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

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Translational Behavioral Medicine

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.

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Notes

  1. It is important to note the difference in what is being described as a model. The term “theory” has been defined variously across various disciplines, but is defined here as a formalized set of concepts that organize observations and inferences, and is meant to predict phenomena (41. Graziano, A. and M. Raulin, Research is a process of inquiry. Research methods: a process of inquiry, 4th Edition. Allyn & Bacon, Needham Heights, MA, 2000: p. 28–53. The term “model”, on the other hand, has been used by different disciplines to mean different things. There are conceptual models, conceived of as proposed causal linkages between a set of concepts believed to be related to a specific outcome (42. Eime, R.M., et al., A systematic review of the psychological and social benefits of participation in sport for children and adolescents: informing development of a conceptual model of health through sport. Int J Behav Nutr Phys Act, 2013. 10: p. 98.which is very similar to the definition of theory given here. There are statistical models, such as Structural Equation Models, a family of multivariate statistical techniques that incorporate factor analysis and path analysis (43. Weston, R. and P.A. Gore, A brief guide to structural equation modeling. The Counseling Psychologist, 2006. 34(5): p. 719–751. This paper proposes the development of computational models of behavior.

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Acknowledgments

This project was funded by the National Science Foundation (CISE IIS-1217464)

We would like to thank participants in the workshop that led to this publication: Jaakko Aarnio, Katerina Martin Abello, René van Bavel, Christina Botella, Niels Boye, Célia Boyer, Marientina Gotsis, Loukianos Gatzoulis, Ross Hammond, Jimi Huh, Holly Brugge Jimison, Pamela Kato, Outi Kenttä, Joni Kettunen, Pedrag Klasnja, Heidi Lehtonen, James Lester, Elina Mattila, Teresa Meneu, Robin Mermelstien, Hannu Nieminen, Ana Paiva, Brigitte Piniewski, Andrew Raij, and Petra Wilson.

Conflict of interest

Donna Spruijt-Metz, Eric Hekler, Niilo Saranummi, Stephen Intille, Ilkka Korhonen, Wendy Nilsen, Daniel E. Rivera, Bonnie Spring, Susan Michie, David A. Asch, Alberto Sanna, Vicente Traver Salcedo, Rita Kukakfa, and Misha Pavel have no conflict of interest to report.

Adherence to ethical principles

This paper reflects discussions and documents developed for the International Workshop on New Computationally-Enabled Theoretical Models to Support Health Behavior Change and Maintenance, October 16–17, 2012, Brussels. All materials can be found here http://www.behaviorchange.ca. There were no human subjects involved.

Author information

Authors and Affiliations

  1. University of Southern California, 635 Downey Way, Suite 305 Building Code: VPD 3332, Los Angeles, CA, 90089-3332, USA

    Donna Spruijt-Metz MFA, PhD

  2. Arizona State University, Tempe, AZ, USA

    Eric Hekler PhD & Daniel E. Rivera PhD

  3. VTT Technical Research Centre of Finland, Espoo, Finland

    Niilo Saranummi PhD & Misha Pavel PhD

  4. Northeastern University, Boston, MA, USA

    Stephen Intille PhD

  5. Tampere University of Technology, Tampere, Finland

    Ilkka Korhonen PhD

  6. National Institutes of Health, Bethesda, MD, USA

    Wendy Nilsen PhD

  7. Northwestern University, Evanston, IL, USA

    Bonnie Spring PhD

  8. University College London, London, UK

    Susan Michie PhD

  9. Wharton School, University of Pennsylvania, Philadelphia, PA, USA

    David A. Asch PhD

  10. Scientific Institute Hospital San Raffaelle, Milano, Italy

    Alberto Sanna PhD

  11. Valencia Polytechnical University, Valencia, Spain

    Vicente Traver Salcedo PhD

  12. Columbia University, New York, NY, USA

    Rita Kukakfa PhD

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  1. Donna Spruijt-Metz MFA, PhD
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Correspondence to Donna Spruijt-Metz MFA, PhD.

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Spruijt-Metz, D., Hekler, E., Saranummi, N. et al. Building new computational models to support health behavior change and maintenance: new opportunities in behavioral research. Behav. Med. Pract. Policy Res. 5, 335–346 (2015). https://doi.org/10.1007/s13142-015-0324-1

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