Date/Time
Date(s) - 10/24/2016
3:00 pm
Abstract:
Poor health-related behaviors represent a major challenge to healthcare due to their significant impact on chronic and acute diseases and their impact effect on the quality of individuals’ life. Recent advances in technology have enabled an unprecedented opportunity to assess objectively, unobtrusively and continuously human behavior and have opened the possibility of optimizing individual-tailored, just-in-time precision interventions within the framework of behavioral informatics. A key prerequisite for this optimization is the ability to assess and predict states of the individuals and the effects of interventions. This is potentially achievable with computational models of behavior and behavior change In this presentation, I plan to describe various approaches to computational modeling with a focus on a new hybrid model based on a dual process theoretical framework for behavior change. The model leverages cognitive learning theories and is shown to be consistent with existing mobile intervention data. We also illustrate how system-theoretic approaches can be used to assess the effect of coaching on participants’ health behaviors. I will discuss the potential of this approach to optimizing interventions.
Bio:
Dr. Misha Pavel is a Professor of Practice jointly appointed between College of Computer and Information Sciences and the Bouvé College of Health Sciences at Northeastern University. Dr. Pavel came to Boston from a position of a Program Director of Smart and Connected Health on a leave from at Oregon Health and Science University where he was a professor at 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 Technical University of Tampere. Previously he served as chair of the Department of Biomedical Engineering (he founded in 2001) and as Director of the Point of Care Laboratory, which focuses on unobtrusive monitoring, neurobehavioral assessment and computational modeling in support of healthcare, with a particular focus on chronic disease and elder care. His earlier academic appointments 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 early 1970s, where his research included network analysis and modeling, and later at AT&T Laboratories with focus on mobile and Internet-based technologies. His current fundamental research is at the intersection of multilevel computational modeling of complex behaviors of biological and cognitive systems, and augmented cognition in combination with transcranial stimulation and game-based training. His most recent efforts are focused on fundamental science and technology that would enable the transformation of healthcare to be proactive, distributed and patient-centered. Together with his colleagues he is applying these behavioral informatics approaches to the development of systems for providing care and rehabilitation for individuals with traumatic brain injuries. Professor Pavel has a Ph.D. in experimental psychology from New York University, an M.S. in electrical engineering from Stanford University, and a B.S. in electrical engineering from the Polytechnic Institute of Brooklyn. Misha Pavel is a Senior Life Member of IEEE.
Current Research Projects
Games for Health: Investigations of computational models for assessment and augmentation of cognitive functions from computer games and computerized versions of neuropsychological tests. Computer games are useful in motivating frequent interactions, but making inferences about cognitive functions require embedding models of cognitive processes in the inference algorithms
Technology for Future Care: In the framework Consortium on Technology for Proactive Care and NU-Care Technology Core Dr. Pavel is leading a group that provides advice and support of technology-based studies and development of self-care and elder care.
SHARP: Strengthening Human Adaptive Reasoning and Problem-solving. Enhancement of adaptive reasoning and problem-solving abilities among high-performing individuals combining game-based cognitive training with low-current transcranial electrical stimulation.
Behavior Change and Rehabilitation: Developing and investigating computational model-based approaches to assessment and inference of aspects of behavior. The goal of this research direction is applying system-theoretic approaches to characterize and optimize interventions.