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. Author manuscript; available in PMC: 2023 Jan 1.
Published in final edited form as: Addict Behav. 2021 Sep 15;124:107120. doi: 10.1016/j.addbeh.2021.107120

Pre-game Drinking among Young Adults and Its Association with Positive and Negative Alcohol Consequences

Brian H Calhoun 1, Ashley N Linden-Carmichael 2
PMCID: PMC8511230  NIHMSID: NIHMS1740641  PMID: 34560423

Abstract

Background

Pre-gaming, or drinking before going out, is common among young adults and associated with heavier drinking and negative consequences. However, findings have been mixed as to whether a unique, day-level association between pre-gaming and negative consequences exists independent of alcohol intake. It is also unknown whether young adults experience more positive consequences of alcohol use on days they engage in pre-gaming. This study tested day-level associations between pre-gaming and positive and negative consequences, controlling for same-day alcohol intake, as well as whether these associations were moderated by person- and day-level variables.

Methods

Participants were 148 young adult heavy drinkers (Mage=20.30, SDage=1.45, 57.4% female) who reported past-month simultaneous alcohol and marijuana use. For up to 14 consecutive days, participants completed electronic surveys asking about their drinking behaviors and consequences the previous day.

Results

Prior to adjusting for alcohol intake, Poisson multilevel models showed that participants reported more negative and positive consequences on days they pre-gamed and those who reported pre-gaming more often throughout the study also experienced more negative and positive consequences overall. After controlling for alcohol intake, a positive, day-level association between pre-gaming and positive consequences remained. There was no evidence of moderation of study associations by person- or day-level variables.

Conclusion

The unique association between pre-gaming and positive consequences may help explain why pre-gaming is linked with heavy drinking and other risky behaviors as positive consequences have been shown to reinforce such behaviors. Findings suggest pre-gaming may be a useful intervention point for alcohol reduction programs.

Keywords: Alcohol, young adults, pre-gaming, alcohol-related consequences, daily diary

1. Introduction

Pre-gaming, or consuming alcohol before going out, is prevalent among US young adults and linked with heavy drinking and greater alcohol-related harms (Pedersen, 2016; Sheehan et al., 2013; Zamboanga & Olthuis, 2016). Approximately two-thirds of drinkers report past-month pre-gaming and approximately one-third of drinking days involve pre-gaming (Zamboanga & Olthuis, 2016). Drinkers who pre-game consume more alcohol on average than those who do not, and drinking tends to be greater on pre-gaming days than on non-pre-gaming drinking days (Barnett et al., 2013; Fairlie et al., 2015).

Prior work has found positive, day-level associations between pre-gaming and negative consequences (e.g., Radomski et al., 2016); however, several knowledge gaps remain regarding pre-gaming and drinking outcomes in daily life, which may ultimately inform prevention efforts targeting pre-gaming. First, it is unclear whether the day-level association between pre-gaming and negative consequences can be explained by heavier drinking on pre-gaming days, as suggested by some studies (Labhart et al., 2013; Read et al., 2010). Others have found unique within-person associations between pre-gaming and greater harms independent of same-day alcohol intake (Barnett et al., 2013; Merrill et al., 2013). Few studies have used assessments of pre-gaming and negative consequences collected daily, instead using Timeline Followback measurements (TLFB; e.g., Radomski et al., 2016) or simply examining the last pre-gaming and non-pre-gaming drinking occasion (e.g., Hummer et al., 2013). Though techniques like TLFB are valid, measurements collected daily provide greater reliability and validity (Gmel & Rehm, 2004).

Second, few studies have examined perceived positive consequences between drinking days with and without pre-gaming, which may underscore immediate reasons for pre-gaming. Young adults tend to report more positive than negative consequences, and positive consequences exhibit strong, positive associations with future drinking behaviors and intentions (Corbin et al., 2008; Lee et al., 2011; Usala et al., 2015). Barnett et al. (2013) found college students reported greater positive consequences in weeks they pre-gamed compared to weeks they did not, independent of weekly alcohol intake. To our knowledge, however, no studies have tested this association at the day level. It is important to understand whether pre-gaming is associated with positive consequences, as it may reinforce risky behaviors or help explain why young adults engage in pre-gaming. If a day-level association exists, it is also important to determine whether it is unique to pre-gaming or can be explained by greater same-day alcohol intake. If evidence supports the former, this may provide further support for pre-gaming being an intervention point for alcohol reduction programs.

Finally, less is known about factors that increase the likelihood of experiencing consequences on pre-gaming days as few studies have tested moderators of day-level associations between pre-gaming and consequences. Sex and age are important determinants of pre-gaming behavior; some studies suggest men and underage drinkers are more likely to pre-game relative to women and drinkers aged 21+, respectively (Ferris et al., 2019). Women relative to men are more likely to experience negative consequences on pre-gaming days (Ahmed et al., 2014). Risky drinking behaviors, such as playing drinking games (Zamboanga et al., 2014), consuming alcohol mixed with energy drinks (AmED; Linden-Carmichael & Lau-Barraco, 2017), drinking liquor (Mochrie et al., 2019), and simultaneously using alcohol and marijuana (SAM; Linden-Carmichael et al., 2020), are associated with experiencing greater negative consequences. Some evidence suggests these risky behaviors commonly occur on pre-gaming days (Borsari et al., 2007; Davis et al., 2020; Linden-Carmichael & Lau-Barraco, 2017). Characteristics of the social setting in which drinking occurs (group size, others’ alcohol use) are associated with greater alcohol consumption (Thrul et al., 2017), which may increase the likelihood of experiencing harms. Characteristics of social settings on pre-gaming days may be important moderators of day-level drinking outcomes.

The current study had two aims. First, we assessed whether pre-gaming was associated with negative and positive consequences. Based on prior research, we hypothesized that pre-gaming would be positively associated with negative and positive consequences before and after controlling for same-day alcohol intake. Second, in exploratory analyses, we examined potential moderators of the associations between pre-gaming and negative and positive consequences. Both day-level (drinking liquor, AmED use, SAM use, playing drinking games, drinking with others [vs. alone], group size, others’ intoxication) and person-level (sex, age) moderators were tested.

2. Materials and Methods

2.1. Participants and Procedures

Data were collected in October 2018-March 2019. Participants were recruited via flyers and a research study database in the Northeast U.S. near a public university. After providing informed consent, participants completed a brief screener to determine eligibility. As the parent study focused on SAM use among a higher-risk population (Linden-Carmichael et al., 2020), eligibility criteria included: 18–25 years old, past 2-week heavy episodic drinking (4+/5+ drinks for females/males), and past-month SAM use. Eligible participants immediately completed a 15-minute online survey and were provided instructions for completing daily surveys.

Participants completed daily surveys across 14 consecutive days remotely. Participants were sent e-mail and text reminders at 9:00am and 11:30am to complete the >5-minute survey about yesterday’s substance use behaviors. Participants received up to $48 for full participation. Procedures were approved by the university’s institutional review board.

Of the 161 participants eligible to participate, 154 (95.7%) completed 1+ daily survey. Daily compliance was high, with participants completing an average of 13.13 (SD=1.95) surveys. Our analytic sample included 148 participants reporting alcohol use on 1+ daily survey. Within the analytic sample, 57.4% were female, and M age was 20.30 (SD=1.45). Regarding race/ethnicity, 73.6% identified as non-Hispanic/Latinx (NHL)-White, 11.5% as NHL-Asian, 5.4% as NHL-Black, 5.4% Hispanic/Latinx, 3.4% as NHL-multiracial, and 0.7% did not report their race/ethnicity. Most (87.8%) were currently attending college. Additional details can be found elsewhere (see Linden-Carmichael et al., 2020).

2.2. Measures

Substance use.

Each day, participants were provided a checklist of substances and indicated all they used the day prior. On days participants reported alcohol use, they were shown examples of standard alcoholic drinks and were provided follow-up questions regarding the number they consumed of each type to determine total number of drinks and whether they used liquor or AmED each day. On days participants used alcohol and marijuana, they were asked, “Did you use alcohol and marijuana together at the same time, such that the effects overlapped?” Responses to this question were used to determine whether it was a SAM day.

Consequences.

Participants completed the Daily Alcohol-Related Consequences and Evaluations Measure for Young Adults (Lee et al., 2017) on days they reported alcohol use, which contained six positive (e.g., I felt relaxed) and seven negative (e.g., I was rude or obnoxious) consequences of using alcohol or other substances. We summed “yes” responses to each subscale, separately, to calculate the total number of positive and negative consequences each day.

Social drinking activities.

On days participants reported alcohol use, they were asked whether they pre-gamed and/or played drinking games. Participants were asked whether they drank with others and, if so, were asked follow-up questions concerning group size (assessed continuously) and their perceptions of others’ intoxication (slider of 0=not at all to 100=very drunk/high).

2.3. Analytic Plan

Poisson multilevel models were estimated in the lme4 package (Bates et al., 2015) in R 4.1.0 (R Core Team, 2021). To address Aim 1, four multilevel models were tested. Models 1 and 2 examined pre-gaming and negative consequences, and Models 3 and 4 examined pre-gaming and positive consequences. In both sets of models, we tested whether pre-gaming was independently linked without (Models 1 and 3) and after (Models 2 and 4) controlling for alcohol consumed. In all models, sex, proportion of pre-gaming days throughout the study, and weekend days (Thursday-Saturday) were controlled. To address Aim 2, we tested day-level and cross-level interactions in the models described above. All Level-1 variables were person-mean-centered to isolate within- and between-person associations at Levels 1 and 2, respectively (Hamaker & Muthén, 2020). All Level-2 variables were grand-mean-centered.

3. Results

3.1. Descriptive Statistics

The analytic sample consisted of 651 (32.2%) drinking days nested within 148 (96.1%) participants. Pre-gaming occurred on 239 (36.7%) drinking days, and 98 (66.2%) participants reported pre-gaming at least once. An average of 8.94 (SD=4.28) and 4.53 (SD=3.52) drinks were consumed on pre-gaming and non-pre-gaming drinking days, respectively. Participants reported an average of 2.30 (SD=1.33) negative and 2.15 (SD=1.39) positive consequences.

3.2. Associations between Pre-gaming and Consequences

Table 1 presents four multilevel models testing associations between pre-gaming and consequences. In Model 1, which did not control for level of alcohol use, participants reported 25% more negative consequences on pre-gaming days than on non-pre-gaming drinking days, and those who pre-gamed more frequently reported experiencing more negative consequences throughout the study. After controlling for alcohol use in Model 2, neither the day- nor person-level association was statistically significant. There was a positive, day-level association between total drinks and negative consequences, but the person-level association between total drinks and negative consequences was non-significant. In Model 3, which did not control for alcohol intake, participants reported 44% more positive consequences on pre-gaming days than on non-pre-gaming drinking days, and those who pre-gamed more frequently reported experiencing more positive consequences throughout the study. After controlling for alcohol use in Model 4, the day-level association, but not the person-level association, remained significant. Participants reported 18% more positive consequences on pre-gaming days than on non-pre-gaming drinking days when controlling for alcohol intake. Total drinks were positively associated with positive consequences at the day- and person-level.

Table 1.

Poisson Multilevel Models Testing Associations between Pre-gaming and Negative and Positive Alcohol Consequences

Outcome: Total Negative Consequences Outcome: Total Positive Consequences
Model 1 Model 2 Model 3 Model 4
Rate Ratio [95% CI] Rate Ratio [95% CI] Rate Ratio [95% CI] Rate Ratio [95% CI]
 Intercept 2.21 [2.06, 2.37]*** 2.19 [2.04, 2.35]*** 2.02 [1.87, 2.18]*** 1.99 [1.84, 2.14]***
Level-1 (Daily level)
 Pre-gaming day 1.25 [1.10, 1.43]** 1.09 [0.94, 1.26] 1.44 [1.25, 1.64]*** 1.18 [1.02, 1.37]*
 Weekend 1.01 [0.90, 1.13] 1.02 [0.92, 1.14] 0.99 [0.88, 1.11] 1.04 [0.92, 1.17]
 Total drinks - 1.04 [1.02, 1.06]*** - 1.06 [1.04, 1.08]***
Level-2 (Person level)
 Prop. of pre-gaming days 1.33 [1.06, 1.66]* 1.16 [0.90, 1.51] 1.58 [1.24, 2.01]*** 1.27 [0.97, 1.68]
 Male sex 0.95 [0.83, 1.09] 0.91 [0.79, 1.05] 0.89 [0.77, 1.04] 0.82 [0.70, 0.97]
 Mean total drinks - 1.02 [1.00, 1.05] - 1.04 [1.01, 1.07]**
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Note. Ndays = 639–640, Npersons = 148. Weekend = Sunday-Wednesday (0), Thursday-Saturday (1). Prop. of pre-gaming days = Proportion of pre-gaming days.

*

p < .05,

**

p < .01,

***

p < .001.

3.2. Moderation

Seven day-level and two person-level variables were tested as moderators of day-level associations between pre-gaming and consequences. None of the nine variables tested moderated the day-level association between pre-gaming and negative consequences, regardless of including alcohol intake as a covariate. One of the nine variables moderated the association between pre-gaming and positive consequences prior to controlling for alcohol intake. The association between pre-gaming and positive consequences was weaker on days participants drank with more people (RR=0.83, 95% CI=[0.69, 1.00]) than on days they drank with fewer people. However, this moderation effect became non-significant once alcohol intake was controlled.

4. Discussion

Findings revealed that pre-gaming was positively associated with negative and positive alcohol-related consequences, however pre-gaming was only uniquely associated with positive consequences after controlling for alcohol intake. We found no evidence of moderation of the associations between pre-gaming and negative or positive consequences. To our knowledge, our study was the first to test a day-level, within-person association between pre-gaming and positive consequences. Our findings extend the work of Barnett et al. (2013), which provided evidence of a positive association between pre-gaming and positive consequences at the week level, independent of total weekly drinks. Whereas Barnett et al.’s (2013) work collected day-level reports of drinking behaviors at a single time point (i.e., TLFB) and aggregated these to the week level, our finding is based on data collected daily (i.e., with shorter recall periods) and analyzed at the actual level pre-gaming occurs (i.e., day level). Given that pre-gaming is associated with greater alcohol intake and risky behaviors (Pedersen, 2016; Zamboanga & Ulthuis, 2016) and positive consequences may reinforce such behaviors (Lee et al., 2011), our findings suggest pre-gaming co-occurs with and may contribute to the perpetuation of risky behaviors, which may in turn put young adults at risk for alcohol-related harms. A second important finding was that pre-gaming was not associated with negative consequences after controlling for same-day alcohol intake. Past work testing this association has produced mixed findings (e.g., Labhart et al., 2013; Merrill et al., 2013). Our null finding suggests that pre-gaming is a common characteristic of heavy drinking days in which more consequences are experienced overall, but that negative consequences may not be uniquely associated with pre-gaming, at least not among heavier substance users.

Lastly, the lack of moderation in our exploratory models suggests that associations between pre-gaming and consequences may be somewhat universal, as opposed to being driven by drinking days and/or individuals with particular characteristics, especially when considering the high prevalence of pre-gaming among young adults (Zamboanga & Ulthuis, 2016). However, we were underpowered to detect moderation effects given the sample size, so the lack of moderation comes with an elevated risk of Type II error.

Strengths of this study include the use of daily reports of drinking behaviors and consequences with shorter recall periods than other methods (Gmel & Rehm, 2004), assessing positive and negative consequences, and the ability to isolate between- and within-person associations. Limitations include the use of a small, niche sample of heavy drinkers who reported SAM use and who were predominantly NHL-White, which may limit generalizability to more general young adult populations.

Highlights.

  • This study examined daily associations between pre-gaming and alcohol consequences.

  • Pre-gaming was linked with positive consequences when alcohol use was controlled.

  • Pre-gaming was not linked with negative consequences independent of alcohol use.

Acknowledgments

This research was supported in part by grants from the National Institute on Alcohol Abuse and Alcoholism (K01 AA026854) and the National Institute on Drug Abuse (P50 DA039838). The NIAAA and NIDA did not have any role in study design, collection, analysis, and interpretation of the data; writing the report; and the decision to submit the report for publication. The authors have no conflicts of interest to report.

Footnotes

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Declarations of interest: none

Contributor Information

Brian H. Calhoun, University of Washington, Department of Psychiatry and Behavioral Sciences, Center for the Study of Health and Risk Behaviors, 1100 NE 45th St., #300, Seattle, WA, 98105,

Ashley N. Linden-Carmichael, Pennsylvania State University, Edna Bennett Pierce Prevention Research Center, 320E Biobehavioral Health Building, University Park, PA 16802,

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