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Data Science for Dynamic Intervention Decision-making Lab

National Institutes of Health (NIH)/National Institute on Alcohol Abuse and Alcoholism (NIAAA) – R01 AA026574
Key collaborators: Inbal Nahum-Shani, Daniel Almirall (Direct Sponsor: University of Minnesota; PI: Megan Patrick)
 

College student alcohol use and associated negative consequences are public health problems. In particular, first-year students transitioning to college are at increased risk. Scarce intervention resources must be used as wisely as possible to address these concerns. One way to address heavy drinking while conserving resources is to first utilize universal interventions, identify students at high risk who do not respond well, and then motivate them to engage in indicated intervention. This approach to prevention is `adaptive’ because information about the student in the course of the intervention (e.g., response status) is used to determine whether more resources should be invested to motivate the student to transition to indicated services. The purpose of the proposed project is to implement adaptive preventive intervention (API) that employs cost- effective, technology-based brief interventions to do the following. First, provide a universal personalized normative feedback (PNF) intervention followed by student self-monitoring (SM). Second, motivate students who continue to drink heavily (i.e., 2+ reports of 4/5+ drinks for women/men, or 1 report of 8/10+ drinks for women/men) to transition to additional intervention resources. To optimize the efficacy of this intervention, we will investigate the best timing for delivering the initial universal PNF+SM intervention (i.e., as an inoculation before moving to college vs. once they are experiencing the college context during their first semester). Additionally, we will examine how best to motivate heavy-drinking students to pursue indicated intervention (i.e., via automated emails vs. online interaction with a personal health coach using mBridge). A sequential multiple assignment randomized trial (SMART) design (N=700) will be used to address these questions. College students will be randomized to receive PNF either before college begins (2 weeks before classes start) or during the beginning of the first semester (about 3 weeks after they arrive on campus), followed by SM every two weeks during the first semester; these SM assessments will be used to identify heavy-drinking students who remain at risk. Once heavy drinking is identified, the student will be re-randomized to either an automated email or mBridge coach to offer indicated intervention resources. The specific aims are to examine: (1) the efficacy of the API compared to an assessment-only control, (2) whether the API can be optimized by altering the timing of the universal intervention and/or the type of message to motivate seeking indicated intervention, and (3) moderators of these effects (e.g., pre-college drinking intentions, high-intensity [compared to binge] drinking during the start of college). Frequency of heavy drinking, alcohol-related consequences, and health services utilization will be assessed prior to the start of classes, and at each follow-up point (the end of the semester, the end of the year, and the following fall). The API to be refined through this project will offer a novel strategy for mitigating both the acute negative health consequences (e.g., injury, alcohol poisoning) and long-term health consequences (e.g., alcohol use disorders) of young adult alcohol use.

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