Using Smartphone Assessments for Personalized Prediction of Problematic Alcohol Use

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PROJECT SUMMARY/ABSTRACT Risky drinking, such as binge (5+/4+ drinks per 2-hour occasion for males/females) and high-intensity (2-3x the rates of binge) drinking, is highly prevalent among young adults and associated with severe acute and longer-term negative behavioral and health outcomes. However, given its prevalence, individuals who engage in such activities comprise a heterogeneous group. Researchers have had a hard time identifying the varied behavioral processes that are predictive of negative alcohol-related consequences and problematic trajectories across time. Predicting who will go on to develop lasting problems and whose risky alcohol use behavior is developmentally-limited is especially challenging. Part of the hindrance comes from the methods that are currently used to study this diverse behavior. In particular, researchers often use cross-sectional studies to look across individuals. While this has reaped invaluable knowledge regarding differences among individuals in their drinking patterns, it does not reveal the dynamic processes that contribute to maintaining such behaviors or make one more likely to have negative consequences. However, increasingly hypotheses pertain to these dynamic processes. This requires arriving at quantitative descriptions of individuals' emotional and behavioral processes. The science can move towards a more nuanced understanding of the varied mechanisms contributing to problematic alcohol use by arriving at valid descriptions of individual-level (i.e., personalized) processes. We propose to make advances towards personalized quantitative models in four ways: 1) develop an informed intensive longitudinal research design that enables acquisition of relevant variables across time on a daily basis and across the span of one year; 2) use innovative measurement technologies that enable objective assessment of contextual features related to drinking; 3) collect data using state-of-the-art phone applications that enable self-report and passive data collection where the user does not need to interface; and 4) implement cutting- edge machine learning algorithms that can reliably arrive at individual-level detection and predictive models that can be used as the foundation for future just-in-time adaptive interventions. We will accomplish this by enrolling N=300 young adult risky drinkers who will complete a 120-day ambulatory assessment protocol completing surveys on smartphones that are also equipped with passive sensors and applications, and then provide 4 waves of data on alcohol use and associated variables (e.g., consequences) over one year. In the end, our endeavors will create novel approaches to measuring and modeling behavioral processes related to high-risk drinking that capture the individuality of each participant. These endeavors will provide the framework for accurate detection and prediction of daily drinking and long-term problematic alcohol use trajectories that support future scientific and clinical efforts.