RCT of web-based personalized normative feedback for college drinking prevention: Are typical student norms good enough?

Abstract

Objectives Personalized normative feedback (PNF) interventions are generally effective at correcting normative misperceptions and reducing risky alcohol consumption among college students. However, research has yet to establish what level of reference group specificity is most efficacious in delivering PNF. This study compared the efficacy of a web-based PNF intervention employing eight increasingly-specific reference groups against a Web-BASICS intervention and a repeated-assessment control in reducing risky drinking and associated consequences. Method Participants were 1663 heavy drinking Caucasian and Asian undergraduates at two universities. The referent for web-based PNF was either the typical same-campus student, or a same-campus student at one (either gender, race, or Greek-affiliation), or a combination of two (e.g., gender and race), or all three levels of specificity (i.e., gender, race, and Greek-affiliation). Hypotheses were tested using quasi-Poisson generalized linear models fit by generalized estimating equations. Results The PNF intervention participants showed modest reductions in all four outcomes (average total drinks, peak drinking, drinking days, and drinking consequences) compared to control participants. No significant differences in drinking outcomes were found between the PNF group as a whole and the Web-BASICS group. Among the eight PNF conditions, participants receiving typical student PNF demonstrated greater reductions in all four outcomes compared to those receiving PNF for more specific reference groups. Perceived drinking norms and discrepancies between individual behavior and actual norms mediated the efficacy of the intervention. Conclusions Findings suggest a web-based PNF intervention using the typical student referent offers a parsimonious approach to reducing problematic alcohol use outcomes among college students.

The Chicken or the Egg: Examining Temporal Precedence Among Attitudes, Injunctive Norms, and College Student Drinking

“…Follow-up rates were 1,017 (89.6%) at 3 months (Time 2) and 1,010 (89.3%) at 6 months (Time 3). Retention rates were similar to previous web-based intervention studies among college students (i.e., LaBrie, et al, 2013;Walters et al, 2009). To protect research participants, we obtained a Federal Certificate of Confidentiality, and study procedures were approved by the Institutional Review Boards of both universities.…”

A Randomized Controlled Pilot Trial of the Functional Assessment Screening Tablet to Engage Patients at the Point of Care

“…Outside of the health care setting, technology-based interventions have shown promise as well, including decreasing problem drinking and gambling. 13,14 In contrast to other studies in primary care that collected health behaviors and HRQoL from patients and provided it exclusively to physicians, 3,4,27 this study engaged patients in their care by providing them with personalized feedback prior to the clinical encounter. Patients in this study were activated to initiate discussions with their resident physicians regarding health behaviors and HRQoL.…”

“…12 Patient activation is one possible solution to remove barriers to behavior change-activated patients are more able to understand their care needs and work with their physician to address those needs. 12 Providing patients with feedback regarding their health has long been used to activate patients to undertake behavior change, 13,14 and can make them more engaged partners in their healthcare. 15 Innovative uses of health information technology (HIT) to provide personalized feedback may help patients take a more proactive role in their healthcare, 16 and allow physicians to focus on patientidentified areas of need.…”

Electronic communication based interventions for hazardous young drinkers: A systematic review

“…Twentytwo papers were excluded due to lack of screening before intervention delivery (Bendtsen et al, 2012;Bingham et al, 2010;Bingham et al, 2011;Collins et al, 2014;Donovan et al, 2015;Fazzino et al, 2015;Foster, Neighbors & Pai, 2015;Hagger, Lonsdale & Chatzisarantis, 2012;Haug et al, 2013;Jouriles et al, 2010;LaBrie et al, 2013;Lovecchio, Wyatt & DeJong, 2010;Murphy et al, 2010;Neighbors et al, 2010;Paschall et al, 2011a;Paschall et al, 2011b;Schuckit et al, 2012;Schuckit et al, 2015;Strohman et al, 2015;Weaver et al, 2014;Wyatt, DeJong & Dixon, 2013).…”