Capturing Habits and Everyday Experiences with Alcohol in Real-Time Study

Can we detect and predict when someone will engage in different drinking behaviors?

This project, funded by the National Institute for Alcohol Abuse and Alcoholism (NIAAA), takes the lab’s efforts to study health-relevant behaviors in real-time using both self-reported (i.e., questionnaires and surveys) and passive-sensing (i.e., collecting streams of data from smartphone use) methods and applies them to a significant public health question: detecting and predicting when individuals engage in drinking. Researchers have had a hard time identifying the varied behavioral processes that are predictive of alcohol use and related consequences and trajectories across time. 

Limited research has explored the day-to-day behavioral and emotional processes within specific individuals that influences their drinking.  The science can move towards a more nuanced understanding of the varied mechanisms contributing to alcohol use by arriving at valid descriptions of individual-level (i.e., personalized) processes. This study aims to advance personalized quantitative models by using innovative measurement methods to capture participants’ daily behaviors leading up to drinking episodes. 

In the end, our endeavors will create novel approaches to measuring and modeling behavioral processes related to 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 alcohol use trajectories that support future scientific and clinical efforts. 

Why is this research important?

Alcohol is socially ingrained in our culture and drinking behaviors come with a variety of positive and negative consequences. In early adulthood, many people drink, and the level of average drinking peaks. Some individuals go on to quit drinking, some continue to drink moderately, and some end up having difficulties with alcohol use. Understanding the context and underlying processes leading up to various types of drinking episodes can help clinicians identify who is truly at risk of developing lasting issues due to alcohol. By predicting drinking episodes, we can apply this knowledge to helping individuals who may want to reduce or discontinue their alcohol use. Our study also approaches this challenge by using an individual-level framework to analyze and explain our findings. Considering individual differences allows researchers to develop more effective, specialized interventions. We hope that this study will lead to larger implications in the field for using these methods in alcohol research.

Study Design and Compensation

Eligible participants will complete short smartphone surveys that are also equipped with passive sensors and applications for a 120-day period. They will also provide 4 waves of follow-up data on alcohol use and associated variables (e.g., consequences) over one year. For their efforts and contributions, participants can be compensated up to $625 during the study period.