DIGITAL Insights Lab

The DIGital Insights for Treating and Assessing Lifestyle Risk Behaviors Lab is co-directed by Dr. Lisa Fucito and Dr. Kelly DeMartini, experts in heavy alcohol use and other lifestyle risk behaviors, digital health, and data science.

Alcohol use and other substance use disorders (AUD/SUD) are significant, costly public health problems. Treatments are only modestly effective, likely due to the complex nature of these conditions with multiple potential etiologies. Thus, innovative strategies are needed.

One solution is to tailor treatment to an individual’s unique etiologic risk mechanisms. This precision medicine approach, widely used for medical diseases like cancer, is relatively new for psychiatric disorders. Core components of precision medicine include elucidating the different mechanisms that cause and maintain disorders and identifying effective interventions to target these mechanisms.

The DIGITAL Insights Lab applies digital health technologies and state-of-the-art data analytics to advance precision medicine for alcohol and other substance use disorders. Digital health technologies, for example smartphones and wearables, afford an unprecedented opportunity to collect behavioral data continuously and unobtrusively as it occurs in daily life and apply these rich data to the understanding of AUD/SUD risk variability within individuals. Compared to traditional diagnostic assessments, digital assessments may offer greater insights regarding the nature of these disorders and individual experiences to enhance treatment discovery and development. In addition, these technologies are easy to scale and translate into interventions capable of providing in-the-moment, more accessible support.

Research out of the DIGITAL Insights Lab contributes to a more complete understanding of the factors that cause and maintain AUD/SUDs to guide the discovery of new precision medicine interventions. Our work spans prevention and early intervention in young adult populations and intervention and management of more chronic disorders in adults. Using a multi-modal approach to data collection and the latest innovations in statistical modeling and machine learning, our research reaches across populations and health behaviors to impact the lives of our participants in real-time.