International Workshop on Dynamic Modeling of Health Behavior Change and Maintenance: Moving the Field Forward

University Of Southern California, Los Angeles CA

Investigators

Abstract

Poor health-related behaviors and habits are responsible for approximately 40% of preventable deaths and the majority of the chronic disease burden. However, our current understanding of health-related behavior is largely based on static snapshots of human behavior, rather than ongoing, dynamic feedback loops of behavior in response to ever-changing biological, social and personal environmental states. Rich streams of continuous data are becoming increasingly available through emerging technologies, including wearable and deployable sensors and mobile phones. This data, combined with sophisticated modeling techniques emanating primarily from engineering fields, can provide unprecedented opportunities to understand behavior in context and in real time. However, to take advantage of these opportunities, dedicated collaborations between behavioral/health scientists and data modelers, as well as across different schools of complex data modeling techniques, are required. The proposed workshop focuses on developing new concepts for modeling the temporally dense, contextually rich and personalized data increasingly afforded by emerging technologies. The major goals of this proposed workshop are to 1) amalgamate techniques from sub-disciplines across different computational modeling approaches, 2) facilitate development of a shared vocabulary/ontology to facilitate communication between modelers, behavioral and health scientists and 3) advance the rigor of dynamic behavioral theories to the next level towards causal and predictive dynamic models. The challenge to 21st century data modeling and health behavior research is to move toward computational, dynamic modeling of behavior that can capture complex and rapid changes in behavioral state and related influencing factors. These new models will pave the way for Just-In-Time, Adaptive Interventions (JITAI) that provide feedback in context, in the moment, when people are most receptive and most likely to benefit. To accomplish this, the workshop proposed here will bring together experts in various types of modeling, for example (but not limited) to systems dynamics, social networks, agent-based modeling, machine learning, and Bayesian inference to work together with behavioral scientists and health care professionals to move this endeavor to the next level. The proposed workshop will follow the International Workshop on Methodologies for Developing and Evaluating Digital Health Interventions, which will be held in London UK, 10-11 September 2015, led by Dr. Susan Michie and Dr. Jeremy Wyatt, sponsored by the Medical Research Council (MRC), UK. The workshop proposed here brings the international leaders in health behavior and mobile interventions together with leaders in big data modeling. This workshop will facilitate an unprecedented collaboration between different streams of "big data" modelers and behaviorists to develop new paradigms for modeling temporally dense, contextualized behavioral data that can guide future JITIAs across health and wellness domains.