research studies demonstrate that college students who drink heavily prefer

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Volume 38 Issue 1 January 1, 2016

Drinking Over the Lifespan: Focus on College Ages

Part of the Topic Series: Alcohol Use Among Special Populations

Jennifer E. Merrill, Ph.D., and Kate B. Carey, Ph.D.

Jennifer E. Merrill, Ph.D., is an assistant professor and Kate B. Carey, Ph.D., is a professor, both at the Center for Alcohol and Addiction Studies and the Department of Behavioral and Social Sciences at Brown University School of Public Health, Providence, Rhode Island.

Many college students drink heavily and experience myriad associated negative consequences. This review suggests that a developmental perspective can facilitate a better understanding of college drinking. Specifically, using an emerging adulthood framework that considers the ongoing role of parents and neurodevelopmental processes can provide insight into why students drink. Most college students drink and tend to drink more and more heavily than their non–college-attending peers. These drinking patterns are affected by environmental and temporal characteristics specific to the college environment, including residential campus living, the academic week, and the academic year. Additional psychosocial factors are of particular relevance to the drinking behavior of college-age people, and include exaggerated peer norms, the development and use of protective behavioral strategies, and mental health considerations. Understanding the unique interaction of person and environment is key to designing prevention/intervention efforts.

Approximately 41 percent of 18- to 24-year-olds are enrolled in a postsecondary degree-granting institution (National Center for Education Statistics 2013). As a group, college students, and particularly those at residential colleges (Presley et al. 2002), often drink heavily and experience myriad associated negative consequences. This selective review discusses the special characteristics of the college age and environment that put students at risk for hazardous drinking and problems with alcohol. The following sections describe the developmental context in which such drinking behavior occurs and then briefly characterize the risky drinking behavior of college students and the temporal and environmental risk factors associated with college attendance. The article then reviews psychosocial predictors of risky drinking that are relevant to this age group and concludes with intervention implications.

Developmental Considerations

The developmental context in which drinking behavior occurs in college-aged men and women is unique, and developmental considerations can inform both basic and intervention research with this population.

Emerging Adulthood

The sociodevelopmental notion of emerging adulthood is a helpful conceptual framework through which to understand risky drinking during the college years (Arnett 2000, 2005). For emerging adults who attend college, graduating from high school is no longer the entry into adulthood. Rather, these individuals typically delay marriage, parenthood, and a career until completing their education. Arnett describes five dimensions that characterize this developmental stage and that may have implications for alcohol use and misuse.

• Identity exploration. During emerging adulthood, when individuals are figuring out their own identity (particularly in the domains of love and work), alcohol use may be a part of exploring a wide range of lifestyle options before adopting adult roles and identity. Students may also use alcohol to cope with identity confusion (Schwartz et al. 2010).
• Instability. The college years are associated with frequent residential moves and changes in friends and partners, educational status, and jobs. Alcohol use often is elevated during periods of transition (Schulenberg and Maggs 2002) and perhaps is used for self-medication or to promote social activity (Kuntsche et al. 2005).
• Self-focus. Upon college entry, students gain independence from their family and relative freedom from obligations and commitments to others. They make independent decisions, and with weaker social controls from family and other institutions, they experience fewer constraints on risk behaviors. Friends may have the most influence on behavior during this time, and students inclined to use alcohol likely establish friendships that support drinking (Abar and Maggs 2010).
• Feeling in-between. Emerging adults may feel neither adolescent nor fully adult, and therefore may feel a sense of responsibility in some domains but not others. For example, they may feel capable of deciding whether or not to use alcohol but may not feel they need to conform to adult standards of comportment. Some students may see the college years as a “time out” from adult responsibilities (Colby et al. 2009) and give themselves permission to enjoy activities such as risky drinking that will be less acceptable later in adulthood.
• Possibilities. Finally, emerging adulthood is a time when people can make dramatic changes in their lives and is characterized by biased optimism. Because college students’ expectations for a positive future are so high, they may not acknowledge that negative consequences related to drinking behavior may occur.

The Unique Role of Parents

As mentioned above, once emerging adults head to college, they depart from the structure and oversight provided when living with parents. However, parents do still matter during the college years. For example, research finds that higher levels of perceived parental involvement may buffer students from the effects of peers on alcohol use and problems (Wood et al. 2004); parental knowledge of how their college student is spending his or her time may influence choice of friends, which in turn may influence drinking behavior (Abar and Turrisi 2008); and parental permissiveness of drinking predicts increases in alcohol use and consequences over time (Walls et al. 2009). Overall, continued parental involvement and communication may serve to protect against high-risk drinking and prevent harm even at this stage of emerging adulthood (Turrisi and Ray 2010).

Neurodevelopmental Factors Affecting Self-Regulation

The developmental context of college drinking is characterized not only by psychosocial but also biological factors. A growing body of research reveals that the brain’s frontal lobes do not fully mature until the mid-20s (Johnson et al. 2009). During adolescence, the bottom-up impulsive system that responds to rewards and social/emotional factors matures before the top-down controls of the prefrontal cortex (Casey and Jones 2010). Importantly, these top-down pathways from the prefrontal cortex help people slow down and consider the long-term outcomes of their behaviors. An imbalance between the impulsive system and the more reflective system may make emerging adults more vulnerable to engaging in addictive behaviors. In addition, some speculate that engaging in behaviors such as substance abuse may strengthen the bottom-up pathways and trigger this imbalance (Bechara 2005). Thus, the observations that late adolescents and emerging adults often choose short-term rewards over long-term goals may reflect the state of their neurocognitive development.

In the next section, we summarize descriptive data about college student drinking and its consequences, keeping in mind that it occurs within this developmental context characterized by the features of emerging adulthood, a changing but still significant role for parents, and continuing neurocognitive development.

Alcohol Use and Consequences Among College Students

Drinking behavior.

National surveys provide valuable data on the drinking habits of college students in the United States. They include the Harvard College Alcohol Study (e.g., Wechsler et al. 2002), the National Epidemiologic Survey on Alcohol and Related Conditions (e.g., Chen et al. 2004; Dawson et al. 2004), the National Survey on Drug Use and Health (Substance Abuse and Mental Health Services Administration 2014), the Core Institute Project (CORE), and the Monitoring the Future studies (Johnston et al. 2014).

White and Hingson (2013) offer a detailed overview of these surveys and their findings; we will provide a brief summary. To start, the majority of college students (approximately 60 percent) report past-month drinking (Johnston et al. 2014; Substance Abuse and Mental Health Services Administration 2014). Those who drink tend to drink heavily: more than one-third of college students report heavy episodic drinking at least once in the past 2 weeks, with heavy drinking defined as 4 or more drinks in one sitting for females and 5 or more drinks in one sitting for males (Johnston et al. 2014). In addition, approximately 1 of 5 males (19.9 percent) and 1 of 10 females (8.2 percent) consume twice this binge threshold (White et al. 2006). It is worth noting that patterns of drinking are heterogeneous with multiple trajectories in binge-drinking behavior across the 4 years of college (Schulenberg and Maggs 2002).

Negative Consequences

Heavy drinking results in negative consequences for both drinking and nondrinking students:

• A total of 646,000 physical assaults, 97,000 sexual assaults, 599,000 unintentional injuries, and 1,825 deaths are linked to alcohol use among college students annually (Hingson et al. 2009).
• Forty percent of college student drinkers report alcohol-induced memory loss, such as blackouts (White et al. 2002), which is associated with future risk for injury and/or increased drinking (Mundt et al. 2012; Read et al. 2013).
• Twenty-one percent of college student drinkers report unplanned sexual activity while drinking, and 10 percent report unprotected sex while drinking (Wechsler et al. 2002). Such behavior can lead to sexually transmitted infections or unplanned pregnancy (Ingersoll et al. 2008) .
• Students also report that drinking alcohol is related to social/interpersonal problems, poor self-care (e.g., eating and/or sleeping poorly), and diminished self-regard (e.g., feeling badly about oneself) (Read et al. 2006).
• Among college students, rates of alcohol abuse as defined in the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition range from 6 to 31 percent, and rates of alcohol dependence range from 6 to 16 percent (Blanco et al. 2008; Dawson et al. 2004; Knight et al. 2002).

From a developmental standpoint, the underdevelopment of the frontal lobes and neurocognitive systems guiding decision making may in part explain some of the consequences of drinking in this age group, particularly those that involve engaging in risky behaviors while drinking. Moreover, as mentioned, because expectations for a positive future are so high during emerging adulthood, college students may feel that they are immune to any negative consequences related to drinking and thus may not take measures to avoid them.

Academic Impairment

Drinking also may influence students’ academics, the primary purpose of attending college. This may manifest in poor performance on exams, missing classes, lower grade-point average (GPA), and even dropping out (for a brief review, see White and Hingson 2013). However, the association between alcohol use and academics may be neither direct nor absolute. Although alcohol involvement has been shown to be associated with academic problems at the end of freshman year, this relationship was explained by historical variables (academic aptitude, class rank) that existed when students entered college (Wood et al. 1997). Binge drinking adversely affects GPA in part by reducing study hours (Wolaver 2002). Further, extreme alcohol involvement— dependence but not abuse—clearly compromises first-year academic performance (Aertgeerts and Buntinx 2002).

Demographic Correlates

Just as in the general population, male and white students (Del Boca et al. 2004; Johnston et al. 2014) are at higher risk for excessive drinking. Certain affiliations associated with college life further enhance this risk, such as being a member of a Greek organization (O’Brien et al. 2013; Park et al. 2008) or a collegiate athletic team (Brenner and Swanik 2007; Yusko et al. 2008). Such affiliations are unique to the college environment and can be important sources of identity and social connectedness, which are both important to emerging adults.

College versus Noncollege Comparisons

The drinking behavior among college students is in some ways distinct from that of their same-age peers who do not attend college (Slutske 2005; Slutske et al. 2004). Every year, from 2002 to 2013, rates of past-month binge drinking (4 or more drinks for women and 5 or more for men) were higher among college-attending young adults, ages 18–22, than their peers who do not attend college (Substance Abuse and Mental Health Services Administration 2014). Similar disparities are seen for alcohol use disorder, but some research finds that differences in alcohol use disorder disappear after adjusting for sociodemographic variables such as gender , race/ethnicity, nativity, marital status, and personal and family income (Blanco et al. 2008). Other studies find that variables such as full-time versus part-time status and type of college may be more directly related t o variations in alcohol consumption than whether a student attends college (Carter et al. 2010). Selection factors associated with the type of college or choice of living situation may partly explain increased risk (Fromme et al. 2008). Nonetheless, college attendance provides an environmental context affording opportunities for high volume drinking. It also may prolong the sense of being in-between childhood and the responsibilities of adulthood.

Risky Drinking Practices Among College Students

One explanation for increased risk for alcohol misuse and consequences among college students is the tendency to engage in specific types of high-risk drinking behaviors. These include but are not limited to pregaming and drinking games.

Sometimes called “preloading,” “frontloading,” or “prepartying,” pregaming is defined as consuming alcohol before attending a social event, where additional alcohol may or may not be available and/or consumed (Read et al. 2010; Wells et al. 2009), and is common on U.S. college campuses. In fact, 70 to 75 percent of college drinkers report pregaming (Barnett et al. 2013; DeJong et al. 2010; Hummer et al. 2013; Pedersen and LaBrie 2007, 2008; Read et al. 2010) and say they engage in the practice on about one-third of drinking days (Labhart et al. 2013; Merrill et al. 2009; Read et al. 2010). Pregaming often takes place in college dorm rooms; is time limited because students need to leave for the primary event; and often involves doing shots of hard liquor, resulting in rapid rates of intoxication (DeJong et al. 2010). When students pregame, compared with drinking episodes when they do not, they consume a greater number of drinks and have higher blood alcohol concentrations (BACs) (Barnett et al. 2013; Borsari et al. 2007 a ; Glindemann et al. 2006; LaBrie and Pedersen 2008; Pedersen and LaBrie 2007; Read et al. 2010). In addition, pregaming is linked to more alcohol- related consequences (Kenney et al. 2010; Labhart et al. 2013; LaBrie and Pedersen 2008; Merrill et al. 2013 a ; Paves et al. 2012; Pedersen et al. 2009), including neglecting responsibilities, feeling sick, passing out, absenteeism at school/work, drunk driving, alcohol poisoning, aggressive or violent acts, and blackouts (DeJong et al. 2010; Hughes et al. 2008; LaBrie and Pedersen 2008; LaBrie et al. 2011; Pedersen and LaBrie 2007; Pedersen et al. 2009).

Drinking Games

Another common and risky practice is playing drinking games (such as beer pong and Kings). According to Zamboanga and colleagues (2013), drinking games involve performing some kind of cognitive and/or physical task, are governed by a set of rules that specify when and how much participants should drink, and are designed specifically to promote increased drinking within short time periods in a social setting. In some cases, a vicious cycle can occur wherein once a participant starts to lose, he or she is forced to drink more as a penalty, thus further diminishing his or her skills in the game and increasing required consumption (Zamboanga 2007 a ; Zamboanga et al. 2010). Individuals may be heckled for refusing to drink during the game (Borsari 2004). It is therefore not surprising that playing drinking games increases risk for heavy drinking and negative alcohol-related outcomes (Ray et al. 2014; Zamboanga et al. 2006). One category of drinking games, including chugging and keg stands, is referred to as consumption or extreme consumption games (Zamboanga et al. 2013). It is this category that may pose the greatest risk for elevated alcohol consumption (LaBrie et al. 2013; Zamboanga et al. 2006, 2007 b ).

Environmental and Temporal Risk Factors for College Students

College attendance places students at increased risk for alcohol consumption and alcohol-related problems in part because of environmental features, including communal living and an academic week that often allows students to select a schedule with long weekends. Furthermore, the rhythm of the academic year includes social holidays and events that happen predictably across college campuses.

Living Situation

Communal living is an important risk factor. For example, Zamboanga and colleagues (2009) found that students at a women’s liberal arts college who lived in residence halls reported higher levels of hazardous alcohol use than students living in house-style residences, and Willoughby and Carroll (2009) demonstrated that students living in co-ed housing were more likely than students living in gender-specific housing to binge drink and consume alcohol. In contrast, students who remain living at home with parents drink less (Valliant and Scanlan 1996). In other work, alcohol dependence rates were highest among college students of both genders who live on campus, and rates of alcohol abuse were highest among college men who live off campus (Dawson et al. 2004, 2005 a ). Within the context of the emerging adulthood framework, living situation during the college years can contribute both to instability (frequent moves) and self-focus (weaker social controls upon moving from home to dormitories where the influence of parents may decline and influence of friends may rise).

The Transition Into College

The transition from high school to the first year of college is associated with increases in alcohol use and heavy drinking (Borsari et al. 2007 b ; Sher and Rutledge 2007). Heavy or frequent drinking early in the college experience can compromise academic success (Hoeppner et al. 2012; Upcraft 1995), as problematic patterns of drinking established during the first weeks often continue throughout college (Schulenberg et al. 2001; Task Force of the National Advisory Council on Alcohol Abuse and Alcoholism 2002). A review by Borsari and colleagues (2007 b ) found that the risk for increased drinking associated with college attendance is moderated by a number of variables, including sensation seeking, race, gender, religiosity, precollege alcohol use, and parental influences. This risk also is explained in part by changes in determinants of drinking that occur upon college entry, including changes in alcohol expectancies, drinking motives, perceived norms, Greek membership, and drinking game participation. The emerging adulthood framework predicts increased drinking during this time frame in that alcohol use is used to cope with the need to rebuild a social life or recreate a social identity; alcohol use also can be a result of an enhanced susceptibility to peer influence (Arnett 2005).

The Academic Week

College students typically drink the heaviest on weekends, and, for some, weekend-like drinking begins on Thursday (Hoeppner et al. 2012). However, this trend is moderated by a student’s schedule; those with no Friday classes drink twice as much on Thursdays as students with early Friday classes (Ward et al. 2013; Wood et al. 2007). Most colleges afford students the ability to select their own schedule, so heavy drinking students may be least likely to enroll in classes that convene on Friday (Paschall et al. 2006), perhaps in an effort to seek more opportunities to drink.

The Academic Year

Importantly, patterns of drinking across the academic year are not uniform; multiple trajectories characterize the overall pattern of drinking across the first college year (Greenbaum et al. 2005). However, at least among first-year students, some of the heaviest drinking occurs not only during the initial weeks of fall semester, as described above, but also during the initial weeks of spring semester (Del Boca et al. 2004; Tremblay et al. 2010). In contrast, the lightest drinking occurs during exam weeks, both midterms and finals (Del Boca et al. 2004). As described below, research also reveals that the heaviest drinking takes place on holidays and during holiday breaks when students are not on campus.

Holidays and Breaks

Drinking among freshmen peaks during Spring Break, Thanksgiving, Christmas, and New Year’s weeks (Del Boca et al. 2004) and tends to be characterized by binge drinking (Beets et al. 2009; Greenbaum et al. 2005). A study of 21-year-old college students found that compared with a typical nonholiday weekend, more students consumed alcohol and they reached higher BACs on New Year’s Eve, New Year’s Day, July 4th, Spring Break, and graduation (Neighbors et al. 2011).

Drinking and related consequences are higher during Spring Break than the typical week (Beets et al. 2009; Del Boca et al. 2004); however, this is particularly true for students who go on trips with friends (Grekin et al. 2007; Lee et al. 2006, 2009). The highest levels of drinking during Spring Break occur among those who report higher levels of intentions to drink before their trip, those who go on longer trips, and those who previously engaged in more heavy episodic drinking (Patrick and Lee 2012). Notably, however, students who typically drink less experience more negative consequences of drinking during Spring Break (Lee et al. 2009).

21st Birthdays

Extreme drinking and negative alcohol-related consequences also are associated with 21st birthdays (Lewis et al. 2009; Neighbors et al. 2005; Rutledge et al. 2008), which typically occur during students’ college years. Half of 21st-birthday drinkers consume more on this day than any other prior occasion (Rutledge et al. 2008), and students drink more than they anticipate they will at this celebration (Brister et al. 2010). Students who do not typically drink heavily but do so the week of their birthday are most likely to experience higher levels of alcohol-related consequences (Lewis et al. 2009). In addition, 21st-birthday drinking is associated with the highest proportion of drinkers and highest BACs compared with other high-risk times (Neighbors et al. 2011).

Campus Events

Drinking also tends to spike during campus- or university-specific events. For example, during “State Patty’s Day,” a student-constructed, party-focused holiday at Pennsylvania State University, first-year students were more likely to drink and drink heavily (Lefkowitz et al. 2012). On this day, students consumed more alcohol than on other weekend days, even after controlling for gender and drinking motives, and local crime rates increased.

Sporting events also are associated with heavy drinking among college students (Glassman et al. 2010; Neal and Fromme 2007) and also seem to increase risk for consequences. For example, college football homegame days see a 9 percent increase in assaults, a 41 percent increase in arrests for alcohol-related disorderly conduct, and a 76 percent increase in liquor-law violations compared with nongame days (Rees and Schnepel 2009). High-profile sporting events (e.g., winning an NCAA championship) increase game-day drinking on average and more so for heavier and more impulsive drinkers (Neal et al. 2005).

The Transition Out of College and Into Adulthood

Despite the often risky nature of drinking during college, most, although not all, students “mature out” of such behavior (Littlefield et al. 2009). The average decline in drinking behavior following the college years has been attributed to events that are delayed for emerging adults who choose to attend college, namely employment, marriage, and parenthood, each of which may accompany reductions in recreational and social activities that involve drinking (Gotham et al. 2003; O’Malley 2004). Age-related changes in personality also may be associated with reductions in drinking during adulthood (Littlefield et al. 2009). However, students with alcohol use disorder are at higher risk for maintaining problematic drinking patterns: about one-half of students who meet alcohol use disorder criteria at age 19 maintain that status at age 25 (Rohde et al. 2001; Sher and Gotham 1999).

Psychosocial Determinants of Drinking During the College Years

College student drinking is affected by several psychosocial determinants that also influence drinking behavior in similar ways during other developmental periods. For example, like the general populations, college students tend to drink more if they believe drinking will have positive effects and consequences, and they tend to drink less if they have negative expectations about drinking (e.g., Gaher and Simons 2007; Wardell and Read 2013). In addition, how positively or negatively students view the expected effects of alcohol (Gaher and Simons 2007) or view actual recently experienced consequences of drinking (Merrill et al. 2013 b ) are also important predictors of college drinking.

A person’s reasons or motives for drinking also influence their alcohol use. For example, drinking to increase positive affect, called enhancement motives, consistently predicts alcohol use and tends to be linked to negative alcohol consequences indirectly, through higher drinking levels (Magid et al. 2007; Merrill and Read 2010; Read et al. 2003). Meanwhile, drink ing to alleviate negative affect, or coping motives, are directly associated with negative alcohol consequences in college students (Jones et al. 2014; Kassel et al. 2000; Merrill and Read 2010; Merrill et al. 2014). Certain personality characteristics, such as sensation seeking or impulsivity (Diulio et al. 2014; Kazemi et al. 2014 a ) and neuroticism (Martin and Sher 1994; Vollrath and Torgersen 2002), have been linked to increased drinking behavior among college students, although findings are mixed. In addition, a person’s drinking level prior to entering college predicts drinking behavior during college (Sher and Rutledge 2007; Varvil-Weld et al. 2013) .

Below we discuss in more detail common psychosocial determinants that exert influence in a way that is unique to the college years. We highlight exaggerated norms, protective behavioral strategies, and mental health.

Exaggerated Norms

Peers influence young adult drinkers in several direct and indirect ways (Borsari and Carey 2001). Perhaps the most studied has been young adults’ perceptions of drinking norms. In fact, when comparing their own drinking behavior (their personal norms) with their perceptions of how much or how often other students drink (descriptive norms) and their perceptions of whether peers approve of drinking and related behaviors (injunctive norms), young adults tend to see others as drinking more and more approving of drinking (Borsari and Carey 2003). When objective evidence of peer drinking is available, the perceived drinking norm is invariably overestimated (e.g., Carey et al. 2006). Research demonstrates the importance of reference group: norms for close friends are more highly correlated with student drinking behavior than those of more distal student groups (Larimer et al. 2009; Neighbors et al. 2008). However, providing students with corrective feedback on drinking norms for other relevant peer groups, because they often are objectively exaggerated, can promote discrepancies that lead to drinking reductions (Larimer et al. 2009). Descriptive and injunctive norms seem to have unique influences on drinking behavior (Larimer et al. 2004). In fact, descriptive norms have a greater influence when there are also permissive injunctive norms, positive outcome expectancies, and higher identification with the referent group (Neighbors et al. 2010; Rimal 2008). The peer-intensive nature of college life affords many opportunities to affiliate with groups that develop their own normative cultures related to drinking (e.g., Greeks, athletic teams, and clubs). Within the context of the emerging adulthood framework, norms are relevant to the factors of both identity exploration (looking to others in the social environment while figuring out his or her own identity) and self- focus (friends as most influential on behavior during this age) .

Protective Behavioral Strategies

In light of all of the contextual and developmental factors that contribute to risk described above, it is essential that students learn to drink safely (if they choose to drink) when navigating the novel drinking environment of college. However, the extent to which college students acquire and use safe drinking skills varies. Most emerging adults leave home for college before they attain the minimum legal drinking age. Thus, peers and not parents or other adults often serve as the primary sources for learning how to drink. Protective behavioral strategies—techniques that can be used to minimize harm associated with alcohol use such as setting drink limits, consuming nonalcoholic in addition to alcoholic drinks, avoiding drinking games, and using a designated driver—have received an increasing amount of attention in the college-drinking literature over the past few decades. A recent review highlights several studies that consistently reveal that individuals who report using more protective behavioral strategies also report drinking less and/or e xperiencing fewer alcohol-related problems (Pearson 2013).

Mental Health

Approximately three-quarters of lifetime mood or anxiety disorders begin by age 24, coinciding with the typical college years (Kessler et al. 2005), and about 11 percent and 12 percent of U.S. college students meet criteria for mood and anxiety disorders, respectively (Blanco et al. 2008). Unfortunately, few college students use mental health services (e.g., Eisenberg et al. 2011), and research finds an association between mental health problems and heavy episodic drinking (Cranford et al. 2009). Moreover, students with mental health symptoms are more likely to experience problems related to alcohol use than students without such symptoms, regardless of drinking level (Dawson et al. 2005 b ; Dennhardt and Murphy 2011; Kenney and LaBrie 2013; LaBrie et al. 2010; Weitzman 2004). Within the emerging adulthood framework, mental health issues are relevant to the factors of both identity exploration (identity confusion may cause distress) and instability (transitions may be disruptive), as alcohol may be used for self-medication purposes among students high on either dimension.

Intervention Implications

The findings reviewed above have several implications for interventions with the special population of college-aged individuals. In general, a harm prevention/harm reduction approach, as opposed to an abstinence-based approach is considered most appropriate for young people who are developing drinking habits and have not exhibited signs of dependence (Ehret et al. 2013; Marlatt and Witkiewitz 2002). Also, given that aspects of the campus environment constitute risk factors for individual drinkers, it is important to implement not only coordinated alcohol abuse prevention efforts involving community and campus environmental management but also group and individual prevention efforts and to identify drinkers in need of treatment services (Toomey et al. 2013; Wolfson et al. 2012). The next section reviews how prevention and intervention efforts can incor porate the patterns and influences we describe above.

Developmental Factors

Despite the importance of the developmental context to college student drinking, to date, developmental considerations have had limited influence on intervention development. A notable exception is parent-based intervention, which has been well received and shows promise both as a standalone intervention (Ichiyama et al. 2009; Turrisi et al. 2013) and a supplement to student-based interventions (Turrisi et al. 2009). In line with the emerging adulthood concept of “possibilities,” interventions highlighting future academic and occupational decisions also may be useful. An example of this comes from a study that modified a traditional brief motivational intervention to include a supplemental session focused on increasing the salience of academic and career goals and discussed behavior patterns that would assist in meeting those goals (Murphy et al. 2012). Students who received the supplement reported fewer alcohol-related consequences at 1- and 6-month followups compared with students who did not receive the supplement. In addition, interventions can address self-regulatory difficulties associated with incomplete prefrontal control by using mobile technologies, which permit real-time assessment (e.g., Mays et al. 2010) and interventions delivered close to drinking events (e.g., Suffoletto et al. 2012). Such approaches seem to be both feasible and acceptable to college students (Kazemi et al. 2014 b ). Additional adaptation of the content and delivery of interventions based on the developmental context of college drinkers is a promising direction for intervention development.

Environmental and Temporal Factors

Tailoring interventions to address environmental issues of the college setting also may be beneficial. Such interventions include establishing substance-free residential options and changing the academic schedule to ensure that students take classes on Fridays and also in the mornings (DeJong and Langford 2002). Increased regulation and/or detection of alcohol use among underage drinkers in particular may be needed at campus events such as football games. Toomey and colleagues (2007) provide a more detailed review of these and other strategies designed for environmental management.

Event-specific prevention (ESP) is an intervention strategy that addresses temporal determinants of drinking behavior (Neighbors et al. 2007). ESP assumes that knowing when and/or where risky drinking will occur provides an opportunity for its prevention. For example, knowing that 21st birthdays and Spring Break are times of greatest risk suggests that resources should be allocated toward prevention around these times, providing a cost-effective approach to preventing alcohol-related consequences associated with these events (Neighbors et al. 2011, 2012). Finally, early preventive interventions for first-year students transitioning into college may help thwart increases in risky drinking behavior. There is modest support that online educational programs are effective for these students (Hustad et al. 2010; Lovecchio et al. 2010). Further, meta-analytic research suggests that behavioral interventions for first-year college students effectively reduce alcohol consumption and alcohol- related problems, with the extent of reductions dependent on intervention content (e.g., personalized feedback provides better outcomes) (Scott-Sheldon et al. 2014).

Psychosocial Determinants

Many of the psychosocial determinants of drinking during emerging adulthood reviewed above have informed the development of alcohol abuse prevention interventions. For example, correcting exaggerated perceived norms is a well-documented active ingredient of successful risk-reduction programs delivered both in person and by computer (Carey et al. 2010; Doumas et al. 2009; Neighbors et al. 2004; Turrisi et al. 2009). Further, interventions increasingly are i ncorporating protective behavioral strategies (Pearson 2013), which have been shown to mediate intervention effects (Barnett et al. 2007; Larimer et al. 2007; Murphy et al. 2012).

Future Directions for Intervention

Tailoring alcohol risk reduction interventions to students with mental health concerns would be another way to integrat e psychosocial determinants of drinking and the emerging adulthood framework into new interventions. For example, interventions that provide alternatives to substance use for coping with negative mood states could prove fruitful for students high on the instability dimension of emerging adulthood and who are experiencing negative affect related to transitions, or for students who experience identity confusion during this time of exploration. In addition, recent data demonstrate that depression may interfere with intervention-related change (Geisner et al. 2015; Merrill et al. 2014). Although the exact mechanisms of this effect are as yet unknown, it may be beneficial to include in brief interventions components that seek to increase substance-free reinforcement (e.g., Murphy et al. 2012) or that broaden students’ coping skills.

To date, we have no evidence-based interventions to reduce high-risk practices such as pregaming or drinking games (Read 2014). Such interventions might involve education about factors affecting BAC and the biphasic curve to help sensitize some drinkers to the risk of consuming large quantities in a short time; corrective normative feedback about the frequency, intensity, or approval of high-risk behaviors by peers; and/or the provision of protective behavioral strategies specific to refusing opportunities to pregame or learning to play drinking games safely.

Much progress has been made in understanding the risk for alcohol misuse among college students. However, there still is room to understand the developmental, social, and environmental factors influencing college student drinking, to best design interventions that can ultimately reduce harm for this special population.

Disclosures

The authors declare that they have no competing financial interests.

Abar, C.C., and Maggs, J.L. Social influence and selection processes as predictors of normative perceptions and alcohol use across the transition to college. Journal of College Student Development 51(5):496–508, 2010. PMID: 21072330

Abar, C.C., and Turrisi, R. How important are parents during the college years? A longitudinal perspective of indirect influences parents yield on their college teens’ alcohol use. Addictive Behaviors 33(10):1360–1368, 2008. PMID: 18635318

Aertgeerts, B., and Buntinx, F. The relation between alcohol abuse or dependence and academic performance in first-year college students. Journal of Adolescent Health 31(3):223–225, 2002. PMID: 12225733

Arnett, J.J. Emerging adulthood: A theory of development from the late teens through the twenties. American Psychologist 55(5):469–480, 2000. PMID: 10842426

Arnett, J.J. The developmental context of substance use in emerging adulthood. Journal of Drug Issues 35(2):235–254, 2005.

Barnett, N.P.; Murphy, J.G.; Colby, S.M.; and Monti, P.M. Efficacy of counselor vs. computer-delivered intervention with mandated college students. Addictive Behaviors 32(11):2529–2548, 2007. PMID: 17707594

Barnett, N.P.; Orchowski, L.M.; Read, J.P.; and Kahler, C.W. Predictors and consequences of pregaming using day- and week-level measurements. Psychology of Addictive Behaviors 27(4):921–933, 2013. PMID: 23438241

Bechara, A. Decision making, impulse control and loss of willpower to resist drugs: A neurocognitive perspective. Nature Neuroscience 8 (11):1458–1463, 2005. PMID: 16251988

Beets, M.W.; Flay, B.R.; Vuchinich, S.; et al. Longitudinal patterns of binge drinking among first year college students with a history of tobacco use. Drug and Alcohol Dependence 103(1–2):1–8, 2009. PMID: 19423242

Blanco, C.; Okuda, M.; Wright, C.; et al. Mental health of college students and their non-college-attending peers: Results from the National Epidemiologic Study on Alcohol and Related Conditions. Archives of General Psychiatry 65(12):1429–1437, 2008. PMID: 19047530

Borsari, B. Drinking games in the college environment: A review. Journal of Alcohol and Drug Education 48(2):29–51, 2004.

Borsari, B.; Boyle, K.E.; Hustad, J.T.; et al. Drinking before drinking: Pregaming and drinking games in mandated students. Addictive Behaviors 32(11):2694–2705, 2007 a . PMID: 17574344

Borsari, B., and Carey, K. B. Peer influences on college drinking: A review of the research. Journal of Substance Abuse 13(4):391–424, 2001. PMID: 11775073

Borsari, B., and Carey, K. B. Descriptive and injunctive norms in college drinking: A meta-analytic integration. Journal of Studies on Alcohol 64(3):331–341, 2003. PMID: 12817821

Borsari, B.; Murphy, J.G.; and Barnett, N.P. Predictors of alcohol use during the first year of college: Implications for prevention. Addictive Behaviors 32(10):2062–2086, 2007 b . PMID: 17321059

Brenner, J., and Swanik, K. High-risk drinking characteristics in collegiate athletes. Journal of American College Health 56(3):267–272, 2007. PMID: 18089508

Brister, H.A.; Wetherill, R.R.: and Fromme, K. Anticipated versus actual alcohol consumption during 21st birthday celebrations. Journal of Studies on Alcohol and Drugs 71(2):180–183, 2010. PMID: 20230714

Carey, K.B.; Borsari, B.; Carey, M.P.; and Maisto, S.A. Patterns and importance of self-other differences in college drinking norms. Psychology of Addictive Behaviors 20(4):385–393, 2006. PMID: 17176173

Carey, K.B.; Henson, J.M.; Carey, M.P.; and Maisto, S.A. Perceived norms mediate effects of a brief motivational intervention for sanctioned college drinkers. Clinical Psychology 17(1):58–71, 2010. PMID: 22238504

Carter, A.C.; Brandon, K.O.; and Goldman, M.S. The college and noncollege experience: A review of the factors that influence drinking behavior in young adulthood. Journal of Studies on Alcohol and Drugs 71(5):742–750, 2010. PMID: 20731981

Casey, B.J., and Jones, R.M. Neurobiology of the adolescent brain and behavior: Implications for substance use disorders. Journal of the American Academy of Child and Adolescent Psychiatry 49(12):1189–1201, 2010. PMID: 21093769

Chen, C.M.; Dufour, M.C.; and Yi, H.-Y. Alcohol consumption among young adults ages 18–24 in the United States: Results from the 2001–2002 NESARC survey. Alcohol Research and Health 28(4):269–280, 2004.

Colby, S.M.; Colby, J.J.; and Raymond, G.A. College versus the real world: Student perceptions and implications for understanding heavy drinking among college students. Addictive Behaviors 34(1):17–27, 2009. PMID: 18774233

Cranford, J.A.; Eisenberg, D.; and Serras, A.M. Substance use behaviors, mental health problems, and use of mental health services in a probability sample of college students. Addictive Behaviors 34(2):134–145, 2009. PMID: 18851897

Dawson, D.A.; Grant, B.F.; and Stinson, F.S. The AUDIT-C: Screening for alcohol use disorders and risk drinking in the presence of other psychiatric disorders. Comprehensive Psychiatry 46(6):405–416, 2005 b . PMID: 16275207

Dawson, D.A.; Grant, B.F.; Stinson, F.S.; and Chou, P.S. Another look at heavy episodic drinking and alcohol use disorders among college and noncollege youth. Journal of Studies on Alcohol 65(4):477–488, 2004. PMID: 15378804

Dawson, D.A.; Grant, B.F.; Stinson, F.S.; et al. Recovery from DSM-IV alcohol dependence: United States, 2001–2002. Addiction 100 (3):281–292, 2005 a . PMID: 15733237

DeJong, W., and Langford, L.M. A typology for campus-based alcohol prevention: Moving toward environmental management strategies. Journal of Studies on Alcohol (Suppl) 14:140–147, 2002. PMID: 12022720

DeJong, W.; DeRicco, B.; and Schneider, S.K. Pregaming: An exploratory study of strategic drinking by college students in Pennsylvania. Journal of American College Health 58(4):307–316, 2010. PMID: 20159754

Del Boca, F.K.; Darkes, J.; Greenbaum, P.E.; and Goldman, M.S. Up close and personal: Temporal variability in the drinking of individual college students during their first year. Journal of Consulting and Clinical Psychology 72(2):155–164, 2004. PMID: 15065951

Dennhardt, A.A., and Murphy, J.G. Associations between depression, distress tolerance, delay discounting, and alcohol-related problems in European American and African American college students. Psychology of Addictive Behaviors 25(4):595–604, 2011. PMID: 21988480

Diulio, A.R.; Silvestri, M.M.; and Correia, C.J. The role of personality variables in drinking game participation. Addictive Behaviors 39(7):1159–1162, 2014. PMID: 24727112

Doumas, D.M.; McKinley, L.L.; and Book, P. Evaluation of two Web-based alcohol interventions for mandated college students. Journal of Substance Abuse Treatment 36(1):65–74, 2009. PMID: 18657941

Doumas, D.M.; Workman, C.; Smith, D.; and Navarro, A. Reducing high-risk drinking in mandated college students: Evaluation of two personalized normative feedback interventions. Journal of Substance Abuse Treatment 40(4):376–385, 2011. PMID: 21295938

Ehret, P.J.; Ghaidarov, T.M.; and LaBrie, J.W. Can you say no? Examining the relationship between drinking refusal self-efficacy and protective behavioral strategy use on alcohol outcomes. Addictive Behaviors 38(4):1898–1904, 2013. PMID: 23380495

Eisenberg, D.; Hunt, J.; Speer, N.; and Zivin, K. Mental health service utilization among college students in the United States. Journal of Nervous and Mental Disease 199(5):301–308, 2011. PMID: 21543948

Fromme, K.; Corbin, W.R.; and Kruse, M.I. Behavioral risks during the transition from high school to college. Developmental Psychology 44(5):1497–1504, 2008. PMID: 18793080

Gaher, R.M., and Simons, J.S. Evaluations and expectancies of alcohol and marijuana problems among college students. Psychology of Addictive Behaviors 21(4):545–554, 2007. PMID: 18072837

Geisner, I.M.; Varvil-Weld, L.; Mittmann, A.J.; et al. Brief Web-based intervention for college students with comorbid risky alcohol use and depressed mood: Does it work and for whom? Addictive Behaviors 42:36–43, 2015.

Glassman, T.J.; Dodd, V.J.; Sheu, J. J. et al. Extreme ritualistic alcohol consumption among college students on game day. Journal of American College Health 58(5):413–423, 2010. PMID: 20304753

Glindemann, K.E.; Ehrhart, l.J.; Maynard, M.L.; and Geller, E.S. Letter to the editor: Alcohol front-loading among college students: Exploring the need for prevention intervention. Journal of Alcohol and Drug Education 50(2):5–13, 2006.

Gotham, H.J.; Sher, K.J.; and Wood, P.K. Alcohol involvement and developmental task completion during young adulthood. Journal of Studies on Alcohol 64(1):32–42, 2003. PMID: 12608481

Greenbaum, P.E.; Del Boca, F.K.; Darkes, J.; et al. Variation in the drinking trajectories of freshmen college students. Journal of Consulting and Clinical Psychology 73(2):229–238, 2005. PMID: 15796630

Grekin, E.R.; Sher, K.J.; and Krull, J.L. College spring break and alcohol use: Effects of spring break activity. Journal of Studies on Alcohol and Drugs 68(5):681–688, 2007. PMID: 17690801

Hingson, R.; Zha, W.; and Weitzman, E.R. Magnitude of and trends in alcohol-related mortality and morbidity among U.S. college students ages 18–24, 1998–2005. Journal of Studies on Alcohol and Drugs (Suppl.) 16:12–20, 2009. PMID: 19538908

Hoeppner, B.B.; Barnett, N.P.; Jackson, K.M.; et al. Daily college student drinking patterns across the first year of college. Journal of Studies on Alcohol and Drugs 73(4):613–624, 2012. PMID: 22630800

Hughes, K.; Anderson, Z.; Morleo, M.; and Bellis, M.A. Alcohol, nightlife and violence: The relative contributions of drinking before and during nights out to negative health and criminal justice outcomes. Addiction 103(1):60–65, 2008. PMID: 17996008

Hummer, J.F.; Napper, L.E.; Ehret, P.E.; and LaBrie, J.W. Event-specific risk and ecological factors associated with prepartying among heavier drinking college students. Addictive Behaviors 38(3):1620–1628, 2013. PMID: 23254208

Hustad, J.T.P.; Barnett, N.P.; Borsari, B.; and Jackson, K.M. Web-based alcohol prevention for incoming college students: A randomized controlled trial. Addictive Behaviors 35(3):183–189, 2010. PMID: 19900763

Ichiyama, M.A.; Fairlie, A.M.; Wood, M.D.; et al. A randomized trial of a parent-based intervention on drinking behavior among incoming college freshmen. Journal of Studies on Alcohol and Drugs (Suppl.) 16:67–76, 2009. PMID: 19538914

Ingersoll, K.S.; Ceperich, S.D.; Nettleman, M.D.; and Johnson, B.A. Risk drinking and contraception effectiveness among college women. Psychology & Health 23(8):965–981, 2008. PMID: 25160922

Johnson, S.B.; Blum, R.W.; and Giedd, J.N. Adolescent maturity and the brain: The promise and pitfalls of neuroscience research in adolescent health policy. Journal of Adolescent Health 45(3):216–221, 2009. PMID: 19699416

Johnston, L.D.; O’Malley, P.M.; Bachman, J.G.; et al. Monitoring the Future National Survey Results on Drug Use, 1975–2013: Volume II, College Students and Adults Ages 19–55 . Ann Arbor: University of Michigan, 2014.

Jones, K.A.; Chryssanthakis, A.; and Groom, M.J. Impulsivity and drinking motives predict problem behaviours relating to alcohol use in University students. Addictive Behaviors 39(1):289–296, 2014. PMID: 24176824

Kassel, J.D.; Jackson, S.I.; and Unrod, M. Generalized expectancies for negative mood regulation and problem drinking among college students. Journal of Studies on Alcohol 61(2):332–340, 2000. PMID: 10757145

Kazemi, D.M.; Cochran, A.R.; Kelly, J.F.; et al. Integrating mHealth mobile applications to reduce high risk drinking among underage students. Health Education Journal 73:262–273, 2014 b .

Kazemi, D.M.; Flowers, C.; Shou, Q.; et al. Personality risk for alcohol consequences among college freshmen. Journal of Psychosocial Nursing and Mental Health Services 52(7):38–45, 2014 a . PMID: 24627978

Kenney, S.R.; Hummer, J.F.; and LaBrie, J.W. An examination of prepartying and drinking game playing during high school and their impact on alcohol-related risk upon entrance into college. Journal of Youth and Adolescence 39(9):999–1011, 2010. PMID: 19904593

Kenney, S.R., and LaBrie, J.W. Use of protective behavioral strategies and reduced alcohol risk: Examining the moderating effects of mental health, gender, and race. Psychology of Addictive Behaviors 27(4):997–1009, 2013. PMID: 24079648

Kessler, R.C.; Berglund, P.; Demler, O.; et al. Lifetime prevalence and age-of-onset distributions of DSM-IV disorders in the National Comorbidity Survey Replication. Archives of General Psychiatry 62(6):593–602, 2005. PMID: 15939837

Knight, J.R.; Wechsler, H.; Kuo, M.; et al. Alcohol abuse and dependence among U.S. college students. Journal of Studies on Alcohol 63(3):263–270, 2002. PMID:

Kuntsche, E.; Knibbe, R.; Gmel, G.; and Engels, R. Why do young people drink? A review of drinking motives. Clinical Psychology Review 25(7):841–861, 2005. PMID: 16095785

Labhart, F.; Graham, K.; Wells, S.; and Kuntsche, E. Drinking before going to licensed premises: An event-level analysis of predrinking, alcohol consumption, and adverse outcomes. Alcoholism: Clinical and Experimental Research 37(2):284–291, 2013. PMID: 23136847

LaBrie, J.W.; Ehret, P.J.; and Hummer, J.F. Are they all the same? An exploratory, categorical analysis of drinking game types. Addictive Behaviors 38(5):2133–2139, 2013. PMID: 23435275

LaBrie, J.W.; Hummer, J.; Kenney, S.; et al. Identifying factors that increase the likelihood for alcohol-induced blackouts in the prepartying context. Substance Use & Misuse 46(8):992–1002, 2011. PMID: 21222521

LaBrie, J.W.; Kenney, S.R.; and Lac, A. The use of protective behavioral strategies is related to reduced risk in heavy drinking college students with poorer mental and physical health. Journal of Drug Education 40(4):361–378, 2010. PMID: 21381463

LaBrie, J.W., and Pedersen, E.R. Prepartying promotes heightened risk in the college environment: An event-level report. Addictive Behaviors 33(7):955–959, 2008. PMID: 18387749

Larimer, M.E.; Kaysen, D.L.; Lee, C.M.; et al. Evaluating level of specificity of normative referents in relation to personal drinking behavior. Journal of Studies on Alcohol and Drugs (Suppl 16):115–121, 2009. PMID: 19538919

Larimer, M.E.; Lee, C.M.; Kilmer, J.R.; et al. Personalized mailed feedback for college drinking prevention: A randomized clinical trial. Journal of Consulting and Clinical Psychology 75(2):285–293, 2007. PMID: 17469886

Larimer, M.E.; Turner, A.P.; Mallett, K.A.; and Geisner, I.M. Predicting drinking behavior and alcohol-related problems among fraternity and sorority members: Examining the role of descriptive and injunctive norms. Psychology of Addictive Behaviors 18(3):203–212, 2004. PMID: 15482075

Lee, C.M.; Lewis, M.A.; and Neighbors, C. Preliminary examination of Spring Break alcohol use and related consequences. Psychology of Addictive Behaviors 23(4):689–694, 2009. PMID: 20025375

Lee, C.M.; Maggs, J.L.; and Rankin, L.A. Spring Break trips as a risk factor for heavy alcohol use among first-year college students. Journal of Studies on Alcohol 67(6):911–916, 2006. PMID: 17061009

Lefkowitz, E.S.; Patrick, M.E.; Morgan, N.R.; et al. State Patty’s Day: College student drinking and local crime increased on a student-constructed holiday. Journal of Adolescent Research 27(3):323–350, 2012. PMID: 22685369

Lewis, M.A.; Lindgren, K.;P.; Fossos, N.; et al. Examining the relationship between typical drinking behavior and 21st birthday drinking behavior among college students: Implications for event-specific prevention. Addiction 104(5):760–767, 2009. PMID: 19344447

Littlefield, A.K.; Sher, K.J.; and Wood, P.K. Is “maturing out” of problematic alcohol involvement related to personality change? Journal of Abnormal Psychology 118(2):360–374, 2009. PMID: 19413410

Lovecchio, C.P.; Wyatt, T.M.; and DeJong, W. Reductions in drinking and alcohol-related harms reported by first-year college students taking an online alcohol education course: A randomized trial. Journal of Health Communication 15(7):805–819, 2010. PMID: 21104507

Magid, V.; Maclean, M.G.; and Colder, C.R. Differentiating between sensation seeking and impulsivity through their mediated relations with alcohol use and problems. Addictive Behaviors 32(10):2046–2061, 2007. PMID: 17331658

Marlatt, G.A., and Witkiewitz, K. Harm reduction approaches to alcohol use: Health promotion, prevention, and treatment. Addictive Behaviors 27(6):867–886, 2002. PMID: 12369473

Martin, E.D., and Sher, K.J. Family history of alcoholism, alcohol use disorders and the five-factor model of personality. Journal of Studies on Alcohol 55(1):81–90, 1994. PMID: 8189730

Mays, D.; Cremeens, J.; Usdan, S.; et al. The feasibility of assessing alcohol use among college students using wireless mobile devices: Implications for health education and behavioural research. Health Education Journal 69:311–320, 2010.

Merrill, J.E., and Read, J.P. Motivational pathways to unique types of alcohol consequences. Psychology of Addictive Behaviors 24(4):705–711, 2010. PMID: 20822194

Merrill, J.E.; Read, J.P.; and Barnett, N.P. The way one thinks affects the way one drinks: Subjective evaluations of alcohol consequences predict subsequent change in drinking behavior. Psychology of Addictive Behaviors 27(1):42–51, 2013 b . PMID: 22985054

Merrill, J.E.; Reid, A.E.; Carey, M.P.; and Carey, K.B. Gender and depression moderate response to brief motivational intervention for alcohol misuse among college students. Journal of Consulting and Clinical Psychology 82(6):984–992, 2014. PMID: 24865872

Merrill, J.E.; Vermont, L.N.; Bachrach, R.L.; and Read, J.P. Is the pregame to blame? Event-level associations between pregaming and alcohol-related consequences. Journal of Studies on Alcohol and Drugs 74(5):757–764, 2013 a . PMID: 23948535

Merrill, J.E., Wardell, J.D., and Read, J.P. Is expectancy reality? Associations between tension reduction beliefs and mood following alcohol consumption. Experimental and Clinical Psychopharmacology 17(6):434–444, 2009. PMID: 19968408

Merrill, J.E.; Wardell, J.D.; and Read, J.P. Drinking motives in the prospective prediction of unique alcohol-related consequences in college students. Journal of Studies on Alcohol and Drugs 75(1):93–102, 2014. PMID: 24411801

Mundt, M.P.; Zakletskaia, L.I.; Brown, D.D.; and Fleming, M.F. Alcohol-induced memory blackouts as an indicator of injury risk among college drinkers. Injury Prevention 18(1):44–49, 2012. PMID: 21708813

Murphy, J.G.; Dennhardt, A.A.; Skidmore, J.R.; et al. A randomized controlled trial of a behavioral economic supplement to brief motivational interventions for college drinking. Journal of Consulting and Clinical Psychology 80(5):876–886, 2012. PMID: 22663899

National Center for Education Statistics (NCES). Enrollment Rates of 18- to 24-year-olds in Degree-granting Institutions, by Level of Institution and Sex and Race/Ethnicity of Student: 1967 through 2012, Washington, DC: NCES, 2013. Available at: https://nces.ed.gov/programs/digest/d13/tables/dt13_302.60.asp . Accessed October 3, 2014.

Neal, D.J., and Fromme, K. Hook ’em horns and heavy drinking: Alcohol use and collegiate sports. Addictive Behaviors 32(11):2681–2693, 2007. PMID: 17662537

Neal, D.J.; Sugarman, D.E.; Hustad, J.T.; et al. It’s all fun and games . . . or is it? Collegiate sporting events and celebratory drinking. Journal of Studies on Alcohol 66(2):291–294, 2005. PMID: 15957681

Neighbors, C.; Atkins, D.C.; Lewis, M.A.; et al. Event-specific drinking among college students. Psychology of Addictive Behaviors 25(4):702–707, 2011. PMID: 21639597

Neighbors, C.; LaBrie, J.W.; Hummer, J.F.; et al. Group identification as a moderator of the relationship between perceived social norms and alcohol consumption. Psychology of Addictive Behaviors 24(3):522–528, 2010. PMID: 20853938

Neighbors, C.; Larimer, M.E.; and Lewis, M.A. Targeting misperceptions of descriptive drinking norms: Efficacy of a computer-delivered personalized normative feedback intervention. Journal of Consulting and Clinical Psychology 72(3):434–447, 2004. PMID: 15279527

Neighbors, C.; Lee, C.M.; Atkins, D.C.; et al. A randomized controlled trial of event-specific prevention strategies for reducing problematic drinking associated with 21st birthday celebrations. Journal of Consulting and Clinical Psychology 80(5):850–862, 2012. PMID: 22823855

Neighbors, C.; O’Connor, R.M.; Lewis, M.A.; et al. The relative impact of injunctive norms on college student drinking: The role of reference group. Psychology of Addictive Behaviors 22(4):576–581, 2008. PMID: 19071984

Neighbors, C.; Spieker, C.J.; Oster-Aaland, L.; et al. Celebration intoxication: An evaluation of 21st birthday alcohol consumption. Journal of American College Health 54(2):76–80, 2005. PMID: 16255318

Neighbors, C.; Walters, S.T.; Lee, C.M.; et al. Event-specific prevention: Addressing college student drinking during known windows of risk. Addictive Behaviors 32(11):2667–2680, 2007. PMID: 17616260

O’Brien, M.C.; McNamara, R.S.; McCoy, T.P.; et al. Alcohol-related injury among Greek-letter college students: Defining a target population for secondary prevention. Journal of Health Psychology 18(4):461–469, 2013. PMID: 22689586

O’Malley, P.M. Maturing out of problematic alcohol use. Alcohol Research and Health 28(4):202–204, 2004.

Park, A.; Sher, K.J.; and Krull, J.L. Risky drinking in college changes as fraternity/sorority affiliation changes: A person-environment perspective. Psychology of Addictive Behaviors 22(2):219–229, 2008. PMID: 18540719

Paschall, M.J.; Kypri, K.; and Saltz, R.F. Friday class and heavy alcohol use in a sample of New Zealand college students. Journal of Studies on Alcohol 67(5):764–769, 2006. PMID: 16847546

Patrick, M.E., and Lee, C.M. Daily variations in Spring Break alcohol and sexual behaviors based on intentions, perceived norms, and daily trip context. Journal of Studies on Alcohol and Drugs 73(4):591–596, 2012. PMID: 22630797

Paves, A.P.; Pedersen, E.R.; Hummer, J.F.; and LaBrie, J.W. Prevalence, social contexts, and risks for prepartying among ethnically diverse college students. Addictive Behaviors 37(7):803–810, 2012. PMID: 22464005

Pearson, M.R. Use of alcohol protective behavioral strategies among college students: A critical review. Clinical Psychology Review 33(8):1025–1040, 2013. PMID: 24036089

Pedersen, E.R., and LaBrie, J. Partying before the party: Examining prepartying behavior among college students. Journal of American College Health 56(3):237–245, 2007. PMID: 18089504

Pedersen, E.R.; LaBrie, J.W.; and Kilmer, J.R. Before you slip into the night, you’ll want something to drink: Exploring the reasons for prepartying behavior among college student drinkers. Issues in Mental Health Nursing 30(6):354–363, 2009. PMID: 19499435

Pedersen, E.R., and LaBrie, J.W. Normative misperceptions of drinking among college students: A look at the specific contexts of prepartying and drinking games. Journal of Studies on Alcohol and Drugs 69(3):406–411, 2008. PMID: 18432383

Presley, C.A.; Meilman, P.W.; and Leichliter, J.S. College factors that influence drinking. Journal of Studies on Alcohol Suppl. 14:82–90, 2002. PMID: 12022732

Ray, A.E.; Stapleton, J.; Turrisi, R.; and Mun, E.Y. Drinking game play among first-year college student drinkers: An event-specific analysis of the risk for alcohol use and problems. American Journal of Drug and Alcohol Abuse 40(5):353–358, 2014. PMID: 25192202

Read, J.P. What’s in a game? Future directions for the assessment and treatment of drinking games. American Journal of Drug and Alcohol Abuse 40(5):415–418, 2014. PMID: 25192211

Read, J.P.; Kahler, C.W.; Strong, D.R.; and Colder, C.R. Development and preliminary validation of the Young Adult Alcohol Consequences Questionnaire. Journal of Studies on Alcohol 67(1):169–177, 2006. PMID: 16536141

Read, J.P.; Merrill, J.E.; and Bytschkow, K. Before the party starts: Risk factors and reasons for “pregaming” in college students. Journal of American College Health 58(5):461–472, 2010. PMID: 20304758

Read, J.P.; Wardell, J.D.; and Bachrach, R.L. Drinking consequence types in the first college semester differentially predict drinking the following year. Addictive Behaviors 38(1):1464–1471, 2013. PMID: 23017734

Read, J.P.; Wood, M.D.; Kahler, C.W.; et al. Examining the role of drinking motives in college student alcohol use and problems. Psychology of Addictive Behaviors 17(1):13–23, 2003. PMID: 12665077

Rees, D.I., and Schnepel, K.T. College football games and crime. Journal of Sports Economics 10:68–87, 2009.

Rimal, R.N. Modeling the relationship between descriptive norms and behaviors: A test and extension of the theory of normative social behavior (TNSB). Health Communication 23(2):103–116, 2008. PMID: 18443998

Rohde, P.; Lewinsohn, P.M.; Kahler, C.W.; et al. Natural course of alcohol use disorders from adolescence to young adulthood. Journal of the American Academy of Child and Adolescent Psychiatry 40(1):83–90, 2001. PMID: 11195569

Rutledge, P.C.; Park, A.; and Sher, K.J. 21st birthday drinking: Extremely extreme. Journal of Consulting and Clinical Psychology 76(3):511–516, 2008. PMID: 18540744

Schulenberg, J.E., and Maggs, J.L. A developmental perspective on alcohol use and heavy drinking during adolescence and the transition to young adulthood. Journal of Studies on Alcohol (Suppl 14):54–70, 2002. PMID: 12022730

Schulenberg, J.E.; Maggs, J.L.; Long, S.W.; et al. The problem of college drinking: Insights from a developmental perspective. Alcoholism: Clinical and Experimental Research 25(3):473–477, 2001. PMID: 11290861

Schwartz, S.J.; Forthun, L.F.; Ravert, R.D.; et al. Identity consolidation and health risk behaviors in college students. American Journal of Health Behavior 34(2):214–224, 2010. PMID: 19814601

Scott-Sheldon, L.A.J.; Carey, K.B.; Elliott, J.C.; et al. Efficacy of alcohol interventions for first-year college students: A meta-analytic review of randomized controlled trials. Journal of Consulting and Clinical Psychology 82(2):177–188, 2014. PMID: 24447002

Sher, K.J., and Gotham, H.J. Pathological alcohol involvement: A developmental disorder of young adulthood. Development and Psychopathology 11(4):933–956, 1999. PMID: 10624733

Sher, K.J., and Rutledge, P.C. Heavy drinking across the transition to college: Predicting first-semester heavy drinking from precollege variables. Addictive Behaviors 32(4):819–835, 2007. PMID: 16860940

Slutske, W.S. Alcohol use disorders among US college students and their non-college-attending peers. Archives of General Psychiatry 62(3):321–327, 2005. PMID: 15753245

Slutske, W.S.; Hunt-Carter, E.E.; Nabors-Oberg, R.E.; et al. Do college students drink more than their non-college-attending peers? Evidence from a population-based longitudinal female twin study. Journal of Abnormal Psychology 113(4):530–540, 2004. PMID: 15535786

Substance Abuse and Mental Health Services Administration (SAMHSA). Results from the 2013 National Survey on Drug Use and Health: Summary of National Findings. Rockville, MD: SAMHSA, 2014

Suffoletto, B.; Callaway, C.; Kristan, J.; et al. Text-message-based drinking assessments and brief interventions for young adults discharged from the emergency department. Alcoholism: Clinical and Experimental Research 36(3):552–560, 2012. PMID: 22168137

Task Force of the National Advisory Council on Alcohol Abuse and Alcoholism. A Call to Action: Changing the Culture of Drinking at U.S. Colleges. NIH Publication No. 02–5010. Rockville, MD: National Institute on Alcohol Abuse and Alcoholism, 2002.

Toomey, T.L.; Lenk, K.M.; and Wagenaar, A.C. Environmental policies to reduce college drinking: An update of research findings. Journal of Studies on Alcohol and Drugs 68(2):208 – 219, 2007. PMID: 17286339

Toomey, T.L.; Nelson, T.F.; Winters, K.C.; et al. Characterizing college systems for addressing student alcohol use: Latent class analysis of U.S. four-year colleges. Journal of Studies on Alcohol and Drugs 74(5):777 – 786, 2013. PMID: 23948538

Tremblay, P.F.; Graham, K.; Wells, S.; et al. When do first-year college students drink most during the academic year? An internet-based study of daily and weekly drinking. Journal of American College Health 58(5):401 – 411, 2010. PMID: 20304752

Turrisi, R.; Larimer, M.E.; Mallett, K.A.; et al. A randomized clinical trial evaluating a combined alcohol intervention for high-risk college students. Journal of Studies on Alcohol and Drugs 70(4):555 – 567, 2009. PMID: 19515296

Turrisi, R.; Mallett, K.A.; Cleveland, M.J.; et al. Evaluation of timing and dosage of a parent-based intervention to minimize college students’ alcohol consumption. Journal of Studies on Alcohol and Drugs 74(1):30 – 40, 2013. PMID: 23200148

Turrisi, R., and Ray, A.E. Sustained parenting and college drinking in first-year students. Developmental Psychobiology 52(3):286 – 294, 2010. PMID: 20213752

Upcraft, M.L. Insights from theory: Understanding first-year student development. First-Year Academic Advising: Patterns in the Present, Pathways to the Future 18:15 – 24, 1995.

Valliant, P.M., and Scanlan, P. Personality, living arrangements, and alcohol use by first year university students. Social Behavior and Personality 24(2):151 – 156, 1996.

Varvil-Weld, L.; Mallett, K.A.; Turrisi, R.; et al. Are certain college students prone to experiencing excessive alcohol-related consequences? Predicting membership in a high-risk subgroup using pre-college profiles. Journal of Studies on Alcohol and Drugs 74(4):542 – 551, 2013. PMID: 23739017

Vollrath, M., and Torgersen, S. Who takes health risks? A probe into eight personality types. Personality and Individual Differences 32(7):1185 – 1197, 2002.

Walls, T.A.; Fairlie, A.M.; and Wood, M.D. Parents do matter: A longitudinal two-part mixed model of early college alcohol participation and intensity. Journal of Studies on Alcohol and Drugs 70(6):908 – 918, 2009. PMID: 19895767

Ward, R.M.; Bonar, R.N.; Taylor, E.A.; et al. Thursday drinking and academic load among college women. Journal of Studies on Alcohol and Drugs 74(6):941 – 949, 2013. PMID: 24172122

Wardell, J.D., and Read, J.P. Alcohol expectancies, perceived norms, and drinking behavior among college students: Examining the reciprocal determinism hypothesis. Psychology of Addictive Behaviors 27(1):191 – 196, 2013. PMID: 23088403

Wechsler, H.; Lee, J.E.; Kuo, M.; et al. Trends in college binge drinking during a period of increased prevention efforts. Findings from 4 Harvard School of Public Health College Alcohol Study surveys: 1993–2001. Journal of American College Health 50(5):203 – 217, 2002. PMID: 11990979

Weitzman, E.R. Poor mental health, depression, and associations with alcohol consumption, harm, and abuse in a national sample of young adults in college. Journal of Nervous and Mental Disease 192(4):269 – 277, 2004. PMID: 15060400

Wells, S.; Graham, K.; and Purcell, J. Policy implications of the widespread practice of “pre-drinking” or “pre-gaming” before going to public drinking establishments: Are current prevention strategies backfiring? Addiction 104(1):4 – 9, 2009. PMID: 19133882

White, A.M., and Hingson, R. The burden of alcohol use: Excessive alcohol consumption and related consequences among college students. Alcohol Research: Current Reviews 35(2):201 – 218, 2013. PMID: 24881329

White, A.M.; Jamieson-Drake, D.W.; and Swartzwelder, H.S. Prevalence and correlates of alcohol-induced blackouts among college students: Results of an e-mail survey. Journal of American College Health 51(3):117 – 119, 122 – 131, 2002. PMID: 12638993

White, A.M.; Kraus, C.L.; and Swartzwelder, H.S. Many college freshmen drink at levels far beyond the binge threshold. Alcoholism: Clinical and Experimental Research 30(6):1006 – 1010, 2006. PMID: 16737459

Willoughby, B.J., and Carroll, J.S. The impact of living in co-ed resident halls on risk-taking among college students. Journal of American College Health 58(3):241 – 246, 2009. PMID: 19959438

Wolaver, A.M. Effects of heavy drinking in college on study effort, grade point average, and major choice. Contemporary Economic Policy 20(4):415, 2002.

Wolfson, M.; Champion, H.; McCoy, T.P.; et al. Impact of a randomized campus/ community trial to prevent high-risk drinking among college students. Alcoholism: Clinical and Experimental Research 36(10):1767 – 1778, 2012. PMID: 22823091

Wood, M.D.; Read, J.P.; Mitchell, R.E.; and Brand, N.H. Do parents still matter? Parent and peer influences on alcohol involvement among recent high school graduates. Psychology of Addictive Behaviors 18(1):19 – 30, 2004. PMID: 15008682

Wood, P.K.; Sher, K.J.; Erickson, D.J.; and DeBord, K.A. Predicting academic problems in college from freshman alcohol involvement. Journal of Studies on Alcohol 58(2):200 – 210, 1997. PMID: 9065898

Wood, P.K.; Sher, K.J.; and Rutledge, P.C. College student alcohol consumption, day of the week, and class schedule. Alcoholism: Clinical and Experimental Research 31(7):1195 – 1207, 2007. PMID: 17451400

Yusko, D.A.; Buckman, J.F.; White, H.R.; and Pandina, R.J. Alcohol, tobacco, illicit drugs, and performance enhancers: A comparison of use by college student athletes and nonathletes. Journal of American College Health 57(3):281 – 290, 2008. PMID: 18980883

Zamboanga, B.L.; Calvert, B.D.; O’Riordan, S.S.; and McCollum, E.C. Ping-pong, endurance, card, and other types of drinking games: Are these games of the same feather? Journal of Alcohol and Drug Education 51(2):26 – 39, 2007 b . PMID:

Zamboanga, B.L.; Horton, N.J.; Tyler, K.M.; et al. The utility of the AUDIT in screening for drinking game involvement among female college students. Journal of Adolescent Health 40(4):359 – 361, 2007 a . PMID: 17367733

Zamboanga, B.L.; Leitkowski, L.K.; Rodríguez, L.; and Cascio, K.A. Drinking games in female college students: More than just a game? Addictive Behaviors 31(8):1485 – 1489, 2006. PMID: 16364557

Zamboanga, B.L.; Olthuis, J.V.; Horton, N.J.; et al. Where’s the house party? Hazardous drinking behaviors and related risk factors. Journal of Psychology 143(3):228 – 244, 2009. PMID: 19455853

Zamboanga, B.L.; Pearce, M.W.; Kenney, S.R.; et al. Are “extreme consumption games” drinking games? Sometimes it’s a matter of perspective. American Journal of Drug and Alcohol Abuse 39(5):275 – 279, 2013. PMID: 23968169

Zamboanga, B.L.; Schwartz, S.J.; Van Tyne, K. et al. Drinking game behaviors among college students: How often and how much? American Journal of Drug and Alcohol Abuse 36(3):175 – 179, 2010. PMID: 20465376

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Social Threat Reduces Alcohol Consumption among College Students

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Jacqueline Hua, Angela E Johnson, Sofia Pino, David Olson, Tony Nguyen, Lacye Lawson, Brendan Bedolla, Jennifer L Howell, Social Threat Reduces Alcohol Consumption among College Students, Alcohol and Alcoholism , Volume 57, Issue 4, July 2022, Pages 508–512, https://doi.org/10.1093/alcalc/agac001

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Past research suggests that people report a greater desire to consume alcohol when they experience social threat—or threats to their social selves, such as social exclusion. Nevertheless, experimental research on the role of social threat in alcohol consumption is limited. The present study examined the causal relationship between social threat and wine consumption.

Undergraduate students ( N  = 83; M age  = 21.8 years old, SD age  = 1.62 years old; 72.3% women; 61.4% Latinx/Hispanic) participated in a study under the pretense that they were in a focus group gauging students’ opinions of a bar being constructed at their university. During the study, participants and two confederate researchers completed a group activity in which they selected design elements for the bar. Participants were randomly assigned to one of two conditions. In the social threat condition, confederates rejected participants’ design choices and socially excluded them during a follow-up task. In the social acceptance condition, confederates supported participants’ choices and did not socially exclude them. All participants then completed a wine taste test.

Contrary to predictions, an independent-samples t-test revealed that participants who experienced social threat consumed significantly less wine than those who were socially accepted, t (81) = −2.22, P  = 0.03, d  = −0.49. Furthermore, a linear regression test revealed that this effect persisted even when controlling for typical alcohol-consumption behavior, b =  56.09 , t  = −2.50, P  = 0.02, d  = −0.61.

The relationship between social threat and alcohol consumption may be more nuanced than anticipated. Discussion centers around two potential moderators including positive affect and identity.

Consuming alcohol can have negative health consequences for college students, such as impairments to cognitive functioning, increased risk of physical assault and even death ( Hingson et al. , 2009 ; White and Hingson, 2013 ). Nevertheless, one review found that 45% of college students reported past-month heavy episodic drinking, and 22% of students admitted to driving while intoxicated ( Hingson et al. , 2009 ). Past correlational research suggests that one important factor in alcohol consumption is social threat (e.g. Kuntsche et al. , 2005 ; Kraemer et al. , 2015 ; Laws et al. , 2017 ). Nevertheless, experimental research on the relationship between social threat and alcohol consumption is limited and has yielded mixed findings (e.g. Bacon et al. , 2015 ; Cloutier et al. , 2021 ). Thus, the present study examines the causal role of social threat in alcohol consumption among college students.

It is well-established that alcohol consumption is a socially driven behavior ( Senchak et al. , 1998 ). For instance, college students are more likely to have multiple drinks when they are in the presence of friends as compared to when they are alone ( Varela and Pritchard, 2011 ). Alcohol consumption is also associated with social norms surrounding drinking, such that college students consume more alcohol to the extent that they perceive their peers are engaging in and approving of this behavior ( Halim et al. , 2012 ). Moreover, the behavior of one’s neighbors in a dormitory environment can influence drinking behavior. For example, evidence suggests that on-campus-living-community norms for drinking may predict how much students drink ( Kenney et al. , 2017 ). Furthermore, one study found that college students’ alcohol consumption is positively associated with their sense of belonging to their college campus, which may implicate students’ desire to fit in with their peers as a reason for their alcohol consumption ( Johnson et al. , 2005 ).

Another social factor that may play an important a role in alcohol consumption behavior is social threat ( Hales et al. , 2015 ). Social threat refers to acute threats to the ‘social self’ such as social exclusion, rejection or ostracism ( Gruenewald et al. , 2004 ). Experiencing social threat is stressful ( Gruenewald et al. , 2004 ) and can motivate people to engage in behaviors to reduce or eliminate threat, including alcohol use ( Hales et al. , 2015 ). Indeed, research finds that college students use alcohol to cope with social anxiety ( Norberg et al. , 2010 ) and social drinking motives—including motives to avoid social threat—positively predict heavy episodic drinking among adolescents ( Schelleman-Offermans et al. , 2011 ). Furthermore, heightened attention to social threat is a risk factor for alcohol use disorders ( Bacon and Ham, 2010 ) and social anxiety is positively associated with alcohol-related problems among college students ( Buckner et al. , 2006 ).

Given the negative health implications of alcohol consumption ( Hingson et al. , 2009 ), it is important to examine factors that underlie this negative health behavior. Although past studies generally suggest that social threat is associated with greater alcohol consumption, research on this topic has been largely correlational (e.g. Kuntsche et al. , 2005 ; Kraemer et al. , 2015 ; Laws et al. , 2017 ) and it remains unclear whether people consume more alcohol in response to social threat or whether people who consume more alcohol have a greater likelihood of experiencing social threat. Thus, research is needed to examine the causality and directionality of this relationship.

Perhaps the most relevant work thus far are four experimental studies investigating the effect of social threat on alcohol consumption. A study by Rabinovitz (2014) suggests that social threat may increase alcohol consumption. Researchers manipulated social exclusion by having college students complete personality questionnaires and giving them feedback that they had a personality type that would hinder their relationships in life. Subsequently, people were more likely to consume alcohol ( Rabinovitz, 2014 ). Although informative, the manipulation from this study limits the generalizability of the results to social threat stemming from false personality feedback and not social interaction.

In contrast, Bacon et al. (2015) suggests that exclusion might decrease alcohol consumption, at least among women. In their study, a very small sample of students ( N  = 40) were socially included or excluded during a virtual ball-toss game (Cyberball) before completing a beer taste test. They found that women (but not men) who were socially excluded consumed significantly less beer than those who were included ( Bacon et al. , 2015 ). Although informative, the study had low power to detect effects of social exclusion (~10 people per cell in a 2 × 2 ANOVA), suggesting replication would be useful before conclusions can be drawn about the size and nature of the effect.

An additional two studies suggest that there is no relationship between social threat and alcohol consumption. First, Cloutier et al. (2021) instructed participants to imagine that they overheard peers criticizing their appearance and behavior at a social event before reporting their willingness to use alcohol. The results revealed that imagining social rejection did not influence willingness to use alcohol ( Cloutier et al. , 2021 ). Second, Bacon and Engerman (2018 ) manipulated ostracism by having research confederates socially exclude a very small group of participants ( N  = 33, approximately eight people per cell in a 2 × 2 ANOVA) during a social interaction. In contrast to previous findings, the results revealed that alcohol consumption did not differ as a function of ostracism, though there was a nonsignificant trend for those in the ostracism condition to drink more beer than those in the control condition ( Bacon and Engerman, 2018 ).

In sum, the current research on the causal role of social threat in alcohol consumption is mixed in its findings with some studies suggesting that social threat increases alcohol consumption ( Rabinovitz, 2014 ), others suggesting it might decrease consumption among women ( Bacon et al. , 2015 ), and others finding no significant relationship ( Bacon and Engerman, 2018 ). Moreover, current experimental studies have important methodological limitations such as the inclusion of small, select samples of mostly White college students, a large focus on consumption of beer, and some social threat manipulations that have limited ecological validity (e.g. imagining exclusion). Thus, using a highly ecologically valid social threat manipulation, the present study aims to bridge these gaps in the literature by examining the causal role of social threat on alcohol consumption among a diverse sample of college students engaged in a social interaction. Specifically, we examine whether experiencing social threat influences wine consumption—an under-studied yet common type of alcohol consumption among college students ( Chen et al. , 2004 ; dePyssler et al. , 2005 ). Given past research which suggests a positive relationship between social threat and alcohol consumption (e.g. Laws et al. , 2017 ) as well as the large body of research demonstrating the role of social threat in alcohol-related problems (e.g. Bacon and Ham, 2010 ), we predicted that participants who experienced social threat would consume more alcohol than those who did not experience threat. The hypothesis and analysis plan were preregistered at https://osf.io/zyhmv/ .

Participants and procedure

We recruited 83 undergraduate students who were 21 years of age or older ( M age  = 21.8 years old, SD age  = 1.62 years old; 72.3% Women; 61.4% Hispanic/Latinx) to participate in a study in exchange for a $7 gift card or partial fulfillment of research participation requirements.

Experimental manipulation

Upon arriving to the lab, a participant and two confederate researchers presented government identification cards to a researcher to verify their age. Participants were randomly assigned to either a social threat or a social acceptance condition. In the social threat condition, the researcher questioned the participant’s student-status as they presented their identification card by saying the phrase ‘Wait, you are a student here, right? Let me see your [student ID] card too.’ In the social acceptance condition, the researcher did not question participants’ student-status. The researcher did not question the confederates’ student-status in either of the conditions.

Following consent procedures, the researcher informed the participant and confederates that they would work together to select design elements for a new bar that was ostensibly coming to the university as part of a campus expansion project. The researcher instructed them to first work individually to create a design scheme (e.g., tile patterns, paint colors, light fixtures) for the bar that they thought other students on campus would enjoy. After making their selections, the researcher asked the confederates and participant to vote on a design scheme (aside from their own) that they felt best suited the student community. In each session, the researcher instructed the participant to vote first. In the social acceptance condition, one of the confederates agreed with the participant’s vote and the other confederate voted for the participant’s selections. The confederates also affirmed the participant’s student identity by saying phrases such as, ‘I think [the participant’s] selections will go best with the school’ and ‘I think [the participant] has a strong sense of the community.’ In the social threat condition, the confederates disagreed with the participant’s vote and induced social threat by saying phrases such as, ‘I just don’t think [the participant] understand[s] what our students like...’ and ‘Maybe [the participant] is a transfer student.’

Afterward, the researcher said that two members of the group would need to pair up and move to another location for a second portion of the study. In the social threat condition, the two confederates volunteered to move to the other location with each other (and not with the participant). In the social acceptance condition, the researchers chose the two confederates to go to the separate location. After the confederates left the room with a second researcher, the primary researcher told the participant they would be sampling wines that would be served in the new bar. They moved them to a cubicle within the room labeled ‘Wine Tasting Station’ where three carafes of dealcoholized wine, cups and a spittoon awaited them atop a tablecloth. Participants were then left alone to complete a wine tasting. After the wine tasting, participants completed measures in an online survey and were fully debriefed. During the debriefing, participants were probed for suspicion regarding the purpose of the study 1 , the presence of confederate researchers, and the fact that the wines were nonalcoholic. One participant was aware that the study involved research confederates and was excluded from analyses. No participants suspected that the wines used in the study were, in fact, nonalcoholic. All procedures were approved by the university’s Institutional Review Board. The full study script as well as the data underlying this article are available at https://osf.io/zyhmv/ .

Manipulation check

To assess whether participants noticed the behavior of the confederate researchers, participants responded to the question: ‘How well did you get along with the other participants in the study?’ (1 =  a great deal , 7 =  not at all ; M =  3.32, SD  = 1.17).

Wine consumption

Wine consumption was calculated by subtracting the weight (measured in grams) of the carafes and spittoon after the experimental session from the weight of the carafes and spittoon before the experimental session ( M consumption  =  131.94 g, SD  = 96.16 g).

Typical alcohol consumption behavior

To assess typical alcohol consumption behavior, participants responded to one item taken from the Alcohol Use Disorders Identification Test ( Saunders et al. , 1993 ). Specifically, they responded to the statement: ‘How many drinks containing alcohol do you have on a typical day when you are drinking alcohol?’ (1 = ‘ 1–3 ,’ 2 = ‘ 4–6 ,’ 3 = ‘ 7–9 ,’ 4 = ‘ 10+ ’; M =  1.46, SD  = 0.63). We chose this item because it is a standardized measure of alcohol consumption during a single-time point and we wished to predict wine consumption at a single-time point.

All analyses were conducted with SPSS version 27. We conducted an independent-samples t-test to determine whether wine consumption differed as a function of condition. We also conducted a multiple linear regression analysis to examine whether wine consumption differed between conditions after controlling for typical alcohol consumption behavior.

Mean alcohol consumption as a function of condition. Note: error bars represent 95% confidence intervals.

Suggesting the participants noticed the confederates’ behavior, those in the social threat condition ( M  = 4.13, SD  = 0.70) reported significantly poorer interactions with the confederates than did those in the social acceptance condition ( M  = 2.57, SD  = 1.02), t (79) = 7.99, P  < 0.001, d  = 1.78.

Alcohol consumption

Results are displayed in Fig. 1 . Contrary to our expectations, participants in the social threat condition consumed significantly fewer grams of wine ( M  = 108.80, SD  = 68.78) than did those in the social acceptance condition ( M  = 154.52, SD  = 113.22), b = − 47.72 , t  = −2.22, P  = 0.03, d  = −0.49. This effect persisted even after controlling for typical alcohol consumption in a linear regression analysis (effect of experimental condition: b =  56.09 , t  = 2.50, P  = 0.02, d  = 0.61; effect of typical alcohol consumption: b =  41.66, t  = 2.33, P  = 0.02; d  = 0.57) 2 .

The present study examined the causal relationship between alcohol consumption and social threat among college students. The results revealed that participants who experienced social threat consumed significantly less alcohol than those who were socially accepted. Although this finding is in the opposite direction of other work examining social threat and alcohol consumption (e.g. Mohr et al. , 2001 ; Rabinovitz, 2014 ), it is perhaps unsurprising for two reasons. First, it is possible that participants who were socially accepted during the study experienced greater positive affect which, in turn, prompted greater alcohol consumption. Indeed, one study found that positive affect, but not negative affect, was associated with greater odds of heavy drinking among college students ( Howard et al. , 2015 ). Similarly, an ambulatory study with heavy drinkers found that positive affect was related to greater drinking whereas negative affect was related to lowered drinking ( Bresin and Fairbairn, 2019 ).

Second, this finding may implicate the importance of the role of college student identity in predicting alcohol consumption behavior. Research finds that college students perceive alcohol consumption to be an important signifier of the college student identity ( Tan, 2012 ) and some students consume alcohol in order to establish their ‘college student identity’ ( Borsari et al. , 2007 ). Furthermore, long-term alcohol consumption may be a stronger signifier of college student identity rather than a single instance of alcohol consumption ( Martinez et al. , 2014 ). It is possible that participants who experienced social threat in the present study deidentified with college students and, thus, felt less desire to consume alcohol in the moment.

Numerous studies suggest that alcohol consumption is a typical behavior among college students ( Hingson et al. , 2009 ). Indeed, the transition to college is difficult for many students and is characterized by challenges such as establishing new social networks away from one’s friends and family at home ( Mattanah et al. , 2010 ). In line with this notion, qualitative research finds that students may experience social threat during this transition ( Pederson et al. , 2019 ) and some students use alcohol as a way to re-establish belonging when they experience social threat ( Hamilton and DeHart, 2016 ). The findings from the present study demonstrate that there may be instances in which social threat actually decreases drinking among college students and emphasize the need to examine other potential factors in this relationship.

Limitations and future directions

Although the present study represents an important initial step in examining the causal role of social threat in alcohol use using a highly ecologically valid manipulation, some limitations should be noted. First, the results from this study may not generalize to other contexts and populations. The study took place in a lab setting in the presence of a researcher, as we aimed to examine the effect of social threat on alcohol consumption in a controlled setting. Moreover, our sample consisted of mostly Hispanic/Latino(a/x) women. Given that cultural and gender norms are associated with drinking patterns ( LaBrie et al. , 2012 ; Patro-Hernandez et al. , 2020 ), further research is needed to determine whether the findings from the current study generalize to even more ecologically valid settings and with other college student samples.

Next, the present study focused on the role of social threat in wine consumption specifically. We believe this represents a strength of the study, since wine consumption has not been extensively examined in the literature on college student drinking. Nevertheless, some research suggests that different types of alcohol are associated with different social identities. For instance, beer is more often associated with masculine in-group status ( Lemle and Mishkind, 1989 ), whereas wine can signify femininity ( Nicholls, 2016 ). Indeed, one study found a significant interaction effect of gender and social threat on alcohol consumption behavior ( Bacon et al. , 2015 ). This may suggest the importance of motives to maintain or re-establish social identity in decisions to consume alcohol following social threat. As such, future research may also consider examining the potential role of various social identities in this relationship.

Finally, we assessed social threat and alcohol consumption at a single-time point. It is possible that the influence of long-term social threat differs from the influence of momentary social threat on alcohol consumption. Put another way, chronic social threat may be more predictive of increased alcohol consumption than single instances of threat. Indeed, this may account for why numerous studies identify sensitivity to threat as a risk factor for alcohol-related problems ( Bacon and Ham, 2010 ). Relatedly, we used a single-item measure of typical alcohol consumption, and this item did not specify to participants how to report standard drinks. Although this item has been used in previous studies to measure alcohol consumption ( Reed Jr et al. , 2005 ), further research is needed to determine whether these findings would replicate while controlling for more extensive measures of alcohol consumption and with long-term experiences of social threat.

Given the numerous negative health implications of consuming alcohol, it is important to understand which factors might underlie this behavior among college students. The present study examined the effect of one such factor: social threat. The results revealed that college students who experienced social threat consumed significantly less alcohol than those who were socially accepted. This relationship persisted even when controlling for students’ typical alcohol consumption behavior. Collectively, the findings suggest that the role of social threat in alcohol consumption may be more complex than previously anticipated.

We conducted extensive pilot testing of the procedures to improve the believability of the study pretense and interactions among the confederate researchers. Participants who participated in the study during the pilot testing phase reported that the nonalcoholic wines tasted like actual wine and that the interactions between confederate researchers were believable.

Given that the wine was dealcoholized, some readers may be interested about the influence of taste on the effects observed here. On the item ‘This sample tastes good,’ participants rated the wines, on average, 4.88/7.00, significantly higher than the ‘neither agree nor disagree’ midpoint, t  = 5.25, P  < 0.001, and ratings were unrelated to wine consumed r  = 0.04, P  = 0.76.

Bacon AK , Engerman B . ( 2018 ) Excluded, then inebriated: a preliminary investigation into the role of ostracism on alcohol consumption . Addict Behav Rep 8 : 25 – 32 .

Google Scholar

Bacon AK , Ham LS . ( 2010 ) Attention to social threat as a vulnerability to the development of comorbid social anxiety disorder and alcohol use disorders: an avoidance-coping cognitive model . Addict Behav 35 : 925 – 39 .

Bacon AK , Cranford AN , Blumenthal H . ( 2015 ) Effects of ostracism and sex on alcohol consumption in a clinical laboratory setting . Psychol Addict Behav 29 : 664 – 72 .

Borsari B , Murphy JG , Barnett NP . ( 2007 ) Predictors of alcohol use during the first year of college: implications for prevention . Addict Behav 32 : 2062 – 86 .

Bresin K , Fairbairn CE . ( 2019 ) The association between negative and positive affect and alcohol use: an ambulatory study . J Stud Alcohol Drugs 80 : 614 – 22 .

Buckner , J. D. , Schmidt , N. B. , Bobadilla , L. , & Taylor , J. ( 2006 ). Social anxiety and problematic cannabis use: evaluating the moderating role of stress reactivity and perceived coping . Behav Res Ther , 44 , 1007 – 1015 . https://doi.org/ 10.1016/J.Brat.2005.08.002

Chen CM , Dufour MC , Yi H . ( 2004 ) Alcohol consumption among young adults ages 18–24 in the United States: results from the 2001–2002 NESARC survey . Alcohol Res Health 28 : 269 – 80 .

Cloutier RM , Anderson KG , Kearns NT et al.  ( 2021 ) An experimental investigation of peer rejection and social anxiety on alcohol and cannabis use willingness: accounting for social contexts and use cues in the laboratory . Psychol Addict Behav Advance online publication .

dePyssler B , Williams VS , Windle M . ( 2005 ) Alcohol consumption and positive study practices among African American college students . J Alcohol Drug Educ 49 : 26 – 44 .

Gruenewald TL , Kemeny ME , Aziz N et al.  ( 2004 ) Acute threat to the social self: shame, social self-esteem, and cortisol activity . Psychosom Med 66 : 915 – 24 .

Hales AH , Williams KD , Eckhardt CI . ( 2015 ) A participant walks into a bar…: subjective intoxication buffers ostracism’s negative effects . Soc Psychol 46 : 157 – 66 .

Halim A , Hasking P , Allen F . ( 2012 ) The role of social drinking motives in the relationship between social norms and alcohol consumption . Addict Behav 37 : 1335 – 41 .

Hamilton HR , DeHart T . ( 2016 ) Drinking to belong: the effect of a friendship threat and self-esteem on college student drinking . Self Identity 16 : 1 – 15 .

Hingson RW , Zha W , Weitzman ER . ( 2009 ) Magnitude of and trends in alcohol-related mortality and morbidity among US college students ages 18-24, 1998-2005 . J Stud Alcohol Durgs Suppl 16 : 12 – 20 .

Howard AL , Patrick ME , Maggs JL . ( 2015 ) College student affect and heavy drinking: variable associations across days, semesters, and people . Psychol Addict Behav 29 : 430 – 43 .

Johnson AM , Rodger SC , Harris JA et al.  ( 2005 ) Predictors of alcohol consumption in university residences . J Alcohol Drug Educ 49 : 9 – 18 .

Kenney SR , Ott M , Meisel MK et al.  ( 2017 ) Alcohol perceptions and behavior in a residential peer social network . Addict Behav 64 : 143 – 7 .

Kraemer KM , McLeish AC , O’Bryan EM . ( 2015 ) The role of intolerance of uncertainty in terms of alcohol use motives among college students . Addict Behav 42 : 162 – 6 .

Kuntsche E , Knibbe R , Gmel G et al.  ( 2005 ) Why do young people drink? A review of drinking motives . Clin Psychol Rev 25 : 841 – 61 .

LaBrie JW , Atkins DC , Neighbors C et al.  ( 2012 ) Ethnicity specific norms and alcohol consumption among Hispanic/Latino/a and Caucasian students . Addict Behav 37 : 573 – 6 .

Laws HB , Ellerbeck NE , Rodrigues AS et al.  ( 2017 ) Social rejection and alcohol use in daily life . Alcohol Clin Exp Res 41 : 820 – 7 .

Lemle R , Mishkind ME . ( 1989 ) Alcohol and masculinity . J Subst Abus Treat 6 : 213 – 22 .

Martinez JA , Sher KJ , Wood PK . ( 2014 ) Drinking consequences and subsequent drinking in college students over 4 years . Psychol Addict Behav 28 : 1240 – 5 .

Mattanah JF , Ayers JF , Brand BL et al.  ( 2010 ) A social support intervention to ease the college transition: exploring main effects and moderators . J Coll Stud Dev 51 : 93 – 108 .

Mohr CD , Armeli S , Tennen H et al.  ( 2001 ) Daily interpersonal experiences, context, and alcohol consumption: crying in your beer and toasting good times . J Pers Soc Psychol 80 : 489 – 500 .

Nicholls E . ( 2016 ) “What on earth is she drinking?‘doing femininity through drink choice on the girls” night out . J Int Women's Stud 17 : 77 – 91 .

Norberg MM , Norton AR , Olivier J et al.  ( 2010 ) Social anxiety, reasons for drinking, and college students . Behav Ther 41 : 555 – 66 .

Patro-Hernandez R , Nieto Robles Y , Liminana-Gras R . ( 2020 ) The relationship between gender norms and alcohol consumption: a systematic review . Adicciones 32 : 145 – 57 .

Pederson JR , Tenzek KE , Kloeber D et al.  ( 2019 ) Social rejection during the first year of college: exploring the factors of communication competence and coping . Iowa J Commun 51 : 253 – 82 .

Rabinovitz S . ( 2014 ) Drowning your sorrows? Social exclusion and anger effects on alcohol drinking . Addict Res Theory 22 : 363 – 70 .

Reed DN Jr , Saxe A , Montanez M et al.  ( 2005 ) Use of a single question to screen trauma patients for alcohol dependence . J Trauma Acute Care Surg 59 : 619 – 23 .

Saunders JB , Aasland OG , Babor TF et al.  ( 1993 ) Development of the alcohol use disorders identification test (AUDIT): WHO collaborative project on early detection of persons with harmful alcohol consumption-II . Addiction 88 : 791 – 804 .

Schelleman-Offermans K , Kuntsche E , Knibbe RA . ( 2011 ) Associations between drinking motives and changes in adolescents’ alcohol consumption: a full cross-lagged panel study . Addiction 106 : 1270 – 8 .

Senchak M , Leonard KE , Greene BW . ( 1998 ) Alcohol use among college students as a function of their typical social drinking context . Psychol Addict Behav 12 : 62 – 70 .

Tan ASL . ( 2012 ) Through the drinking glass: an analysis of the cultural meanings of college drinking . J Youth Stud 15 : 119 – 42 .

Varela A , Pritchard ME . ( 2011 ) Peer influence: use of alcohol, tobacco, and prescription medications . J Am Coll Heal 59 : 751 – 6 .

White A , Hingson R . ( 2013 ) The burden of alcohol use: excessive alcohol consumption and related consequences among college students . Alcohol Res 35 : 201 – 18 .

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Problem Drinking Behaviors among College Students

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Problematic drinking behaviors, such as frequent intoxication and binge drinking, have long been assumed to be more prevalent among college students than the general population. Researchers have dedicated several decades to better understanding problem drinking among college students and helping to prevent the serious consequences of this behavior.

Prevalence of Problem Drinking Among College Students

In a 2012 national survey conducted by the University of Michigan Institute for Social Research, 41% of male college students reported that in the previous two weeks they had one or more episodes where they consumed five or more drinks in a row; 34% of female college students reported the same. 1  While these high rates certainly indicate a need for intervention, many policy makers and scientists have wondered whether increased binge drinking patterns are unique to college students or indicative of all young adults within this age group. Without a clear understanding of the underlying issue, intervention efforts can be ineffective at improving the problem.

Historically, studies examining binge drinking between college students and young adults in the same age group who are not attending college have had mixed results. 2  While college students have always tended to report higher rates of heavy drinking, it is often attributed to uncontrolled confounds, such as subject background and living arrangements; when these effects are controlled for, the significant association between college attendance and increased drinking is sometimes lost. 3  A recent twin study provided strong evidence that college attendance did indeed correlate with significantly higher volumes of alcohol consumed, including binge drinking (see figure); although, when controlling for demographic and lifestyle characteristics, overall frequency of drinking and frequency of binge drinking were no longer significantly correlated with college attendance. 2  There remained, however, a significant association between frequency of intoxication and college attendance, even when carefully controlling for demographic, biological, and social characteristics. 2  These findings indicate that cultural norms specific to college campuses across the United States may encourage drinking to intoxication, more so than cultural norms among other young adult peer groups not attending college.

Consequences of Problem Drinking

Whether problematic drinking behaviors are uniquely correlated to college attendance or a result of a combination of demographic and lifestyle factors shared by students attending college, they pose a significant risk. An investigation of alcohol-related incidents among young adults found that in 2005, there was a total of 5,534 alcohol-related injury deaths among 18- to 24-year-olds in the United States; 1,825 (32.9%) of those who died were college students. 4  In 2001, an estimated 599,000 college students sustained an alcohol-related injury, 646,000 were assaulted by another student who had been drinking, and 97,000 were victims of sexual assault or rape perpetrated by a student who had been drinking. 5  More than 36% of students attending colleges with widespread patterns of heavy drinking admitted to driving after drinking in 2005, and nearly 15% reported driving after consuming five or more drinks. 6  In addition to serious physical and legal consequences, college students who reported recent heavy drinking behavior also endorsed consequences that included missing classes, falling behind in school work, having unprotected sex, and damaging property. 6

Strategies to Decrease Problem Drinking at Colleges

Given the wide range of factors that contribute to problem drinking risk among college students, developing content and strategies for intervention is an extremely difficult challenge. The majority of recent and historical research on heavy drinking among college students agrees that social and lifestyle factors are extremely influential in predicting drinking behaviors and outcomes, and these areas should be addressed in any intervention. 2,7  Alcohol consumption has been significantly predicted by fraternity/sorority membership, the established and perceived social norms related to drinking, and beliefs that alcohol consumption will positively enhance experiences or increase conformity to one’s peers. 7

With so many of these factors interacting and affecting drinking behavior, success of current interventions to address and prevent problem drinking is often more of a function of the individual who is receiving the intervention rather than the intervention itself. 8, 9, 10  Researchers have found that women, upperclassmen, students who began drinking later in life, students who do not participate in drinking games, and those without social norms aligned with heavy drinking are the most responsive to interventions. 9  More research is needed on strategies that better engage and support young, male students who regularly interact with a social environment that enables or glorifies problem drinking. 9  It is recommended that any intervention should aim to address students at an individualized level that considers environmental, personality/psycho-pathological, and cognitive aspects related to heavy drinking. 10

Despite the significant influence of individual student characteristics, some strategies have been found to be generally more effective than others. The impact of social norms across studies demonstrates the importance of incorporating peers and family members in interventions. 7  By correcting inflated perceptions of peer and parent approval of heavy drinking, these interventions can make a considerable impact on the effect of social norms on alcohol consumption. 7  Students who have experienced serious medical or legal consequences as a result of their drinking have been found to be responsive to brief motivational interviews, 8  and all students demonstrate better outcomes following face-to-face interventions as compared to computer-administered interventions. 11  The effects of most interventions tend to fade significantly over the year following the intervention, which indicates the need for follow-up after the initial administration. 9

More than 36% of students attending colleges with widespread patterns of heavy drinking admitted to driving after drinking

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How to Use This Information

University Officials:  Problem drinking among college students is heavily influenced by cultural factors, and universities that foster a heavy drinking atmosphere often face more common and more severe consequences of heavy drinking than campuses that do not have a heavy drinking culture. 6 Working with professional organizations to implement evidence-based intervention and prevention strategies can be a great way to reduce the perception of problem drinking norms at your university.

Parents:  Studies have shown that parent involvement in problem drinking interventions, as well as in establishing clear parental disapproval for heavy drinking, can be effective in reducing problem drinking among college students. 8 It is important to foster a home environment where heavy drinking is clearly not the norm. Students who do not report actual or perceived parental approval of heavy drinking are much less likely to develop problem drinking behaviors in college.

• Johnston, L. D., O'Malley, P. M., Bachman, J. G., & Schulenberg, J. E. (2013). Monitoring the Future national survey results on drug use, 1975-2012, volume I: Secondary school students. Ann Arbor, MI: The University of Michigan Institute for Social Research.
• Slutske, W. S., Hunt-Carter, E. E., Nabors-Oberg, R. E., Sher, K. J., Bucholz, K. K., Madden, P. A. F., … Heath, A. C. (2004). Do college students drink more than their non-college-attending peers? Evidence from a population-based longitudinal female twin study. Journal of Abnormal Psychology, 133(4), 530-540.
• Gfroerer, J. C., Greenblatt, J. C., & Wright, D. A. (1997). Substance use in the U.S. college-age population: Differences according to educational status and living arrangement. American Journal of Public Health, 87, 62-65.
• Hingston, R. W., Zha, W., & Weitzman, E. R. (2009). Magnitude of and trends in alcohol-related mortality and morbidity among U.S. college students ages 18-24, 1998-2005. Journal of Studies on Alcohol and Drugs, 16, 12-20.
• Hingston, R., Heeren, T., Winter, M., & Wechsler, H. (2005). Magnitude of alcohol-related mortality and morbidity among U.S. college students ages 18-24: Changes from 1998 to 2001. Annual Review of Public Health, 26, 259-279.
• Nelson, T. F., Xuan, Z., Lee, H., Weitzman, E. R., & Wechsler, H. (2009). Persistence of heavy drinking and ensuing consequences at heavy drinking colleges. Journal of Studies on Alcohol and Drugs, 70, 726-734.
• Neighbors, C., Lee, C. M., Lewis, M. A., Fossos, N., & Larimer, M. E. (2007). Are social norms the best predictor of outcomes among heavy-drinking college students? Journal of Studies on Alcohol and Drugs, 68(4), 556-565.
• Mun, E. Y., White, H. R., & Morgan, T. J. (2009). Individual and situational factors that influence the efficacy of personalized feedback substance use interventions for mandated college students. Journal of Consulting and Clinical Psychology, 77(1), 88-102.
• Henson, J. M., Pearson, M. R., & Carey, K. B. (2015). Defining and characterizing differences in college alcohol intervention efficacy: A growth mixture modeling application. Journal of Consulting and Clinical Psychology, 83(2), 370-381.
• Ham, L. S. & Hope, D. A. (2003). College students and problematic drinking: A review of the literature. Clinical Psychology Review, 23, 719-759.
• Carey, K. B., Scott-Sheldon, L. A., Elliott, J. C., Garey, L., & Carey, M. P. (2012). Face-to-face versus computer-delivered alcohol interventions for college drinkers: A meta-analytic review, 1998 to 2010. Clinical Psychology Review, 32(8), 690-703.

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Social influence processes and college student drinking: the mediational role of alcohol outcome expectancies

Affiliation.

• 1 Department of Psychology and Cancer Prevention Research Center, University of Rhode Island, Kingston 02881, USA. [email protected]
• PMID: 11271962
• DOI: 10.15288/jsa.2001.62.32

Objective: Social influences are among the most robust predictors of adolescent substance use and misuse. Studies with early adolescent samples have supported the need to distinguish among various types of social influences to better delineate relations between social factors and alcohol use and problems.

Method: The first major goal of the present study (N = 399, 263 women) was to examine unique relations between particular facets of social influence and alcohol use and problems in a relatively heavy-drinking population (i.e., college students). We hypothesized that active social influences (offers to drink alcohol) and passive social influences (social modeling and perceived norms) would demonstrate positive associations with measures of alcohol use and problems. We also tested the hypothesis that alcohol outcome expectancies would mediate associations between social influences and drinking behaviors.

Results: Structural equation modeling analyses provided strong support for the first hypothesis. Social modeling demonstrated the strongest association with alcohol use and problems, and active social influences demonstrated significant associations with both use and problems. Perceived norms were related to alcohol use, but not directly with alcohol problems. Support for the second hypothesis was positive but limited to one type of social influence. Strong evidence for a mediational role of outcome expectancies was found for relations between social modeling and alcohol use and problems.

Conclusions: Together, these findings demonstrate the unique and relative contribution of active and passive social influences and provide limited support for a hypothesized process by which social factors influence cognitions and alcohol-related behaviors.

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Home > Electronic Theses and Dissertations > Psychology ETDs > 262

Psychology ETDs

Drinking norms and college student alcohol outcomes: systematic review and meta-analysis.

Kylee Hagler , University of New Mexico - Main Campus Follow

Publication Date

Summer 7-14-2018

Despite efforts to reduce problematic alcohol use on college campuses, students continue to drink heavily and experience alcohol-related consequences (e.g., Hingson, Zha, & Smyth, 2017.) Descriptive/injunctive norms positively relate to college students’ own alcohol use. Despite substantial research, there have been few efforts to statistically synthesize these data. The present study was a correlation-based, random-effects meta-analysis. Articles published on drinking norms and alcohol outcomes published in English-language peer-reviewed journals between 2003 and 2015 were identified, coded, and subjected to meta-analytic integration. There was an overall medium, positive association found between descriptive norms and college student alcohol behaviors (rw = 0.36). A relatively weaker small positive association was found between injunctive norms and college student alcohol behaviors (rw = 0.18). Analyses revealed little evidence of publication bias. This research suggests that drinking norms are a viable target for college student drinking interventions. Future analyses should consider moderators of the relationships between norms and alcohol outcomes to optimize targeted interventions.

Degree Name

Level of degree, department name, first committee member (chair).

Dr. Kamilla Venner

Second Committee Member

Dr. Katie Witkiewitz

Third Committee Member

Dr. J. Scott Tonigan

Fourth Committee Member

Dr. Matthew Pearson

Fifth Committee Member

Dr. Clayton Neighbors

college students; alcohol; drinking norms; normative beliefs

Document Type

Dissertation

Recommended Citation

Hagler, Kylee. "Drinking Norms and College Student Alcohol Outcomes: Systematic Review and Meta-Analysis." (2018). https://digitalrepository.unm.edu/psy_etds/262

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• Research article
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• Published: 28 June 2013

Alcohol drinking among college students: college responsibility for personal troubles

• Vincent Lorant 1 ,
• Pablo Nicaise 1 ,
• Victoria Eugenia Soto 1 , 2 &
• William d’Hoore 1  

BMC Public Health volume  13 , Article number:  615 ( 2013 ) Cite this article

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One young adult in two has entered university education in Western countries. Many of these young students will be exposed, during this transitional period, to substantial changes in living arrangements, socialisation groups, and social activities. This kind of transition is often associated with risky behaviour such as excessive alcohol consumption. So far, however, there is little evidence about the social determinants of alcohol consumption among college students. We set out to explore how college environmental factors shape college students' drinking behaviour.

In May 2010 a web questionnaire was sent to all bachelor and master students registered with an important Belgian university; 7,015 students participated (participation = 39%). The survey looked at drinking behaviour, social involvement, college environmental factors, drinking norms, and positive drinking consequences.

On average each student had 1.7 drinks a day and 2.8 episodes of abusive drinking a month. We found that the more a student was exposed to college environmental factors, the greater the risk of heavy, frequent, and abusive drinking. Alcohol consumption increased for students living on campus, living in a dormitory with a higher number of room-mates, and having been in the University for a long spell. Most such environmental factors were explained by social involvement, such as participation to the student folklore, pre-partying, and normative expectations.

Conclusions

Educational and college authorities need to acknowledge universities’ responsibility in relation to their students’ drinking behaviour and to commit themselves to support an environment of responsible drinking.

Peer Review reports

In 2007 one young adult in two has entered university education in Western countries and this proportion is likely to increase in the future [ 1 ]. Many of these young students will be exposed to substantial changes in living arrangements and social activities. This kind of transition is often associated with an increase in heavy and risky alcohol use [ 2 ].

Indeed, it is reckoned that college students are particularly exposed to alcohol during their college years. An international study of alcohol consumption among students found wide geographical variation in the prevalence of risky drinking behaviour, with more than 40% of students aged 17-30 having drunk heavily in the U.S.A. and in several European countries [ 3 ]. Risky drinking has also been found to be a common practice [ 4 ].

Risky alcohol consumption among young people is becoming a key public health priority because of its important health and educational consequences. Among those aged 15-29, alcohol accounts for more than 10% of the overall burden of disease and injury [ 5 ]. In addition to morbidity and mortality, alcohol has a significant important effect on student academic performance and on antisocial behaviour [ 6 , 7 ]. The case for alcohol could be weakened if adolescent drinking patterns became more mature in adulthood. However, a review of cohort studies shows that higher consumption in late adolescence continues into adulthood [ 8 ].

Risky alcohol consumption has first been approached from an individual perspective, with a strong emphasis on individual risk factors, such as gender, age, and psychological factors, and on drinking motives [ 3 , 9 , 10 ]. Adolescents often report drinking for motives such as social enhancement, enjoyment, image enhancement, or coping motives; thus, they may drink because of positive consequences that outweigh, at least in the short term, negative consequences [ 11 – 13 ].

International comparison, however, shows there is wide cross-country variation in the prevalence of risky drinking among college students [ 3 ]. Within the U.S.A., there is compelling evidence that college drinking varies dramatically between colleges [ 14 ]. This indicates that alcohol use may be sensitive to contextual factors. Alcohol use among college students occurs in specific social environments characterised by independent living, reduced parental control, increased social homogeneity, wide availability of alcohol-related social activities such as pre-partying [ 15 ] and student folklore (traditional, extra-curricular, and generally recreational activities managed by student organisations) [ 16 ]. The transition to the college environment brings about changes in adolescents’ adjustment to their social environment, which in turns influence alcohol use [ 2 ]. We thus need to better understand upstream factors that shape drinking at college. With some exceptions [ 10 ], there has, however, been little research into the college-related environmental risk factors affecting drinking by college students in Europe. Moreover, research needs to better understand the contribution of the college and university context to alcohol-drinking behaviour. The European university system and legal provisions related to alcohol consumption differ considerably from those in North America. Thus, research of the kind suggested might indicate opportunities for community health preventive interventions.

The study analyses alcohol consumption among college students from a community health perspective. We aim to understand how the college-related environment shapes students’ drinking behaviour. In particular, we assess the role of living arrangements, college social activities, and social norms in drinking patterns. We explore two questions: (1) does the college-related environment influence alcohol use? (2) How do social and normative factors contribute to these college influences on alcohol use?

Design and participants

This study is part of an important multi-method investigation into alcohol drinking among college students. It was carried out in a Belgian university with two main campuses, one in Louvain-La-Neuve, a town of 20,000 inhabitants, half of whom are students living in dormitories. The other campus, mainly devoted to health sciences, is located in Brussels, 30 km away from the main campus.

A web survey was carried out in May 2010. An e-mail invitation was sent to all bachelor and master students registered with the university (n = 18,137), with a link to a web-survey questionnaire. No financial or material incentive was provided. The students could request a copy of the final report, and 62% of the respondents made such request. The form included 31 questions related to socio-demographics, living arrangements, study programmes, involvement in student activities, alcohol use, injunctive and descriptive norms, and positive and negative consequences of alcohol use. On average, filling in the questionnaire took 12 minutes and very few break-offs were recorded. The study was approved for ethical issues by the Social and Student Affairs review board of the university on the 26 th March 2010.

After up to two reminders, 7,015 students (39%) participated, a rate well above the average web survey participation rate [ 17 ]. Compared with a face-to-face survey, our participation rate may look low. But for a web survey it is a very satisfactory result, as it corresponds to the median web-survey participation [ 18 ]. In addition, there is no evidence that online surveys with lower response rates produce biased estimates in higher education evaluation or surveys [ 19 ] or in surveys in general [ 20 , 21 ]. However, to assess the risk of bias we compared the distribution of our sample with the distribution of the population. Analysis of non-participants suggests that women were a bit more likely to participate (OR = 1.10) than men; no differences of participation regarding age or year of study were noted. There is thus little evidence of an important bias linked to factors associated with alcohol consumption.

Alcohol consumption measures came from the Eurostat European Health Interview Survey schedule and from the European School Survey Project on Alcohol and Other Drugs (ESPAD) questionnaire [ 22 ]. We modelled the weekly average number of drinks in the last year, the monthly frequency of drinking, and the monthly frequency of abusive drinking (more than 6 drinks on one occasion [ 23 ]). A drink was defined as a glass of any alcoholic beverage (beer, wine, spirits, other), assuming that a standardised glass of beer, wine, or spirit contains a similar quantity of alcohol (from 10 to 13 g).

College environmental factors included curricular and extra-curricular features. The former consisted of the number of years the student had been studying and the study programme (a student class within a curriculum). The study programme factor was expected to capture the peer effect linked to the culture of alcohol consumption specific to faculties. The extra-curricular features were: living arrangements (living in a dormitory or with parents), living on the campus (yes or no), and the number of room-mates (0 for staying with parents).

Social involvement was measured by involvement in traditional student folklore, pre-partying, and being a student representative. Student folklore shares some similar features with the sororities and fraternities in the U.S.A. It has long played a traditional role in European university social life: traditional students’ organisations contribute to welcoming freshmen and to rites of passage; they organise parties and other recreational activities that may or may not involve drinking. Some student associations also manage student accommodation and sit on consultative bodies related to student social affairs. Involvement in this kind of traditional student folklore was measured by a score ranging from a low of 0 to a high of 3 according to rites of passage or positions of responsibility in student folklore. One point was given for each of the following: participation in hazing activities at the beginning of the academic year, after which one is labelled “baptisé”; participation in another traditional activity that upgrades the student’s prestige and allows him/her to wear a ritual cap called a “calotte”; and participation in the folklore organisation. The score was categorised in three groups (0 = none, 1 = medium, 2-3 = high). Finally, the university provides students with many curricular or extra-curricular social organisations. We asked the students whether they were members of any organisation of that kind.

Pre-partying was defined as the consumption of alcohol with friends while preparing to go out for the night. Pre-partying helps to improve sociability and conviviality, easing the discomfort associated with meeting new people at a party [ 15 , 24 – 26 ]. Students were requested to report their pre-partying frequency per month.

The normative factors included descriptive and injunctive norms. Alcohol injunctive norms were covered by a four-item questionnaire measuring approval by friends of four kinds of drinking behaviour: drinking every weekend, daily, after driving, and enough to be drunk [ 27 ]. Each item had a score ranging from 0 (strong disapproval) to 4 (strong approval). The overall sum of the four items ranged between 0 and 16 and measures “permissiveness”. Descriptive norms measure the perceived drinking behaviour of referent others and were assessed by the Drinking Norm Rating Forms, which ask the student to estimate the average daily number of drinks individuals of three different reference groups consume (students in general, same-sex students, and friends) [ 27 ]. According to social comparison theory proximal comparisons are more relevant than distal comparisons, so we expected that friends’ average consumption would have a greater influence than typical same-sex student consumption [ 28 ].

In order to also include experiential reporting, students’ positive drinking consequences were registered via the Positive Drinking Consequences Questionnaire, a 14-item scale [ 29 ]. This scale measures actual and past perceived positive consequences of alcohol use and differs from expectations. The scale mainly records consequences of drinking in terms of improved social interaction (11 items out of 14) such as social enhancement and stress reduction. This seemed relevant for young people in transition to adulthood and experiencing a dramatic change in their living conditions. We counted the number of times students reported a positive consequence of drinking over the last year.

Data analysis

The analysis was in two stages, according to our two research questions. First, we investigated the role of socio-demographics and college environmental factors. Second, we added to the analysis social-involvement, normative, and experiential factors that might contribute to the influence of college environmental factors. However, cross-sectional analysis of drinking behaviour is vulnerable to selection bias: unobserved heterogeneity across individuals may explain why some vulnerable individuals self-select into an at-risk college environment, as predicted by the theory of increased heterogeneity [ 2 ]. We assessed this kind of bias by sensitivity analysis: we checked the robustness of the models by including age at first drink, a factor strongly linked to poor executive function and an individual risk factor for subsequent drinking and drug abuse [ 30 ]. Because number of drinks and frequency of abusive drinking are not normally distributed and because of over-dispersion (a minority may never drink), we used a negative binomial mixed regression model. All models included a random component capturing the intra-study programme correlation. Statistical procedures were carried with SAS 9.2.

On average, students were aged 21.5 (std = 3.3), and mainly female (Table  1 ). Women were slightly younger compared with men (21.4 vs 21.8, F = 21.5, p < 0.01) but there was no significant association between gender and study year ( χ 2 =1.18, p  = 0.28). On average students have been attending the University for about 2.8 years (std = 1.7) and were pursuing bachelor degrees. A majority of students were staying on the campus (66.9%) and in a dormitory (64%), with an average of 4.4 room-mates (std = 4.2). A minority of the students (12.3%) was highly involved in traditional student folklore with a score of 2 to 3. Most students pre-party (67%), with an average of 2.3 pre-parties per month (std = 3.3).

There were some socio-demographic differences between our sample and the population. Compared with the overall University population, our sample had a higher frequency of females (sample: 57.3%; population: 54.6%), was younger (21.5 y vs 21.9 y), and had a higher proportion of undergraduates (62% vs 59%). Overall, these differences were small and do not indicate a systematic tendency towards more or less frequent drinking: women generally drink less than men and undergraduates generally drink more than postgraduates.

On average, students had their first drink at the age of 15.7 (std = 1.8), while only a small percentage had never drink alcohol (6%). In Belgium, the legal drinking age is 16. On average, a student drank seven times a month (std = 6.6), had 1.7 drinks a day (std = 21), and 2.8 episodes of abusive drinking per month (std = 4.4). Over the last year, the students acknowledged on average 5 positive consequences (std = 3.1). The three most frequent consequences were to “approach a person that I probably wouldn’t have spoken to otherwise” (68%), to “find it easy to engage in a conversation in a situation in which I would usually have stayed quiet” (65%), and to feel “like I had enough energy to stay out all night partying or dancing” (64%).

College students overestimated what a typical student drinks; this overestimation decreased for closer-reference students: 4.2 (std = 4.7) daily drinks for students in general, 3.9 (std = 4.9) for same-sex students, 3.5 (std = 4.4) for friends (to be compared with a self-declared 1.7). College students overestimated their friends’ drinking by 2 drinks a day.

Overall socio-demographic variables played a more important role for abusive drinking and number of drinks than for the frequency of drinking (Table  2 , Model 1). Men drank more, more frequently, and drank abusively more often than women. But the gender difference was somewhat lower for frequency of drinking (OR = 1.58) and higher for abusive drinking (OR = 2.29). Older students were less likely to drink and, in particular, less likely to engage in abusive drinking. For each additional year of age, the frequency of abusive drinking decreased by 9% and the frequency of drinking decreased by 2%.

Higher exposure to college environmental factors meant, in most cases, more frequent, and more abusive drinking (Table  2 , Model 1). These risk factors were, in general, more important for excessive drinking than for frequency of drinking. For each additional year spent at the university, drinking became more frequent and the frequency of abusive drinking increased (OR = 1.11). Compared with not living on the campus, living on the campus meant more frequent and more abusive drinking behaviour (OR = 1.56). The greater the number of room-mates, the higher the risk of frequent and abusive drinking behaviour. Each additional room-mate increased the frequency of abusive drinking by 6%. There was one exception to these college environmental factors: staying in a dormitory was associated with less frequent drinking behaviour. This could be due to the collinearity with living on the campus and the number of room-mates: as 93% of those living in dorms were on the campus, it was difficult to disentangle the campus effect from the dormitory effect. We checked this issue in two ways. First, we ran Model 1 for the number of drinks per day, by excluding the “living on the campus” variable. We found that, indeed, living in a dormitory was associated with an increased number of drinks compared with living with parents (OR =1.12 95% CI: 1.06-1.18). Second, we compared the two campuses, the one in Louvain-la-Neuve, which is mainly a student town and is known to expose students to numerous drinking opportunities, with the one in Brussels, which has a much more mixed population, controlling for all other variables of Model 1. We found that the Brussels campus had a lower risk (OR = 0.68, 95% CI: 0.60-0.76) compared with the Louvain-La-Neuve campus, suggesting that living on the campus is a more potent predictor of frequent abusive drinking than living in a dormitory.

There was a small intra-class correlation linked to the study programme and this was more important for abusive drinking (0.23) than for frequency of drinking (0.03), suggesting a slight programme effect on abusive drinking but not on drinking frequency. We found the Faculty of Engineering and the Faculty of Social Sciences to have a higher number of drinks per day (Engineering mean = 2.2, p  < 0.001; Social Sciences Mean = 2.1, p  < 0.001), with a lower number in the faculties of medicine (1.21, p < 0.001) and psychology (mean = 1.17 NS).

Model 2 adds social-involvement, normative, and experiential factors (Table  2 ). The more a student was involved in traditional student folklore, the more frequent his or her drinking behaviour, even at the intermediate level of involvement. This was particularly obvious for abusive drinking (OR = 2.11), with a somewhat lower risk for drinking frequency (OR = 1.46). More frequent pre-partying was associated with increased drinking: one additional monthly occasion of pre-partying increased abusive drinking by 8%. However, not all university involvement increased drinking frequency. Being elected as a student representative was associated with a lower risk of drinking, particularly of abusive drinking (OR = 0.84).

Drinking was more frequent as the number of positive consequences increased and as drinking norms became more favourable to drinking. The more a student thought his friends were drinking, the more and the more frequently he drank (OR = 1.02). Likewise, the more a student thought his friends were permissive regarding drinking, the higher the risk of all drinking behaviour, particularly for drinking frequency (OR = 1.08). Drinking frequency or quantity increased by at least 10% for each additional positive consequence a college student experienced.

In most cases, controlling for social engagement, normative, and experiential factors led to a reduction in the risk associated with the college environment. The effect of the number of years attending the University on abusive drinking decreased from OR = 1.11 to OR = 1.05, while the effect of the number of room-mates decreased from 1.06 to 1.02. The effect of living arrangements became insignificant or very small.

The model’s robustness was checked by including age at first drink, a factor likely to capture individual vulnerability. In most cases, the ORs were only slightly affected: the effect of time attending the university on abusive drinking decreased from 1.11 (Model 1, without controlling for age at first drink) to 1.109 (Model 1, with control for age at first drink); the effect of living on the campus on abusive drinking frequency decreased from 1.56 to 1.52; the effect of traditional student folklore from 2.11 to 2.09. Pre-partying frequency was not affected by this kind of sensitivity analysis.

Main findings

This study confirmed that excessive alcohol consumption is common among college students, with an average of 3 episodes of abusive drinking per month. Greater exposure to college environmental factors, such as living on the campus, a longer spell at university meant more frequent drinking. These community risk factors were more pronounced for excessive drinking patterns than for the quantity or frequency of drinking. Time had a double and mixed effect: older students drink less and less excessively than younger students; however, the longer the period a student has spent in the university, the higher his/her risk of drinking. These effects of college environmental factors were partly explained by social-involvement, experiential, and normative expectations: college students drank for the positive consequences, because they over-estimate the drinking of their friends, or because of other normative expectations.

Consistency with previous studies

The role of living arrangements has been shown in previous American [ 31 ], European [ 10 , 32 ], and cross-comparative [ 3 , 33 ] studies in which living with parents, not living on the campus, and not living in fraternity and sorority houses protected against heavy or abusive drinking. We found that living on the campus was a more potent predictor of frequent abusive drinking than living in a dormitory (both in model 1 and model 2). On the surface, this might seem to contradict a previous European review [ 10 ]. However, this is in part because of the strong association between living on the campus and living in a dormitory. This is also consistent with the Harvard School of Public Health college alcohol study which found that living off-campus was a stronger and more significant factor than staying in a dormitory [ 31 ]. The finding that the dormitory became non-significant in model 2 suggests that social-involvement, experiential, and normative expectations contribute to explain college environmental factors of drinking behaviours.

Yet, our study shows that the college environment influences drinking behaviour in a much more complex way that involves not only where students live but also the kind of living arrangements, participation in traditional student folklore, the duration of college training, and the type of faculty in which the student is studying. In particular, living in a dormitory with a high number of room-mates and being highly involved in traditional student folklore also play a role in the frequency of abusive drinking. There is thus not one college environmental risk factor but several that relate to different aspects of student life. This may explain why living away from home had a slightly greater effect on heavy drinking in the American (OR = 1.72) or in the international comparison study (OR = 1.61) than in ours (OR = 1.57). The role of dormitory size needs, in particular, to be emphasized and could be explained by innovation diffusion. As adolescent social network studies have shown, teenagers who have a denser social network are more likely to drink than those with less dense social networks [ 34 ]. The finding on that pre-partying contribution to the relationship between college environmental factors and frequency of abusive drinking supports this hypothesis. As in previous studies [ 15 ], pre-partying was revealed to be a common practice contributing to both drinking behaviour and the influence of community factors on drinking behaviour. College students pre-party to ease the discomfort or awkwardness associated with meeting new people. As hypothesized in a qualitative study, the pre-party is a base to build on when you get to a party, a way to bond with friends, and a social lubricant at a later event to help “hook up” with a partner [ 26 ].

Our study shows that abusive drinking increased with the period attending the college, whereas it decreased with age. These two opposite effects were of similar magnitude: this may explain why previous studies have found no clear relationship between age and drinking behaviour [ 10 ]: it all depends on the time spent in the university. Few studies have controlled for the time spent in college, so that the protective maturing effect of age was confounded by the risk attached to the time spent attending college. One important prospective American study found, moreover, that heavy drinking decreased with age [ 35 ], while there is wide evidence of an association between late adolescent drinking behaviour and subsequent drinking into adulthood [ 8 ]. Why did older students drink less while, at the same time, more years at the University were associated with more drinking? Firstly, the correlation between age in years and number of years attending the university was not very high (correlation coefficient = 0.33), suggesting that not all students follow the same trajectory. Some start a postgraduate programme later in life, while working part-time. These “older” students generally spend a shorter period at university (2-3 years) and, possibly, have less time for student activities involving alcohol. Secondly, age and time at the University capture different risks linked to drinking alcohol: age may also capture a cohort effect and, in particular, changes in drinking habits: older students may not only adapt their consumption but may also have started drinking later than the younger age group. This is supported by our data, as we found a small but significant positive correlation between age and age at first drink (correlation = 0.22, p <0.001), although, with our cross-sectional design, these correlations must be approached with caution. A third possible explication is that a significant proportion of students had studied outside the University for their first undergraduate degree and where thus not exposed to the campus for as long as those who followed both under- and postgraduate programmes on the same campus. Our study suggests that the maturing effect on heavy drinking is modest and depends on the time spent attending the University, leaving one particular group of college students at risk: those starting university at a younger age and studying there for longer periods. But these results should be approached with caution. Truncation may affect our results, as younger students who failed to graduate because of heavy alcohol consumption are less likely to be observed at a later stage; this makes the comparison between younger and older students problematic: the latter are observed if they haven’t dropped out of the University.

We found that students overestimate other students’ average number of daily drinks. To compare our results with previous studies of self-other comparison in drinking, we computed the Z Fisher transformation correlation between self-reported daily number of drinks and friends’ numbers of drinks. Our Z Fisher correlation was 0.36 ( p  < 0.001), which compares quite well a Z fisher value of 0.29 from a previous meta-analytic integration of 23 studies [ 29 ]. The college social environment increases drinking through a combination of social activities and normative and motivational expectations. It puts students at risk of frequent and abusive drinking because students expect positive social consequences, because of social activities such as pre-parties, and because of injunctive and descriptive drinking norms. The role of such social and normative influences, evidenced in previous studies [ 36 , 37 ], may result from two different and complementary processes: social learning, in which drinking behaviour is acquired through social interaction, and social control, which emphasizes the role of social expectations such as norms and peer pressure [ 38 ]. We found that college students overestimate other students’ alcohol consumption and this overestimation decreases with social distance: drinking behaviour was more related to the quantities drunk by friends than to the quantities drunk by students overall. Finally, pre-partying and participation in traditional student folklore, both of which provide strong opportunities for social learning, emerged as strong predictors of drinking behaviour. All this suggests that social learning is a key factor that contributes to the effect of the college social environment on drinking behaviour, as found elsewhere [ 39 ].

Limitations

Our cross-sectional study is vulnerable to reversed causality, so the results need to be interpreted with caution. It could be that involvement in student life and drinking behaviour are confounded by unobserved vulnerability. Extraverted individuals are sensitive to positive social rewards and, thus, more likely to engage in socially-motivated drinking, so the relationship between traditional student folklore and drinking behaviour may be biased upwards. Moreover, the dose-response relationship with involvement in traditional student folklore or with the number of room-mates may downplay this risk of confounding without totally removing it. To assess the risk of confounding we replicated the analysis controlling for the age at which the student reported that he or she started drinking, a factor known to predict a heavy alcohol consumption trajectory [ 40 ]. Our results suggest that this kind of self-selection risk may slightly affect our conclusions.

The second limitation has to do with the setting, which unlike other campuses in Belgium or abroad, is much less socially mixed, giving the college environmental factors more clout while mitigating other social control effects. Our results, nevertheless, are in line with a cross-comparative study such as the College Alcohol study in the U.S.A. [ 23 ] or the European Amsterdam-Antwerp comparison, which showed living arrangements to be a strong predictor of problematic alcohol use [ 33 ]. Finally, it could be that our setting provides a pessimistic picture of community factors and is, in that sense, a good model for reflecting on the community risk factors linked to college drinking behaviour.

Conclusion: relevance for community health promotion

It is foreseen that in the future most young adults will attend university where, our study shows, they will be exposed to frequent and intensive drinking behaviour. That experience will have subsequent and important consequences lasting into adulthood [ 8 ]. Colleges need thus to acknowledge their role in this issue and to commit themselves to lower exposure to excessive alcohol consumption. In particular, they need to combine multi-level strategies: individual, group, and organization-level, from a community health promotion perspective. One danger would be a top-down approach of undertaking community actions in ways that do not consider the realities of student life. A first step would be to involve members of the community in identifying realistic objectives, e.g. limiting excessive consumption, and defining targets, e.g. male students involved in traditional and folklore activities in which hazardous alcohol intake peaks. A second step would be to define interventions, e.g. social-norm interventions that could correct gross miss-perceptions and effectively reduce alcohol consumption [ 41 – 43 ]. Third and fourth steps would be to evaluate what has been implemented, to provide feedback in order to improve and extend interventions, which requires sustained funding, and to analyse gaps between national policies and what is locally feasible. More community-based research is needed to face the problem of hazardous alcohol use, which is persistent and pervasive.

Organisation for Economic Co-operation and Development, Centre for Educational Research and Innovation: Education at a glance 2009. 2009, Paris: OECD indicators, 471-

Google Scholar  

Schulenberg JE, Maggs JL: A developmental perspective on alcohol use and heavy drinking during adolescence and the transition to young adulthood. J Stud Alcohol. 2002, 54-70. 14

Dantzer C, Wardle J, Fuller R, et al: International study of heavy drinking: Attitudes and sociodemographic factors in university students. J Am Coll Health. 2006, 55: 83-89. 10.3200/JACH.55.2.83-90.

Article   PubMed   Google Scholar  

Stock C, Mikolajczyk R, Bloomfield K, et al: Alcohol consumption and attitudes towards banning alcohol sales on campus among European university students. Public Health. 2009, 123: 122-129. 10.1016/j.puhe.2008.12.009.

Article   CAS   PubMed   Google Scholar  

Rehm J, Mathers C, Popova S, et al: Global burden of disease and injury and economic cost attributable to alcohol use and alcohol-use disorders. Lancet. 2009, 373: 2223-2233. 10.1016/S0140-6736(09)60746-7.

Aertgeerts B, Buntinx F: The relation between alcohol abuse or dependence and academic performance in first-year college students. J Adolesc Health. 2002, 31: 223-225. 10.1016/S1054-139X(02)00362-2.

Singleton RA, Wolfson AR: Alcohol Consumption, Sleep, and Academic Performance Among College Students. J Stud Alcohol Drugs. 2009, 70: 355-363.

McCambridge J, McAlaney J, Rowe R: Adult Consequences of Late Adolescent Alcohol Consumption: A Systematic Review of Cohort Studies. PLoS Med. 2011, 8: e1000413-10.1371/journal.pmed.1000413.

Article   PubMed   PubMed Central   Google Scholar  

Kuntsche E, Knibbe R, Gmel G, et al: Who drinks and why? A review of socio-demographic, personality, and contextual issues behind the drinking motives in young people. Addict Behav. 2006, 31: 1844-1857. 10.1016/j.addbeh.2005.12.028.

Wicki M, Kuntsche E, Gmel G: Drinking at European universities? A review of students' alcohol use. Addict Behav. 2010, 35: 913-924. 10.1016/j.addbeh.2010.06.015.

Kuntsche E, Knibbe R, Gmel G, et al: Why do young people drink? A review of drinking motives. Clin Psychol Rev. 2005, 25: 841-861. 10.1016/j.cpr.2005.06.002.

Lee CM, Patrick ME, Neighbors C, et al: Exploring the role of positive and negative consequences in understanding perceptions and evaluations of individual drinking events. Addict Behav. 2010, 35: 764-770. 10.1016/j.addbeh.2010.03.003.

Park CL: Positive and negative consequences of alcohol consumption in college students. Addict Behav. 2004, 29: 311-321. 10.1016/j.addbeh.2003.08.006.

Dowdall GW: College drinking : reframing a social problem. 2009, Westport, Conn: Praeger

Read JP, Merrill JE, Bytschkow K: Before the party starts: risk factors and reasons for "pregaming" in college students. J Am Coll Health. 2010, 58: 461-472. 10.1080/07448480903540523.

Borsari B, Bergen-Cico D, Carey KB: Self-reported drinking-game participation of incoming college students. J Am Coll Health. 2003, 51: 149-154. 10.1080/07448480309596343.

Shih TH, Xitao F: Comparing response rates from web and mail surveys: a meta-analysis. Field Methods. 2008, 20: 249-271. 10.1177/1525822X08317085.

Article   Google Scholar  

Cook C, Heath F, Thompson RL: A meta-analysis of response rates in Web- or internet-based surveys. Educ Psychol Meas. 2000, 60: 821-836. 10.1177/00131640021970934.

Bennett L, Nair CS: A recipe for effective participation rates for web-based surveys. Assess Eval Higher Educ. 2010, 35: 357-365. 10.1080/02602930802687752.

Miller ET, Neal DJ, Roberts LJ, et al: Test-retest reliability of alcohol measures: is there a difference between internet-based assessment and traditional methods?. Psychol Addict Behav. 2002, 16: 56-63.

Groves RM, Couper M: Nonresponse in household interview surveys. 1998, New York: Wiley

Book   Google Scholar  

Hibell B, et al: The ESPAD report 2007. Substance use among students in 35 European countries. 2007, Stockholm: Council for Informaiton on Alcohol and other Drugs, 2009-

Wechsler H, Davenport A, Dowdall G, et al: Health and Behavioral Consequences of Binge-Drinking in College - A National Survey of Students at 140 Campuses. JAMA. 1994, 272: 1672-1677. 10.1001/jama.1994.03520210056032.

Pedersen ER, Labrie J: Partying before the party: examining prepartying behavior among college students. J Am Coll Health. 2007, 56: 237-245. 10.3200/JACH.56.3.237-246.

Borsari B, Boyle KE, Hustad JTP, et al: Drinking before drinking: Pregaming and drinking games in mandated students. Addict Behav. 2007, 32: 2694-2705. 10.1016/j.addbeh.2007.05.003.

DeJong W, DeRicco B, Schneider SK: Pregaming: an exploratory study of strategic drinking by college students in Pennsylvania. J Am Coll Health. 2010, 58: 307-316. 10.1080/07448480903380300.

Neighbors C, O'Connor RM, Lewis MA, et al: The relative impact of injunctive norms on college student drinking: the role of reference group. Psychol Addict Behav. 2008, 22: 576-581.

Borsari B, Carey KB: Descriptive and injunctive norms in college drinking: A meta-analytic integration. J Stud Alcohol. 2003, 64: 331-341.

Corbin WR, Morean ME, Benedict D: The Positive Drinking Consequences Questionnaire (PDCQ): Validation of a new assessment tool. Addict Behav. 2008, 33: 54-68. 10.1016/j.addbeh.2007.06.003.

Brown SA, Mcgue M, Maggs J, et al: A developmental perspective on alcohol and youths 16 to 20 years of age. Pediatrics. 2008, 121: S290-S310. 10.1542/peds.2007-2243D.

Weitzman ER, Nelson TF, Wechsler H: Taking up binge drinking in college: the influences of person, social group, and environment. J Adolesc Health. 2003, 32: 26-35. 10.1016/S1054-139X(02)00457-3.

D'Alessio M, Baiocco R, Laghi F: The problem of binge drinking among Italian university students: A preliminary investigation. Addict Behav. 2006, 31: 2328-2333. 10.1016/j.addbeh.2006.03.002.

Boot CR, Rosiers JF, Meijman FJ, et al: Consumption of tobacco, alcohol and recreational drugs in university students in Belgium and the Netherlands: the role of living situation. Int J Adolesc Med Health. 2010, 22: 527-534.

Ennett ST, Bauman KE, Hussong A, et al: The peer context of adolescent substance use: Findings from social network analysis. J Res Adolesc. 2006, 16: 159-186. 10.1111/j.1532-7795.2006.00127.x.

Sher KJ, Rutledge PC: Heavy drinking across the transition to college: Predicting first-semester heavy drinking from precollege variables. Addict Behav. 2007, 32: 819-835. 10.1016/j.addbeh.2006.06.024.

Borsari B, Carey KB: Peer influences on college drinking: A review of the research. J Subst Abuse. 2001, 13: 391-424. 10.1016/S0899-3289(01)00098-0.

Collins RL, Parks GA, Marlatt GA: Social Determinants of Alcohol-Consumption - the Effects of Social-Interaction and Model Status on the Self-Administration of Alcohol. J Consult Clin Psychol. 1985, 53: 189-200.

Ennett ST, Foshee VA, Bauman KE, et al: The social ecology of adolescent alcohol misuse. Child Dev. 2008, 79: 1777-1791. 10.1111/j.1467-8624.2008.01225.x.

Durkin KF, Wolfe TW, Clark GA: College students and binge drinking: An evaluation of social learning theory. Sociol Spectr. 2005, 25: 255-272. 10.1080/027321790518681.

Maggs JL, Schulenberg JE: Initiation and course of alcohol consumption among adolescents and young adults. Recent Dev Alcohol. 2005, 17: 29-47. 10.1007/0-306-48626-1_2.

Anderson P, Chisholm D, Fuhr DC: Alcohol and Global Health 2 Effectiveness and cost-effectiveness of policies and programmes to reduce the harm caused by alcohol. Lancet. 2009, 373: 2234-2246. 10.1016/S0140-6736(09)60744-3.

Moreira MT, Smith LA, Foxcroft D: Social norms interventions to reduce alcohol misuse in university or college students. Cochrane Database Syst Rev. 2009, CD006748-

Saltz RF, Paschall MJ, McGaffigan RP, et al: Alcohol risk management in college settings: the safer California universities randomized trial. Am J Prev Med. 2010, 39: 491-499. 10.1016/j.amepre.2010.08.020.

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Acknowledgments

This research was supported by the Université catholique de Louvain, in particular the Vice-Rector for Student and Social Affairs.

The research was also carried out with the help of students in the Faculty of Public Health: Anémone Bruneau, Alessandra Ausloos, Anne-Sophie Dehanne, Céline Denis, François Leruth, and Sandrine Race.

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Vincent Lorant, Pablo Nicaise, Victoria Eugenia Soto & William d’Hoore

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VL conceived the study, carried out the survey, performed the data analysis, and drafted the manuscript. PN participated in the design of the study, carried out the survey, contributed to analysis and helped draft the manuscript. VES contributed to analysis and helped draft the manuscript. WD contributed to analysis and helped draft the manuscript. All authors read and approved the final manuscript.

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Lorant, V., Nicaise, P., Soto, V.E. et al. Alcohol drinking among college students: college responsibility for personal troubles. BMC Public Health 13 , 615 (2013). https://doi.org/10.1186/1471-2458-13-615

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Frequently Asked Questions

About monitoring campus alcohol problems, how do you recommend monitoring the extent of campus alcohol problems and the effects of our intervention efforts.

Monitoring problems and progress on your campus doesn’t have to be complicated, and at least some of the information you need may already be available. First, you’ll need to determine what types of information will be helpful in assessing the nature of alcohol problems at your school and the effects of your efforts. Then, you have several choices: collecting new data, using data already collected on your campus, viewing data from existing surveys or other sources, or combining these options.

Consider collecting three types of information: data on students’ drinking itself, plus its consequences at both the individual and campus levels. To follow are some options for measures and tools.

Possible measures of  student drinking itself :

• Frequency of alcohol use (e.g., number of days per week)
• Quantity of alcohol consumed in a typical drinking day (e.g., number of standard drinks per day)
• Peak quantity of alcohol use (e.g., maximum number of drinks consumed in a single day)
• Frequency of binge drinking (e.g., number of binge drinking occasions in a 2-week period). NIAAA defines binge drinking as 5 or more standard drinks for men, or 4 or more standard drinks for women, in a 2-hour period.

Possible assessments for  individual-level   consequences  of alcohol use that have been validated for use with college students follow:

• Consequences of alcohol use can be assessed using the Rutgers Alcohol Problem Index ( RAPI ) or the Young Adult Alcohol Consequences Questionnaire ( YAACQ ).
• The severity of alcohol-related psychopathology can be assessed using the Alcohol Use Disorders Identification Test‒Concise ( AUDIT-C ) or the Alcohol, Smoking, and Substance Involvement Screening Test ( ASSIST ).

Possible measures of  campus-level   consequences  of alcohol use (many campuses may already be monitoring and tracking at least some of this information):

• Emergency room transports (if possible, record blood alcohol concentration)*
• Sexual assaults
• Alcohol-involved deaths (intentional and unintentional, by both injuries and poisoning)
• Costs for repairs of vandalized property
• Police services: calls, citations, or arrests for alcohol-related offenses
• Incident reports from judicial affairs, student life, or public safety
• Documented problems at sporting events
• Neighborhood complaints
• Mapped locations of problem areas using police/emergency call and response data (heavy drinking areas can also be identified in student surveys)

*Note: An increase in emergency room transports may be a  positive  consequence, depending on the focus of your intervention.

College alcohol intervention experts strongly advise conducting an annual survey of a random sample of students to assess self-reported alcohol use and alcohol-related problems. Several commercial surveys are available to monitor student alcohol use, including but not limited to the Core Institute’s Alcohol and Other Drug Use Survey and the American College Health Association’s National College Health Assessment. Using one of these surveys may increase your ability to compare your institution with other colleges. You also have the option of developing an instrument that you tailor to your own needs, which can incorporate validated measures of alcohol use and its consequences. Collecting survey data both before and after implementation of an intervention will help you to assess its impact.

A word about gathering broad vs. more specific data: Although gathering broad data on alcohol consumption and subsequent problems is important, by itself it may not help you select or evaluate your strategies. You’ll likely need to supplement broad, generic measures with more specific data on student subgroups or specific times and places where problems arise. If, for instance, you choose to focus on alcohol use related to sporting events such as football games, a global measure of ’binge drinking’ is unlikely to demonstrate success. Instead, be sure you either already measure alcohol consumption at those events or start to do so.

Likewise, if you want to see if an individual-level approach is working, it is important to track who has received the intervention and examine changes in their data before and after. A broad survey cannot capture individual-level changes if it mixes data for those who did and did not receive the intervention.

About selecting and implementing strategies

General questions about selecting strategies, why does collegeaim recommend both individual-level and environmental-level strategies.

Your greatest chance for making a safer campus will likely come from a combination of individual- and environmental-level interventions that work together to maximize positive effects. Individual-level strategies, which generally aim to reduce drinking levels in students who drink heavily, may seem a logical place to start. For two reasons, however, it’s best not to stop there:

• Students don’t live in a vacuum. Ready availability of alcohol in the broader campus and community environment has the potential to undercut the impact of a carefully chosen and well-implemented individual-level intervention. Addressing the environment includes enforcement strategies and policies that help to curb access and act as a deterrent, as well as providing consequences to reduce recidivism.
• Students who drink heavily are not the only ones in need of prevention attention. Those who drink moderately have, on average, a lower individual risk of harmful consequences, but they outnumber students who drink heavily. Studies estimate that students who drink at non-extreme levels―four or fewer drinks on occasion―experience one-third to one-half of all college drinking-related problems (Gruenewald, Johnson, Ponicki, & Lascala, 2010; Weitzman & Nelson, 2004). Moreover, students who typically drink moderately, but occasionally engage in heavier drinking, may be at greater risk for negative consequences than students who regularly drink heavily (Toomey & Wagenaar, 2002). This underscores the need to curb student drinking at all levels by reducing alcohol availability, while providing targeted approaches for those who drink most heavily and therefore have the greatest individual risk for harm.

The challenge for campus staff and administrators is to put together a manageable number and combination of strategies that fits the priorities and meets the needs of their campus.

Gruenewald PJ, Johnson FW, Ponicki WR, & Lascala EA.  A dose-response perspective on college drinking and related problems . Addiction, 105(2):257–69, 2010.

Toomey TL, & Wagenaar AC.  Environmental policies to reduce college drinking: Options and research findings . Journal of Studies on Alcohol Supplement, Mar(14):193–205, 2002.

Weitzman ER, & Nelson TF.  College student binge drinking and the “prevention paradox”: Implications for prevention and harm reduction . Journal of Drug Education, 34(3):247‒66, 2004.

We have seen recommendations for multi-component strategies to address college student drinking. Are any particular elements of these strategies more effective than others?

In general, your greatest chance for creating a safer campus will likely come from a combination of individual- and environmental-level interventions that work together to maximize positive effects. The research literature supports combining individual and environmental strategies to reduce availability of alcohol in multiple ways. Individual-level strategies generally aim to assist students identified as problem, at-risk, or alcohol-dependent drinkers. It is important to engage these students as early as possible. Environmental strategies seek to affect the behavior of the overall student population by addressing the factors that accommodate or promote underage and high-risk drinking. Reducing the availability of alcohol in the broader campus and community environment, for example, can have wide-ranging positive effects for all students and the campus as a whole. Within the environmental category, two approaches have particularly strong research evidence—enforcing the minimum legal drinking age and increasing the price of alcohol (or limiting deep discounts and specials on alcohol). Using strategies that focus on these two approaches is suggested.

Keep in mind that many successful strategies have multiple components. In these cases, the value of specific elements or smaller combination of elements has not been identified so it is best to implement the entire intervention as described.

Overall, each campus and community needs to assess what environmental and individual factors are contributing to the drinking rates and related problems on their campus and in the surrounding area and then focus on strategies that best address those factors.

At times we hear about campuses trying out interesting strategies that CollegeAIM doesn’t identify, or that have too few studies to rate effectiveness. Should we follow suit?

Campus officials sometimes implement new, untested strategies that appear to offer quick, low-cost solutions to the complex problem of student alcohol use, or they try novel interventions because they are popular at other schools.  CollegeAIM  was created to give campuses a more systematic and effective way to choose strategies, allowing you to see which ones have a strong evidence base and which do not.

It’s best to implement strategies with evidence of effectiveness first, and only then consider exploring other, less well-evaluated strategies.   At times, though, it may be appropriate to experiment with less well-studied approaches. If you choose to do so, then:

• Check  CollegeAIM  and make sure the strategy is not among those that have been shown to be ineffective, as these are not worth pursuing in any case.
• Check the latest literature for any evidence of effectiveness. If you find supporting research, consider consulting with evaluation experts on campus to discuss the study’s rigor and the implications of its findings.
• Incorporate a strong evaluation component in your planning process. It’s important to conduct a more comprehensive evaluation for untested strategies than for those already rated as effective in  CollegeAIM . For this step, consider partnering with faculty with expertise in prevention or program evaluation.
• Publish the results of the evaluation to aid your own college as well to inform and guide other schools.
• Campuses may also benefit from implementing a new strategy on a small scale, utilizing a  plan-study-do-act cycle  as used in the National College Health Improvement Program. Starting small allows campuses to test and refine strategies before rolling them out on a larger scale.

The following question, submitted by a campus reviewer of  CollegeAIM , regards a serious problem for which staff members are considering a strategy that lacks evidence for effectiveness:

Q:   Our campus had an alcohol overdose death and we are working diligently to prevent this from ever happening again. We had been considering implementing a bystander intervention strategy, but  CollegeAIM  lists this as not having sufficient research to support an effectiveness rating. Many campuses are looking to utilize this strategy, which is often used to prevent sexual assault. Should we give it a try?

A:   After the tragedy of losing a student to an alcohol overdose death, it’s understandable to direct efforts to prevent that specific outcome. In general, though, it is best to focus on interventions that currently have proven effectiveness for reducing alcohol-related problems. Although some research has examined the use of bystander interventions to prevent college sexual assault, we don’t yet have evidence regarding their use to prevent alcohol overdose, and it’s not likely that you can generalize results from one research area to the other. Still, with a very specific intended outcome such as preventing overdoses, a campus might think through whether situations that give rise to alcohol overdose present opportunities for bystander interventions analogous to situations that give rise to sexual assault. If they see similarities and can find a bystander intervention  that has been proven to be effective , then they might adapt that program. It would be important to evaluate it fully to see if it works for their outcome in their setting, and to publish the findings to share with the field.

About specific individual-level strategies

How do we choose strategies to target specific subgroups such as first-year students, student athletes, members of greek organizations, and mandated students.

The chart below lists strategies from the full  CollegeAIM  that (1) focused on specific subgroups and (2) were shown to be effective in the majority of at least four studies. Five strategies for freshmen and two for mandated students met these criteria. Although a handful of strategies focused on student-athletes and members of the Greek system, there were too few studies for each strategy to draw strong conclusions about effectiveness.

Of course, students can be members of more than one high-risk group, such as freshmen who are student athletes or pledges or both. You may wish to consider how these groups overlap on your campus when deciding on a program.

Among the choices listed above, the size and duration of effects on drinking differ, so be sure to check details in the  individual-level  or  environmental-level  summaries before making a selection. And keep in mind that an effective means of reducing alcohol use among all students, including those in these subgroups, is to use multiple strategies and include both individual- and environmental-level approaches.  

Many of our incoming freshmen students arrive on campus with established drinking habits. How can we address this issue?

It is common for students to bring risky drinking habits with them to college and to have expectations that excessive alcohol use is widespread and accepted on campus. Keep in mind that in most cases, the college environment produces a significant increase in alcohol consumption whatever the students’ previous drinking history. The question is whether the school facilitates those tendencies or moderates them.

Be proactive in addressing freshman alcohol use before and soon after they arrive on campus. Anecdotal evidence suggests that the first 6 weeks of enrollment are critical to first-year student success. If students initiate or increase alcohol use during this phase, they may adapt less successfully to campus life.

During the summer prior to matriculation, you might offer a parent-based intervention (see IND-13), which helps facilitate conversations between parents and students about alcohol use and have been shown to reduce drinking during freshmen year (Ichiyama et al., 2009; Turrisi et al., 2001). Online pre-matriculation programs that include personalized feedback also have proven effective over the short term.

Once students are on campus, assess the extent of the problem. If your campus is not assessing the drinking habits of incoming students, set up a system to do so ( see the FAQ on monitoring campus alcohol problems ). If your campus is assessing incoming freshmen, develop rapid ways to process data, identify students at risk for alcohol-related problems, and tailor prevention strategies accordingly.

Early in the fall semester, plan to distribute campus alcohol policies with information about services for students who are struggling with alcohol use, including specific resources for those who identify as being in recovery.

See also the  FAQ on specific subgroups  for a list of individual-level interventions shown effective with freshmen. Keep in mind, however, that the campus environment has a powerful influence on students’ drinking behavior, and that environmental-level strategies have proven effectiveness in reducing the availability and appeal of alcohol for all students.

References Ichiyama MA, Fairlie AM, Wood MD, Turrisi R, Francis DP, Ray AE, et al.  A randomized trial of a parent-based intervention on drinking behavior among incoming college freshmen .  Journal of Studies on Alcohol and Drugs   Supplement , Jul(16):67–76, 2009.

Turrisi R, Jaccard J, Taki R, Dunnam H, & Grimes J.  Examination of the short-term efficacy of a parent intervention to reduce college student drinking tendencies .  Psychology of Addictive Behaviors,  15(4):366–72, 2001.

How can we assess the potential effectiveness of commercial products before we invest our limited resources in them?

Be aware, first, that many commercial products have been modified over time. Before investing in any commercial product, clarify which version of the product you are considering, then request or look for empirical support for that specific version. Research on a previous version may not apply to later ones. Beyond studies conducted by the commercial firm, see if you can find evaluations by independent researchers.

Study quality varies significantly, so you might want to consult with an evaluation expert on your campus, perhaps in the behavioral or social sciences departments, to help assess the scientific rigor and neutrality of the studies. If you are still uncertain about the product, request a free trial period from the product developer and conduct your own evaluation to make sure it addresses your campus goals.

Personalized feedback interventions (PFIs) and personalized normative feedback (PNF) are among the more effective individual-level strategies in CollegeAIM. What are PFI and PNF? Some of these are listed as “generic” strategies—what does “generic” mean? Where can we learn to implement a generic strategy?

Personalized feedback interventions (PFIs) for alcohol provide individual students with tailored feedback on their alcohol use, their risk of potential consequences, their expectations about alcohol’s effects, and their perceptions of campus drinking norms as compared with actual drinking behaviors. Personalized normative feedback (PNF) focuses solely on the norms perception component, which seeks to reduce alcohol use by correcting the common misperception that most students drink to excess.

Some PFI and PNF strategies are branded, or packaged, interventions available from commercial or noncommercial sources (such as eCHECKUP TO GO) or that have a consistent, standardized process (such as 21st birthday cards). The strategies labeled “generic” covered the content areas described above, but did not meet the branded or standardized criteria and had unique designs that were examined in one or just a few studies.

To develop and implement your own version of a PFI or PNF, contact the lead author of one or more papers that tested a generic approach (see References for  Personalized feedback intervention (PFI): Generic/other  and  Normative re-education: Electronic/mailed personalized normative feedback (PNF): Generic/other , respectively) or partner with behavioral scientists on campus to determine what you might include in your program. For ideas on comparative measures, see the methods sections of studies of generic programs. At a minimum, creating a generic PFI or PNF requires these measures:

• For the campus as a whole: How much and how often students drink
• For individuals targeted by the intervention: Drinking behavior, perceived drinking behaviors of peers, alcohol-related problems, and, often, the use of protective strategies such as alternating between alcoholic and non-alcoholic beverages

To provide students with tailored feedback based on their responses, you might recruit a campus colleague or student with expertise in design, computing, or a related field to create a database that allows you to integrate assessment responses into feedback messages. Most studies of PFI and PNF included in  CollegeAIM  delivered this feedback electronically to students, either in person on a computer in a campus office or via a URL sent to the student via email.  

We are planning to conduct routine alcohol screenings and interventions through our health and counseling centers. Which screening tools should we use? Where can we find resources to train staff to deliver screenings and interventions with fidelity?

Start by using screening tools that have been tested in a college setting. One study (Schaus et al., 2009) screened 8,700 students who presented as new patients at a large university health center using a single question about binge drinking in the past 2 weeks (5 or more drinks per occasion for men or 4 or more drinks for women). They then conducted a randomized trial of a two-session intervention with the 2,500 students (28 percent) who screened positive, which indicated that brief interventions delivered by primary care providers in a student health center could reduce high-risk drinking. Additional screening tools are ranked in a comparative study by Winters et al. (2011), which recommends those found most effective in identifying student alcohol problems that required intervention. If you plan to provide interventions along with screening, you can find free and low-cost training and resources through the  Center of Excellence for Integrated Health Solutions , funded by the Substance Abuse and Mental Health Services Administration (SAMHSA) and operated by the National Council for Mental Wellbeing, as well as the  Higher Education Center for Alcohol and Drug Misuse Prevention and Recovery  at Ohio State University.

Resources needed to train staff on the use of an intervention will vary, depending on which intervention you choose (e.g., BASICS, ASTP, or personalized feedback intervention). SAMHSA’s  Evidence-Based Practices Resource Center  provides information on trainers for specific programs. You might also contact the lead authors of papers describing the efficacy of a particular approach to ask for trainer recommendations. In addition, the  Addiction Technology Transfer Center  maintains a registry of trainers and offers training through its regional centers. Whichever course you choose, speak with multiple trainers before entering into an agreement to ensure a good fit. Be sure to request cost estimates and inquire about minimum training requirements; meaningful differences exist across trainers that may make an intervention more or less feasible for your campus.

Schaus JF, Sole ML, McCoy TP, Mullett N, & O’Brien MC.  Alcohol screening and brief intervention in a college student health center: A randomized controlled trial .  Journal of Studies on Alcohol and Drugs Supplement , Jul(16):131‒41, 2009.

Winters KC, Toomey T, Nelson TF, Erickson D, Lenk K, & Miazga M.  Screening for alcohol problems among 4-year colleges and universities .  Journal of American College Health , 59(5): 350‒57, 2011.

About specific environmental-level strategies

Where can we find models of campus-community collaboration that have been effective in reducing student alcohol use and related consequences.

Several campus-community collaborations can serve as models in reducing student drinking, such as the following:

• Impact of a Randomized Campus/Community Trial to Prevent High-Risk Drinking Among College Students (Wolfson et al., 2012);
• Alcohol Risk Management in College Settings: The Safer California Universities Randomized Trial (Saltz et al., 2010);
• Effects of a college community campaign on drinking and driving with a strong enforcement component (McCartt et al., 2009);
• Evaluating a Comprehensive Campus-Community Prevention Intervention to Reduce Alcohol-Related Problems in a College Population (Saltz et al., 2009); and
• Reducing DUI Among US College Students: Results of an Environmental Prevention Trial (Clapp et al., 2005.

The Safer California Universities study, funded by NIAAA, examined a variety of environmental-level strategies that could be implemented on campuses and in their surrounding communities. A  free toolkit  for implementing the collaborative model is available.

References Clapp JD, Johnson M, Voas RB, Lange JE, Shillington A, & Russell C.  Reducing DUI among US college students: Results of an environmental prevention trial .  Addiction , 100(3): 327‒34, 2005.

McCartt AT, Hellinga LA, & Wells JK.  Effects of a college community campaign on drinking and driving with a strong enforcement component .  Traffic Injury Prevention , 10(2):141‒47, 2009.

Saltz RF, Paschall MJ, McGaffigan RP, & Nygaard PMO.  Alcohol risk management in college settings: The Safer California Universities Randomized Trial .  American Journal of Preventive Medicine , 39(6):491–99, 2010.

Saltz RF, Welker LR, Paschall MJ, Feeney MA, & Fabiano PM.  Evaluating a comprehensive campus-community prevention intervention to reduce alcohol-related problems in a college population .  Journal of Studies on Alcohol and Drugs Supplement , Jul(16):21-27, 2009.

Wolfson M, Champion H, McCoy TP, Rhodes SD, Ip EH, Blocker JN, et al.  Impact of a randomized campus/community trial to prevent high-risk drinking among college students .  Alcohol Clinical and Experimental Research , 36(10):1767–78, 2012.

Many alcohol-related deaths among college students nationwide result from driving under the influence. What can alcohol and other drug program staff, working with campus leadership, do about this?

Any strategy that reduces alcohol use by students who drive can help prevent driving under the influence (DUI). In addition to intervening to reduce alcohol consumption directly, if alcohol-impaired driving is a current or potential problem at your school, you may wish to:

• Become familiar with interventions shown effective in reducing alcohol-impaired driving, both in college and in general populations. A number of proven strategies for reducing driving under the influence in general populations also apply to the college population. Some of the most effective include sobriety checkpoints, well-planned mass-media campaigns, and community mobilization. Two good resources are the Guide to Community Preventive Services’  Motor Vehicle-Related Injury Protection: Reducing Alcohol-Impaired Driving  and the National Highway Safety and Transportation Administration’s  Countermeasures That Work  report.
• Encourage and support campus and community police department efforts to implement and enforce evidence-based DUI-prevention measures.
• Seek models of campus-community collaboration toward this goal; see, for example, a program that effectively combined a campus social marketing campaign with increased community law enforcement of DUI laws (Clapp 2005; McCartt et al., 2009).

Most of the strategies you’ll find in  CollegeAIM  focus on reducing student alcohol use as a way of lowering the risks of all harmful consequences. One of the few exceptions is safe-rides program strategy, which is included because many colleges employ this strategy with the hope of reducing DUI. Although college safe-rides programs make sense on the surface and are widely used, to date they have not been studied enough to determine their effectiveness.

Clapp JD, Johnson M, Voas RB, Lange JE, Shillington A, & Russell C.  Reducing DUI among US college students: Results of an environmental prevention trial .  Addiction , 100(3): 327‒34, 2005.

McCartt AT, Hellinga, LA, & Wells JK.  Effects of a college community campaign on drinking and driving with a strong enforcement component .  Traffic Injury Prevention , 10(2):141‒47, 2009.

About responding to potential objections or challenges

How do i respond to people who say, “college drinking has been around forever and students are always going to drink, so why bother”.

A good counterpoint question is, “With so many students drinking alcohol and so many negative consequences, how can we  not  take action?” Supporting points include:

• Campuses are responsible for the safety and well-being of their students. The consequences of underage and high-risk student drinking are many, sometimes lifelong, and often severe.
• Campuses that do not use evidence-based prevention strategies may put themselves at legal risk if there is civil litigation.
• Colleges have a mission to educate students and prepare them for successful careers and futures. Alcohol use interferes with this mission by lowering academic engagement and grades and by raising the risk of “stopping out” or dropping out.
• If campuses do not change alcohol access, expectations, norms, and consequences for violations, then students will correctly assess that the costs of alcohol use are low while perceiving high social benefits. Student drinking and problems will likely continue at the same levels.
• A significant percentage of college students drink very little or not at all, and their safety and quality of life is often negatively affected by the drinking of others. Campuses have a responsibility to protect them and their rights.

Many public health problems share the history of “being around forever” (e.g., smoking cigarettes, traffic injuries and fatalities). However, many of these same public health problems have demonstrated progress over many years of concerted efforts. While we cannot eliminate them altogether, we can reduce their toll.

Some people continue to wonder if campus officials could better manage student drinking if the minimum legal drinking age were reduced to age 18. What does the research say?

Research consistently affirms the public health benefits of setting the minimum legal drinking age (MLDA) at 21 years old. In short, the age 21 MLDA saves hundreds of lives every year. It also reduces alcohol-related harm in the long run as well as the short term. Each year, up to 900 lives are saved because of fewer alcohol-related traffic fatalities among underage drivers, according to estimates from the National Highway Transportation Safety Administration. Moreover, a recent literature review (DeJong & Blanchett, 2014) states that the age 21 MLDA has reduced alcohol consumption among youth, along with a number of associated harms they might have experienced as adults, including alcohol dependence and suicide. The review also notes that the majority of U.S. adults age 18 and up oppose lowering the drinking age to 18, and concludes that “the current law has served the nation well by reducing alcohol-related traffic crashes and consumption among youth, while also protecting drinkers from long-term negative outcomes.”

DeJong W, & Blanchette J.  Case closed: Research evidence on the positive public health impact of the age 21 minimum legal drinking age in the United States .  Journal of Studies on Alcohol and Drugs , 75(Suppl. 17):108‒15, 2014.

How do I respond to comments that efforts to reduce alcohol-related problems on our campus may just shift them to off-campus locations?

Research suggests that displacement does not necessarily occur nor is even likely, particularly when campuses and communities collaborate in prevention efforts. Studies of campuses and communities that took part in two major multisite programs―the Safer California Universities Randomized Trial (Saltz et al., 2010) and the “A Matter of Degree” program (Nelson, Weitzman, & Wechsler, 2005)―did not find evidence of problem displacement among drinking locations or an increase in drinking and driving. Even had there been some displacement, however, a key question is whether the overall (or “net”) effect is for the better.

To reduce the likelihood of problem displacement, develop community partnerships or form a coalition to implement complementary strategies. Because drinking contexts and alcohol problems are naturally dynamic, one important joint activity is to monitor where students drink and experience problems. In addition, work with local law enforcement and outlets to reduce access and enforce consequences. Alcohol-related problems should not shift off-campus unless access to alcohol is easier there, and the consequences weaker.

Nelson TF, Weitzman ER, & Wechsler H.  The effect of a campus-community environmental alcohol prevention initiative on student drinking and driving: Results from the “A Matter of Degree” program evaluation .  Traffic Injury Prevention , 6(4):323‒30, 2005.

Saitz RF, Paschall MJ, McGaffigan RP, & Nygaard PMO.  Alcohol risk management in college settings: The Safer California Universities Randomized Trial .  American Journal of Preventive Medicine , 39(6):491–99, 2010.

Campus revenue is declining. How can we build a case for investing in prevention?

A strong argument is that  not  taking steps to reduce student alcohol use and related problems has its own costs, many of which affect the campus budget and may be more expensive than the prevention costs in the long run.

For colleges, student alcohol use contributes to the costs of campus security and health care as well as repair of damaged property. Colleges also could be at risk of civil litigation for failing to enact prevention measures before serious injuries or damages occur. In addition, alcohol-related attrition can generate substantial costs to recruit and enroll replacement students.

For students and their families, student alcohol use can result in poor academic performance, withdrawal from school, and difficulty in finding post-graduate employment, which may in turn also reduce alumni donations. Each of these outcomes represents the decreased value or loss of an investment in a college education.

Consider building your case for prevention around the economic benefits of addressing campus alcohol-related problems. Any plans to improve student retention, achievement, and post-graduate employment rates should include efforts to reduce student alcohol misuse and related harm.

What can we accomplish with a limited budget?

A good first step in determining where to invest limited funds is to take a look at your existing prevention and intervention strategies. Are there opportunities to redirect financial or staff resources from ineffective strategies to effective strategies? In  CollegeAIM , strategies with higher effectiveness and lower costs are listed in the upper-left quadrant.

Collaborate with local community organizations, such as local law enforcement, to share costs of implementing and enforcing environmental prevention strategies. Additional avenues for collaboration and cost-cutting can include partnering with other local and regional colleges to diffuse costs in areas such as:

• Training--by holding joint training workshops
• Service delivery--by sharing health care providers, intervention trainers, and emergency transport services
• Ongoing maintenance of services--by sharing computer support staff for personalized feedback interventions and staff supervisors to maintain fidelity of in-person interventions

We’ve tried prevention strategies in the past and were not successful—how can we stay motivated?

It’s important to think long-term about success. Changes in social norms or the local drinking environment, for example, happen slowly. In addition, consider how you are defining success. An increase in alcohol-related transports to the emergency department may appear to indicate a lack of success with a prevention program, but it actually may represent a reduction in severe alcohol-use consequences because students are more willing to call for help for a friend who needs it.

If your efforts are not achieving their stated goals, it may be that the target of your efforts, your measures of success, or both were too broad to make it possible to show effectiveness.  For example, you may be quite successful in reducing intoxication at football games, but a global measure of alcohol consumption would be unlikely to reveal that improvement. Focus on a specific time, place, subgroup, or combination of these variables, then use an outcome measure tailored to that strategy.

In moving forward:

• Think comprehensively and focus on a plan of action using evidence-based strategies.
• Engage a variety of constituencies. Work collaboratively with campus and community partners to strengthen the commitment to prevention. Students can be a source of energy and ideas for enhancing the safety and quality of life on their campus.
• Set realistic, measurable objectives.
• Gather new evaluation data to chart your progress ( see the FAQ on monitoring campus alcohol problems ).
• Keep up with the research literature ( see the FAQ on this topic ).
• Find ways to institutionalize structures and programs to make them less susceptible to changes in administration.
• Celebrate the small successes along the way.

About  CollegeAIM  and ongoing research

How did the research teams arrive at ratings for the various strategies.

Six leading college alcohol intervention researchers worked in two teams of three, one team for individual-level approaches and one for environmental-level approaches, to produce ratings for the strategies. Their goal was to rate not only the interventions’ relative effectiveness, but also practical measures such as the relative costs to adopt and maintain the strategies and the magnitude of the implementation barriers. In addition, they assessed the quality and amount of research and coded for helpful descriptors such as public health reach, target and research populations, and staffing expertise needed. 

The teams first searched the research literature through 2012 to find studies and reviews for each strategy. Seminal studies from 2013 were added following the first round of reviews. The ratings were derived through both quantitative and qualitative processes. Researchers used the quantitative methods specified in the matrices’ legends and footnotes to estimate the effectiveness and amount of research for individual-level strategies, as well as the amount and quality of research for the environmental-level strategies. For estimated effectiveness for the environmental strategies, as well as estimated costs and barriers for all strategies, they used a qualitative process of assigning rating codes independently—based on literature reviews, direct knowledge of strategies in practice, or both—then resolving discrepancies through discussion and referral to the literature to reach a consensus.

Once the  CollegeAIM  analysis was completed, an additional 10 prominent college alcohol intervention researchers reviewed the many ratings and designations—720 data points in total—and provided insightful comments. The teams discussed all comments with NIAAA and incorporated the feedback into revisions, which then went through additional cycles of review and revision. Thus,  CollegeAIM  reflects a multistage process involving analysis, consensus, and review by a total of 16 prominent professionals with expertise in addressing alcohol issues on college campuses.

To keep  CollegeAIM  current, NIAAA plans to update it regularly.

What are some ways to keep up with the research literature on college alcohol interventions?

One convenient way is to have research abstracts or summaries delivered by email on a regular basis. A good source of curated studies is newsletters from professional support organizations, such as the National Center on Safe Supportive Learning Environments’  Higher Education e-Digest  or the  Higher Education Center’s   UReport .

You can also set up automated searches for college alcohol intervention studies, using the National Institutes of Health’s  PubMed  database. For example, an NIAAA search under “AA [grant support] college student alcohol intervention” produced this  list of studies . PubMed offers a  tutorial  on how to automate these searches and have results e-mailed to you, or your campus library might help you set this up.

If you find a new study that appears useful, be sure to assess the rigor of the study methods before making program decisions. If this is not your area of specialty, it’s a good idea to consult with campus experts in the behavioral and social sciences for their perspectives on the study methods and conclusions. ( See the FAQ on strategies that are less well studied .)

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Heavy Drinking in College Students: Who Is at Risk and What Is Being Done About It?

Problem drinking and related consequences are a major social issue plaguing college campuses across the United States. Each year, alcohol is responsible for fatalities, assaults, serious injuries, and arrests that occur among college students. The authors review and discuss the risk factors, drinking patterns, and consequences that are relevant to the general student population. In addition, the authors highlight individuals at an increased risk of experiencing alcohol-related problems, such as Greek-letter social organization members and student athletes. The authors also discuss the interventions that attempt to reduce risky drinking and related problems in these subgroups as well as the future directions for research.

COLLEGE STUDENT HEAVY DRINKING and alcohol-related consequences are major social problems in the United States ( Perkins, 2002 ; Wechsler, Dowdall, Maenner, Gledhill-Hoyt, & Lee, 1998 ). Twenty years of research has revealed that the highest proportion of heavy drinkers and individuals with diagnosable alcohol-use disorders and multiple substance dependencies are in the age range encompassing over 90% of all enrolled college students, the majority of these individuals being between the ages of 18 and 21 ( Grant, 1997 ; O'Malley & Johnston, 2002 ). Alcohol drinkers are more likely to have been insulted by others; been confronted with unwanted sexual advances; been a victim of date rape or sexual assault; been in a serious argument or quarrel; been pushed, hit, or assaulted; had their property damaged; been in a situation where they had unplanned sexual activity; put themselves in situations where they were more susceptible to sexually transmitted diseases such as HIV; been injured or had life-threatening experiences; driven while intoxicated, or ridden in a car with an intoxicated driver ( Abbey, 2002 ; Cooper, 2002 ; Hingson, Heeren, Zakocs, Kopstein, & Wechsler, 2002 ). Reports such as the following are not uncommon ( Turrisi, Jaccard, Taki, Dunnam, & Grimes, 2001 ):

“My friend had a drinking contest with her boyfriend. They each had five shots of Wild Turkey, two beers, and then started a ‘power hour’ or ‘century’––one shot of beer per minute for 60 minutes. My friend began falling down and looked ill. She laid down to go to sleep and began throwing up for two hours straight. She rolled over and almost choked in her vomit” (anonymous college freshman).

Heavy drinkers are not the only ones who have experienced adverse consequences. Nondrinking college students have their own stories to tell about how others' drinking has affected them:

“My roommate came home very drunk. I didn't want to deal with it. I had three tests the next day and had planned to study instead of playing ‘mom.’ I was really scared though. She was throwing things everywhere and crying. She really stunk and was disgusting. I especially didn't want her to puke in my room. I flunked one test and skipped another; I was so drained. I didn't speak to her at all the next day” (anonymous freshman college student).

Although these anecdotal reports of experiences may be enlightening and motivating to administrators, teachers, public health officials, and anyone else close to students, the epidemiological data reveal just how widespread and damaging the consequences of college student drinking can be. For example, Hingson and colleagues (2002) estimated that approximately 42%, or over 3 million of the 8 million students attending colleges in the U.S. have consumed five or more drinks during a single drinking occasion within the past 30 days. Alcohol is cited as being responsible each year for 1,400 student deaths; 500,000 unintentional injuries; 600,000 student assaults; 112,000 arrests; and 2.1 million cases (approximately 1 in 4) of driving under the influence of alcohol (Hingson et al.).

For some students, excessive alcohol consumption and the related negative consequences emerge after matriculation to college. However, research also consistently indicates that, for many students, excessive consumption in college represents a continuation or escalation of drinking patterns established earlier ( Baer, Kivlahan, & Marlatt, 1995 ; Gonzalez, 1989 ; Leibsohn, 1994 ; Lo & Globetti, 1993 ; Schulenberg & Maggs, 2000 ; Wechsler, Davenport, Dowdall, Moeykens, & Castillo, 1994 ; Wechsler, Dowdall, Davenport, & Castillo, 1995 ). Researchers have argued for improved efforts to detect and prevent heavy drinking earlier, such as the first year of high school or sooner ( Baer, 1993 ; Baer et al., 1995 ; Johnston, O'Malley, & Bachman, 1997 ; Schulenberg, O'Malley, Bachman, Wadsworth, & Johnston, 1996 ). Although early detection and prevention efforts may reduce high-risk college drinking, there is sufficient evidence that additional efforts should target the transition period between high school and college and post-matriculation. For example, a significant number of students adopt heavy-drinking tendencies for the first time during their first year in college. Consider a random sample of 1,000 male high school students. Johnston and colleagues (1997) found approximately 10% of men in high school have heavy-drinking tendencies. On the basis of a study by Wechsler, Issac, Grodstein, and Sellers (1994) , approximately 80% of these high school heavy episodic drinkers are likely to be college student heavy drinkers. Using estimates from Wechsler, Issac, and colleagues (1994) , of the remaining 900 students who did not drink heavily in high school, approximately 1 out of 4 (225 individuals) develop heavy-drinking tendencies in college. Thus, of the 305 men who exhibit heavy-drinking tendencies in college, a substantial percentage may not be identified as being at-risk as a function of their drinking behavior in high school. Reports with different estimates of high school and college drinking tendencies yield slightly different results, but the example suggests that a significant percentage of individuals develop heavy-drinking tendencies in college who were not heavy drinkers in high school. Intervention efforts aimed to reduce the number of new heavy drinkers are an important component of strategies geared toward lowering heavy drinking among college students.

In response to these reports, college administrators have adopted more intensive on-campus alcohol and drug abuse education and prevention programs ( Dodge, 1991 ; Kunz, Irving, & Black, 1993 ; Magner, 1988 ; Morritz, Seehafer, & Maatz-Majestic, 1993 ). Despite efforts, the magnitude of college student drinking and alcohol-related problems has not decreased significantly in the past 15 years ( Hingson et al., 2002 ; Schuckit, Klein, Twitchell, & Springer, 1994 ; Wechsler, Issac, et al., 1994 ). However, the climate of research on college drinking has changed dramatically in recent years, primarily because of significant efforts by the National Institute on Alcohol Abuse and Alcoholism (NIAAA). In 1998, the NIAAA established a Task Force on College Drinking that include college presidents and research scientists. The Task Force focused on assessing the current needs of college administrators and accumulating findings from the scientific literature that could be useful in addressing those needs. Concurrently, the NIAAA developed funding mechanisms to support the development and evaluation of efficacious interventions and to assess issues in the implementation of evidence-based interventions on college campuses. Together, these efforts have resulted in good reviews of college student drinking, morbidity, and mortality (Hingson et al.); college environments ( Presley, Meilman, & Leichliter, 2002 ); and individual-focused interventions ( Larimer & Cronce, 2002 ). They have also resulted in decreases in risky sexual behavior ( Cooper, 2002 ), sexual assaults ( Abbey, 2002 ), and other negative sexual consequences of drinking ( Perkins, 2002 ). In addition, there have been several evidence-based intervention approaches that are now being evaluated for implementation on campuses or in the neighboring communities that were not in existence 10 years ago ( Barnett et al., 2004 ; Fromme & Corbin, 2004 ; Neighbors, Larimer, & Lewis, 2004 ; Turrisi et al., 2001 ). Despite these advances, there are still areas of need. For example, there remain groups of college students who are at an increased risk of engaging in risky drinking and experiencing alcohol-related problems. Data from efficacy studies that target these individuals are limited, and more research is necessary to develop sound interventions. In our review, the focus is on groups of students who research shows are at the greatest risk for heavy drinking and alcohol-related problems: Greek-letter social organization members and college athletes. We also examine the etiology and intervention efforts to illustrate recent advances and highlight areas where research is needed.

Members of Greek-Letter Social Organizations

Within the general college student population, members of social fraternities and sororities are more likely than are other students to engage in high-risk drinking and substance use and to experience related problems ( Alva, 1998 ; Borsari & Carey, 1999 ; Caron, Moskey, & Hovey, 2004 ; Cashin, Presley, & Meilman, 1998 ; McCabe et al., 2005 ; Meilman, Leichliter, & Presley, 1999 ; Presley et al., 2002 ; Weschler, Kuh, & Davenport, 1996 ). In particular, men living in social fraternity houses drink more in terms of both quantity and frequency, and as a result, experience more adverse consequences than do non-Greek student members (Alva; Borsari & Carey; Meilman et al.; Wechsler et al., 1996 ). Specifically, McCabe and colleagues found that, compared with nonmembers, significantly more fraternity and sorority members (70% of men and 50% of women) engaged in binge drinking (defined as consuming five or more drinks during a single drinking occasion for men and four or more drinks for women) during the 2 weeks prior to the study (42% of men, 29% of women). In addition, Cashin and colleagues reported that the average number of drinks consumed per week is significantly higher for Greek fraternity and sorority members (men: 12 drinks per week, women: 6 drinks per week) than for non-Greek fraternity and sorority members (men: 6 drinks, women: 2 drinks). Furthermore, although many of the negative consequences experienced by Greek members are prevalent among college students (e.g., hangovers, blackouts, unplanned sexual activity, and academic problems), fraternity and sorority members report experiencing these consequences at a much higher rate than do nonmembers (see Table 1 ). However, there are exceptions. Larimer, Anderson, Baer, and Marlatt (2000) found that, within sororities, women who were classified as low- and high-frequency drinkers experienced similar rates of adverse consequences. However, the total number of such consequences was lower for the sorority women than for high-frequency female drinkers who lived in the residence halls, which suggests that being a sorority member may provide some protection against negative consequences for high-frequency drinkers. In addition to consequences typically experienced by college students, a significant subset of social and fraternity sorority members also report more severe symptoms typically associated with the diagnosis of alcohol dependence (Diagnostic and Statistical Manual of Mental Disorders [ DSM-IV ]; American Psychological Association, 1994 ), such as difficulty stopping or controlling drinking, increased tolerance, and withdrawal. Successfully implementing interventions to reduce problem drinking in this environment has been challenging because fraternity members generally do not express concern about their drinking behavior and live in an environment that often supports heavy drinking ( Borsari & Carey, 1999 ; Dielman, 1990 ; Goodwin, 1989 ; Harrington, Brigham, & Clayton, 1999 ; Klein, 1989 ; Larimer et al., 2000 ; Shore, Gregory, & Tatlock, 1991 ; Tampke, 1990a , 1990b ).

Percentage of Greek and Non-Greek Members Experiencing Selected Drinking-Related Consequences

Note. Greek membership refers to a person's membership in either a fraternity or sorority. N = 28,341 (from 61 universities). A total of 25,411 students answered the necessary survey items from “Alcohol use in the Greek system: Follow the leader?” by J. R. Cashin, C. A. Presley, and P. W. Meilman, 1998, Journal of Studies on Alcohol, 59 , pp. 63−70. Reprinted with permission from the author.

Individual characteristics have an impact on individual's decisions to join the Greek social system. Research has shown that individuals with positive expectancies and attitudes toward alcohol use tend to gravitate toward environments and social groups that foster high-risk drinking ( Borsari & Carey, 1999 ; Cashin et al., 1998 ; Klein, 1992 ). Furthermore, these individuals also tend to feel more favorable toward heavy drinking in both themselves and others, and have experience with alcohol and substance abuse ( Baer et al., 1995 ; Cashin et al.; Klein; Larimer et al., 2000 ; McCabe et al., 2005 ). Research has shown that individuals who engaged in heavy alcohol and substance use and experienced related problems in high school often self-select into fraternities and sororities ( Larimer et al., 2000 ; McCabe et al.; Read, Wood, Davidoff, McLacken, & Campbell, 2002 ); however, this pattern is more prevalent in men than in women ( Baer et al., 1995 ). This self-selection process into fraternities in particular, is higher for men who tend to place a high priority on socialization, peer relationships, and have a higher need for acceptance (Borsari & Carey; Marlowe & Auvenshire, 1982 ; Wilder, Hoyt, Surbeck, Wilder, & Carney, 1986 ). Greek affiliation and constant exposure to other fraternity and sorority members is thought to reinforce or exacerbate preexisting risky drinking patterns ( Bartholow, Sher, & Krull, 2003 ; Lo & Globetti, 1995 ; McCabe et al.).

Peer influences also play a role in the heavy drinking of fraternity and sorority members. The presence of heavy-drinking peers significantly increases alcohol consumption, whereas exposure to light-drinking peers has the reverse effect ( Caudill & Marlatt, 1975 ). Considering that most of the alcohol consumed by Greek social members takes place in fraternity houses ( Arnold & Kuh, 1992 ; Wittman, 1989 ), observing heavy drinking by peers is common. Inflated normative perceptions such as quantity, frequency, and acceptability of drinking among Greek members have been shown to perpetuate heavy drinking ( Baer, Stacy, & Larimer, 1991 ; Goodwin, 1989 ; Larimer, 1992 ; Larimer, Turner, Mallett, & Geisner, 2004 ; Sher, Bartholow, & Nanda, 2001 ). Typical fraternity members approve of heavy alcohol use and perceive it as a common behavior among peers ( Borsari & Carey, 1999 ; Cashin et al., 1998 ). Unfortunately, alcohol use is valued and directly influences the popularity of being in a fraternity ( Larimer, Irvine, Kilmer, & Marlatt, 1997 ). The perceived norms that value heavy drinking, in conjunction with peer modeling of excessive drinking behavior within the Greek system, constitute barriers to individual change, and serve as challenges to implementing successful interventions, particularly in heavy-drinking Greek-letter social organizations ( Harrington et al., 1999 ; Larimer et al., 1997 ).

Interventions Aimed at Greek Fraternity and Sorority Members

With little research to guide intervention efforts for drinking problems that occur within the Greek social system, and the potential for liability of related accidents, injuries, and deaths on campus, many universities have implemented policies that aim to reduce problem drinking and to manage liability. Although there is a growing body of literature on policy interventions to reduce drinking and its related consequences ( Wagenaar & Toomey, 2002 ), this continues to be an area in which research is lacking with respect to college students. In addition, some research suggests that such policies have actually increased risky drinking behavior and its related negative consequences among college students ( George, Crowe, Abwender, & Skinner, 1989 ; Gonzalez, 1990 ; Schall, Kemeny, & Maltzman, 1991 ; Williams, Kirkman-Liff, & Szivek, 1990 ). Considering fraternity and sorority members, Kilmer, Larimer, Parks, Dimeff and Marlatt (1999) evaluated a policy by the University of Washington to replace kegs with a “bring your own booze” policy at Greek social functions. The policy also prohibited using pooled funds to buy alcohol and mandated enforcing laws prohibiting alcohol consumption by individuals less than 21 years of age. In addition, both fraternities and sororities were required to participate in one educational program per quarter that focused on issues of alcohol and related topics. Kilmer and colleagues found that, one year after implementation, fraternity and sorority members were drinking less frequently, but consuming more alcohol per occasion. These findings suggest that the policy inadvertently increased risky drinking behavior in an attempt to curb it.

As shown in Table 2 , starting in the early 1990s, individual prevention approaches began to emerge with demonstrated efficacy among fraternity and sorority members ( Baer et al., 1992 ; Fournier, Earhart, Glindemann, & Geller, 2004 ; Garvin, Alcorn, & Faulkner, 1990 ; Larimer et al., 2001 ; Marlatt et al., 1998 ; Thompson, 1996 ; see Larimer & Cronce 2002 , for a comprehensive review of college student drinking interventions). Both Baer and colleagues and Marlatt and colleagues utilized a brief motivational enhancement approach combined with feedback and skills training of both members and nonmembers of fraternities and sororities. Incoming first-year students, who were classified as being at high risk for alcohol problems on the basis of their high school drinking patterns, were provided with a 1-hour feedback interview to reveal their alcohol use, related consequences, expectancies, and beliefs. Although the content of this feedback interview was similar to other cognitive-behavioral skills training approaches, the style or process of the interview was developed on the basis of the theory and techniques of motivational interviewing ( Miller & Rollnick, 2002 ). Motivational interviewing emphasizes the provision of accurate, nonjudgmental feedback to the client's risks and experience of alcohol-related problems, while avoiding labeling, confrontation, or specific interviewer-generated goals for behavior change. With fraternity and sorority members, results showed that individuals who received the feedback interview reported less drinking and fewer adverse consequences than those in the control group after 3 months. At a 3-year follow-up, participants had maintained these reductions. It is important to note that, although the study demonstrated modest success in reducing drinking among Greek system members, these students, particularly fraternity members, continued to report significantly heavier drinking and significantly more adverse alcohol-related consequences than did students who were not members of fraternities or sororities.

Outcomes of Interventions That Successfully Reduced Alcohol Consumption in Members of Fraternities and Sororities ( Larimer & Cronce, 2002 , 2005)

Larimer and colleagues (2001) randomly assigned pledge class (first year) members of fraternities to receive either a 1-hour feedback session about their self-reported drinking behaviors, beliefs, and related consequences, or treatment as usual (typically a 1-hour didactic alcohol education lecture delivered to the entire fraternity chapter). Each feedback session included a skills-training component and was delivered using the principles of the motivational interviewing approach described earlier ( Miller & Rollnick, 2002 ). At the 1-year follow-up, the study showed a significant reduction in alcohol consumption among pledge class members who received the intervention compared with participants in the treatment-as-usual group. Larimer and colleagues noted no significant differences in the number of adverse drinking consequences reported by participants in the two groups.

Garvin and colleagues (1990) obtained similar results with a selective prevention approach. Entire pledge classes of four fraternities were randomly assigned to four conditions: (a) a typical alcohol education class, (b) a cognitive–behavior alcohol skills training class, (c) training in self-monitoring of alcohol consumption alone, or (d) no intervention. Overall, participants in both the skills training condition and the self-monitoring-only condition showed significant reductions in drinking as compared with those in the other two conditions at the final 6-month follow-up assessment. Again, participants who received these interventions continued to drink more than did nonmembers of fraternities, reporting an average of 15−20 standard drinks per week at follow-up. The study had several limitations, including a small sample size and a possible confound between program effects and organization effects in that the authors did not take into account the differences in alcohol consumption across particular fraternities. Another limitation is that some fraternities tend to consider the pledge period an initiation process where there may be hazing that involves peer pressure to drink. Future research should address such practices and the implications for alcohol abuse and consequences in these social contexts.

Research has also shown that members will drink less at fraternity parties if given incentive to do so. For example, Fournier et al. (2004) gave students information about blood alcohol content (BAC), including personalized BAC charts, and told them they were eligible to win a cash prize if they kept their blood alcohol level below .05 during the course of the evening. Individuals at such intervention parties had significantly lower blood alcohol levels compared with individuals at control parties.

Although some successful interventions have emerged, certain interventions that have shown promise in the general college student population have not shown the same success in fraternities and sororities. For example, interventions aimed to correct inaccurate and inflated peer norms of drinking behavior and approval of heavy drinking have faced numerous barriers in the Greek organization population ( Barnett, Far, Mauss, & Miller, 1996 ; Carter & Kahnweiler, 2000 ). First, research has indicated that, in some of the heaviest-drinking fraternities, members accurately estimate the normative drinking patterns of their fellow members instead of overestimating it ( Larimer et al., 1997 , 2004 ). Second, the actual normative drinking behavior among fraternity and sorority members, particularly among the men, is extremely risky, and, therefore, it is difficult to argue that a healthy drinking norm exists about which to inform students. Further, general campus drinking norms may not be viewed as relevant to Greek members, and thus may be unlikely to influence their behavior (Carter & Kahnweiler). This has led to questions regarding the efficacy of normative feedback interventions for curbing fraternity and sorority drinking (Carter & Kahnweiler), though others have argued that normative feedback is an important component of efficacious interventions for this population ( Larimer et al., 2001 , 2004 ).

Student Athlete Drinking

Athletes, like university Greek-letter social organization members, are considered a high-risk college group for problem alcohol use and associated negative consequences ( Larimer & Cronce, 2002 ; Meilman et al., 1999 ). Early studies suggested that athletic participation served as a protective factor for students from alcohol-related issues ( Strauss & Bacon, 1953 ); however, although research focusing on drinking etiology in college athletes is limited, recent research shows that athletes drink more frequently and consume more per occasion than do their nonathlete peers ( Hildebrand, Johnson, & Bogle, 2001 ; Leichliter, Meilman, Presley, & Cashin, 1998 ; Nattiv & Puffer, 1991 ; Nelson & Wechsler, 2001 ; Selby, Weinstein, & Bird, 1990 ; Wechsler, Fulop, Padilla, Lee, & Patrick, 1997 ). Furthermore, existing studies on athlete drinking tendencies show that, as athletic participation increases, so does alcohol consumption (Leichliter et al.; Meilman et al.; Nattiv & Puffer; Wechsler et al., 1997 ).

In studies involving college student athletes, researchers collected data on substance use and abuse habits from athletes who competed in various sports in a large, nationally representative sample of collegiate institutions in all National Collegiate Athletic Association (NCAA) divisions (i.e., I, II, III). In the multiple studies examining collegiate athlete alcohol use, consistent themes have emerged from the large data collections. Overall, researchers have identified athletes as consuming more alcohol, and experiencing a higher rate of alcohol-related negative consequences as compared with nonathletes. In additional, more athletes than nonathletes have been categorized as heavy episodic, or binge, drinkers ( Hildebrand et al., 2001 ; Leichliter et al., 1998 ; Nelson & Wechsler, 2001 ; Wechsler, Davenport, Dowdall, Grossman, & Zanakos, 1997 ). Some studies have shown that college and high school athletes begin drinking at earlier ages (Hildebrand et al.; Thombs, 2000 ; Wechsler et al., 1997 ), and engage in more risky behaviors than do individuals who have never been athletes (Hildebrand et al.; Leichliter et al.; Meilman et al., 1999 ). In comparisons between men and women athletes, few significant gender differences have been observed in past studies; however, drinking pattern differences based on race or ethnicity are notable in that Caucasian athletes drink at higher rates than do African American or other racial and ethnic group athletes ( Green, Uryasz, Petr, & Bray, 2001 ; Hildebrand et al.; Leichliter et al.; NCAA, 2001 ; Nelson & Wechsler; Wechsler et al., 1997 ). In addition, Leichliter et al. found no support for the hypothesis that athletes in leadership positions use alcohol more responsibly than do other team members.

A number of studies have been conducted to examine athlete groups independently of nonathlete groups to gain a sense of drinking patterns, rates, and experiences related to alcohol use. In the 2001 NCAA Study of Substance Use Habits of College Student Athletes, a comprehensive study of 21,225 athletes reported 79.5% of collegiate athletes drank alcoholic beverages in the past 12 months. According to student athlete data from this study, 65.4% reported having had a hangover, 52.4% reported nausea or vomiting, 43.7% reported doing something they later regretted, 43.2% reported missing a class, 35.1% got into an argument or fight, 33.3% reported doing poorly on a test, 29.7% drove a car while under the influence, 29.3% had a memory loss, 20% have been hurt or injured, 17.5% have been in trouble with police, residence hall, or other college authorities, and 11.5% have been taken advantage of sexually as a direct result of using alcohol or drugs in the past 12 months. Despite these high rates of negative consequences associated with student athlete drinking, drinking rates have remained high over the past decade, with only slight decreases in overall alcohol consumption ( NCAA, 2001 ). Interestingly, studies have shown drinking decreases among athletes during their competitive sport season ( Bower & Martin, 1999 ; Martin, 1998 ; NCAA; Selby et al., 1990 ; Shields, 1998 ). It is also important to note that few differences have been observed in sport affiliation; variations in men's alcohol consumption range from 68.8% of track and field athletes (low) to 95.6% of ice hockey athletes (high). For women's sports, 71.3% of track and field athletes (low) report drinking, compared with the highest reported group, lacrosse players (93.4%; NCAA). Finally, comparisons among NCAA Division athletes show Division III athletes consuming alcohol at higher rates than their Division I and II peers ( Green et al., 2001 ; NCAA).

With an understanding of the high prevalence of student athlete alcohol consumption and its connected consequences, the reasons behind alcohol use and nonuse have been widely debated. A variety of motives for alcohol use among athletes have been suggested, including a coping mechanism or for conformity ( Damm & Murray, 1996 ), the increase in social opportunity and availability ( Tricker, Cook, & McGuire, 1989 ), and the experience and enhancement gained from use ( Nattiv, Puffer, & Green, 1997 ). With such a wide range of suggested motives and beliefs, it is difficult to determine the underlying reasons for heavy drinking in this population. Studies have identified social purposes (drinking to feel good, peer influence, drinking to deal with the stress of school and athletics, and drinking to have fun) as primary reasons for alcohol use ( Bower & Martin, 1999 ; Green et al., 2001 ; Martin, 1998 ). Also, it is important to identify the reasons athletes choose not to use alcohol. Health, sports performance, coaches' rules, the taste, and weight gain were the primary deterrents for individuals (Bower & Martin; Martin).

An additional group of studies has been conducted in an attempt to identify underlying factors associated with high drinking rates of student athlete populations, which have led to a more comprehensive view of athlete drinking etiology ( Miller, Miller, Verhegge, Linville, & Pumariega, 2002 ; Storch, Storch, Killiany, & Roberti, 2005 ; Wilson, Pritchard, & Schaffer, 2004 ). Miller and colleagues focused on underlying psychiatric symptoms in which athlete populations have reported higher levels of alcohol abuse in connection with higher levels of depressive and other psychiatric symptoms. As participants' severity of depressive and general symptoms increased, so did their level of alcohol misuse (Miller et al.). However, Wilson and colleagues (2004) noted that men athletes tended to drink for social reasons and to “get high.” In this study, college women athletes and nonathletes, and men nonathletes tended to drink for coping reasons (i.e., they used alcohol to feel better), which was significantly related to greater quantity and frequency of alcohol consumption and incidence of drunkenness. Therefore, coping tactics seemed to explain alcohol abuse for each group except men athletes. Storch and colleagues suggested that elite intercollegiate women athletes experience elevated levels of depressive symptoms, social anxiety, and a perception of less social support than do their nonathlete women peers. However, in the sample of studies by Storch and colleagues, mental health problems were not especially prominent among the women athletes in the group. In the absence of mental health issues, the relationship between gender and drinking within the student athlete population tends to mirror general student body trends in that men drink more heavily and more frequently than do women ( Green et al., 2001 ; Hildebrand et al., 2001 ; Wechsler et al., 1997 ; Wilson et al.).

Research with college student athletes also identified peer influence as having an impact on drinking tendencies. Seventy-two percent of NCAA athletes reported that more than half of their team consumed alcohol within the last year ( NCAA, 2001 ). In a study by Thombs (2000) , student athletes tended to perceive that their teammates consumed more alcohol than they did themselves, while they believed that the typical university student consumed more alcohol than did their typical teammate. Consistent with the general student body, student athletes tend to overestimate the amount and frequency of alcohol use by peers. The social norms theory contends that substance abuse behavior is influenced by the biased perceptions social group members have of their peers ( Berkowitz, 1997 ; Thombs & Hamilton, 2002 ).

Interventions Aimed at Athletes

Because of the overall lack of understanding regarding influences that affect student athlete drinking, few interventions have been developed to target this high-risk group. Marcello, Danish, and Stolberg (1989) and Gregory (2001) published intervention control trials for college athletes, and Thombs and Hamilton (2002) discussed the effects of a social norm feedback campaign with Division I student athletes. Marcello and colleagues found no reduced drinking effect when evaluating a multicomponent skills training intervention with college athletes. Gregory focused on college athletes and met minimum inclusion criteria in a controlled intervention trial. In the study, Gregory compared three interventions: (a) a three-session feedback and skills group that contained personalized feedback on alcohol use, norms, consequences, as well as risk-reduction skills; (b) a two-session feedback with minimal focus on skills training; and (c) a group that used a workbook with similar information as the other two groups but independently. Individuals in the three-session feedback group had the largest decrease in perceived drinking-related norms and positive expectancies related to alcohol use. In addition, individuals in both feedback groups reported experiencing significantly fewer alcohol-related negative consequences than did those in the workbook group. It is important to note that, although Gregory observed reductions in alcohol-related norms, expectancies, and consequences, there were no significant decreases in actual alcohol consumption within any of the conditions. Because of the lack of a no-treatment control group, more research that examines intervention effects within the college athlete population is needed.

Thombs and Hamilton (2002) evaluated the effects of a social-norms-based feedback campaign at three Division I universities in Ohio. Their goal in this nonexperimental study was to assess the campaign's effects on the perceived drinking norms and behaviors of athletes at the participating institutions. Thombs and Hamilton used display ads, bus-rider signage, high traffic area poster displays, dining area table tents, athletic department promotional material, classes and small-group presentations, and mass mailings to publicize the campus norm messages to the targeted population. They surveyed 566 student athletes over a 4-week period to evaluate the estimated blood-alcohol content, number of drinks consumed, alcohol-related consequences in the past 30 days, binge drinking rates, in- and out-of-season drinking rates, typical drinking patterns, age of drinking onset, and perceptions of peer norms. General findings showed that athletes exposed to the social-norm campaign materials perceived less alcohol use in their campus environment when compared with nonexposed peers. Data showed that, after three campaign semesters, there was a positive impact on perceived drinking norms, yet no effect on drinking behaviors. Although the exposed participants reported significantly lower levels of peer drinking, there was no significant group difference on the typical number of drinks consumed by closest friends. Results showed that social norms feedback campaigns can effectively alter most perceptions of campus drinking norms for Division I athletes, yet there is no evidence to show that close-friend-alcohol-use perceptions or changes in personal drinking behaviors were affected. Again, because of the nonexperimental design of this study and the absence of collected baseline information, Thombs and Hamilton recommend caution in the use of their data in interpreting the findings of the study. The authors do, however, identify the challenge of using social-norm feedback interventions with high-risk populations such as collegiate student athletes.

Overall Summary

High-risk drinking and related consequences continue to be problems within fraternities and sororities and among student athletes. Although more research is clearly needed, some evidence of promising research and demonstrations of the efficacy of interventions targeting fraternity and sorority members have emerged. Overall, few interventions have used the collegiate student athlete population in an effort to reduce high-risk drinking behavior. College administrators have established multiple educational programs, as well as mandated drug testing, in hopes of curbing many of the substance abuse problems and associated behaviors in this population. However, the efficacy of these programs has yet to be effectively studied ( Gay, Minelli, Tripp, & Keilitz, 1990 ; Tricker & Cook, 1988 ). In addition, studies need to include athletes at the club-sport team, intramural, and the National Association of Intercollegiate Athletes levels to allow a more comprehensive understanding of these issues regarding collegiate athletes.

Many challenges face researchers working with fraternity, sorority members, and student athletes, such as strong ingrained alcohol use traditions, low concern about personal drinking habits, low motivation to modify behavior, outright resistance to change, difficulty in gaining access to the populations, and various unsupported policies being implemented by institutions in an attempt to curb alcohol-related problem behavior. Regardless, these are college subpopulations that are at high risk and in need of further research attention. Studies that focus on the use of interventions, as well as continued studies on underlying themes behind higher levels of alcohol use, are needed to more clearly understand and derive appropriate interventions that impact drinking behaviors and their adverse consequences for these populations.

Acknowledgments

This manuscript was prepared with support from the National Institute on Alcohol Abuse and Alcoholism grants R01 AA 12529 to Rob Turrisi and U01 AA014742 awarded to Mary Larimer.

• Abbey A. Alcohol-related sexual assault: A common problem among college students. Journal of Studies on Alcohol. 2002; 63 (Suppl 14):118–128. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Alva SA. Self-reported alcohol use of college fraternity and sorority members. Journal of College Student Development. 1998; 39 :3–10. [ Google Scholar ]
• American Psychological Association . Diagnostic and Statistical Manual of Mental Disorders. 4th ed. Author; Washington, DC: 1994. [ Google Scholar ]
• Arnold JC, Kuh GD. Brotherhood and the bottle: A cultural analysis of the role of alcohol in fraternities. Center for the Study of the College Fraternity; Bloomington, IN: 1992. [ Google Scholar ]
• Baer JS. Etiology and secondary prevention of alcohol problems with young adults. In: Baer JS, Marlatt GA, McMahon RJ, editors. Addictive behaviors across the lifespan. Sage; Newbury Park, CA: 1993. pp. 111–137. [ Google Scholar ]
• Baer JS, Kivlahan DR, Marlatt GA. High-risk drinking across the transition from high school to college. Alcoholism: Clinical and Experimental Research. 1995; 19 :54–61. [ PubMed ] [ Google Scholar ]
• Baer JS, Marlatt GA, Kivlahan DR, Fromme K, Larimer ME, Williams E. An experimental test of three methods of alcohol risk reduction with young adults. Journal of Consulting and Clinical Psychology. 1992; 60 :974–979. [ PubMed ] [ Google Scholar ]
• Baer JS, Stacy A, Larimer M. Biases in the perception of drinking norms among college students. Journal of Studies on Alcohol. 1991; 52 :580–586. [ PubMed ] [ Google Scholar ]
• Barnett LA, Far JM, Mauss AL, Miller JA. Changing perceptions of peer norms as a drinking reduction program for college students. Journal of Alcohol and Drug Education. 1996; 41 :39–62. [ Google Scholar ]
• Barnett NP, Tevyaw TO, Fromme K, Borsari B, Carey KB, Corbin WR, et al. Brief alcohol interventions with mandated or adjudicated college students. Alcoholism: Clinical and Experimental Research. 2004; 28 :966–976. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Bartholow BD, Sher KJ, Krull JL. Changes in heavy drinking over the third decade of life as a function of collegiate fraternity and sorority involvement: A prospective multilevel analysis. Health Psychology. 2003; 22 :616–626. [ PubMed ] [ Google Scholar ]
• Berkowitz G. Substance and shadow: Women and addiction in the United States. Addiction. 1997; 92 :765–766. [ Google Scholar ]
• Borsari BE, Carey KB. Understanding fraternity drinking: Five recurring themes in the literature,1980−1998. Journal of American College Health. 1999; 48 :30–37. [ PubMed ] [ Google Scholar ]
• Bower BL, Martin M. African American female basketball players: An examination of alcohol and drug behaviors. Journal of American College Health. 1999; 48 :129–273. [ PubMed ] [ Google Scholar ]
• Caron SL, Moskey EG, Hovey CA. Alcohol use among fraternity and sorority members: Looking at change over time. Journal of Alcohol and Drug Education. 2004; 47 :57–66. [ Google Scholar ]
• Carter CA, Kahnweiler WM. The efficacy of the social norms approach to substance abuse prevention applied to fraternity men. Journal of American College Health. 2000; 49 :66–71. [ PubMed ] [ Google Scholar ]
• Cashin JR, Presley CA, Meilman PW. Alcohol use in the Greek system: Follow the leader? Journal of Studies on Alcohol. 1998; 59 :63–70. [ PubMed ] [ Google Scholar ]
• Caudill BD, Marlatt GA. Modeling influences in social drinking: An experimental analogue. Journal of Consulting and Clinical Psychology. 1975; 43 :405–415. [ PubMed ] [ Google Scholar ]
• Cooper ML. Alcohol use and risky sexual behavior among college students and youth: Evaluating the evidence. Journal of Studies on Alcohol. 2002; 63 (Suppl 14):101–117. [ PubMed ] [ Google Scholar ]
• Damm J, Murray P. Alcohol and other drug use among college student-athletes. In: Etzel EF, Ferrante AP, Pinkney JW, editors. Counseling college student-athletes: Issues and interventions. Fitness Information Technology; Morgantown, WV: 1996. pp. 185–220. [ Google Scholar ]
• Dielman TE. Prevention of substance abuse on the college campus: A summary of the literature. Report prepared for the University of Michigan Task Force on Substance Abuse Prevention Committee. 1990.
• Dodge S. The use of beer kegs banned by some colleges and national fraternities. The Chronicle of Higher Education. 1991:24–27. [ Google Scholar ]
• Fournier AK, Earhart IJ, Glindemann KE, Geller ES. Intervening to decrease alcohol abuse at university parties: Differential reinforcement of intoxication level. Behavior Modification. 2004; 28 :167–181. [ PubMed ] [ Google Scholar ]
• Fromme K, Corbin W. Prevention of heavy drinking and associated negative consequences among mandated and voluntary college students. Journal of Consulting and Clinical Psychology. 2004; 72 :1038–1049. [ PubMed ] [ Google Scholar ]
• Garvin RB, Alcorn JD, Faulkner KK. Behavioral strategies for alcohol abuse prevention with high-risk college males. Journal of Alcohol and Drug Education. 1990; 36 :23–34. [ Google Scholar ]
• Gay JE, Minelli MJ, Tripp D, Keilitz D. Alcohol and athlete: A university's response. Journal of Alcohol and Drug Education. 1990; 35 :81–86. [ Google Scholar ]
• George WH, Crowe LC, Abwender D, Skinner JB. Effects of raising the drinking age to 21 years in New York State on self-reported consumption by college students. Journal of Applied Social Psychology. 1989; 19 :623–635. [ Google Scholar ]
• Gonzalez GM. Early onset of drinking as a predictor of alcohol consumption and alcohol-related problems in college. Journal of Drug Education. 1989; 19 :225–230. [ PubMed ] [ Google Scholar ]
• Gonzalez GM. Effects of raising the drinking age and related campus initiatives on student alcohol consumption and alcohol-related problems. Journal of College Student Development. 1990; 31 :181–183. [ Google Scholar ]
• Goodwin L. Explaining alcohol consumption and related experiences among fraternity and sorority males. Journal of College Student Development. 1989; 30 :448–458. [ Google Scholar ]
• Grant BF. Prevalence and correlates of alcohol use and DSM-IV alcohol dependence in the United States. Results of the national longitudinal alcohol epidemiologic survey. Journal of Studies on Alcohol. 1997; 58 :464–473. [ PubMed ] [ Google Scholar ]
• Green GA, Uryasz FD, Petr TA, Bray CD. NCAA study of substance use and abuse habits of college student-athletes. Clinical Journal of Sport Medicine. 2001; 11 :51–56. [ PubMed ] [ Google Scholar ]
• Gregory B. College alcohol and life skills study with student-athletes. Florida Atlantic University; Boca Raton: 2001. Unpublished doctoral dissertation. [ Google Scholar ]
• Harrington NG, Brigham NL, Clayton RR. Alcohol risk reduction for fraternity and sorority members. Journal of Studies on Alcohol. 1999; 60 :521–527. [ PubMed ] [ Google Scholar ]
• Hildebrand KM, Johnson DJ, Bogle K. Comparison of patterns of alcohol use between high school and college athletes and non-athletes. College Student Journal. 2001; 35 :358–365. [ Google Scholar ]
• Hingson RW, Heeren T, Zakocs RC, Kopstein J, Wechsler H. Magnitude of alcohol-related mortality and morbidity among U.S. college students ages 18–24. Journal of Studies on Alcohol. 2002; 63 :136–144. [ PubMed ] [ Google Scholar ]
• Johnston LD, O'Malley PM, Bachman JG. Drug use among American high school seniors, college students and young adults, 1975−1990. U.S. Department of Health and Human Services; Rockville, MD: 1995. [ Google Scholar ]
• Johnston LD, O'Malley PM, Bachman JG. National Survey Results on Drug Use From the Monitoring the Future Study, 1975−1995. Vol 2. College Students and Young Adults. U.S. Department of Health and Human Services; Bethesda, MD: 1997. (NIH Publication No. 98−4140) [ Google Scholar ]
• Kilmer JR, Larimer ME, Parks GA, Dimeff LA, Marlatt GA. Liability management or risk management? Evaluation of a Greek system alcohol policy. Psychology of Addictive Behaviors. 1999; 13 :269–278. [ Google Scholar ]
• Klein H. Helping the college student problem drinker. Journal of College Student Development. 1989; 30 :323–331. [ Google Scholar ]
• Klein H. College students' attitudes toward the use of alcohol beverages. Journal of Alcohol and Drug Education. 1992; 37 :35–52. [ Google Scholar ]
• Kunz ML, Irving WM, Black DR. Health services at large public institutions: Funding, utilization, and staffing. Journal of American College Health. 1993; 42 :3–14. [ PubMed ] [ Google Scholar ]
• Larimer ME. Alcohol abuse and the Greek system: An exploration of fraternity and sorority drinking. University of Washington; Seattle: 1992. Unpublished doctoral dissertation. [ Google Scholar ]
• Larimer ME, Anderson BK, Baer JS, Marlatt GA. An individual in context: Predictors of alcohol use and drinking problems among Greek and residence hall students. Journal of Substance Abuse. 2000; 11 (1):53–68. [ PubMed ] [ Google Scholar ]
• Larimer ME, Cronce JM. Identification, prevention and treatment: A review of individual-focused strategies to reduce problematic alcohol consumption by college students. Journal of Studies on Alcohol. 2002; 63 (Suppl 14):148–163. [ PubMed ] [ Google Scholar ]
• Larimer ME, Irvine DL, Kilmer JR, Marlatt GA. College drinking and the Greek system: Examining the role of perceived norms for high-risk behavior. Journal of College Student Development. 1997; 38 :587–598. [ Google Scholar ]
• Larimer ME, Turner AP, Anderson BK, Fader JS, Kilmer JR, Palmer RS, et al. Evaluating a brief alcohol intervention with fraternities. Journal of Studies on Alcohol. 2001; 62 :370–380. [ PubMed ] [ Google Scholar ]
• Larimer ME, Turner AP, Mallett KA, Geisner IM. Predicting drinking behavior and alcohol-related problems among fraternity and sorority members: Examining the role of descriptive and injunctive norms. Psychology of Addictive Behaviors. 2004; 18 :203–212. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Leibsohn J. The relationship between drug and alcohol use and peer group associations of college freshmen as they transition from high school. Journal of Drug Education. 1994; 24 :177–192. [ PubMed ] [ Google Scholar ]
• Leichliter JS, Meilman PW, Presley CA, Cashin JR. Alcohol use and related consequences among students with varying levels of involvement in college athletics. Journal of American College Health. 1998; 46 :257–262. [ PubMed ] [ Google Scholar ]
• Lo CC, Globetti G. A partial analysis of the campus influence on drinking behavior: Students who enter college as nondrinkers. The Journal of Drug Issues. 1993; 23 :715–725. [ Google Scholar ]
• Lo CC, Globetti G. The facilitating and enhancing roles Greek associations play in college drinking. International Journal of the Addictions. 1995; 30 :1311–1322. [ PubMed ] [ Google Scholar ]
• Magner DK. Alcohol-related problems have not decreased on most college campuses, survey indicates. Chronicle of Higher Education. 1988:A35. [ Google Scholar ]
• Marcello RJ, Danish SJ, Stolberg AL. An evaluation of strategies developed to prevent substance abuse among student-athletes. Sport Psychologist. 1989; 3 :196–211. [ Google Scholar ]
• Marlatt GA, Baer JS, Kivlahan DR, Dimeff LA, Larimer ME, Quigley LA, et al. Screening and brief intervention with high-risk college student drinkers: Results from a two-year follow-up assessment. Journal of Consulting and Clinical Psychology. 1998; 66 :604–615. [ PubMed ] [ Google Scholar ]
• Marlowe AF, Auvenshire CD. Greek membership: Its impact on the moral development of college freshmen. Journal of College Student Personnel. 1982; 23 :53–57. [ Google Scholar ]
• Martin M. The use of alcohol among NCAA division I female college basketball, softball, and volleyball athletes. Journal of Athletic Training. 1998; 33 :163–167. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• McCabe SE, Schulenberg JE, Johnston LD, O'Malley PM, Bachman JG, Kloska DD. Selection and socialization effects of fraternities and sororities on U.S. college student substance use: A multi-cohort national longitudinal study. Addiction. 2005; 100 :512–524. [ PubMed ] [ Google Scholar ]
• Meilman P, Leichliter JS, Presley CA. Greeks and athletes: Who drinks more? Journal of American College Health. 1999; 47 :187–190. [ PubMed ] [ Google Scholar ]
• Miller BE, Miller MN, Verhegge R, Linville HH, Pumariega AJ. Alcohol misuse among college athletes: Self-medication for psychiatric symptoms? Journal of Drug Education. 2002; 32 (1):41–52. [ PubMed ] [ Google Scholar ]
• Miller WR, Rollnick S. Motivational Interviewing. 2nd ed. Guilford; New York: 2002. [ Google Scholar ]
• Morritz T, Seehafer RW, Maatz-Majestic E. A student competition to develop an innovative alcohol education strategy. Journal of American College Health. 1993; 41 :283–286. [ PubMed ] [ Google Scholar ]
• National Collegiate Athletics Association (NCAA) NCAA study of substance use habits of college student-athletes. 2001.
• Nattiv A, Puffer JC. Lifestyles and health risks of collegiate athletes. The Journal of Family Practice. 1991; 33 :585–590. [ PubMed ] [ Google Scholar ]
• Nattiv A, Puffer JC, Green GS. Lifestyles and health risks of collegiate athletes: A multi-center study. Clinical Journal of Sport Medicine. 1997; 7 :262–272. [ PubMed ] [ Google Scholar ]
• Neighbors C, Larimer ME, Lewis MA. Targeting misperceptions of descriptive drinking norms: Efficacy of a computer delivered personalized normative feedback intervention. Journal of Consulting and Clinical Psychology. 2004; 72 :434–447. [ PubMed ] [ Google Scholar ]
• Nelson TF, Wechsler H. Alcohol and college athletes. Medicine and Science in Sports and Exercise. 2001; 33 (1):43–47. [ PubMed ] [ Google Scholar ]
• O'Malley PM, Johnston LD. Epidemiology of alcohol and other drug use among American college students. Journal of Studies on Alcohol. 2002; 63 (Suppl 14):23–39. [ PubMed ] [ Google Scholar ]
• Perkins HW. Surveying the damage: A review of research on consequences of alcohol misuse in college populations. Journal of Studies on Alcohol. 2002; 63 (Suppl 14):91–100. [ PubMed ] [ Google Scholar ]
• Presley CA, Meilman PW, Leichliter JS. College factors that influence drinking. Journal of Studies on Alcohol. 2002; 63 (Suppl 14):82–90. [ PubMed ] [ Google Scholar ]
• Read JP, Wood MD, Davidoff OJ, McLacken J, Campbell JF. Making the transition from high school to college: The role of alcohol-related social influence factors in students' drinking. Substance Abuse. 2002; 23 (1):53–65. [ PubMed ] [ Google Scholar ]
• Schall M, Kemeny A, Maltzman I. Drinking by university dormitory residents: Its prediction and amelioration. Journal of Alcohol and Drug Education. 1991; 36 :75–86. [ Google Scholar ]
• Schuckit MA, Klein JL, Twitchell GR, Springer LM. Increases in alcohol related problems for men on a college campus between 1980 and 1992. Journal of Studies on Alcohol. 1994; 55 :739–742. [ PubMed ] [ Google Scholar ]
• Schulenberg J, Maggs JL. A developmental perspective on alcohol use and heavy drinking during adolescence and the transition to young adulthood; Paper presented for the National Institute on Alcohol Abuse and Alcoholism Subcommittee on College Drinking,; Washington, DC. Jun, 2000. [ PubMed ] [ Google Scholar ]
• Schulenberg J, O'Malley PM, Bachman JG, Wadsworth KN, Johnston LD. Getting drunk and growing up: Trajectories of frequent binge drinking during the transition to young adulthood. Journal of Studies on Alcohol. 1996; 57 :289–304. [ PubMed ] [ Google Scholar ]
• Selby R, Weinstein HM, Bird TS. The health of university athletes: Attitudes, behaviors, and stressors. Journal of American College Health. 1990; 39 :11–18. [ PubMed ] [ Google Scholar ]
• Sher KJ, Bartholow BD, Nanda S. Short- and long-term effects of fraternity and sorority membership on heavy drinking: A social norms perspective. Psychology of Addictive Behaviors. 2001; 15 :42–51. [ PubMed ] [ Google Scholar ]
• Shields EW. Relative likelihood of in-season and off-season use of alcohol by high school athletes in North Carolina: Trends and current status. Journal of Alcohol and Drug Education. 1998; 43 :48–63. [ Google Scholar ]
• Shore ER, Gregory T, Tatlock L. College students reaction to a designated driver program: An exploratory study. Journal of Alcohol and Drug Education. 1991; 37 :1–6. [ Google Scholar ]
• Storch EA, Storch JB, Killiany EM, Roberti JW. Self-reported psychopathology in athletes: A comparison of intercollegiate student-athletes and non-athletes. Journal of Sport Behavior. 2005; 28 (1):86–98. [ Google Scholar ]
• Strauss R, Bacon SD. Drinking in college. Yale University Press; New Haven, CN: 1953. [ Google Scholar ]
• Tampke DR. Alcohol behavior, risk perception, and fraternity and sorority membership. NASPA Journal. 1990a; 28 :71–77. [ Google Scholar ]
• Tampke DR. Residential differences in alcohol consumption, risk perception, and motivation. Journal of College and University Student Housing. 1990b; 20 :25–29. [ Google Scholar ]
• Thombs DL. A test of the perceived norms model to explain drinking patterns among university student athletes. Journal of American College Health. 2000; 49 :75–83. [ PubMed ] [ Google Scholar ]
• Thombs DL, Hamilton MJ. Effects of a social norm feedback campaign on the drinking norms and behavior of division I student-athletes. Journal of Drug Education. 2002; 32 :227–244. [ PubMed ] [ Google Scholar ]
• Thompson ML. A review of prevention research institute programs. Eastern Kentucky University; Richmond: 1996. (A report to the division for substance abuse Kentucky cabinet for human resources). [ Google Scholar ]
• Tricker R, Cook DL. The current status of drug intervention & prevention in college athletic programs. Journal of Alcohol and Drug Education. 1988; 34 :38–45. [ Google Scholar ]
• Tricker R, Cook DL, McGuire R. Issues related to drug abuse in college athletics: Athletes at risk. Sport Psychologist. 1989; 3 :155–165. [ Google Scholar ]
• Turrisi R, Jaccard J, Taki R, Dunnam H, Grimes J. Examination of the short-term efficacy of a parent intervention to reduce college student drinking tendencies. Psychology of Addictive Behaviors. 2001; 15 :366–372. [ PubMed ] [ Google Scholar ]
• Wagenaar AC, Toomey TL. Effects of minimum drinking age laws: Review and analyses of the literature. Journal of Studies on Alcohol. 2002; 63 (Suppl 14):206–225. [ PubMed ] [ Google Scholar ]
• Wechsler H, Davenport A, Dowdall G, Moeykens B, Castillo S. Health and behavioral consequences of binge drinking in college: A national survey of students at 140 campuses. Journal of the American Medical Association. 1994; 272 :1672–1677. [ PubMed ] [ Google Scholar ]
• Wechsler H, Davenport AE, Dowdall GW, Grossman SJ, Zanakos SI. Binge drinking, tobacco, and illicit drug use and involvement in college athletics: A survey of students at 140 American colleges. Journal of American College Health. 1997; 45 :195–200. [ PubMed ] [ Google Scholar ]
• Wechsler H, Dowdall GW, Davenport A, Castillo S. Correlates of college studentbinge drinking. American Journal of Public Health. 1995; 85 :921–926. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Wechsler H, Dowdall GW, Maenner G, Gledhill-Hoyt J, Lee H. Changes in binge drinking and related problems among American college students between 1993 and 1997: Results of the Harvard School of Public Health College Alcohol Study. Journal of American College Health. 1998; 47 :57–68. [ PubMed ] [ Google Scholar ]
• Wechsler H, Fulop M, Padilla A, Lee H, Patrick K. Binge drinking among college students: A comparison of California with other states. Journal of American College Health. 1997; 45 :273–277. [ PubMed ] [ Google Scholar ]
• Wechsler H, Issac NE, Grodstein F, Sellers D. Continuation and initiation of alcohol use from the first and second year of college. Journal of Studies on Alcohol. 1994; 55 :41–45. [ PubMed ] [ Google Scholar ]
• Wechsler H, Kuh G, Davenport A. Fraternities, sororities and binge drinking: Results from a national study of American colleges. National Association of Student Personnel Administrators. 1996; 33 :260–279. [ Google Scholar ]
• Wilder DH, Hoyt AE, Surbeck BS, Wilder JC, Carney PI. Greek affiliation and attitude change in college students. Journal of College Student Personnel. 1986; 27 :510–519. [ Google Scholar ]
• Williams FG, Kirkman-Liff BL, Szivek PH. College student drinking behaviors before and after changes in state policy. Journal of Alcohol and Drug Education. 1990; 35 :12–25. [ Google Scholar ]
• Wilson GS, Pritchard ME, Schaffer J. Athletic status and drinking behavior in college students: The influence of gender and coping styles. Journal of American College Health. 2004; 52 :269–273. [ PubMed ] [ Google Scholar ]
• Wittman FD. Planning and programming server intervention initiatives for fraternities and sororities: Experiences at a large university. Journal of Primary Prevention. 1989; 9 :247–269. [ PubMed ] [ Google Scholar ]

• Study Protocol
• Open access
• Published: 18 June 2022

A mobile-based pregaming drinking prevention intervention for college students: study protocol for a randomized controlled trial

• Eric R. Pedersen   ORCID: orcid.org/0000-0002-8017-6246 1 ,
• Justin F. Hummer 2 ,
• Jordan P. Davis 3 ,
• Reagan E. Fitzke 1 ,
• Nina C. Christie 4 ,
• Katie Witkiewitz 5 &
• John D. Clapp 6  

Addiction Science & Clinical Practice volume  17 , Article number:  31 ( 2022 ) Cite this article

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Pregaming is a high-drink context popular among college students that often leads to elevated blood alcohol levels and negative consequences. Over 15 years of research studies have demonstrated that pregaming represents one of the riskiest known behaviors among college students, yet no pregaming-specific interventions have been developed to help prevent this behavior. General brief interventions for students do not reduce pregaming behavior and may not be appropriate, as they do not help students develop skills unique to the pregaming context that could help them drink less. We developed a brief, mobile-based intervention that is proposed to prevent heavy drinking during pregaming for college students, with the ultimate goal that behavioral reductions in this risky practice will ultimately affect global drinking and prevent consequences.

Methods/Design

The intervention, Pregaming Awareness in College Environments (PACE), was developed by combining two innovations to facilitate behavior change: (1) a mobile-based application that increases accessibility, is easy and engaging to use, and broadens the reach of the intervention content and (2) personalized pregaming-specific intervention content with harm reduction and cognitive behavioral skills proven to be mechanisms preventing and reducing heavy drinking among college students. After a develop and beta-test phase, we propose to test the efficacy of PACE in a preliminary randomized controlled trial with 500 college students who pregame at least once per week. Pregaming, general drinking, and alcohol-related consequences outcomes will be examined in the immediate (2 weeks post-intervention) and short-terms (six and 14-week post-intervention). We will also evaluate moderator effects for age, sex, and heaviness of drinking to allow for more refined information for a planned larger test of the intervention to follow this initial trial of PACE.

This pregaming intervention clinical trial, if found to be efficacious, will culminate with an easily-disseminated mobile-based intervention for college student drinkers. It has the potential to reach millions of college students, perhaps as a clinical tool used by college counseling centers as an adjunct to formal care or as a preventive tool for first-year students or other high-risk groups on campus.

Trial registration : ClinicalTrials.gov Identifier NCT04016766.

Despite substantial intervention efforts to reduce college student drinking and resulting consequences [ 1 – 3 ], both continue to be national public health concerns and thus, important foci of prevention and intervention efforts [ 4 – 6 ]. National data from 2019 indicate most college students drink alcohol (78% annual prevalence, 62% past month prevalence), 32% engage in heavy episodic drinking (five or more drinks in a row in the past two weeks), and 35% report being drunk in the past month [ 7 ]. These national data also indicate that “high intensity drinking” is common, with 12% of college students reporting consumption of 10 or more drinks in a row on an occasion during the past two weeks [ 8 , 9 ]. Prevalence rates of high intensity drinking increase from age 18 to 22, with the steepest increases occurring over time for college students compared to young adults not attending college [ 8 ]. The consequences of heavy college drinking are well documented and include academic problems, physical injuries and fights, risky sexual behavior and sexual assaults, memory blackouts and passing out, sustained cognitive deficits, alcohol poisoning, and even death [ 10 ].

Several interventions have been designed to address heavy drinking and related consequences among college students. These interventions are often brief in order to be acceptable by students and feasible to deliver to large numbers of students in universal or selective prevention efforts. Many college students do not consider their drinking to be problematic or something that needs to be changed [ 9 ]; many are in a stage of change where they are not yet considering making any changes to their drinking (called the precontemplation stage in the transtheoretical model) or are considering making some changes but have no plan of action to make such changes (called contemplation ) [ 11 ]. Most of the interventions use components first documented together in the Brief Alcohol Screening and Intervention for College Students (BASICS) approach [ 12 ], typically combine strategies from Motivational Interviewing (MI), [ 13 ] (e.g., strategies to increase motivation to change/reduce risky behavior), cognitive behavioral skills training [ 14 ] (e.g., drink refusal skills), harm reduction strategies [ 15 , 16 ] (e.g., protective strategies for limiting consumption), and personalized feedback (e.g., review of personalized consequences such as alcohol calories consumed each week). Yet, studies on in-person BASICS and adaptations of the program’s components into group, computer, web-based, and mobile formats have demonstrated modest effects at best [ 3 , 17 – 19 ], which has sparked debate about whether such brief interventions are clinically meaningful or impactful on a broader public health scale [ 20 – 23 ]. It is becoming increasingly clear that brief interventions for college students need to be refined or enhanced in order to have a larger impact on changing the ingrained heavy drinking culture present on many college campuses today.

Although brief interventions with students often include aspects based on a relapse prevention approach, wherein they identify high-risk situations and apply specific skills to manage these situations with minimal or no use of alcohol [ 12 , 14 , 16 ], global interventions can be vague with regard to when to use certain skills. The college context is diverse, with individual student’s drinking levels varying between specific contexts (e.g., drinking more at a bar versus at a party, drinking less on a Wednesday than on a Thursday) [ 24 , 25 ]. To expand upon brief interventions that target behavior at a global level, contemporary prevention programs prepare students for the inevitable risks associated with specific high-risk drinking events. Such events include spring break, 21st birthdays, holidays such as St. Patrick’s Day, and study abroad trips [ 26 – 31 ], which are periods where students drink at heavy or high-intensity levels, placing them at even greater risk than during a typical week on campus. Thus, these “event-specific” approaches lay out a clear framework for the specific skills that students can implement in discrete circumstances, often in preparation for an upcoming risky event where heavy drinking is likely to occur.

Event-specific prevention programs have been tested with promising effects, [ 32 – 34 ] and they represent an approach to combat college drinking beyond a global level, wherein students learn specific skills to prepare for an event anticipated to involve risky drinking. Targeted preventive education reduces ambiguity about how, when, and where to use a learned skill, which can thereby increase the successful implementation of that skill in real life. Although event-specific prevention outcomes are generally positive, their effects are often short-lived (e.g., spring break is just week, 21st birthdays are just one day). Less clear is if modifying drinking behavior in one specific context (e.g., a 21st birthday celebration) can translate to sustained behavior change in other diverse drinking contexts. Ideally, an event-specific prevention program would target a high-risk drinking behavior involved in most drinking contexts so that event-specific skills learned in the program could be employed more frequently and broadly.

One such frequent, yet risky, drinking behavior that has received growing empirical attention and heightened concern is called “pregaming”. Pregaming’s etymology stems from its roots in “tailgating” prior to sporting events, but local and regional vernacular has evolved to include terms such as prepartying, preloading, predrinking, and front-loading. The behavior has expanded well beyond tailgating-specific events, as students report pregaming across a number of different drinking contexts, such as before going to bars, parties, concerts, football games, or on dates; with friends or alone; while playing drinking games; while getting ready to go out; and even while driving to their destination for the night [ 35 – 38 ]. Pregaming is prevalent among American college students, ubiquitous across college drinking contexts, and consistently involves or leads to high intensity drinking [ 35 , 39 – 43 ]. During pregaming, people consume multiple drinks during a brief period prior to going to an event or social gathering where more alcohol is typically consumed. It is highly prevalent among students, with over 40% of all college students reporting past month pregaming and past month prevalence rates among student drinkers ranging from 50 to 85% across studies [ 39 , 44 ]. Pregaming is not specific to U.S. college students, as the behavior has been studied among young people in several other countries, such as Switzerland, the United Kingdom, and Australia, with similar findings related to its prevalence and risks [ 38 , 45 , 46 ].

Among U.S. students, around one-third or more of all drinking days involve pregaming [ 35 , 40 , 47 , 48 ], with students typically consuming between three to five drinks within just one to two hours [ 35 , 49 ]. Such quick-paced drinking can lead to high blood alcohol levels (BALs), which are reached even before students leave for their intended destination, at which point they often go on to drink more. To wit, most pregaming events involve further drinking once students reach their intended destination [ 49 , 50 ], which typically results in a total drink count for the night indicative of high intensity drinking (i.e., 10 or more drinks) [ 51 ]. High BALs can lead to negative consequences on a night out, and severe alcohol-related consequences have been linked to pregaming drinking, including hospitalizations, regretted sex, driving after drinking, blacking out, and passing out [ 35 , 40 , 43 , 49 , 52 – 55 ]. Further, students drink more on pregaming nights than on non-pregaming nights [ 35 , 40 , 43 , 49 , 54 , 55 ], and longitudinal research shows that pregaming frequency predicts heavy drinking behavior and alcohol-related consequences even up to one year later [ 42 ], suggesting long term impacts on risky alcohol use trajectories.

Given the risks associated with pregaming, it would be important for interventions that target drinking globally to also affect changes in pregaming behavior specifically. Yet, interventions that target general drinking patterns do not show effects on pregaming behavior. For example, one published study evaluated pregaming outcomes after a global, brief, group intervention with mandated students and failed to find significant reductions in pregaming post-intervention, even if pregaming was mentioned (albeit infrequently) by students in the discussion portions of the intervention [ 56 ]. Another study found that a general alcohol-reduction intervention for student-athletes did not reduce athletes’ pregaming behavior one- and four-months post-intervention [ 57 ]. Thus, approaches specifically targeting pregaming may be necessary for reductions to occur in pregaming-specific heavy drinking. Such an approach can selectively target the specific behavior known to lead to subsequent consequences and heavy drinking both on the pregaming day and more generally.

Only three studies to our knowledge have examined the effects of a pregaming-specific intervention on pregaming behaviors. There is promise from a small experimental study that found that providing female students with fabricated normative information that other students pregame less frequently than they perceived prevented pregaming during a subsequent drinking occasion [ 58 ]. A second study by Caudwell and colleagues [ 59 ] examined the efficacy of two online interventions that shared Australian national drinking guidelines with students who were assigned to either complete an exercise based on autonomy support (e.g., reminders that drinking less during pregaming could help reduce negative consequences) or on implementation intentions (e.g., intentions to use protective behavioral strategies to limit consumption, like drinking a glass of water after consuming a pregaming drink). Participants in all conditions (including an intervention condition where participants completed both exercises and a control group that received neither intervention) did not differ at a four-week follow-up in their reductions in pregaming drinking and alcohol-related consequences. A third study by Cadigan and colleagues [ 60 ] evaluated a very brief text-message intervention delivered to students prior to attending a tailgating event at a college football game, finding that students who received the intervention consumed fewer drinks and reached lower estimated BALs than those in a control condition. Moreover, the intervention, though targeted toward a specific one-time football game event, was associated with fewer alcohol-related problems one month later. The findings lend support to the notion that helping students change how they drink during one specific high-risk event may translate to lower risk drinking during other events in the near term.

The present study

The prior studies evaluating pregaming interventions are promising, but perhaps limited due to their brevity and focus on targeting perceived norms or providing prompts/reminders only, thus not incorporating multiple components of brief alcohol interventions known to help students reduce heavy drinking [ 18 ]. Without multiple evidence-based components that have been tested in interventions targeting broader, global drinking behaviors, lasting change may be difficult to obtain. Changing the way students drink during pregaming could not only prevent heavy drinking and its consequences following the pregaming event on a particular night, but it could subsequently reduce overall drinking behavior and alcohol-related consequences more globally for an individual. We designed a brief mobile intervention to address the high-risk drinking behavior of pregaming, targeting the multitude of different pregaming contexts (e.g., before going to a concert, party, bar, or date) beyond tailgating before football games. Targeting a high-frequency event that occurs in many different contexts has potential for greater impact on total consumption than other event-specific interventions (e.g., those targeted on 21st birthdays or spring breaks). As such, an empirically-supported approach focusing on pregaming, a behavior known to lead to both event-specific and global consequences, would improve upon existing global and event-specific interventions. Table 1 contains Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT) recommended sections within this protocol.

For this study, we propose to develop and test a brief mobile (i.e., mobile phone-friendly website) intervention directly targeting pregaming among college students. First, we developed the intervention content and programmed the online intervention. We then beta-tested the developed program with heavy drinking college students who reported pregaming at least once per week on average and gathered feedback regarding feasibility and acceptability of intervention content. Final edits to the brief intervention were made based on these students’ feedback. Next, we will conduct a randomized controlled trial (RCT) of the intervention with approximately 500 college students from one private southern California university, assigning half to intervention and half to control. We will evaluate immediate term (from two weeks pre-intervention to two weeks post-intervention) and short term (from one month pre-intervention to six weeks and 14 weeks post-intervention) drinking during pregaming, overall drinking, and consequences.

For the RCT, we will recruit (1) full time undergraduate students at the university who are (2) between the ages of 18 and 24 and (3) report pregaming at least once per week in the past month. No other eligibility criteria beyond these will use in an effort to obtain students who pregame but who may not be considering making changes to their drinking. Participants will be recruited by emails sent to a random selection of undergraduates, via a list of student emails obtained from the university’s registrar. Participants will complete a screening questionnaire to determine if they meet eligibility criteria. Those screening into the study will complete a 20-min baseline survey, followed by two weeks of daily surveys. Participants will then be randomized to receive the brief pregaming intervention or a control condition program (randomized by computer-generated random numbers). After viewing one or the other program, they will complete two additional weeks of daily surveys, followed by a 20-min one-month follow-up survey (completed online six weeks post-intervention) and a final 20-min follow-up survey two months later (completed online 14-weeks post-intervention). See Fig.  1 for RCT study flow. Participants receive a $20 gift card (multiple options to choose from such as Amazon, clothing stores, and coffee shops) for each of the three 20-min surveys. For each of the 28 two-minute daily surveys they complete, participants will receive $2 added to a gift card balance, for a total of $56 if all daily surveys are completed. It is made clear to participants that incentives are provided for completing the surveys, not for completing the intervention (or the control condition). All procedures have been approved by the Institutional Review Board at the university where the research is being conducted.

Flow of the RCT

Development of the intervention

The intervention, called Pregaming Awareness in College Environments (PACE), is comprised of a theoretically-informed, brief, accessible, and personalized intervention to address pregaming drinking among college students that is based on empirically-supported intervention components. It is tailored toward an individual’s personal goals, beliefs (perceptions, expectancies, self-efficacy), and behavior (protective strategies), focusing on the core components of brief interventions that mediate the effects of multiple component intervention programs (e.g., correcting perceived norms, use of protective behavioral strategies, increased self-efficacy, challenging expectancies, BAL feedback) [ 17 , 18 ]. Content was informed by the BASICS approach [ 61 ], which is based on aspects of both Motivational Interviewing [ 62 ] and relapse prevention [ 63 ] and rooted in a harm reduction framework [ 15 , 16 ]. BASICS for general drinking has been efficacious when delivered in individual and group formats [ 64 , 65 ] and recently has been adapted for use on mobile phones [ 66 ]. Researchers found in a study of 94 heavy drinking college students that components of the BASICS intervention in a mobile format led to limited drinking during the 14 days of the study [ 66 ]. Yet, the intervention failed to demonstrate one-month effects on heavy drinking behavior compared to control. It is possible that minimal lasting effects were found after the intervention ended due to lengthy, non-targeted content (e.g., participants received a mean of 23 modules, 3–5 min each, with additional focus on smoking cessation). The innovation in the PACE intervention comes from its targeting of pregaming behavior directly, with components of BASICS modified to address this risky drinking practice. PACE presents videos with a female narrator, combined with interactive activities to engage participants and help them consider making changes to their pregaming behavior.

Format of the intervention

Nearly all college students own and use smartphones regularly [ 67 ]. Young adults report checking these phones constantly throughout the day at an average of 74 times per day [ 68 ]. Moreover, alcohol interventions based on BASICS components that are delivered online through computers, tablets, or mobile phones have shown efficacy and are a means to reach individuals with intervention efforts that may not have otherwise sought in-person care [ 69 , 70 ]. Thus, we opted to deliver PACE to participants on mobile phones and created a mobile-friendly website to host the program. Students could log in and view the program, which takes an average of 25 min to complete. The mobile format increases access to the intervention, without need for in-person facilitator delivery or use of a desktop or laptop computer.

Intervention content

PACE begins with definitions and activities to help students better understand standard drinks (e.g., 12 oz of beer or one shot of most liquors), BAL, and alcohol metabolism rates. Students learn that alcohol takes time to be processed; thus, when consuming many drinks in a short period of time, this can place them at higher than anticipated levels of intoxication once arriving at the event. Often students do not feel the full effects of pregaming until they arrive at their destination [ 39 , 44 ]. Participants receive BAL feedback and learn about alcohol’s biphasic curve and the point of diminishing returns (BAL of 0.05–0.06), where the “good things” about alcohol (e.g., feel relaxed and social) are maximized and there is less (not no) likelihood of experiencing the “not so good things” (e.g., consequences). This is important as students reach high BALs even before leaving for the night out; as BALs rise, potential for consequences increases [ 35 , 49 ].

Students then learn that students at their school pregame less frequently and drink significantly less during pregaming than they perceived. Research shows that students have misperceptions of pregaming perceived norms and such misperceptions associate with their own pregaming drinking behavior [ 71 ]. Prior work has shown that reductions in perceived norms are one of the driving components of change in brief interventions with students, [ 18 ] and that reductions in tailgating norms (i.e., tailgating being one of the many contexts where pregaming occurs), specifically, mediated changes observed in a tailgating-focused brief intervention with students during a football game on campus [ 60 ]. Thus, during PACE, students are asked about the typical pregaming behavior (frequency in past 30 days, amount consumed per pregaming occasion) of their peers on campus, as well as how much they drink themselves. Graphs with descriptions, narration, and the source of the norms are presented on screen, with content showing the discrepancies between one’s own perceptions and actual norms, as well as between one’s own use and actual norms. Students see pregaming norms for both males and females on campus. This is followed by a video describing the theory behind how social norms work to perpetuate heavy drinking in college [ 72 ]. Campus norms were collected in the spring of 2019 in a first phase of the study, among 527 students from the university recruited through a random list from the university registrar. This sample was similar in demographics to the larger university community (mean age 20; 62% female, 56% racial/ethnic minority students), with 69% reporting pregaming behavior. Details about the Phase 1 norms documentation can be found elsewhere [ 73 ].

PACE content also focused on goals for the night (i.e., reasons for pregaming) and students learn that they can get what they want out of the night (e.g., feel relaxed, be more social) by drinking moderately or not at all during pregaming. Relatedly, participant’s pregaming-specific expectancies and beliefs that pregaming will make their night better are challenged through a presentation on alcohol placebo studies, where students hear about experiments where college students display the social effects of drinking even without consuming actual alcohol. This is important because students with positive outcome expectancies (e.g., it would be easier to talk to people) are more likely to pregame, and pregaming mediates the relationship between expectancies and hazardous drinking [ 74 ]. Participant’s drink refusal skills are reviewed with alternate strategies to use if feeling pressured to drink heavily during pregaming, as greater drink refusal self-efficacy associates with less pregaming [ 75 ]. As in a relapse prevention approach, which targets the people, situations, and feelings that may lead one to drink heavily [ 63 ], risky situations specific to the students (e.g., when in a large group, when getting ready to meet a potential romantic partner later that night) are reviewed. Protective drinking strategies specific to pregaming [ 73 ] are selected by the students and they are asked to try during their next pregaming event strategies they do not normally use.

The intervention concludes with a video summarizing the content and a personalized feedback sheet with resources, that also gets emailed to participants. The personalized feedback sheet contains the information from the intervention in a format viewable at a later time, as well as resources for seeking help on campus and in the community for drinking, sexual violence, and mental health.

Beta test of the intervention

Participants completing the norms documentation survey were asked at the end of the survey if they would be interested in attending a focus group to offer feedback on the intervention once it was completed. Of the 527 participants, 75 met eligibility criteria and expressed interest in attending a focus group. In August 2020, we invited these 75 participants and obtained consent from 13 of them to review the first draft of the PACE intervention and provide feedback. These participants attended one of three online focus groups to provide feedback to our research team on what they liked and did not like, what could be improved regarding content and functionality, and ideas for improving engagement with the program. Focus groups were conducted online rather than in person as initially proposed, due to COVID-19 pandemic safety protocols. Focus group participants were provided with a $50 Amazon gift card.

Focus group participants’ feedback was primarily positive and generally focused on what they liked about the PACE intervention. Still, we prodded students to generate suggestions for improvement. Feedback that could feasibly be addressed within the scope of the budget was incorporated in the intervention and prepared for a final version to test in the RCT. Suggestions included modifying the graphs displaying the normative drinking patterns to improve readability, adding brief text introductions to each section to facilitate fluidity between sections, modifying images used for standard drinks (including adding an image of sake), adding additional protective strategies (e.g., avoiding use of motorized scooters after pregaming), correcting a few typos and modifying some color schemes, and adding additional campus-specific resources to the resources page.

Control condition

Participants in the control condition of the RCT will be asked to view a series of text-based slides regarding general drinking behavior. These slides were accessible to be viewed on mobile phones, with content based on information obtained from the Rethinking Drinking website from the National Institute on Alcohol Abuse and Alcoholism (NIAAA).

Analytic plan

Main effects.

Main effects of the intervention will be evaluated for pregaming drinking, general drinking (i.e., drinks consumed both during and after pregaming), and alcohol-related consequences in the immediate term and short-term. In the immediate term, we will evaluate whether intervention participants pregame less frequently (i.e., fewer days per week), reach lower BALs on pregaming days, and consume fewer drinks during pregaming from the two weeks prior to the intervention to the two weeks post-intervention than those in the control condition. Estimated BALs will be calculated using Widmark’s formula, which is the standard method for estimating BAL (using sex, weight, amount consumed, time). We will also evaluate whether, compared to control, intervention participants drink fewer days overall (i.e., pregaming days and non-pregaming days), consume fewer drinks over the course of each drinking day, and report fewer consequences on drinking days from the two weeks prior to the intervention to the two weeks post-intervention. In the short term (baseline to six and 14-weeks post-intervention), we will evaluate main effects of the intervention on pregaming frequency (i.e., pregaming days in the past 30 days) and pregaming quantity (i.e., typical amount consumed during pregaming on pregaming days in the past 30 days). We will also evaluate main effects of the intervention on overall drinking days (i.e., pregaming days and non-pregaming days) in the past 30 days, average consumed on a typical drinking day in the past 30 days, and number of alcohol-related consequences experienced in the past 30 days.

We will evaluate moderation by augmenting main effect models with interactions between four moderators of interest and the intervention. Significant interactions with sex, for example, will be indicative of an effect modification where the impact of the intervention can be different for men and women even if both groups realize a significant impact of the intervention. We will test four moderators of intervention efficacy: sex, age, baseline hazardous drinking scores on the Alcohol Use Disorders Identification Test (AUDIT) [ 76 ], and baseline motivation to change drinking. These moderators were selected based on the pregaming literature and to help determine the feasibility of this approach in spite of variations in behavior during the event. First, female students have been found to be at particular risk from pregaming, including higher pregaming BALs and subsequent hospitalizations [ 40 , 49 , 52 , 77 – 79 ]. Thus, we hypothesize that women will benefit most from the intervention. Second, though there are few differences observed in pregaming frequency between students under 21 and 21 or older, students under age 21 have reported reaching higher BALs during pregaming than of-age students and are hypothesized to benefit most [ 36 , 80 ]. Third, baseline levels of hazardous drinking will also be explored as a moderator, as heavier global drinkers drink more during pregaming [ 41 , 81 – 83 ]. We hypothesize baseline heavier drinkers will benefit most.

Outcome measures

On all surveys, we will define pregaming behavior for participants as the following: “When we ask you about pregaming (a.k.a., prepartying), we are talking about the consumption of alcohol prior to attending an event or activity. For example, drinking before going to a party, bar, concert, sporting event, date, meeting, or any other event or activity at which more alcohol may or may not be consumed. This can be an event that has a large number of people or very few people.” Participants will also be provided with a graphic depicting standard drinks (i.e., 12 oz of beer with 5% alcohol/volume, 8–9 oz of craft beer with approximately 7% alcohol/volume, 4–5 oz of wine with approximately 13% alcohol/volume, 12 oz of hard seltzer with 5% alcohol/volume, 1.5 oz of 80 proof liquor with 40% alcohol/volume in either a shot glass or in a mixed drink).

Baseline and follow-up surveys

On the screening questionnaire, participants will be asked, “During the past 30 days, how often did you engage in pregaming,” with response options of never, just once, a couple of times, about once per week, a couple of times per week, and daily or almost daily. Those endorsing about once per week or more will screen into the study and complete the baseline survey. Overall drinking frequency will be assessed with an item asking, “During the past 30 days, how many days did you have at least one drink of any alcoholic beverage, such as beer, wine, hard seltzer, mixed drinks, or shots of liquor,” with response options from 0 to 30 days. To assess overall drinking quantity, participants will then be asked to consider their typical drinking behavior over the past 30 days with, “During the past 30 days, on the days when you drank, about how many drinks did you drink on average,” with response options from 0 to 30 drinks. Pregaming frequency will then be asked with the item, “During the past 30 days, how many days did you engage in pregaming,” with response options from 0 to 30 days. Pregaming quantity will be assessed with an item asking, “During the past 30 days, on the days when you drank, about how many drinks did you drink during pregaming,” with response options from 0 to 30 drinks. Alcohol consequences will be assessed with the Brief Young Adult Alcohol Consequences Questionnaire (B-YAACQ) [ 84 , 85 ], where participants will indicate which (yes/no) of 24 alcohol-related consequences have happened to them in the past 30 days (e.g., I drove a car when I knew I had too much to drink to drive safely, I did not remember large stretches of time while drinking heavily). Participants will indicate race/ethnicity and class year (for descriptive purposes), age and biological sex (for moderation analyses), weight (for calculating estimated BAL), complete the 10-item AUDIT [ 76 ] (for moderation analyses), and indicate how motivated they are to drink less using a change ruler (scale from 0–10) modified from other work [ 86 , 87 ] (for moderation analyses). Follow-up surveys will also ask intervention and control participants how long they spent viewing the content and whether they returned to review the content after initial viewing. Back-end data connected to PIN codes can also be used to determine whether participants finished viewing the intervention or control content or only completed a portion of either.

Daily surveys

Daily surveys will be delivered in the morning and ask about the day before. On the daily surveys, participants will first be asked if they drank yesterday (yes/no). If so, they will be asked how much they drank overall with an item assessing, “How may drinks did you have total yesterday,” with response options of 0 to 30 drinks. They will then be asked if they pregamed yesterday; if so, they will be asked, “How many of the [drinks they had overall] did you have while pregaming, with response options from 0 and capping at the overall amount they indicated for that day. On days they drank (i.e., pregaming or non-pregaming day), they will then be asked if they experienced (yes/no) any of the 24 BYAACQ consequences that day. We will evaluate any of the 24 consequences as an outcome (summed score ranging from 0 to 24).

This brief, personalized, and easily accessible mobile phone-based intervention focused on pregaming is proposed to help college students develop and use drinking-reduction skills to limit the amount they drink while pregaming. The advent of smartphones has led to increased intervention opportunities to target risky behaviors among those who may not otherwise have sought help for their drinking [ 88 , 89 ]. As college students typically do not pursue treatment to address alcohol use despite engaging in frequent heavy drinking [ 90 , 91 ], having a brief intervention available to them that is both easy to use and engaging is essential. Smartphone and app-based interventions have gained popularity, with the few available ones demonstrating promise on reducing alcohol use outcomes [ 66 , 92 ]. Similar smartphone-based text message interventions have also shown promise of efficacy with college drinkers [ 93 ]. Though hundreds of alcohol apps exist in the public domain for download onto smartphones, few if any include empirically-supported behavioral change techniques or have demonstrated efficacy at actually reducing drinking [ 94 , 95 ]. For example, apps with BAL information are available for download, but they provide inaccurate estimates, misleading information (e.g., asking users to blow into the phone’s microphone to estimate BAL), do not provide personalized feedback, and are not empirically based [ 96 – 98 ]. Importantly, the intervention we designed and propose to test in the RCT represents one of the first to address pregaming specifically. That is, for this project, the intervention is specifically designed to focus upon the drinking behavior that is known to be perhaps the riskiest drinking practice for many students, and content is personalized to help students address their own personal risk factors for drinking during pregaming. The content may help address underlying traits associated with problem drinking in general (e.g., practicing refusal skills for those with little refusal self-efficacy) and prepare students who pregame less frequently to avoid problems that may emerge on an impromptu pregame night involving greater consumption than what is typical. Given that upwards of 80% of student drinkers report pregaming behavior in the past month alone [ 39 ], the intervention has broad applicability to the majority of college students for both intervention and prevention efforts.

The proposed research is innovative in four main respects. First, there has been a call for research and interventions targeting high intensity drinking [ 51 ] and, as stated, this is among the first intervention studies to directly target pregaming–a popular and risky aspect of the college drinking culture that cuts across specific contexts leading to high intensity drinking and resulting problems. Second, by using a pregaming-specific mobile-based intervention, the intervention expands on promising preexisting smartphone app-based brief interventions that target non-specific events, global in-person and web-based approaches with small effect sizes, and event-specific prevention programs that target a single risky event (e.g., tailgating). Third, the smartphone-based intervention can be widely available to students outside of research settings to increase access to a theoretically-informed and evidence-based brief intervention. If found to be efficacious, it has the potential to reach millions of college students, perhaps as a clinical tool used by college counseling centers, an intervention for adjudicated students on campus, or modified for use as a brief orientation program for incoming first year students to prevent pregaming during the high-risk initial weeks on campus. The easy-to-use tool could be adopted for use beyond college students with high school students and non-college young adults who also report frequent pregaming [ 41 , 47 , 83 ]. Fourth, the pregaming intervention is tailored toward the individual student, in that it targets personalized beliefs and behaviors known in the literature on brief college drinking interventions to lead to positive outcomes, such as by targeting one’s positive expectations to result from pregaming, correcting misperceived norms of pregaming, and encouraging use of protective strategies during prepartying [ 17 , 18 ].

Limitations and alternative methods considerations

We have considered potential limitations of the research design and planned for them where possible. First, by design, participants in both intervention and control groups complete daily assessments of their drinking behaviors for 28 days. Regarding assessment reactivity during the 28 days of these daily surveys, research has cited minimal reactivity to daily diary assessments; there is no evidence that prompting individuals to assess their alcohol use leads one to drink [ 66 , 99 ]. However, repeatedly self-reporting on drinking (or self-monitoring as it has been called) can be a form of intervention that could lead to reductions in drinking [ 100 ]. Though our analytic plan calls for analyses of pre-intervention and post-intervention daily data, it is possible the control group may be impacted to change their drinking by this self-monitoring. Thus, any significant intervention effects we find will need to be interpreted as occurring over the effects of simply self-monitoring.

Second, the intervention is quite brief (20–30 min) by design. It is delivered on one occasion to capitalize on the innovation and brevity of this approach. Other brief interventions are delivered over several drinking days, but these can be burdensome, and the feasibility of such an approach is low. Therefore, we desire to show support for a one-time event-specific approach–the effects of which are anticipated to generalize to future pregaming events once individuals learn to moderate their pregaming drinking effectively. As this is the first randomized controlled trial test of the intervention, we want to determine if the intervention targeting pregaming alone is efficacious. If it is not, or if it is only efficacious for certain students, then future work can refine this initial approach to enhance the intervention to possibly include repeated delivery after pregaming or perhaps during multiple pregaming events.

Lastly, though the PACE intervention was completed and ready to be implemented in the RCT in late 2019 (with a plan to enroll participants starting in the spring semester of 2020), the COVID-19 pandemic and stay-at-home orders prevented us from starting the study on time. We waited until students were back on campus and living in residence halls again; thus, the study began with recruitment in the fall semester of 2021.

In conclusion, this pregaming-specific intervention has potential for impacting heavy college drinking as it targets a popular dangerous activity that, if reduced, could possibly lead to reduced drinking overall. This study will inform future grant efforts and the smartphone-based app could be delivered to millions of pregaming college students, at any desired interval, for a host of qualifying reasons, to prevent heavy pregaming drinking for a fraction of the cost it would take to intervene individually with students who have established heavy drinking patterns.

Abbreviations

Alcohol Use Disorders Identification Test

Blood alcohol level

Brief Alcohol Screening and Intervention for College Students

Brief Young Adult Alcohol Consequences Questionnaire

Motivational Interviewing

National Institute on Alcohol Abuse and Alcoholism

Pregaming Awareness in College Environments

Randomized controlled trial

DeJong W, Larimer ME, Wood MD, Hartman R. niaaa’s rapid response to college drinking problems initiative: reinforcing the use of evidence-based approaches in college alcohol prevention. J Stud Alcohol Drugs Suppl. 2009;16:5–11.

Article   Google Scholar  

Hingson RW, White AM. Magnitude and prevention of college alcohol and drug misuse: US college students aged 18–24. Mental Health Care College Commun. 2010;33:289–324.

Cronce JM, Larimer ME. Individual-focused approaches to the prevention of college student drinking. Alcohol Res Health. 2011;34(2):210–21.

PubMed   PubMed Central   Google Scholar  

National Institute on Alcohol Abuse and Alcoholism. A call to action: changing the culture of drinking at U.S. colleges. NIH Publication No 02–5010. 2002: Rockville, MD, Author.

National Institute on Alcohol Abuse and Alcoholism. What Colleges Need to Know Now: An Update on College Drinking Research NIH Publication No 07–5010. 2011: Rockville, MD, Author.

Alcoholism NIoAAa. Planning Alcohol Interventions Using NIAAA’s COLLEGE AIM Alcohol Intervention Matrix. NIH Publication No 15-AA-8017. 2015: Rockville, MD. Author.

Johnston LD, Miech RA, O'Malley PM, Bachman JG, Schulenberg JE, Patrick ME. Monitoring the future national survey results on drug use, 1975-2020: overview, key findings on adolescent drug use. Institute for social research. 2021.

Patrick ME, Terry-McElrath YM, Kloska DD, Schulenberg JE. High-intensity drinking among young adults in the united states: prevalence, frequency, and developmental change. Alcoholism: Clin Exp Res. 2016

Schulenberg J, Johnston L, O'Malley P, Bachman J, Miech R, Patrick M. Monitoring the Future national survey results on drug use, 1975–2019: Volume II, college students and adults ages 19–60. 2020.

White A, Hingson R. The burden of alcohol use: excessive alcohol consumption and related consequences among college students. Alcohol Res. 2014;35(2):201–18.

PubMed Central   Google Scholar  

Prochaska JO, DiClemente CC, Norcross JC. In search of how people change. Applicat Addictive Behav Am Psychol. 1992;47(9):1102–14.

CAS   Google Scholar  

Dimeff LA, Baer JS, Kivlahan DR, Marlatt GA. Brief Alcohol Screening and Intervention for College Students (BASICS) A harm reduction approach. New York: Guilford Press; 1999.

Google Scholar  

Miller WR, Rollnick S. Motivational Interviewing, Third Edition: Helping People Change (Applications of Motivational Interviewing). New York: Guilford Press; 2012.

Marlatt GA, Donovan DM. Relapse Prevention: Maintenance strategies in the treatment of addictive behaviors. New York: Guilford Press; 2005.

Logan DE, Marlatt GA. Harm reduction therapy: a practice-friendly review of research. J Clin Psychol. 2010;66(2):201–14.

Marlatt GA, Larimer ME, Witkiewitz K. Harm Reduction: Pragmatic Strategies for Managing High-Risk Behaviors (2nd edition). New York: Guilford Press; 2011.

Carey KB, Scott-Sheldon LAJ, Elliott JC, Garey L, Carey MP. Face-to-face versus computer-delivered alcohol interventions for college drinkers: a meta-analytic review, 1998 to 2010. Clin Psychol Rev. 2012;32(8):690–703.

Article   PubMed   PubMed Central   Google Scholar  

Reid AE, Carey KB. Interventions to reduce college student drinking: State of the evidence for mechanisms of behavior change. Clin Psychol Rev. 2015;40:213–24.

Larimer ME, Kilmer JR, Cronce JM, Hultgren BA, Gilson MS, Lee CM. Thirty years of BASICS: dissemination and implementation progress and challenges. Psychol Addict Behav. 2021. https://doi.org/10.1037/adb0000794 .

Article   PubMed   Google Scholar  

Grant S, Pedersen ER, Osilla KC, Kulesza M, D’Amico EJ. Reviewing and interpreting the effects of brief alcohol interventions: comment on a Cochrane review about motivational interviewing for young adults. Addiction. 2016;111(9):1521–7.

Tanner-Smith EE, Lipsey MW. Brief alcohol interventions for adolescents and young adults: a systematic review and meta-analysis. J Subst Abuse Treat. 2015;51:1–18.

Foxcroft DR, Coombes L, Wood S, Allen D, Almeida Santimano NM, Moreira MT. Motivational interviewing for the prevention of alcohol misuse in young adults. Cochrane Database Syste Rev. 2016;7:CD007025.

Mun EY, Atkins DC, Walters ST, et al. Is motivational interviewing effective at reducing alcohol misuse in young adults? A critical review of Foxcroft et al. (2014). Psychol Addict Behav. 2015;29(4):836–46.

Harford TC, Wechsler H, Seibring M. Attendance and alcohol use at parties and bars in college: a national survey of current drinkers. J Stud Alcohol. 2002;63(6):726–33.

Baer JS. Student factors: understanding individual variation in college drinking. J Stud Alcohol Suppl. 2002;14:40–53.

Lee CM, Lewis MA, Neighbors C. Preliminary examination of spring break alcohol use and related consequences. Psychol Addict Behav. 2009;23(4):689–94.

Neal DJ, Fromme K. Hook ’em horns and heavy drinking: alcohol use and collegiate sports. Addict Behav. 2007;32(11):2681–93.

Neighbors C, Atkins DC, Lewis MA, Lee CM, Kaysen D, Mittmann A, et al. Event-specific drinking among college students. Psychol of Addict Behav. 2011;25(4):702–7.

Rutledge PC, Park A, Sher KJ. 21st birthday drinking: extremely extreme. J Consult Clin Psychol. 2008;76(3):511–6.

Henslee AM, Irons JG, Bonn-Miller MO. Celebrating St. Patrick’s Day: Students’ Expectations, Intent, And Behavior. J Psychoactive Drugs. 2016;48(2):146–51.

Pedersen ER, D’Amico EJ, LaBrie JW, Klein DJ, Farris C, Griffin BA. Alcohol and sexual risk among american college students studying abroad. Prev Sci. 2020;21:926–36.

Lee CM, Neighbors C, Geisner IM, Mittmann A, Atkins DC, Zheng C, et al. Reducing high-risk spring break drinking: a randomized controlled trial. Society for Prevention Research; San Francisco, CA2013.

Neighbors C, Lee CM, Atkins DC, Lewis MA, Kaysen D, Mittmann A, et al. A randomized controlled trial of event-specific prevention strategies for reducing problematic drinking associated with 21st birthday celebrations. J Consult Clin Psychol. 2012;80(5):850–62.

Neighbors C, Walters ST, Lee CM, Vader AM, Vehige T, Szigethy T, et al. Event-Specific prevention: addressing college student drinking during known windows of risk. Addict Behav. 2007;32(11):2667–80.

Pedersen ER, LaBrie J. Partying before the party: examining prepartying behavior among college students. J Am Coll Health. 2007;56(3):237–45.

Pedersen ER, LaBrie JW, Kilmer JR. Before you slip into the night, you’ll want something to drink: exploring the reasons for prepartying behavior among college student drinkers. Issues Ment Health Nurs. 2009;30(6):354–63.

Zamboanga BL, Casner HG, Olthuis JV, Borsari B, Ham LS, Schwartz SJ, et al. Knowing where they’re going: destination-specific pregaming behaviors in a multiethnic sample of college students. J Clin Psychol. 2013;69(4):383–96.

Labhart F, Graham K, Wells S, Kuntsche E. Drinking before going to licensed premises: an event-level analysis of predrinking, alcohol consumption, and adverse outcomes. Alcoholism Clin Exp Res. 2013;37(2):284–91.

Zamboanga BL, Olthuis JV. What Is pregaming and how prevalent is it among U.S. college students an introduction to the special issue on pregaming. Substance Use Misuse. 2016;51(8):953–60.

Merrill JE, Vermont LN, Bachrach RL, Read JP. Is the pregame to blame? Event-level associations between pregaming and alcohol-related consequences. J Stud Alcohol Drugs. 2013;74(5):757–64.

Barry AE, Stellefson ML, Piazza-Gardner AK, Chaney BH, Dodd V. The impact of pregaming on subsequent blood alcohol concentrations: an event-level analysis. Addict Behav. 2013;38(8):2374–7.

LaBrie JW, Earle AM, Hummer JF, Boyle SC. Is prepartying a cause of heavy drinking and consequences rather than just a correlate? A longitudinal look at the relationship between prepartying, alcohol approval, and subsequent drinking and consequences. Subst Use Misuse. 2016;51(8):1013–23.

Fairlie AM, Maggs JL, Lanza ST. Prepartying, drinking games, and extreme drinking among college students: a daily-level investigation. Addict Behav. 2015;42:91–5.

Pedersen ER. Using the solid research base on pregaming to begin intervention development an epilogue to the special issue on pregaming. Substance Use Misuse. 2016;51(8):1067–73.

Østergaard J, Andrade SB. Who pre-drinks before a night out and why? Socioeconomic status and motives behind young people’s pre-drinking in the United Kingdom. J Substance Use. 2014;19(3):229–38.

Zamboanga BL, George AM, Van Hedger K, Olthuis JV, Pilatti A, Dresler E. Participation in drinking games and predrinking among university students in Argentina, Australia, Canada, and New Zealand. Alcohol Alcohol. 2021;56(6):683–8.

Kenney SR, Hummer JF, LaBrie JW. An examination of prepartying and drinking game playing during high school and their impact on alcohol-related risk upon entrance into college. J Youth Adolesc. 2010;39(9):999–1011.

Barnett NP, Orchowski LM, Read JP, Kahler CW. Predictors and consequences of pregaming using day- and week-level measurements. Psychol Addict Behav. 2013;27(4):921–33.

LaBrie JW, Pedersen ER. Prepartying promotes heightened risk in the college environment: an event-level report. Addict Behav. 2008;33(7):955–9.

Chaney BH, Martin RJ, Barry AE, Lee JGL, Cremeens-Matthews J, Stellefson ML. Pregaming: a field-based investigation of alcohol quantities consumed prior to visiting a bar and restaurant district. Subst Use Misuse. 2019;54(6):1017–23.

Patrick ME. A call for research on high-intensity alcohol use. Alcoholism Clin Exper Res. 2016;40(2):256–9.

Ahmed R, Hustad JT, LaSalle L, Borsari B. Hospitalizations for students with an alcohol-related sanction: gender and pregaming as risk factors. J Am Coll Health. 2014;62(5):293–300.

Hummer JF, Napper LE, Ehret PE, LaBrie JW. Event-specific risk and ecological factors associated with prepartying among heavier drinking college students. Addict Behav. 2013;38(3):1620–8.

Read JP, Merrill JE, Bytschkow K. Before the party starts: risk factors and reasons for “pregaming” in college students. J Am Coll Health. 2010;58(5):461–72.

Radomski S, Blayney JA, Prince MA, Read JP. PTSD and Pregaming in college students: a risky practice for an at-risk group. Subst Use Misuse. 2016;51(8):1034–46.

Borsari B, Merrill JE, Yurasek A, Miller MB, Carey KB. Does a brief motivational intervention reduce frequency of pregaming in mandated students? Subst Use Misuse. 2016;51(8):1056–66.

Zamboanga BL, Merrill JE, Olthuis JV, Milroy JJ, Sokolovsky AW, Wyrick DL. Secondary effects of myPlaybook on college athletes’ avoidance of drinking games or pregaming as a protective behavior strategy: a multisite randomized controlled study. Soc Sci Med. 2019;228:135–41.

Burger JM, LaSalvia CT, Hendricks LA, Mehdipour T, Neudeck EM. Partying before the party gets started: the effects of descriptive norms on pregaming behavior. Basic Appl Soc Psychol. 2011;33(3):220–7.

Caudwell KM, Mullan BA, Hagger MS. Testing an online, theory-based intervention to reduce pre-drinking alcohol consumption and alcohol-related harm in undergraduates: a randomized controlled trial. Int J Behav Med. 2018;25(5):592–604.

Cadigan JM, Martens MP, Dworkin ER, Sher KJ. The efficacy of an event-specific, text message, personalized drinking feedback intervention. Prevention Sci. 2019;20(6):873–83.

Dimeff LA, Baer JS, Kivlahan DR, Marlatt GA. Brief Alcohol Screening and Intervention for College Students (BASICS)A Harm Reduction Approach. New York: Guilford Press; 1999.

Miller WR, Rollnick S. Motivational Interviewing Helping People Change. New York: Guilford Press, 3rd Edition; 2012.

Marlatt GA, Donovan DM. Relapse Prevention, Second Edition: Maintenance Strategies in the Treatment of Addictive Behaviors. Donovan GAMDM, editor: The Guilford Press, 2nd Edition; 2007.

Whiteside U, Cronce JM, Pedersen ER, Larimer ME. Brief motivational feedback for college students and adolescents: a harm reduction approach. J Clin Psychol. 2010;66(2):150–63.

Fachini A, Aliane PP, Martinez EZ, Furtado EF. Efficacy of brief alcohol screening intervention for college students (BASICS): a meta-analysis of randomized controlled trials. Subst Abuse Treat Prev Policy. 2012;7:40.

Witkiewitz K, Desai SA, Bowen S, Leigh BC, Kirouac M, Larimer ME. Development and evaluation of a mobile intervention for heavy drinking and smoking among college students. Psychol Addict Behav. 2014;28(3):639–50.

Pew Research Center. College students and technology. Washington: DC Author; 2011.

Deloitte. 2015 Global Mobile Consumer Survey: US Edition. The rise of the always-connected consumer. New York, NY: Author; 2015.

Kaner EF, Beyer FR, Garnett C, Crane D, Brown J, Muirhead C, et al. Personalised digital interventions for reducing hazardous and harmful alcohol consumption in community-dwelling populations. Cochrane Database Syst Rev. 2017;9(9):Cd011479.

PubMed   Google Scholar  

Kazemi DM, Li S, Levine MJ, Auten B, Granson M. Systematic review of smartphone apps as a mhealth intervention to address substance abuse in adolescents and adults. J Addict Nurs. 2021;32(3):180–7.

Pedersen ER, LaBrie JW. Normative misperceptions of drinking among college students: a look at the specific contexts of prepartying and drinking games. J Stud Alcohol Drugs. 2008;69(3):406–11.

Perkins HW. Social norms and the prevention of alcohol misuse in collegiate contexts. J Stud Alcohol. 2002;14:164–72.

Pedersen ER, Davis JP, Hummer JF, DiGuiseppi G, Sedano A, Rodriguez A, et al. Development of a measure to assess protective behavioral strategies for pregaming among young adults. Substance Use Misuse. 2019;55:1–12.

Zamboanga BL, Schwartz SJ, Ham LS, Borsari B, Van Tyne K. Alcohol expectancies, pregaming, drinking games, and hazardous alcohol use in a multiethnic sample of college students. Cogn Ther Res. 2010;34(2):124–33.

Kenney SR, Napper LE, LaBrie JW. Social anxiety and drinking refusal self-efficacy moderate the relationship between drinking game participation and alcohol-related consequences. Am J Drug Alcohol Abuse. 2014;40(5):388–94.

Saunders JB, Aasland OG, Babor TF, de la Fuente JR, Grant M. Development of the Alcohol Use Disorders Identification Test (AUDIT): WHO collaborative project on early detection of persons with harmful alcohol consumption–II. Addiction. 1993;88(6):791–804.

Article   CAS   PubMed   Google Scholar  

Boyle A, Wee N, Harris R, Tompkins A, Soper M, Porter C. Alcohol-related emergency department attendances: is preloading a risk factor? Cross-sectional survey. Int J Emerg Med. 2010;3(3):151–5.

Hughes K, Anderson Z, Morleo M, Bellis MA. Alcohol, nightlife and violence: the relative contributions of drinking before and during nights out to negative health and criminal justice outcomes. Addiction. 2008;103(1):60–5.

Miller MB, Borsari B, Fernandez AC, Yurasek AM, Hustad JT. Drinking location and pregaming as predictors of alcohol intoxication among mandated college students. Subst Use Misuse. 2016;51(8):983–92.

Glindemann KE, Ehrhart IJ, Maynard ML, Geller ES. Alcohol front-loading among college students: exploring the need for prevention intervention. J Alcohol Drug Educat. 2006;50:5.

Foster JH, Ferguson C. Alcohol “Pre-loading”: a review of the literature. Alcohol Alcohol. 2014;49(2):213–26.

Hummer JF, LaBrie JW, Kenney SR. Prepartying and Pregaming. In: Levesque RJR, editor. Encyclopedia of Adolescence. New York: Springer, New York; 2011. p. 2138–44.

Chapter   Google Scholar  

Zamboanga BL, Borsari B, Ham LS, Olthuis JV, Van Tyne K, Casner HG. Pregaming in high school students: relevance to risky drinking practices, alcohol cognitions, and the social drinking context. Psychol Addict Behav. 2011;25(2):340–5.

Kahler CW, Hustad J, Barnett NP, Strong DR, Borsari B. Validation of the 30-day version of the brief young adult alcohol consequences questionnaire for use in longitudinal studies. J Stud Alcohol Drugs. 2008;69(4):611–5.

Kahler CW, Strong DR, Read JP. Toward efficient and comprehensive measurement of the alcohol problems continuum in college students: the brief young adult alcohol consequences questionnaire. Alcohol Clin Exp Res. 2005;29(7):1180–9.

Prochaska JO, Velicer WF, Rossi JS, Goldstein MG, Marcus BH, Rakowski W, et al. Stages of change and decisional balance for 12 problem behaviors. Health Psychol. 1994;13(1):39–46.

Williams EC, Horton NJ, Samet JH, Saitz R. Do brief measures of readiness to change predict alcohol consumption and consequences in primary care patients with unhealthy alcohol use. Alcoholism Clin Exp Res. 2007;31(3):428–35.

Berman AH, Gajecki M, Sinadinovic K, Andersson C. Mobile Interventions targeting risky drinking among university students: a review. Curr Addict Rep. 2016;3(2):166–74.

Heron KE, Smyth JM. Ecological momentary interventions: incorporating mobile technology into psychosocial and health behavior treatments. Br J Health Psychol. 2010;15(Pt 1):1–39.

Buscemi J, Murphy JG, Martens MP, McDevitt-Murphy ME, Pederson AA, Skidmore JR. Help-Seeking for alcohol-related problems in college students: correlates and preferred resources. Psychol Addict Behav. 2010;24(4):571–80.

Cellucci T, Krogh J, Vik P. Help seeking for alcohol problems in a college population. J Gen Psychol. 2006;133(4):421–33.

Gajecki M, Berman AH, Sinadinovic K, Rosendahl I, Andersson C. Mobile phone brief intervention applications for risky alcohol use among university students: a randomized controlled study. Addict Sci Clin Prac. 2014;9(1):1–12.

DeMartini KS, Fucito LM, O’Malley SS. Novel approaches to individual alcohol interventions for heavy drinking college students and young adults. Curr Addict Rep. 2015;2(1):47–57.

Crane D, Garnett C, Brown J, West R, Michie S. Behavior change techniques in popular alcohol reduction apps: content analysis. J Med Internet Res. 2015;17(5):e118.

Cohn AM, Hunter-Reel D, Hagman BT, Mitchell J. Promoting behavior change from alcohol use through mobile technology: the future of ecological momentary assessment. Alcoholism Clin Exp Res. 2011;35(12):2209–15.

Weaver RE, Horyniak RD, Jenkinson R, Dietze P, Lim SCM. ?Let?s get Wasted!? and other apps: characteristics, acceptability, and use of alcohol-related smartphone applications. JMIR Mhealth Uhealth. 2013;1(1): e9.

Pagoto S, Schneider K, Jojic M, DeBiasse M, Mann D. Evidence-based strategies in weight-loss mobile apps. Am J Prev Med. 2013;45(5):576–82.

Abroms LC, Padmanabhan N, Thaweethai L, Phillips T. iPhone apps for smoking cessation: a content analysis. Am J Prev Med. 2011;40(3):279–85.

Shiffman S. Ecological momentary assessment (EMA) in studies of substance use. Psychol Assess. 2009;21(4):486–97.

McCambridge J, Kypri K. Can simply answering research questions change behaviour? Systematic review and meta analyses of brief alcohol intervention trials. PLoS ONE. 2011;6(10):e23748.

Article   CAS   PubMed   PubMed Central   Google Scholar  

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Acknowledgements

The authors wish to thank the software and design teams at Emberex for programming the intervention and Andy Langdon at Good Pictures for filming the video portions of the intervention.

This work was supported by a grant from the National Institute on Alcohol Abuse and Alcoholism (R34AA025968 “Mobile Application Intervention Targeting the High-Risk Drinking Practice of Prepartying”) awarded to Eric R. Pedersen.

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Keck School of Medicine, Department of Psychiatry and Behavioral Sciences, USC Institute for Addiction Science, University of Southern California, 2250 Alcazar Street, Suite 2200, Los Angeles, CA, 90033, United States

Eric R. Pedersen & Reagan E. Fitzke

RAND Corporation, Santa Monica, United States

Justin F. Hummer

Suzanne Dworak-Peck School of Social Work; USC Center for Artificial Intelligence in Society; USC Center for Mindfulness Science; USC Institute for Addiction Science, University of Southern California, Los Angeles, United States

Jordan P. Davis

Department of Psychology, University of Southern California, Los Angeles, United States

Nina C. Christie

Department of Psychology, University of New Mexico, Albuquerque, United States

Katie Witkiewitz

Suzanne Dworkak-Peck School of Social Work; Department of Population and Public Health Sciences, Keck School of Medicine; USC Institute for Addiction Science, University of Southern California, Los Angeles, United States

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ERP, JFH, and JPD conceptualized the study and obtained funding. KW and JDC assisted with efforts to obtain funding. ERP has overall responsibility for the execution of the intervention, data collection, and reporting. JPD will oversee analyses. JFH and ERP drafted an initial version of the intervention and JPD, KW, and JC provided edits. JFH created the name Pregaming Awareness in College Environments (PACE). REF and NCC led the focus group efforts. All authors assisted with the design and evaluation of the intervention and will assist with quantitative data analyses. ERP drafted an initial version of this paper and all authors provided edits and contributed to all sections. All authors read and approved the final manuscript.

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Correspondence to Eric R. Pedersen .

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The University of Southern California Social Behavioral Review Committee (FWA 00007099) approved the study and all procedures. All participants consented to be included in the study.

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Pedersen, E.R., Hummer, J.F., Davis, J.P. et al. A mobile-based pregaming drinking prevention intervention for college students: study protocol for a randomized controlled trial. Addict Sci Clin Pract 17 , 31 (2022). https://doi.org/10.1186/s13722-022-00314-5

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Received : 30 November 2021

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Published : 18 June 2022

DOI : https://doi.org/10.1186/s13722-022-00314-5

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