Libby’s research program is focused on intensive longitudinal methods for examining temporal dynamics of affective, social, and health behavior experiences using ecological momentary assessment and sensor-based data collected from individuals in their daily lives. Her goals are to understand how behavioral processes unfold across multiple time-scales and contexts, and how this knowledge can be used to build personalized interventions that can facilitate health behavior change with long-term impacts on both lifespan and high quality of life years (healthspan). Data visualization is also an important component of Libby's work as a way to better understand complex behavioral processes, to generate new ideas, and to use as a tool for scientific communication. Currently, Libby is writing a NIH K01 focused on developing a reinforcement learning algorithm for personalizing decision rules in a just-in-time adaptive intervention (JITAI) for smoking cessation.

For more details on Libby's published research, please click on the CV page on this website or her Google Scholar page.