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

Grace Lyden, David M. Vock, Aparajita Sur, Nicole Morrell, Christine M. Lee, Megan E Patrick

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)1053-1064
Number of pages12
JournalPrevention Science
Issue number6
StatePublished - Aug 2022

Bibliographical note

Funding Information:
Data collection and manuscript preparation were supported by the National Institute on Alcohol Abuse and Alcoholism (R01AA026574, PI M. Patrick). The study sponsors had no role in the study design, collection, analysis or interpretation of the data, writing of the manuscript, or the decision to submit the paper for publication. The content is solely the responsibility of the authors and does not necessarily represent the official views of the study sponsor.

Publisher Copyright:
© 2022, Society for Prevention Research.


  • Adaptive treatment strategies
  • Alcohol
  • Dynamic treatment regimes
  • Reinforcement learning
  • Sequential multiple assignment randomized trial
  • Substance use
  • m-out-of-n bootstrap

PubMed: MeSH publication types

  • Journal Article
  • Randomized Controlled Trial
  • Research Support, N.I.H., Extramural


Dive into the research topics of 'Deeply Tailored Adaptive Interventions to Reduce College Student Drinking: a Real-World Application of Q-Learning for SMART Studies'. Together they form a unique fingerprint.

Cite this