Personalized Booster Feedback After Alcohol Health Education

Heavy episodic alcohol use within the college student population is widespread, creating problems for student drinkers, their peers, and their institutions. Negative consequences from heavy alcohol use can be mild (e.g., hangovers, missed classes), to severe (e.g., assault, even death). Although online interventions targeting college student drinking reduce alcohol consumption and associated problems, they are not as effective as in-person interventions. Online interventions are cost-effective, offer privacy, reduce stigma, and may reach individuals who would otherwise not receive treatment. In a recently completed randomized, controlled trial, an emailed booster with personalized feedback improved the efficacy of a popular online intervention (Braitman \& Henson, 2016). Although promising, the booster incorporated in the study needs further empirical refinement. The current project seeks to build on past progress by further developing and refining the booster. In particular, one aspect missing from online interventions is a connection with a person invested in improving the student's outcomes. The current study aims to generate a personal connection for online interventions through a follow-up booster emailed by a member of the research staff. Outcomes will be compared for participants who receive a follow-up booster with similar content, but is clearly automatically generated and not from any particular individual. There are 3 conditions: all participants receive the initial online intervention targeting college drinking. Condition 1 (the control group) receives an email with a reminder to complete the follow-up surveys, but no feedback (i.e., no booster). Condition 2 receives an emailed booster with normative feedback plus protective strategies feedback, clearly automatically generated. Condition 3 receives an emailed booster with normative feedback plus protective strategies feedback, from a member of the research staff. The booster content alone (automatical