Translating Domain Knowledge into Mechanistic Process Models: Illustrating the Need with a Just-in-Time Adaptive Intervention
Date: Thursday, March 18, 2021
Time: 9:00 – 10:00 AM Pacific / 12:00 – 1:00 PM Eastern Time
Register to receive Zoom access link. This event is FREE to attend, but pre-registration is required. Presentations will be streamed live on Zoom, recorded, and archived on YouTube.
Dr. Eric Hekler, Ph.D., University of California, San Diego
Dr. Misha Pavel, Ph.D., Northeastern University
Dr. Donna Spruijt-Metz, Ph.D., M.F.A., University of Southern California
Overview: Digital technologies present radically new possibilities for studying and developing insights related to both advancing fundamental understanding of social and behavioral processes and, simultaneously, improving behavioral interventions built on said knowledge. These new possibilities force us to find more robust ways to translate domain knowledge about processes (e.g., insights from operant and classical learning, cognitive science, and affective learning) into robust process models that rigorously specify temporal understanding of the dynamics and context-dependencies of said processes. The possibilities and challenges are particularly evident in developing just-in-time adaptive interventions (JITAI), which seek to provide support during states when a person would have the opportunity to engage in a positive behavior and be receptive to receiving support and when engagement would result in positive internalized adaptation toward participating in the desired behavior, eventually without the need for the JITAI. In this webinar the team will describe efforts to establish more robust approaches for translating domain knowledge about processes into computational models that account for theorized dynamics and to offer some initial steps to advance the field.
The Training in Advanced Data Analytics for Behavioral and Social Sciences (TADA-BSSR) Webinar Series is a virtual lecture series that covers advanced data analytics and data science underlying modern behavioral and social sciences research, with presentations from experts showing the basics of data management, representation, computation, statistical inference, data modeling, causal inference, and various other topics relevant to “big data” and teaching for behavioral and social sciences researchers. The TADA-BSSR program supports Behavioral and Social Sciences Research (BSSR) predoctoral training programs that focus on innovative computational and/or data science analytic approaches and their incorporation into training for the future BSSR health research workforce. The vision of the TADA-BSSR program is to support the development of a cohort of specialized predoctoral candidates who will possess advanced competencies in data science analytics to apply to an increasingly complex landscape of behavioral and social health-related big data. This series highlights research projects and topics in scope of the Computational Social Science Training Program at BIDS, as well as related programs at other national universities that are funded by the National Institutes of Health (NIH) Office of Behavioral and Social Sciences Research (OBSSR). The program at BIDS is aligned with the Eunice Kennedy Shriver National Institute of Child Health and Human Development.
Dr. Eric Hekler, Ph.D., is the director of the Center for Wireless & Population Health Systems within the Qualcomm Institute at University of California, San Diego (UCSD), interim director of the Design Lab at UCSD, and associate professor in the Herbert Wertheim School of Public Health and Human Longevity Science at UCSD. Three interdependent themes characterize his research for advancing (1) methods for optimizing adaptive behavioral interventions; (2) methods and processes to help people help themselves; and (3) advancement of collective action and facilitation of equitable political action focused on advancing vitality and justice for all, human and non-human. He is internationally recognized as an expert in digital health and optimization methods.
Dr. Misha Pavel, Ph.D., is a professor of practice jointly appointed between Khoury College of Computer and Information Sciences and the Bouvé College of Health Sciences at Northeastern University. Dr. Pavel came to Boston from the position of program director of Smart and Connected Health on leave from Oregon Health & Science University (OHSU), where he was a professor in the Department of Biomedical Engineering with a joint appointment in the Department of Medical Informatics and Clinical Epidemiology. He is also a visiting professor at the Halmstad University in Sweden and Technical University of Tampere. Previously he served as chair of the Department of Biomedical Engineering (which he founded in 2001) and as Director of the Point of Care Laboratory at OHSU, which focuses on unobtrusive monitoring, neurobehavioral assessment, and computational modeling in support of health care, with a particular focus on chronic disease and elder care. His earlier academic appointments have included positions at New York University and Stanford University. In addition to his academic career, Professor Pavel was a member of the technical staff at Bell Laboratories in the early 1970s, where his research included network analysis and modeling, and later at AT&T Laboratories with focus on the development of mobile and internet-based technologies. Capitalizing on his multidisciplinary background that includes biomedical engineering, electrical engineering, computer science, and experimental psychology, Dr. Pavel has been developing tools for inference and predictions of the dynamics of cognitive and sensory-motor processes leveraging behavioral and physiological data with applications to the care of older adults. Dr. Pavel leveraged his experience with networked, wireless, mobile, and context-aware applications for the development of unobtrusive monitoring technology. His prior research in sensor fusion, modeling of pattern recognition in sensory-motor systems, human cognition, and human-computer communication systems are at the heart of the analytic techniques needed to ensure the modeling and inference efforts' success. In close collaboration with computer scientists and engineers, Dr. Pavel advanced transdisciplinary and computational modeling approaches in several Defense Advanced Research Projects Agency programs, including Augmented Cognition and Neurotechnology for Intelligence Analysts. In his most recent work, he has been developing computational modeling of behavioral change to enable a just-in-time assessment to optimize JITAI.
Dr. Donna Spruijt-Metz, Ph.D., M.F.A., is a research professor in psychology and professor in preventive medicine, housed in the University of Southern California Dornsife Center for Economic and Social Research. Her work meshes 21st century technologies with transdisciplinary metabolic, behavioral, and environmental research to facilitate dynamic, personalized, and contextualized behavioral interventions that can be adapted on the fly. She has a deep interest in harnessing mobile health and new media modalities to bring researchers and research systems into interaction, engage people in their own data, and bring about lasting change in public health. One of her primary focuses is combining sensor and self-report data that are continuous, temporally rich, and contextualized. Using data and innovative modeling techniques, Spruijt-Metz collaborates with engineers, health professionals, and data modelers to create new mathematical models of human health–related behavior in real time. She is one of the first to undertake a JITAI in youth and envisions most or all future interventions being JITAI.