Translational Behavioral Medicine

, Volume 6, Issue 4, pp 483–495 | Cite as

Development of a dynamic computational model of social cognitive theory

  • William T. RileyEmail author
  • Cesar A. Martin
  • Daniel E. Rivera
  • Eric B. Hekler
  • Marc A. Adams
  • Matthew P. Buman
  • Misha Pavel
  • Abby C. King
Original Research


Social cognitive theory (SCT) is among the most influential theories of behavior change and has been used as the conceptual basis of health behavior interventions for smoking cessation, weight management, and other health behaviors. SCT and other behavior theories were developed primarily to explain differences between individuals, but explanatory theories of within-person behavioral variability are increasingly needed as new technologies allow for intensive longitudinal measures and interventions adapted from these inputs. These within-person explanatory theoretical applications can be modeled as dynamical systems. SCT constructs, such as reciprocal determinism, are inherently dynamical in nature, but SCT has not been modeled as a dynamical system. This paper describes the development of a dynamical system model of SCT using fluid analogies and control systems principles drawn from engineering. Simulations of this model were performed to assess if the model performed as predicted based on theory and empirical studies of SCT. This initial model generates precise and testable quantitative predictions for future intensive longitudinal research. Dynamic modeling approaches provide a rigorous method for advancing health behavior theory development and refinement and for guiding the development of more potent and efficient interventions.


Dynamical system modeling Computational modeling Control systems engineering Health behavior theory Social cognitive theory 



DR and EH were supported by a grant from National Science Foundation (IIS-1449751). DR also was supported by a grant from the National Institute on Drug Abuse (K25DA021173). MP was supported by a grant from the National Institute of Nursing Research (P20NR015320). AK was supported by grants from National Heart, Lung, and Blood Institute (R01HL116448), National Institute of Diabetes and Digestive and Kidney Diseases (R01DK102016), and National Institute of Biomedical Imaging and Bioengineering (U54EB020405). This paper represents the views and perspectives of the authors, not the funders or the National Institutes of Health. All authors participated in this article and take responsibility for the integrity of the conceptualizations and models described.

Compliance with ethical standards

This article presents conceptual and computational models; no human subject data were collected.

Conflict of interest

The authors declare that they have no competing interests.


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Copyright information

© Society of Behavioral Medicine 2015

Authors and Affiliations

  • William T. Riley
    • 1
    Email author
  • Cesar A. Martin
    • 2
  • Daniel E. Rivera
    • 2
  • Eric B. Hekler
    • 3
  • Marc A. Adams
    • 3
  • Matthew P. Buman
    • 3
  • Misha Pavel
    • 4
  • Abby C. King
    • 5
  1. 1.Behavioral Research Program, Division of Cancer Control and Population SciencesNational Cancer InstituteRockvilleUSA
  2. 2.Control Systems Engineering Laboratory (CSEL), School for Engineering of Matter, Transport, and EnergyArizona State UniversityTempeUSA
  3. 3.School of Nutrition and Health PromotionArizona State UniversityTempeUSA
  4. 4.Northeastern UniversityBostonUSA
  5. 5.Department of Health Research and Policy, and Stanford Prevention Research Center, Department of MedicineStanford University School of MedicineStanfordUSA

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