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Transl Behav Med. 2015 Sep;5(3):335-46. doi: 10.1007/s13142-015-0324-1.

Building new computational models to support health behavior change and maintenance: new opportunities in behavioral research.

Author information

1
University of Southern California, 635 Downey Way, Suite 305 Building Code: VPD 3332, Los Angeles, CA 90089-3332 USA.
2
Arizona State University, Tempe, AZ USA.
3
VTT Technical Research Centre of Finland, Espoo, Finland.
4
Northeastern University, Boston, MA USA.
5
Tampere University of Technology, Tampere, Finland.
6
National Institutes of Health, Bethesda, MD USA.
7
Northwestern University, Evanston, IL USA.
8
University College London, London, UK.
9
Wharton School, University of Pennsylvania, Philadelphia, PA USA.
10
Scientific Institute Hospital San Raffaelle, Milano, Italy.
11
Valencia Polytechnical University, Valencia, Spain.
12
Columbia University, New York, NY USA.

Abstract

Adverse and suboptimal health behaviors and habits are responsible for approximately 40 % of preventable deaths, in addition to their unfavorable effects on quality of life and economics. Our current understanding of human 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, personal, and environmental states. This paper first discusses how new technologies (i.e., mobile sensors, smartphones, ubiquitous computing, and cloud-enabled processing/computing) and emerging systems modeling techniques enable the development of new, dynamic, and empirical models of human behavior that could facilitate just-in-time adaptive, scalable interventions. The paper then describes concrete steps to the creation of robust dynamic mathematical models of behavior including: (1) establishing "gold standard" measures, (2) the creation of a behavioral ontology for shared language and understanding tools that both enable dynamic theorizing across disciplines, (3) the development of data sharing resources, and (4) facilitating improved sharing of mathematical models and tools to support rapid aggregation of the models. We conclude with the discussion of what might be incorporated into a "knowledge commons," which could help to bring together these disparate activities into a unified system and structure for organizing knowledge about behavior.

KEYWORDS:

Computational models of behavior; Connected health; Health-related behavior; Just-in-time adaptive interventions; Mobile health; Real-time interventions; mHealth

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