A Dynamical Systems Model for Understanding Behavioral Interventions for Weight Loss

  • J. -Emeterio Navarro-Barrientos
  • Daniel E. Rivera
  • Linda M. Collins
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6007)


We propose a dynamical systems model that captures the daily fluctuations of human weight change, incorporating both physiological and psychological factors. The model consists of an energy balance integrated with a mechanistic behavioral model inspired by the Theory of Planned Behavior (TPB); the latter describes how important variables in a behavioral intervention can influence healthy eating habits and increased physical activity over time. The model can be used to inform behavioral scientists in the design of optimized interventions for weight loss and body composition change.


behavioral interventions theory of planned behavior dynamical systems energy balance weight loss 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Ajzen, I., Madden, T.J.: Prediction of goal-directed behavior: attitudes, intentions, and perceived behavioral control. J. Exp. Soc. Psychol. 22, 453–474 (1986)CrossRefGoogle Scholar
  2. 2.
    Baranowski, T., Cullen, K.W., Nicklas, T., Thompson, D., Baranowski, J.: Are current health behavioral change models helpful in guiding prevention of weight gain efforts? Obesity Research 11, 23S–43S (2003)CrossRefGoogle Scholar
  3. 3.
    Blanchard, C.M., Fisher, J., Sparling, P.B., Shanks, T.H., Nehl, E., Rhodes, R.E., et al.: Understanding adherence to 5 serving of fruits and vegetables per day: A theory of planned behavior perspective. J. Nutr. Edu. Behav. 41(1), 3–10 (2009)CrossRefGoogle Scholar
  4. 4.
    Bollen, K.A.: Structural equations with latent variables. Series in probability and mathematical statistics. Wiley, Chichester (1989)MATHGoogle Scholar
  5. 5.
    Symons Downs, D., Hausenblas, H.A.: The theories of reasoned action and planned behavior applied to exercise: A meta-analytic update. J. Phys. Act. Health 2, 76–97 (2005)Google Scholar
  6. 6.
    FAO/WHO/UNU Expert Consultation, Human energy requirements, Food and Nutrition Technical Report Series 1, FAO, Rome (October 2001)Google Scholar
  7. 7.
    Fishbein, M., Ajzen, I.: Belief, attitude, intention and behavior: An introduction to theory and research. Addison-Wesley, Reading (1975)Google Scholar
  8. 8.
    Forbes, G.B.: Lean body mass-body fat interrelationships in humans. Nutr. Rev. 45, 225–231 (1987)CrossRefGoogle Scholar
  9. 9.
    Garrow, J.S.: Energy balance and obesity in man, 2nd edn. Elsevier/North-Holland Biomedica Press, Amsterdam (1978)Google Scholar
  10. 10.
    Hall, K.D.: Computational model of in vivo human energy metabolism during semistarvation and refeeding. Am. J. Physiol. Endocrinol Metab. 291, E23–E37 (2006)CrossRefGoogle Scholar
  11. 11.
    Hall, K.D., Jordan, P.N.: Modeling weight-loss maintenance to help prevent body weight regain. Am. J. Clin. Nutr. 88, 1495–1503 (2008)CrossRefGoogle Scholar
  12. 12.
    Hall, K.D., Chow, C.: A simple dynamic model of body weight and composition change. personal communicationGoogle Scholar
  13. 13.
    Keim, N.L., Blanton, C.A., Kretsch, M.J.: America’s obesity epidemic: Measuring physical activity to promote an active lifestyle. J. Am. Diet. Assoc. 104(9), 1398–1409 (2004)CrossRefGoogle Scholar
  14. 14.
    Merrill, A.L., Watt, B.K.: Energy value of foods - basis and derivation. In: Agriculture Handbook. U.S. Department of Agriculture, vol. 74 (1973)Google Scholar
  15. 15.
    Ljung, L.: System Identification: Theory for the User. Prentice Hall Information and System Sciences Series. Prentice Hall, Englewood Cliffs (1987)MATHGoogle Scholar
  16. 16.
    Raykov, T., Marcoulides, G.A.: A First Course in Structural Equation Modeling, 2nd edn. Erlbaum, Mahwah (2006)Google Scholar
  17. 17.
    Rivera, D.E., Pew, M.D., Collins, L.M.: Using engineering control principles to inform the design of adaptive interventions: A conceptual introduction. Drug and Alcohol Dependence 88, S31–S40 (2007)CrossRefGoogle Scholar
  18. 18.
    Schwartz, J.D., Wang, W., Rivera, D.E.: Optimal tuning of process control-based decision policies for inventory management in supply chains. Automatica 42, 1311–1320 (2006)MATHCrossRefGoogle Scholar
  19. 19.
    Trumbo, P., Schlicker, S., Yates, A.A., Poos, M.: Dietary reference intakes for energy, carbohydrate, fiber, fat, fatty acids, cholesterol, protein and amino acids. J. Am. Diet Assoc. 102(11), 1621–1630 (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • J. -Emeterio Navarro-Barrientos
    • 1
  • Daniel E. Rivera
    • 1
  • Linda M. Collins
    • 2
  1. 1.Control Systems Engineering Laboratory, School of Mechanical, Aerospace, Chemical and Materials EngineeringArizona State UniversityTempeUSA
  2. 2.The Methodology Center and Department of Human Development and Family StudiesPenn State UniversityState CollegeUSA

Personalised recommendations