Advancing Intensive Longitudinal Data Analysis, Visualization, and Personalization Methods Pertinent to Mobile Health Interventions

Advancing Intensive Longitudinal Data Analysis, Visualization, and Personalization Methods Pertinent to Mobile Health Interventions

Thursday December 8, 2022

12:00 p.m. ET

Thursday December 8, 2022

12:00 p.m. ET

Location

In-Person – ISR-Thompson, Room 1430BD

Virtual – Zoom Link

Speaker

Lizbeth ‘Libby’ Benson
Postdoctoral Research Fellow at the TSET Health Promotion Research Center at the NCI-designated Stephenson Cancer Center and University of Oklahoma Health Sciences Center

Lizbeth ‘Libby’ Benson, PhD, is a Postdoctoral Research Fellow at the TSET Health Promotion Research Center at the NCI-designated Stephenson Cancer Center and University of Oklahoma Health Sciences Center. Libby earned her BA in Psychology with honors from the University of Wisconsin Madison, spent three years working as a research coordinator at the University of Pennsylvania Positive Psychology Center, and most recently earned her PhD from the Pennsylvania State University in the department of Human Development and Family Studies. Libby’s research program is focused on intensive longitudinal, computational, and machine learning 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 to 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 Dr. Benson’s work as a way to better understand complex behavioral processes, to generate new ideas, and to use as a tool for scientific communication.

Description

Advances in personal sensing – such as smart phones and watches – have increased the variety, volume and velocity of data for capturing how individuals’ experiences and behaviors (e.g., emotions, smoking) vary over time and across contexts. In this talk, I will present research on how intraindividual variability metrics, differential equation models, and Bayesian multiple time-scale models can be used to describe, analyze, and visualize these multivariate intensive longitudinal data. Throughout, I will discuss how these methods afford opportunities to conduct innovative behavior change research pertaining to individuals’ health and well-being in the context of mobile health interventions. I will conclude with discussion of future directions including the National Institutes of Health K01 grant I am writing, where I propose to develop a reinforcement learning algorithm for automated personalization of intervention content in a just-in-time adaptive intervention for smoking cessation.

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