Comparison of Pregaming Alcohol Use and Consequences by Season Status and Sex in College Student Athletes

Abstract

As student athletes exhibit unique alcohol use patterns based on being in- versus out-of-season and biological sex, we aimed to explore student athlete (N = 442) alcohol use, pregaming behaviors, and associated negative outcomes. Results suggest being out-of-season and male are positively associated with negative alcohol-related consequences, and male athletes report greater numbers of pregame specific alcohol-related consequences than female athletes (p < .05). Female athletes indicated significantly higher estimated blood alcohol concentrations than male athletes on pregaming nights. No differences emerged between in- and out-of-season athletes on pregame consequences. Results suggest that further emphasis on the role season status and sex has on pregaming behaviors and experiencing negative outcomes may be an important next step toward enhancing prevention and intervention approaches.

Keywords

pregaming college student athletes alcohol consequences

Student alcohol use on college campuses is a nationwide problem, with nearly two thirds (61.7%) of college students indicating alcohol use within the last 30 days (American College Health Association, 2018). Of this alcohol using group, 38.3% had at least one binge drinking episode (American College Health Association, 2018). Binge drinking, or what is also referred to as heavy episodic drinking, is the consumption of five or more drinks for men or four or more drinks for women within a two-hour period (Hingson et al., 2017; Wechsler et al., 1994). Heavy episodic drinking is commonly tied to a range of negative social, academic, and physical consequences impacting the day-to-day lives of college students (Hingson et al., 2017). Although common among the entire college student population, student athletes are a specific subpopulation of college students who report engaging in heavy episodic drinking more regularly (Green et al., 2014; Mastroleo et al., 2018). As such, they also report higher levels of estimated blood alcohol concentration (eBAC) and are at heightened risk for negative alcohol-related consequences (American College Health Association, 2018; Mastroleo et al., 2018).

Student Athlete Drinking

Over the last few decades, student athletes have been consistently shown to engage in heavy episodic drinking and to experience a broad range of negative alcohol-related consequences (Leichliter et al., 1998; Turrisi et al., 2006; Wechsler et al., 1997, 2009). The National Collegiate Athletic Association (NCAA) reported 77% of student athletes used alcohol within the last year, with 42% reporting past-year binge drinking (44% of men, 39% of women; NCAA, 2018). Across multiple studies, student athletes report greater alcohol consumption, being drunk more frequently, and experiencing more negative alcohol-related consequences than nonathletes (Doumas et al., 2007; Leichliter et al., 1998; Mastroleo et al., 2018). Specifically, out-of-season athletes (i.e., those not in their traditional season in which a championship is held) reported consuming greater quantities of alcohol and experiencing more negative consequences than in-season athletes (Mastroleo et al., 2018). Consequences of these behaviors have included: blackouts, symptoms of depression, injuries, decreased academic performance, legal problems, and sexual assaults (Brenner et al., 2014; Leichliter et al., 1998). Two common activities associated with heavy episodic drinking are engaging in pregaming and playing drinking games (Grossbard et al., 2007).

Pregaming in College Settings

Pregaming has traditionally been defined as the practice of consuming large amounts of alcohol in a short period of time prior to a planned social event (e.g., party, sporting event, dance) where alcohol may or may not be present (Borsari et al., 2007; Read et al., 2010). Recently, Zamboanga and Olthuis (2016) defined pregaming as “drinking (which may or may not involve getting ‘buzzed’ or drunk) alone or with people before going to an event or gathering where more alcohol may or may not be consumed” (Zamboanga & Olthuis, 2016, p. 954). Other common terms used to describe the behavior include preloading, prepartying, front-loading, and prefunking (Labrie et al., 2012).

Although research on student athlete pregaming is in its infancy, studies of nonathletes have shown that college students who engage in pregaming consume more drinks on an average drinking day than on days they do not pregame (Grossbard et al., 2007; Pedersen & Labrie, 2007; Read et al., 2010; Zamboanga et al., 2008). Subsequently, students who pregame also have higher eBACs than those who do not (Grossbard et al., 2007; Zamboanga et al., 2008). In addition, frequency of pregaming has been positively associated with experiencing alcohol-related consequences (i.e., hangovers or feeling sick, personality changes, having a bad time, fighting or doing mean things to others) in the general student population (Pedersen & Labrie, 2007). One study conducted by Merrill et al. (2013) found that pregaming significantly predicted an increase in the number of alcohol-related consequences experienced at the daily level (i.e., same day), and that this stayed true even when number of drinks and BAC level were taken into account. The growing literature supports the notion that pregaming may lead to student athletes experiencing negative alcohol-related consequences at even higher rates than the general college student population (Grossbard et al., 2009; Pedersen & Labrie, 2007; Zamboanga et al., 2010).

Sex Differences and Student Pregaming

Research shows that both male and female college students report higher rates of alcohol use and alcohol-related consequences when pregaming compared to students with no pregaming behavior (Paves et al., 2012). One study designed to examine differences between male and female pregaming behaviors found that both sexes engaged in more heavy episodic drinking on pregaming days than on nonpregaming days (84% for men, 83% for women; Pedersen & Labrie, 2007). Men, however, reported consuming more alcohol overall on pregaming days than women, though studies are conflicting on if men consumed more during the pregaming period or afterwards (Paves et al., 2012; Pedersen & Labrie, 2007; Read et al., 2010). It is also unclear if men or women typically reach higher eBACs while pregaming (Barnett et al., 2013; Read et al., 2010). In a separate study, male student athletes reported greater alcohol consumption and more alcohol-related consequences than females when participating in drinking games, yet females reported higher eBACs and peak eBACs than men (Grossbard et al., 2007).

Student Athlete Pregaming

To our knowledge, there are no studies examining sex differences among college student athletes’ pregaming behavior. In fact, despite researchers showing that student athletes are a unique college subpopulation at higher risk for heavy episodic drinking, it is not clear if athletes participate in pregaming at higher rates than nonathletes. Most of what is known about student athlete pregaming is limited to studies done on participation in drinking games (e.g., Grossbard et al., 2007). Studies have shown a relationship exists between pregaming and drinking games, as the two activities are often done concurrently (DeJong et al., 2010). Nonetheless, they remain distinct behaviors in which people may participate in one and not necessarily the other (Borsari et al., 2007; Zamboanga et al., 2010). In a study by Grossbard et al. (2007), intramural and intercollegiate athletes reported drinking more on a weekly basis, having higher typical eBACs, higher peak eBACs, and experiencing more alcohol-related consequences than their nonathlete peers. Likewise, these athletes also reported greater drinking game participation than nonathletes (Grossbard et al., 2007). Yet how these outcomes were related specifically to pregaming was not explored.

To date, researchers have not identified if pregaming behavior varies for student athletes based on their season status (i.e., in- vs. out-of-season). Despite mixed reviews on when nonathlete college students drink the most, it has been clearly shown that athletes drink more during off-season training than during their championship competition seasons (Martens et al., 2006; Mastroleo et al., 2018). Given that practice and competition schedules vary from week to week and throughout the year, it is possible that student athlete pregaming patterns will be distinct based on their current status as an in-season or out-of-season athlete. Marzell et al. (2015) suggest that the limited opportunities to socialize during in-season may influence athlete decisions to maximize partying opportunities. Furthermore, student athletes report having higher negative alcohol-related consequences than nonathletes (Mastroleo et al., 2018) and males engage in pregaming at higher rates than their nonathlete peers (Rutledge et al., 2014). However, it is still unknown if specific patterns of pregaming exist based on season status and if negative alcohol-related consequences are associated with student athlete pregaming behavior.

As such, there remains a critical need for researchers to continue to explore potential relationships between student athlete drinking behaviors and consequences and, in particular, their participation and association with pregaming. The failure to consider in-season and out-of-season status for teams considerably limits understanding of their behaviors. The intent of this study is to examine the differences in pregaming behaviors based on season status (in- vs. out-of-season) and the number of alcohol-related consequences associated with these patterns. We further sought to examine differences between male and female athlete pregaming behaviors and alcohol-related consequences.

Materials and Methods

Participants

Undergraduate students from a private, medium-sized, research intensive university in the Northeastern United States were randomly selected by the university registrar with one half of students surveyed each semester (N = 2,984; 57% female, 61% White; 16% athlete, n = 442). Email messages (containing a link to a web-based survey) were sent to selected students inviting them to complete the survey. All data were collected without identifying information and upon completion participants were entered in a lottery to win one of several prizes ranging in value from $5 to $150 (i.e., dining services gift cards, t-shirts, iPad). As this study was a component of a campus-based evaluation of student alcohol use, the University Institutional Review Board deemed the research exempt from human subject review.

Measures

Demographics

Gender, race, ethnicity, year in school, and weight (for eBAC), as well as involvement in intercollegiate athletics, were collected. Athlete status was determined by asking participants, “Are you a member of an intercollegiate athletic team?” Athlete season status was identified by asking, “What is your traditional sport season (i.e., the season in which your sport has a national championship)?” The response options for this question were Fall (typically August through December), Winter (typically October through March), and Spring (typically January through May). Answers to this question were crossed with the semester the participant completed the survey to establish whether athletes were in- or out-of-season at the time they completed their survey. As an extra attempt to ensure anonymity of the data collected, team affiliation (i.e., sport-type) was not collected in this study.

Alcohol Use

A two week alcohol Timeline Follow Back (TLFB; Sobell & Sobell, 2003) approach was used to collect frequency and quantity of drinking. This measure has been used in various drinking populations (e.g.,, adults, adolescents, high-risk drinking) and can provide a range of information regarding an individual’s drinking (i.e., pattern, variability, and magnitude of drinking; Sobell et al., 1979). Clinical and general studies have found content, construct, and criterion validity with this measure, and it is often recommended for use when participants need to distinguish between high or low drinking days, and/or precise self-report estimates are needed (Sobell et al., 1979).

Participants were presented with a calendar of the past two weeks and asked, “How much alcohol (measured in number of drinks) did you drink on each day?” A detailed standard drink definition (12 oz. beer, 5 oz. wine, 1 shot of liquor) and examples were provided. Frequency was derived using a count of the number of drinking days. Total number of drinks consumed in the past two weeks was also calculated. For each of the days on which drinking was reported, participants were asked the length of the drinking episode. Using number of drinks, time spent drinking, and the participant’s gender and weight, an eBAC was calculated (Matthews & Miller, 1979) and the day with the highest value was labeled peak eBAC.

For pregaming, participants were asked, “Did you ‘pregame’ or ‘pre-party’ in the past two weeks?” The definition of pregaming was listed on the survey as follows:

Pregaming is when an individual consumes alcohol prior to going out to the main event for the night (e.g., drinking in your home/room or a friends’ home/room). The behavior is characterized by drinking while waiting for people to gather for the evening, or drinking in order to ‘get buzzed’ before going to a party/function at which alcohol will be expensive (e.g., at a bar or club) or difficult to obtain (e.g., at a school function).

In addition, on the days that drinking was reported on the two week TLFB, participants were asked “How many of these drinks did you have while pregaming?” Each drinking day was subsequently coded as to whether it included pregaming or not and was collected for one full week of data reported on the TLFB.

Alcohol-Related Consequences

Negative alcohol-related consequences were assessed using the Brief Young Adult Alcohol Consequences Questionnaire (B-YAACQ; Kahler et al., 2005), a 24-item measure of alcohol-related problems with dichotomous (no/yes) response options. Kahler et al. (2008) found this measure to be valid within the college student population, with minimal item redundancy, ceiling/floor effects, and sensitivity to changes in drinking levels post intervention. This measure shows retainment of the variance as indicated by the original 48 item YAACQ (Kahler et al., 2005; Read et al., 2006). Questions were repeated for participants who endorsed pregaming such that students were asked to indicate which negative consequences occurred on the nights they pregamed. All items were presented with a dichotomous (yes/no) response option and were based on the same two-week period that they answered for the TLFB.

Data Analysis

A series of independent t tests were conducted to verify in and out-of-season athletes were similar in age, year in school, and ethnicity. Chi-square tests were used to verify in- and out-of-season athletes were similar on race and sex. A mixed-model analysis of variance (ANOVA) tested group (in vs. out-of-season) effects on pregaming outcomes including average eBAC on pregame days, average eBAC on nonpregame days, percent of pregame drinking days, number of drinks during pregame, total alcohol-related consequences (B-YAACQ Total), and pregaming specific alcohol-related consequences (B-YAACQ Pregaming Total). Second, a series of ANOVAs were used to identify group (female athlete vs. male athlete) effects on pregaming outcomes including average eBAC on pregame days, average eBAC on nonpregame days, percent of drinking days pregamed, number of drinks during pregame, total alcohol-related consequences (B-YAACQ Total), and pregaming alcohol-related consequences (B-YAACQ Pregaming Total). A final examination was conducted to test for interaction effects between sex (male/female) and season status (in/out).

Results

Sample Information

A total of 5,911 students were invited to participate, of which 2,984 (50.5%) enrolled in the survey, and 2,794 contributed some alcohol use data and completed the question about athlete status. A total of 442 (15.8%) participants endorsed being an intercollegiate athlete, with 265 athletes determined to be in-season when they completed their survey, and 175 were identified as out-of-season (60.0% and 39.6% of athletes, respectively). The complete athlete sample was 62.4% female, 83.7% non-Hispanic White, and was well distributed across school years. Additional demographic information is presented in Table 1. There were no significant differences between in- and out-of-season athletes on demographic variables (all p values > .05; see Table 1).

Table 1.

Sample Demographics.

Variable	N (%) (N = 442)	In-season (n = 265; 60%)	Out-of-season (n = 175; 39.6%)	p
Birth sex
Female	276 (62.4)	169 (63.8)	106 (60.6)
Male	166 (37.6)	96 (36.2)	69 (39.4)	.497
Hispanic
No	417 (94.3)	251 (94.7)	164 (93.7)
Yes	25 (5.7)	14 (5.3)	11 (6.3)	.376
Race
White	370 (83.7)	227 (85.7)	142 (81.1)
Non-White	65 (14.7)	34 (12.8)	30 (17.1)	.205
Year in school
First	120 (27.1)	62 (23.4)	58 (33.1)
Second	123 (27.8)	79 (29.8)	43 (24.6)
Third	95 (21.5)	61 (23.0)	33 (18.9)
Fourth/Fifth	104 (23.5)	63 (23.8)	41 (23.4)	.187
Residence
On campus	325 (73.5)	192 (72.5)	131 (74.9)
Off campus	115 (26.0)	72 (27.2)	43 (24.6)	.230
Fraternity/Sorority
No	381 (86.2)	230 (86.8)	149 (85.1)
Yes	60 (13.6)	34 (12.8)	26 (14.9)	.241
Pregaming (2 weeks)
No	138 (31.2)	86 (32.5)	51 (29.1)
Yes	280 (63.3)	162 (61.1)	117 (66.9)	.061

Note. Discrepancy in the number of participants who responded to an item and the total sample N are due to skipped items by participants; percentages were calculated among valid responses.

Alcohol Use and Season Status

Overall, there was no significant difference between in- and out-of-season athletes on average eBAC on pregame and nonpregame days (p > .05). There was also no significant difference between groups on the percent of drinking days pregamed or the number of drinks consumed on pregaming days (see Table 2).

Table 2.

Comparison of In and Out of Sport Season Athletes on Past 2-Week Drinking and Alcohol Problems.

	In-season(n = 265)N(%) or M(SD)	Out-season(n = 175)N(%) or M(SD)	p
Alcohol use
Avg. eBAC on pregaming days (n = 176)	.108 (.064)	.121 (.073)	.193
Avg. eBAC on non-pregaming days (n = 170)	.052 (.046)	.051 (.053)	.863
Percent of drinking days pregamed	63.72 (42.36)	57.50 (41.48)	.260
Number of drinks on pregaming day	2.71 (1.38)	4.42 (.31)	.096
Total consequences
BYAACQ total (n= 376)	2.53 (3.26)	3.53 (3.98)	.008**
Pregame consequences
BYAACQ pregame total (n= 233)	1.96 (2.58)	2.38 (3.67)	.305

Note. eBAC = estimated Breath Alcohol Concentration (Matthews & Miller, 1979); B-YAACQ = Brief Young Adult Alcohol Consequences Questionnaire. Alcohol problems measures were analyzed for participants who reported drinking in the past two weeks. Two athletes did not indicate their sport season. Analyses were one-way ANOVA for continuous outcomes and multinomial logistic regression for binary outcomes. All variables were past 2-weeks except pregaming, which was past week. * p < .05, ** p < .01, ***p < .001

Alcohol-Related Consequences

Total Alcohol-Related Consequences

There was a significant difference between season status and total B-YAACQ score—F(1, 374) = 7.202, p = .008, η² = .019. Athletes who were out-of-season reported significantly more alcohol-related consequences (M = 3.53) than athletes in-season (M = 2.53) (see Table 2).

Pregame Consequences

There was no significant difference between in- and out-of-season athletes’ total B-YAACQ pregaming scores (M = 1.96; M = 2.38; Table 2).

Male and Female Alcohol-Related Consequences

A significant difference was found between male and female athletes on their total B-YAACQ scores—F(1, 376) = 10.867, p = .001, η² = .028 and pregame B-YAACQ scores—F(1, 232) = 5.872, p = .016, η² = 0.025. In both instances, male athletes reported more overall total and pregaming consequences than their female counterparts. Finally, a significant difference was found between groups on average eBAC on pregaming days—F(1, 174) = 5.028, p = .026, η² = 0.028, where female athletes reported higher eBACs (M = .123) than male athletes (M = .10). Nonsignificant differences were found for all other drinking variables (p > .05). In addition, there was no interaction between sex and season status (p > .05; see Table 3).

Table 3.

Comparison of Female and Male Athletes on Past 2-Week Drinking and Alcohol Problems.

	Female athletes (n = 276) N(%) or M(SD)	Male athletes (n = 166) N(%) or M(SD)	p
Alcohol use
Avg. eBAC on pregaming days (n = 176)	.123 (.070)	.100 (.063)	.026*
Avg. eBAC on non-pregaming days (n = 170)	.053 (.048)	.049 (.052)	.621
Percent of drinking days pregamed (n = 286)	52.57 (44.6)	46.00 (44.7)	.219
Number of drinks on pregaming day (n = 19)	3.40 (2.50)	4.22 (1.71)	.421
Non-pregame consequences
BYAACQ total (n= 378)	2.47 (3.12)	3.72 (4.20)	.001***
Pregame consequences
BYAACQ pregame total (n= 233)	1.75 (2.58)	2.74 (3.69)	.016*

Note. eBAC = estimated Breath Alcohol Concentration (Matthews & Miller, 1979); B-YAACQ = Brief Young Adult Alcohol Consequences Questionnaire. Alcohol problems measures were analyzed for participants who reported drinking in the past two weeks. Two athletes did not indicate their sport season. Analyses were one-way ANOVA for continuous outcomes and multinomial logistic regression for binary outcomes. All variables were past two weeks except pregaming, which was past week. * p < .05, ** p < .01, ***p < .001

Discussion

This study examined the relationship between college student athlete season status, pregaming behavior, and associated alcohol-related outcomes and consequences. Overall, results indicate that out-of-season athletes reported a significantly higher number of overall negative consequences on nights they were not pregaming, while no differences existed between groups on pregaming consequences. In addition to season status differences, sex differences were found indicating male athletes reported a higher number of alcohol-related consequences, as well as pregaming consequences. Interestingly, female athletes were found to have significantly higher eBACs than male athletes on pregaming nights. Taken together, the findings continue to indicate season status and sex play an important role in understanding drinking behaviors of college student athletes. Given the current knowledge of Brief Motivational Intervention (BMI) having little to no effect on reducing the frequency of pregaming (Borsari et al., 2016) even when discussed explicitly in the intervention with students, and web-based interventions also having no impact on student athlete pregaming (Zamboanga et al., 2019), outcomes of this study further reinforce the need for intentional prevention and intervention approaches for pregaming as a whole.

Season Status and Alcohol-Related Consequences

Athletes who were out-of-season reported significantly more total negative alcohol-related consequences than their in-season peers. Out-of-season athletes also reported a higher number of negative alcohol-related consequences on nights they pregamed, yet this difference was not significant. Although the percentage of days athletes pregamed were similar across season status, the average number of drinks consumed on pregaming days by out-of-season athletes was greater. A possible inference from these findings is the greater number of negative consequences experienced by out-of-season athletes may be associated with the greater number of drinks consumed. This would be consistent with other studies that have found the greater number of drinks consumed, and the higher a student’s eBAC, the more negative consequences they incur (Neal & Carey, 2007; Turner et al., 2004). Another possibility is that athlete’s motives for drinking may be driving some of the behaviors (Martens et al., 2011; Marzell et al., 2015). For example, athletes who are in-season may be more conscientious about their drinking decisions (i.e., not getting injured while drinking or in trouble with police) and recognize the ramifications those decisions could have on their individual and team performance. In contrast, these same in-season athletes may engage in pregaming or heavier alcohol consumption as a way to celebrate a victory or good performance (i.e., sport-related positive reinforcement motives; Martens et al., 2011). As the motives behind athlete drinking are not well understood, more research is needed to clearly understand these associations and the event-level behaviors of student athletes while they are in- versus out-of-season.

Alcohol-Related Consequences for Men and Women

The number of negative alcohol-related consequences reported by athletes in this study was associated with participant’s biological sex. Congruent with previous findings of males experiencing more negative consequences related to alcohol use (Grossbard et al., 2007), male participants in this study reported higher total alcohol-related consequences and pregame specific consequences than females. Females reported higher eBACs on nights they pregamed than males in the study, suggesting a possible difference in drinking patterns when athletes pregame. These findings are consistent with the Grossbard et al. (2007) study that indicated males consume more alcohol and have more consequences than females, yet females reach higher peak eBACs. Although it was not collected in this survey, the way in which alcohol is consumed during pregaming is important to consider and could partially explain the differences between male and female eBACs. Grossbard et al. (2007) found that drinking games mediate the relationship between alcohol consumption and negative consequences in athletes. It is also known that participating in drinking games can result in higher eBACs (Borsari et al., 2007); however, it was unknown if participants in this study participated in various drinking games as these data were not collected. Alternatively, it is possible that men and women have similar, if not identical, drinking patterns and their eBACs are due to biological differences in how they process alcohol (Frezza et al., 1990; Li et al., 1998).

Limitations

Although important information was found, there are limitations to this study which should be addressed in future research. The first is this study did not include questions regarding motives for pregaming, which is a measure that would assist in better understanding in- versus out-of-season athlete pregaming behavior. For example, in-season athletes may pregame to “catch up” after a road trip or out-of-season athletes may pregame because they have more free time to recover from a hangover. Given the unique experiences of student athletes due to competition and travel schedules, a more comprehensive exploration of pregaming engagement may be helpful in understanding pregaming behaviors. As we only collected one week of pregaming and associated consequences tied to the TLFB data, future studies should consider a more expansive approach to studying daily drinking, pregaming, and associated consequences. Another limitation to this study is that information on the different types of pregaming or specific pregaming behaviors student athletes engaged in was not gathered. For example, it is possible that athletes may engage in different pregaming behaviors based on season status. Understanding these distinct differences in choices surrounding pregaming may help explain correlations between higher eBACs and negative consequences associated with season status. Third, although a comprehensive definition of pregaming was included in the survey, it is possible that individuals may have answered questions based on their own interpretation of pregaming.

Another limitation of the study was the decision to not collect information on sports-type and team affiliation. Prior literature suggests that specific teams may differ in their alcohol use patterns and that alcohol use may even differ by contact versus noncontact sport participation. The study also did not collect data using daily reporting (e.g., daily diary) or event-level approaches (e.g., Ecological Momentary Assessment; EMA) which limits the ability to make specific inferences associated with real time data collection methods. However, the approach of a retrospective TLFB in which participants were able to recall past 2-week drinking along with pregaming behaviors and associated consequences using a detailed calendar offers confidence in participants’ recent recall of drinking events. Finally, the survey was sent to only one campus, creating a homogeneous population and results may not generalize to other college campuses.

Conclusions and Implications for Future Research

As research begins to expand on college athlete drinking and on college student pregaming behavior in general, this study provides a starting point for understanding how the two intersect. University officials and athletic department representatives should take into consideration the season status of athletes when designing programming for athletes; specifically incorporating information about the harms of pregaming and harm reduction approaches that may reduce negative alcohol-related consequences. More specifically, future research should seek to better understand how biological sex of athletes may interact with season status on the number of negative alcohol-related consequences experienced. Furthermore, future research should be designed to identify and test numerous variables known to be associated with pregaming and alcohol use among student athletes (i.e., via Structural Equation Modeling).

Results of this study provide evidence that college student athletes who pregame may experience more negative alcohol-related consequences than athletes who do not pregame. At the same time, there is initial evidence to suggest sex may serve as a moderator of these findings. Female student athletes reported higher eBACs on pregaming nights, while male student athletes reported a greater number of negative alcohol-related consequences on nights when they engaged in pregaming. Future studies are needed to more comprehensively assess specific pregaming behaviors among student athletes and to explore whether the differences seen among males and females are due to their season status or some other variable.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Callon M. Williams

Author Biographies

Callon M. Williams, earned her BA in psychology from Binghamton University (SUNY) while competing in Division I lacrosse. She is currently working towards her PhD in Clinical Psychology from Binghamton University. Her research interests focus on substance use (i.e., alcohol and cannabis) among high-risk populations (i.e., college athletes),with an emphasis towards understanding the relationship between substance use and pain (e.g., injury).

Linda M. Reynolds, is currently working towards her PhD in Community Research and Action from Binghamton University (MS West Virginia University and BA St. Bonaventure University). Her research interests are broadly in the areas of college student substance use and the role of leadership and communication styles in prevention and intervention work. As a higher education professional, she is interested in developing evidence-based interventions that target special subpopulations of college students such as student athletes.

Nadine R. Mastroleo, earned her PhD in Counselor Education and Supervision from The Pennsylvania State University. She is an Associate Professor in the Department of Psychology at Binghamton University (SUNY). Her research interests focus on brief interventions for alcohol and other substance use with high risk populations. She has a specific focus on college student-athletes and interventions to reduce substance use and related problems using a Motivational Interviewing approach.

References

American College Health Association. (2018). American College Health Association-National College Health Assessment II: Reference Group Executive Summary Fall 2017. American College Health Association.

Barnett

N. P.

Orchowski

L. M.

Read

J. P.

Kahler

C. W.

(2013). Predictors and consequences of pregaming using day- and week-level measurements. Psychology of Addictive Behaviors, 27(4), 921–933. https://doi.org/10.1037/a0031402

Borsari

Merrill

J. E.

Yurasek

Miller

M. B.

Carey

K. B.

(2016). Does a brief motivational intervention reduce frequency of pregaming in mandated students? Substance Use & Misuse, 51(8), 1056–1066. https://doi.org/10.3109/10826084.2016.1152494

Borsari

Murphy

J. G.

Barnett

N. P.

(2007). Predictors of alcohol use during the first year of college: Implications for prevention. Addictive Behaviors, 32(10), 2062–2086. https://doi.org/10.1016/j.addbeh.2007.01.017

Brenner

J. W.

Metz

S. M.

Entriken

Brenner

C. J.

(2014). Experiences and attitudes of collegiate athletic trainers regarding alcohol- related unintentional injury in athletes. Journal of Athletic Training, 49(1), 83. https://doi.org/10.4085/1062-6050-48.6.02

DeJong

DeRicco

Schneider

S. K.

(2010). Pregaming: An exploratory study of strategic drinking by college students in Pennsylvania. Journal of American College Health, 58(4), 307–316. https://doi.org/10.1080/07448480903380300

Doumas

D. M.

Turrisi

Coll

K. M.

Haralson

(2007). High-risk drinking in college athletes and nonathletes across the academic year. Journal of College Counseling, 10(2), 163–174. https://doi.org/10.1002/j.2161-1882.2007.tb00016.x

Frezza

di Padova

Pozzato

Terpin

Baraona

Lieber

C. S.

(1990). High blood alcohol levels in women. New England Journal of Medicine, 322(2), 95–99. https://doi.org/10.1056/NEJM199001113220205

Green

Nelson

T. F.

Hartmann

(2014). Binge drinking and sports participation in college: Patterns among athletes and former athletes. International Review for the Sociology of Sport, 49(3–4), 417–434. https://doi.org/10.1177/1012690213509257

10.

Grossbard

J. R.

Geisner

I. M.

Mastroleo

N. R.

Kilmer

J. R.

Turrisi

Larimer

M. E.

(2009). Athletic identity, descriptive norms, and drinking among athletes transitioning to college. Addictive Behaviours, 34(4), 352–359. https://doi.org/10.1016/j.addbeh.2008.11.011

11.

Grossbard

J. R.

Geisner

I. M.

Neighbors

Kilmer

J. R.

Larimer

M. E.

(2007). Are drinking games sports? College athlete participation in drinking games and alcohol-related problems. Journal of Studies on Alcohol and Drugs, 68(1), 97–105. https://doi.org/10.15288/jsad.2007.68.97

12.

Hingson

R. W.

Zha

White

A. M.

(2017). Drinking beyond the binge threshold: Predictors, consequences, and changes in the U.S. American Journal of Preventive Medicine, 52(6), 717–727. https://doi.org/10.1016/j.amepre.2017.02.014

13.

Kahler

C. W.

Hustad

Barnett

N. P.

Strong

D. R.

Borsari

(2008). Validation of the 30-day version of the Brief Young Adult Alcohol Consequences Questionnaire for use in longitudinal studies. Journal of Studies on Alcohol and Drugs, 69(4), 611–615. https://doi.org/10.15288/jsad.2008.69.611

14.

Kahler

C. W.

Strong

D. R.

Read

J. P.

(2005). Toward efficient and comprehensive measurement of the alcohol problems continuum in college students: The brief young adult alcohol consequences questionnaire. Alcoholism: Clinical and Experimental Research, 29(7), 1180–1189. https://doi.org/10.1097/01.alc.0000171940.95813.a5

15.

Labrie

J. W.

Hummer

J. F.

Pedersen

E. R.

Lac

Chithambo

(2012). Measuring college students’ motives behind prepartying drinking: Development and validation of the prepartying motivations inventory. Addictive Behaviors, 37(8), 962–969. https://doi.org/10.1016/j.addbeh.2012.04.003

16.

Leichliter

Meilman

Presley

Cashin

(1998). Alcohol use and related consequences among students with varying levels of involvement in college athletics. Journal of American College Health, 46(6), 257–262. https://doi.org/10.1080/07448489809596001

17.

T.-K.

Beard

J. D.

Orr

W. E.

Kwo

P. Y.

Ramchandani

V. A.

(1998). Gender and ethnic differences in alcohol metabolism. Alcoholism: Clinical and Experimental Research, 22(3), 771–772. https://doi.org/10.1111/j.1530-0277.1998.tb04345.x

18.

Martens

M. P.

Dams-O’Connor

Duffy-Paiement

(2006). Comparing off-season with in-season alcohol consumption among intercollegiate athletes. Journal of Sport & Exercise Psychology, 28(4), 502–510.

19.

Martens

M. P.

Pedersen

E. R.

Smith

A. E.

Stewart

S. H.

O’Brien

(2011). Predictors of alcohol-related outcomes in college athletes: The roles of trait urgency and drinking motives. Addictive Behaviors, 36(5), 456–464. https://doi.org/10.1016/j.addbeh.2010.12.025

20.

Marzell

Morrison

Mair

Moynihan

Gruenewald

P. J.

(2015). Examining drinking patterns and high-risk drinking environments among college athletes at different competition levels. Journal of Drug Education, 45(1), 5–16. https://doi.org/10.1177/0047237915575281

21.

Mastroleo

N. R.

Barnett

N. P.

Bowers

K. M.

(2018). Association between sex, race/ethnicity, season, day of week, and alcohol use and related risks in college student athletes and nonathletes. Journal of American College Health, 67(5), 422–432. https://doi.org/10.1080/07448481.2018.1484367

22.

Matthews

D. B.

Miller

W. R.

(1979). Estimating blood alcohol concentration: Two computer programs and their applications in therapy and research. Addictive Behaviors, 4, 55–60. https://doi.org/10.1016/0306-4603(79)90021-2

23.

Merrill

J. E.

Vermont

L. N.

Bachrach

R. L.

Read

J. P.

(2013). 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. https://doi.org/10.15288/jsad.2013.74.757

24.

National Collegiate Athletic Association. (2018). NCAA national study of substance use habits of college student-athletes: Executive summary.

25.

Neal

D. J.

Carey

K. B.

(2007). Association between alcohol intoxication and alcohol related problems: An event-level analysis. Psychology of Addictive Behaviors, 21(2), 194–204. https://doi.org/10.1037/0893-164X.21.2.194

26.

Paves

A. P.

Pedersen

E. R.

Hummer

J. F.

Labrie

J. W.

(2012). Prevalence, social contexts, and risks for prepartying among ethnically diverse college students. Addictive Behaviors, 37(7), 803–810. https://doi.org/10.1016/j.addbeh.2012.03.003

27.

Pedersen

Labrie

(2007). Partying before the party: Examining prepartying behavior among college students. Journal of American College Health, 56(3), 237–245. https://doi.org/10.3200/JACH.56.3.237-246

28.

Read

J. P.

Kahler

C. W.

Strong

D. R.

Colder

C. R.

(2006). Development and preliminary validation of the young adult alcohol consequences questionnaire. J Stud Alcohol, 67(1), 169–177. https://doi.org/10.15288/jsa.2006.67.169

29.

Read

J. P.

Merrill

J. E.

Bytschkow

(2010). Before the party starts: Risk factors and reasons for “pregaming” in college students. Journal of American College Health, 58(5), 461–472. https://doi.org/10.1080/07448480903540523

30.

Rutledge

P. C.

McCarthy

B. J.

Lendyak

R. M.

(2014). Let’s get this party started: The effects of descriptive and injunctive norms on preparty behavior in college students. Substance Use & Misuse, 49(1/2), 166–175. https://doi.org/10.3109/10826084.2013.824473

31.

Sobell

L. C.

Sobell

M. B.

(2003). Alcohol consumption measures. In J. P. Allen & V. Wilson (Eds.), Assessing alcohol problems (2nd ed., pp. 78–99). National Institute on Alcohol Abuse and Alcoholism.

32.

Sobell

L. C.

Maisto

S. A.

Sobell

M. B.

Cooper

A. M.

(1979). Reliability of alcohol abusers’ self-reports of drinking behavior. Behaviour Research and Therapy, 17(2), 157–160. https://doi.org/10.1016/0005-7967(79)90025-1

33.

Turner

J. C.

Bauerle

Shu

(2004). Estimated blood alcohol concentration correlation with self-reported negative consequences among college students using alcohol. Journal of Studies on Alcohol, 65(6), 741–749. https://doi.org/10.15288/jsa.2004.65.741

34.

Turrisi

Mallett

K. A.

Mastroleo

N. R.

Larimer

M. E.

(2006). Heavy drinking in college students: Who is at risk and what Is being done about it? The Journal of General Psychology, 133(4), 401–420. https://doi.org/10.3200/GENP.133.4.401-420

35.

Wechsler

Davenport

Dowdall

Grossman

Zanakos

(1997). 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, 45(5), 195. https://doi.org/10.1080/07448481.1997.9936884

36.

Wechsler

Davenport

Dowdall

Moeykens

Castillo

(1994). Health and behavioral consequences of binge drinking in college: A national survey of students at 140 campuses. The Journal of the American Medical Association, 272(21), 1672–1677. https://doi.org/10.1001/jama.1994.035202100560

37.

Wechsler

Kuh

Davenport

A. E.

(2009). Fraternities, sororities and binge drinking: Results from a national study of American colleges. NASPA Journal, 46(3), 395–416. https://doi.org/10.2202/1949-6605.5017

38.

Zamboanga

B. L.

Olthuis

J. V.

(2016). What is pregaming and how prevalent is it among U.S. college students? An introduction to the special issue on pregaming. Substance Use & Misuse, 51(8), 953–960. https://doi.org/10.1080/10826084.2016.1187524

39.

Zamboanga

B. L.

Merrill

J. E.

Olthuis

J. V.

Milroy

J. J.

Sokolovsky

A. W.

Wyrick

D. L.

(2019). Secondary effects of myPlaybook on college athletes’ avoidance of drinking games or pregaming as a protective behavior strategy: A multisite randomized controlled study. Social Science & Medicine, 228, 135–141. https://doi.org/10.1016/j.socscimed.2019.02.016

40.

Zamboanga

B. L.

Rodriguez

Horton

N. J.

(2008). Athletic involvement and its relevance to hazardous alcohol use and drinking game participation in female college athletes: A preliminary investigation. Journal of American College Health, 56(6), 651–656. https://doi.org/10.3200/JACH.56.6.651-656

41.

Zamboanga

B. L.

Schwartz

S. J.

Ham

L. S.

Borsari

Tyne

(2010). Alcohol expectancies, pregaming, drinking games, and hazardous alcohol use in a multiethnic sample of college students. Cognitive Therapy and Research, 34(2), 124–133. https://doi.org/10.1007/s10608-009-9234-1