditions: assessment only control (AOC), typical PBS feedback (typical strategies PFI), or PBS normative feedback (strategy norms PFI). Feedback profiles were emailed to participants within 24 hours of baseline. Participants were asked to confirm their receipt and review of the feedback profile by responding to the email (i.e. providing a “read receipt”). Participants were sent up to 5 email reminders. Those who did not return a read receipt were not invited to complete the 1-month follow-up.

One month later, participants were invited to complete the online follow-up assessment. Participants were emailed the link to the survey up to 5 times. They received course credit for completion of the baseline and follow-up, and after follow-up, they were provided a list of treatment referrals. All procedures were approved by the university’s Institutional Review Board.

Due to the pilot nature of the trial, an a priori power analysis was not conducted. Participants (N = 408) were eligible if they were between 18 and 30 years old and endorsed 2+ heavy drinking episodes (4+/5+ drinks for women/men in a single sitting) in the past month at baseline. Participants were excluded from analyses for the following reasons: they declined participation prior to being randomized (n = 33); were outside the age range of interest (n = 1); denied 2+ heavy drinking episodes in the past month (n = 35); provided unlikely responses, such as 480 drinks in a typical week (n=4); self-reported dishonest responding (n = 10); or did not provide a PFI read receipt (n = 57). The final sample consisted of 268 participants (see Figure 1). Sample characteristics are depicted in Table 1.

Figure 1.

Figure 1.

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CONSORT Diagram

Note. AOC = assessment-only control. A total of N = 268 participants met inclusion/exclusion criteria and were included in the analysis regardless of follow-up using missing data techniques described in the manuscript.

Table 1.

Participant characteristics (N = 268).

Age, M (SD) 19.79 (1.65)
Gender (n, %)
 Male 104 (39%)
 Female 164 (61%)
Year in School (n, %)
 First year 99 (37%)
 Sophomore 80 (30%)
 Junior 48 (18%)
 Senior 39 (15%)
 Post-Baccalaureate 2 (1%)
Race/ethnicity (n, %)
 White 221 (83%)
 Hispanic/Latino 14 (5%)
 Native American or Native Alaskan 12 (5%)
 Black/African American 7 (3%)
 Biracial/Mixed 5 (2%)
 Other 4 (2%)
 Asian 3 (1%)
 Native Hawaiian or Pacific Islander 2 (<1%)
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Measures

Alcohol consumption

Participants completed the Daily Drinking Questionnaire (DDQ; (Collins, Parks, & Marlatt, 1985) at baseline and 1-month follow-up. Participants reported the number of drinks they consumed each day of a typical week in the past 30 days. The seven values were summed to calculate a total number of drinks consumed in a typical week. Participants reported the number of drinks consumed on their peak drinking day in the two weeks prior to the survey.

Protective behavioral strategies (PBS)

The Protective Behavioral Strategies Scale (PBSS; (Martens et al., 2005) was used to assess participants’ past-month use of PBS. The PBSS assesses 15 strategies that may be used to mitigate consequences of drinking, such as “put extra ice in your drink,” “avoid taking shots,” and “use a designated driver.” Items were assessed on a 6-point Likert scale from 1 (never) to 6 (always). A total score was obtained by summing all 15 items. Cronbach’s alpha in the current study was .88 and .89 for the baseline and follow-up assessments, respectively.

PBS norms

Participants’ normative perceptions of 15 protective behavioral strategies were assessed at baseline and 1-month follow-up using a modified version of the PBSS (Martens et al., 2005). Participants indicated “how often you believe the typical college student engages in the following behaviors when using alcohol or ‘partying.’” The items and anchors paralleled those in the PBSS. Cronbach’s alpha at baseline and follow-up was .90 and .92, respectively.

Alcohol-related consequences

Past-month experience with alcohol-related consequences was measured at baseline and 1-month follow-up using the Brief Young Adult Alcohol Consequences Questionnaire (B-YAACQ) (Kahler, Strong, & Read, 2005). The B-YAACQ assesses experience with 24 alcohol-related consequences of varying severity in a dichotomous (yes/no) fashion. Items were summed to calculate a total score in which higher scores indicated experience with a greater number of consequences. Cronbach’s alpha for the current study was .89 at baseline and .90 at follow-up.

Interventions

Typical Strategies PFI

Consistent with traditional feedback on PBS, participants in the typical strategies PFI condition received a list indicating how often they reported using each of the 15 PBS at baseline. The feedback stated, “Below is a list of strategies people may use to keep from experiencing problems while drinking. The extent to which you reported using each strategy is included below.”

Strategy Norms PFI

Participants in the strategy norms PFI condition received a list similar to that included in the typical strategies PFI. However, the feedback also indicated how often the participant believes the typical college student at his/her university uses each strategy (perceived norms), how often the typical student actually reported using each strategy (actual norms), and the proportion of students who use each strategy more often than they do (percentile rank). The five PBS with the greatest discrepancies between perceived norms and actual norms for each participant were highlighted.

Data Screening and Analysis

Four hundred eight participants were assessed for eligibility. Of the 268 participants who met inclusion criteria, 41.8% (n = 112) did not complete the 1-month follow-up (see Figure 1). A Chi Squared test indicated no statistically significant relationship between condition and follow-up attrition, X2(2) = 3.657, p = .16. Logistic regression was used to assess whether baseline levels of any of the five outcomes predicted attrition at 1-month follow-up; each measure was used as a single predictor in a separate model. Perceptions of others’ engagement with PBS was not a statistically significant predictor of missing at follow-up, eb = 1.00, Wald = .02, p = .90. Use of PBS (eb = .97, Wald = 10.27, p < .01), drinks per week (eb = 1.02, Wald = 4.41, p = .04), peak drinking quantity (eb = 1.05, Wald = 7.45, p < .01), and alcohol-related consequences (eb = 1.05, Wald = 4.57, p = .03.) were statistically significant predictors of missingness at 1-month follow-up.

Missing data were imputed using the multiple imputation procedure in Mplus (Muthén, 2018) to reduce nonresponse bias (Baraldi & Enders, 2010; Enders, 2017; Schafer & Graham, 2002). Consistent with recommendations, imputation was conducted at the item level and scale scores were created after imputation (Enders, 2017; Gottschall, West, & Enders, 2012). A separate imputation procedure was conducted for each of the five outcomes because imputation that incorporated all study variables failed to converge. For all analyses, appropriate auxiliary variables were identified and used to support the imputation routine (Collins, Schafer, & Kam, 2001). Auxiliary variables were chosen for a given model based on their correlation with variables with missingness and, in some cases, predictiveness of missingness. For use of PBS and normative perceptions of PBS, baseline drinks per week and peak drinking quantity were used as auxiliary variables. For alcohol related consequences, total PBS and baseline number of drinks on peak drinking day were used as auxiliary variables. For drinks per week, baseline number of alcohol-related consequences was included as an auxiliary variable. Finally, for the model assessing peak drinking quantity, total PBS was used as the auxiliary variable. For each analysis, 20 imputed data sets were created. The between-imputation burn-in interval (range 300–25,000) was based on the values of the potential scale reduction factor, with values below 1.10 regarded as acceptable (Gelman & Rubin, 1992; Gelman & Shirley, 2011).

In order to facilitate the use of multiple imputation, analyses were conducted using regression instead of analysis of covariance (ANCOVA). Group membership was dummy coded (D1 = typical strategy PFI group coded as 1; D2 = strategy norms PFI group coded 1), with AOC as the reference group (coded 0 on D1 and D2). The following regression model was used:

MeasureTime2=b1D1+b2D2+bGenderGender+bMeasureMeasureTime1

Based on the coding scheme, the regression coefficient b1 represents the difference in the outcome at Time 2 in the typical strategies PFI group relative to the AOC group, controlling for gender and the outcome at Time 1. The values of these regression coefficients are akin to pairwise comparisons in ANCOVA in that they represent expected differences in the outcome between groups (i.e. b1 represents the difference for the typical strategies PFI vs. AOC group, b2 represents strategy norms PFI vs. AOC, and b3 represents typical strategies PFI vs. strategy norms PFI). To assess whether the set of dummy codes together are statistically significant (akin to the main effect of group in ANCOVA), we used the Wald joint significance (Asparouhov & Muthén, 2010; Liu & Enders, 2017) test to evaluate the significance of D1 and D2 simultaneously.

Results

Group Equivalence at Baseline

Intervention and control groups did not differ significantly at baseline in terms of age, gender, or White versus non-White ethnicity (all p > .05). There were no statistically significant baseline differences between groups in regard to alcohol consumption, alcohol-related consequences, use of PBS, or perceptions of peers’ use of PBS at baseline (see Table 2).

Table 2.

Descriptive statistics for primary outcomes.

Baseline
 1 Month
Condition M SD M SD Cohen’s d
PBS norm
 Typical Strategies PFI 41.27 9.62 46.89a 12.13 0.51
 Strategy Norms PFI 41.36 10.98 48.31a 13.06 0.58
 AOC 41.11 12.95 42.55b 11.63 0.12
PBSS total
 Typical Strategies PFI 51.48 12.49 53.22 12.50 0.14
 Strategy Norms PFI 51.89 14.41 53.09 14.22 0.08
 AOC 50.11 14.21 51.21 13.87 0.08
Drinks per week
 Typical Strategies PFI 14.45 12.59 14.17 12.58 0.02
 Strategy Norms PFI 18.14 17.78 14.08 14.88 0.25
 AOC 14.29 11.56 13.43 12.60 0.07
Peak drinking quantity
 Typical Strategies PFI 9.68 7.99 8.51 6.93 0.16
 Strategy Norms PFI 9.34 7.41 8.97 7.23 0.05
 AOC 9.89 8.57 8.15 7.02 0.22
Alcohol consequences
 Typical Strategies PFI 8.93 5.30 8.35 6.32 0.10
 Strategy Norms PFI 8.41 5.47 8.45 6.31 0.01
 AOC 8.91 5.52 9.79 6.51 0.15
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Note. All analyses calculated using multiple imputation in Mplus (N = 268). AOC = assessment only (n = 101). Typical Strategies PFI = typical strategies personalized feedback intervention (n = 81). Strategy Norms PFI = strategy norms personalized feedback intervention (n = 86). PBS = protective behavioral strategies. PBSS = Protective Behavioral Strategies Scale. Non-matching superscripts indicate significant between-group differences.

Primary Outcomes

Descriptive statistics for all outcomes are presented in Table 2. Consistent with theory and the rationale for the intervention, participants underestimated the extent to which the typical student on campus engaged in PBS use at baseline [perceived M = 41.24, SD = 11.14; actual M = 51.09, SD = 13.79; paired samples t(267) = 10.97, p < .001]. There were significant between-group differences in perceptions of typical students’ use of PBS at 1-month follow-up, controlling for perceptions at baseline (Wald = 8.77, df = 2, p = .01). Compared to participants in the assessment only condition (M = 41.11), for both the strategy norms PFI (M = 48.31) and the typical strategies PFI (M = 46.89), participants’ perceptions of other students’ use of PBS became more accurate, such that they reported believing that the typical student utilizes a greater number of PBS at 1-month follow-up (b1 = 4.38, p = .03; b2 = 5.46, p < .01). There was not a statistically significant difference between perceptions of other students’ use of PBS between the typical strategies and the strategy norms PFI (b3 = 1.075, p = .61).

There were no significant between-group differences in actual use of PBS (Wald = 0.40, df = 2, p = .82; b1 = 0.52, p = .77; b2 = 0.96, p = .53; b3 = 0.44, p = .79), drinks consumed per week (Wald = 1.76, df = 2, p = .41; b1 = 0.53, p = .79; b2 = −2.0, p = .37; b3 = −2.485, p = .19), alcohol related consequences (Wald = 3.51, df = 2, p = .17; b1 = −1.46, p = .07; b2 = −0.89, p = .27; b3 = 0.57, p = .52), or peak drinking quantity (Wald = .80, df = 2, p = .67; b1 = 0.47, p = .70; b2 = 1.13, p = .39; b3 = 0.66, p = .53) at 1-month follow-up.

Discussion

This study aimed to determine if descriptive normative feedback on PBS use increased the efficacy of traditional PBS feedback for reducing alcohol consumption and related consequences among college students. This study is the first to utilize normative feedback regarding engagement in PBS compared to typical strategies PFI and assessment-only control in a brief personalized feedback intervention for college students. Normative feedback regarding typical student alcohol consumption is an effective intervention for heavy college alcohol use (Lewis, Neighbors, Oster-Aaland, Kirkeby, & Larimer, 2007; M. B. Miller et al., 2013; Neighbors, Larimer, & Lewis, 2004; Neighbors et al., 2010). However, normative feedback with the specific aim of increasing use of typical strategies has not been utilized within interventions. Consistent with previous research, participants underestimated their peers’ use of PBS (Lewis et al., 2009). This may be because, in comparison to drinking behaviors, peer engagement in PBS is often not easily observed and may not be discussed with peers, making peer PBS use less salient compared to actual drinking behaviors (Lewis et al., 2009). If this is the case, interventions that incorporate normative feedback on PBS use will be important in bringing attention to engagement in PBS by other university students, something that college students may not easily observe on their own.

Consistent with hypotheses and similar to other interventions incorporating personalized normative feedback (Lewis et al., 2007; Neighbors et al., 2004; Neighbors et al., 2010), the current intervention was successful in correcting participants’ perceptions of other students’ PBS use. This finding is important, since descriptive norms predict use of PBS (Benton et al., 2008; Lewis et al., 2009). Interestingly, alcohol outcomes were similar across groups, despite increased perceptions of peer PBS use only in the intervention conditions. Theoretically, change in norms are expected to correspond with change in behavior (Perkins, 2002); and the efficacy of stand-alone normative feedback on drinking quantity/frequency support this idea (Dotson, Dunn, & Bowers, 2014). However, recent research suggests that attitudes (perceptions of alcohol use as good, beneficial, enjoyable, etc.) may be more strongly associated with subsequent drinking than perceptions of peer use/approval (Cooke, Dahdah, Norman, & French, 2016; DiBello, Miller, Neighbors, Reid, & Carey, 2018). Therefore, it is possible that normative feedback on PBS alone is insufficient to produce changes in the attitudes associated with drinking behavior. Future studies examining the impact of normative feedback on alcohol use attitudes and the potential for attitudes to explain feedback effects on alcohol use are encouraged.

In contrast to hypotheses, both the strategy norms and typical strategies PFIs increased accuracy of perceived PBS norms, suggesting that the addition of normative PBS feedback may not be necessary to promote changes in perceptions of other students’ use of PBS. In this case, the mechanism by which PBS feedback leads to changes in PBS norms is unclear. It is possible that PBS feedback in general increases awareness of PBS, thereby increasing the likelihood that students notice PBS use among others. Indeed, although the authors did not measure norms specific to PBS, another intervention with an emphasis on PBS (Martens, Smith, & Murphy, 2013) was successful in increasing use of PBS at both 1- and 6-month follow-up when combined with in-person brief motivational interviewing. Thus, non-normative feedback on PBS may be sufficient for increasing the efficacy of existing interventions.

The current study is not without limitations. First, neither PFI incorporated normative feedback on alcohol consumption. Inclusion of such feedback is common in PFIs for college students (M. B. Miller et al., 2013) and may increase the magnitude of the effect in future studies. Second, the current sample consisted of a relatively high proportion of females, younger (freshmen/sophomore) students, and students that identified as Caucasian; therefore, the findings in the current study may not generalize to all college students. Third, the current study used the 15-item PBSS to assess PBS. After initiation of this study, a 20-item version of the PBSS was published that demonstrated improved content validity (Treloar, Martens, & McCarthy, 2015); therefore, future research should utilize more updated versions of this measure. Finally, while participants were required to confirm receipt and review of the feedback profile, it remains unclear the extent to which participants viewed and understood the feedback. Future research should consider inclusion of more sophisticated measures to ensure receipt and understanding of feedback, including testing recall of the information included in the PFI.

This study identified and tested a novel potential target to enhance the efficacy of existing PFIs for heavy-drinking college students. In contrast to hypotheses, both the typical and norms-based PBS feedback were effective in correcting perceptions of other students’ use of PBS. Data suggest that normative feedback may not enhance the efficacy of PFIs in increasing PBS use. Thus, future research aiming to enhance the efficacy of PFIs may target another aspect or component of the intervention (e.g., improving mode of delivery or focus on particularly negative consequences). In particular, given the brevity of the current intervention, research examining the extent to which in-person motivational enhancement or conventional normative feedback on alcohol consumption increases the efficacy of PBS interventions is encouraged.

Acknowledgements

The authors would like to thank Rachel Feddor, MS, Melissa Burson, BS, and Alonzo Johnson, BS for helping to create feedback profiles throughout the study; thank you to Susanna Lopez, MS for her contributions through editing and providing input throughout the manuscript preparation.

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