Using the Solid Research Base on Pregaming to Begin Intervention Development: An Epilogue to the Special Issue on Pregaming

Abstract

The pregaming research to date, up to and including the innovative studies included in this special issue, has centered on defining the behavior, learning which students may be most at-risk for pregaming, exploring which contexts may be most risky for pregaming, and quantifying the function of pregaming so that individual-level programs can be developed to target the risky practice. Although there is room for continuing to expand and refine our understanding of pregaming, much of the formative work has been conducted-primarily by the researchers highlighted in this special issue. Now it is time to use the understanding we have of this risky behavior to develop and empirically test programs specifically directed toward reducing or eliminating the behavior, not just at the individual level, but by targeting the social, cultural, economic, environmental, and policy level factors that sustain the behavior in the population. This may include targeting the behavior directly through use of techniques that are shown to be effective for young adult behavior change, but this may also require broadening beyond the psychological literature and utilizing expertise from other domains to affect behavior change.

Heterogeneity of Pregamers by Consumption and Reinforcement Reasons: A Latent Profile Analysis

“…Pregaming is a prevalent and high‐risk drinking behavior, and there is currently no evidence that traditional alcohol intervention efforts reduce this highly reinforced drinking behavior (Pedersen, ). This study utilized pregaming‐specific alcohol use and motives to identify 5 distinct profiles of students who pregame.…”

“…Brief Motivational Interventions (BMIs), often variations of Brief Alcohol Screening and Intervention in College Students (Dimeff et al., ), are generally efficacious interventions for reducing overall use and consequences (Carey et al., , , ; Ray et al., ; White et al., ). However, a recent study has suggested that effects of these interventions remain small (Huh et al., ), and to date, BMIs addressing general alcohol use and consequences have not demonstrated an ability to reduce pregaming frequency or quantity (see Pedersen, ). A recent study (Borsari et al., ) that delivered BMIs with college students who were sanctioned for a drinking offense and also had not responded to a brief advice session found that BMIs did not affect pregaming frequency even when pregaming was explicitly discussed (quantity was not assessed).…”

“…Variability within pregamers is currently not well understood, and important for designing programming (e.g., incorporating pregaming in personalized feedback or implementing targeted social media campaigns) to address this risky drinking behavior (Pedersen, ). This study extended the existing latent class modeling study on pregaming (Haas et al., 2016) to address 3 primary limitations: utilization of exclusively consumption‐based indicators, performance at a single site, and possible generalizability issues to students who are not entering freshmen.…”

Testing an Online, Theory-Based Intervention to Reduce Pre-drinking Alcohol Consumption and Alcohol-Related Harm in Undergraduates: a Randomized Controlled Trial

“…Students tend to engage in predrinking in an attempt to reduce their overall expenditure on alcohol by purchasing alcohol at lower prices from retailers (e.g., supermarkets, grocery stores) to consume in a private setting, as opposed to at a bar or nightclub, and to maximize intoxication prior to attending a subsequent event [7,11]. Given the inherent risks associated with pre-drinking, there is a need to develop pre-drinking interventions that seek to reduce excessive alcohol consumption and the associated risk of alcohol-related harm [12].…”

“…Kruskal-Wallis tests indicated no statistically significant differences between the total number of implementation intentions selected or formed, for pre-drinking alcohol consumption (χ 2 (5) = 2.47, p = .649) and alcohol-related harm (χ 2 (5) = 1.68, p = .794). A total of 38 participants selected one or more experimenter-provided implementation intentions only, with 18generating at least one implementation intention either in conjunction with an experimenterprovided(12), or outright(8). The self-generated implementation intentions were assessed for compliance with instructions, scored using a dichotomous scale as to whether the content complied (1) or not (0).…”