As a quantitative methodologist and behavioral scientist, Libby integrates data science, digital technologies, and theory-driven approaches to optimize interventions that promote mental and physical health by meeting individuals’ unique and evolving needs in real-time, real-world settings. Specifically, her research program is focused on (1) developing intensive longitudinal models for understanding how psychological (emotions, stress, cognitions) and behavioral (physical activity, smoking) processes unfold in daily life and intervention contexts, and (2) using knowledge of these processes to personalize intervention content (e.g., personalized feedback, AI-driven messages) and delivery (e.g., just-in-time adaptive interventions [JITAIs] and artificial intelligence [AI] reinforcement learning algorithms). Innovative study designs that integrate ecological momentary assessments (EMAs) and sensor-based data from smartphones and wearable devices are central to this work, providing the real-time, context-rich information needed to model dynamic processes and personalize interventions. As an investigator on the Methodologies for Adapting and Personalizing Prevention, Treatment and Recovery Services for SUD and HIV (MAPS Center; P50DA054039), Libby currently leads a pilot project to develop personalized feedback visualizations for integrating into JITAIs. To date, Libby has published 34 peer reviewed journal articles, 1 peer reviewed encyclopedia article, and 1 editorial for a special issue she co-edited for the Journal of Social and Personal Relationships. Her interdisciplinary research program includes publications in both substantive and methodological journals including Frontiers in Digital Health, Annals of Behavioral Medicine, Developmental Psychology, Structural Equation Modeling: A Multidisciplinary Journal, and Psychological Assessment.

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