Deeply Tailored Adaptive Interventions to Reduce College Student Drinking: A Real-World Application of Q-Learning for SMART Studies

Autor/inn/en	Lyden, Grace R.; Vock, David M.; Sur, Aparajita; Morrell, Nicole; Lee, Christine M.; Patrick, Megan E.
Titel	Deeply Tailored Adaptive Interventions to Reduce College Student Drinking: A Real-World Application of Q-Learning for SMART Studies
Quelle	In: Prevention Science, 23 (2022) 6, S.1053-1064 (12 Seiten) PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Patrick, Megan E.)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1389-4986
DOI	10.1007/s11121-022-01371-7
Schlagwörter	Drinking; Alcohol Abuse; College Freshmen; Student Characteristics; Intervention + Suchen Sie Ihr Suchwort? Trinken; Studienanfänger
Abstract	M-bridge was a sequential multiple assignment randomized trial (SMART) that aimed to develop a resource-efficient adaptive preventive intervention (API) to reduce binge drinking in first-year college students. The main results of M-bridge suggested no difference, on average, in binge drinking between students randomized to APIs versus assessment-only control, but certain elements of the API were beneficial for at-risk subgroups. This paper extends the main results of M-bridge through an exploratory analysis using Q-learning, a novel algorithm from the computer science literature. Specifically, we sought to further tailor the two aspects of the M-bridge APIs to an individual and test whether deep tailoring offers a benefit over assessment-only control. Q-learning is a method to estimate decision rules that assign optimal treatment (i.e., to minimize binge drinking) based on student characteristics. For the first aspect of the M-bridge API (when to offer), we identified the optimal tailoring characteristic post hoc from a set of 20 candidate variables. For the second (how to bridge), we used a known effect modifier from the trial. The results of our analysis are two rules that optimize (1) the timing of universal intervention for each student based on their motives for drinking and (2) the bridging strategy to indicated interventions (i.e., among those who continue to drink heavily mid-semester) based on mid-semester binge drinking frequency. We estimate that this newly tailored API, if offered to all first-year students, would reduce binge drinking by 1 occasion per 2.5 months (95% CI: decrease of 1.45 to 0.28 occasions, p < 0.01) on average. Our analyses demonstrate a real-world implementation of Q-learning for a substantive purpose, and, if replicable in future trials, our results have practical implications for college campuses aiming to reduce student binge drinking. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01