The Effects of a Web-Based Alcohol Prevention Program on Social Norms, Expectancies, and Intentions to Prevent Harm among College Student-Athletes

Abstract

College athletes are at risk for heavy alcohol use, which jeopardizes their general health, academic standing, and athletic performance. Effective prevention programming reduces these risks by targeting theory-based intermediate factors that predict alcohol use while tailoring content to student-athletes. The purpose of this study was to examine the impact of the myPlaybook online prevention program on student-athletes’ social norms, negative alcohol expectancies, and intentions to use alcohol-related harm prevention strategies. NCAA Division II student-athletes were recruited from 60 institutions across the United States to complete myPlaybook and pretest/posttest surveys measuring demographics and targeted outcome variables. Participants were randomly assigned to the treatment group (pretest-program-posttest; final n = 647) or the delayed treatment “control” group (pretest-posttest-program; final n = 709). Results revealed significant program effects on social norms (p < .01) and intentions to use harm prevention strategies (p < .01), while the effect on negative alcohol expectancies was nonsignificant (p = .14). Implications for future research and practice are discussed.

A longitudinal examination of relations between competitive athletic participation, drinking norms, impulsivity, and sensation seeking and binge drinking throughout college.

“…The role of perceived drinking norms on drinking behaviors has been substantiated in both athlete and nonathlete populations, with some studies showing stronger associations for athletes (Perkins, 2002; Perkins et al, 1999; Quinn & Fromme, 2011). Further, brief interventions targeting both descriptive norms (i.e., web-based interventions, Fearnow-Kenney et al, 2016; personalized normative feedback, Lewis et al, 2014) and injunctive norms (e.g., Prince & Carey, 2010) are effective at reducing alcohol use among college-aged drinkers, strengthening the argument that normative perceptions are closely related to the development and maintenance of drinking behavior. In contrast, research concerning normative perceptions in athletes are relatively underdeveloped.…”

“…While controlling for potential covariates (e.g., personality), efforts to address at-risk drinking among athletes would benefit from a better understanding of mechanisms of risk. Evidence suggests that social norms (e.g., perceptions of peer drinking behavior and attitudes) may be one important mechanism (Dams-O’Connor et al, 2007; Fearnow-Kenney et al, 2016; Grossbard et al, 2009). In support, social norms theory (SNT; Zhou & Heim, 2014) suggests that drinking behavior is predicated on the perceptions of others’ behaviors and attitudes, such that perceptions of more permissive attitudes and heavier drinking by one’s peers is associated with heavier drinking (Zhou & Heim, 2014).…”

Substance Use and Addiction in Athletes: The Case for Neuromodulation and Beyond

“…Another study demonstrated that online personalized drinking feedback (PDF) coupled with athlete specific information led to a lower peak blood alcohol concentration (BAC) at 6-month follow-up along with in-season athletes reporting less drinks per week than those in the control group [ 41 ]. Other online programs have found that providing web-based feedback or online modules in college athletes could lead to significant reductions in drinking, as well as improvement in assessment of social norms related to drinking [ 42 , 43 ]. Alcoholics anonymous (AA) and Narcotics anonymous (NA) meetings along with finding a sponsor are effective methods as well with no data in the athletic world at this time.…”

A Hybrid Evaluation-Optimization Trial to Evaluate an Intervention Targeting the Intersection of Alcohol and Sex in College Students and Simultaneously Test an Additional Component Aimed at Preventing Sexual Violence

“…Two of the itMatters intervention components targeted specific proximal outcomes from our conceptual model (i.e., descriptive and injunctive norms) [20]. We identified proximal outcome measures from previous intervention research with college students [25][26][27]. The descriptive norms scale (α t1 = 0.84) measured students' perceptions of the prevalence of behaviors among college students (e.g., "In the past 30 days, approximately what percentage of college students do you think have [done the following]?").…”