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Control Systems Engineering for Optimizing Behavioral mHealth Interventions

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Abstract

Control systems engineering is a broad-based field that examines how system variables can be adjusted over time to improve important process outcomes. In recent years, control engineering approaches have been proposed as the basis for modeling and optimizing personalized, timevarying interventions in behavioral health. This chapter describes how control systems engineering principles, particularly system identification and model predictive control, can be applied to serve as dynamic modeling methods and optimal decision policies, respectively, for intensively adaptive interventions in behavioral mHealth applications. The role that behavioral theory plays in determining model structure and enabling semi-physical system identification is explained. The combined system identification-model predictive control strategy is illustrated with examples of interventions for fibromyalgia, smoking cessation, and enhancing physical activity.

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References

  1. Adams, M.A., Sallis, J.F., Norman, G.J., Hovell, M.F., Hekler, E.B., Perata, E.: An adaptive physical activity intervention for overweight adults: A randomized controlled trial. PLoS ONE 8(12), e82,901 (2013)

    Article  Google Scholar 

  2. Bandura, A.: Social Foundations of Thought and Action: A Social Cognitive Theory. Prentice-Hall Series in Social Learning Theory (1986)

    Google Scholar 

  3. Bandura, A.: Human agency in social cognitive theory. The American Psychologist 44(9), 1175–1184 (1989)

    Article  Google Scholar 

  4. Bemporad, A., Morari, M.: Control of systems integrating logic, dynamics, and constraints. Automatica 35, 407–427 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  5. Butte, N.F., Ellis, K.J., Wong, W.W., Hopkinson, J.M., Smith, E.O.: Composition of GWG impacts maternal fat retention and infant birth weight. Am J Obst Gynecol 189(5), 1423–1432 (2003)

    Article  Google Scholar 

  6. Clague, J., Bernstein, L.: Physical activity and cancer. Current Oncology Reports 14(6), 550–558 (2012)

    Article  Google Scholar 

  7. Collins, L.: Unpacking the black box: engineering more potent behavioral interventions to improve public health. Evan G. and Helen G. Pattishall Outstanding Research Achievement Award lecture presented at Penn State University, State College, PA on March 20 (2012)

    Google Scholar 

  8. Collins, L.M., Murphy, S.A., Bierman, K.L.: A conceptual framework for adaptive preventive interventions. Prevention Science 5(3), 185–196 (2004)

    Article  Google Scholar 

  9. Deshpande, S.: A control engineering approach for designing an optimized treatment plan for fibromyalgia. Master’s thesis, Electrical Engineering, Arizona State University, USA (2011)

    Book  Google Scholar 

  10. Deshpande, S.: Optimal input signal design for data-centric identification and control with applications to behavioral health and medicine. Ph.D. thesis, Electrical Engineering, Arizona State University, USA (2014)

    Google Scholar 

  11. Deshpande, S., Nandola, N.N., Rivera, D.E., Younger, J.W.: Optimized treatment of fibromyalgia using system identification and hybrid model predictive control. Control Engineering Practice 33, 161–173 (2014)

    Article  Google Scholar 

  12. Deshpande, S., Rivera, D.E., Younger, J.: Towards patient-friendly input signal design for optimized pain treatment interventions. Proceedings of the 16th IFAC Symposium on System Identification pp. 1311–1316 (2012)

    Google Scholar 

  13. Deshpande, S., Rivera, D.E., Younger, J.W., Nandola, N.N.: A control systems engineering approach for adaptive behavioral interventions: illustration with a fibromyalgia intervention. Translational Behavioral Medicine 4(3), 275–289 (2014)

    Article  Google Scholar 

  14. Erhardt, L.: Cigarette smoking: An undertreated risk factor for cardiovascular disease. Atherosclerosis 205(1), 23–32 (2009)

    Article  Google Scholar 

  15. Ferster, C.B.: Schedules of reinforcement with Skinner. In: P.B. Dews (ed.) Festschrift for B. F. Skinner, Century psychology series, pp. 37–46. New York, Appleton-Century-Crofts (1970)

    Google Scholar 

  16. Gwaltney, C.J., Metrik, J., Kahler, C.W., Shiffman, S.: Self-efficacy and smoking cessation: A meta-analysis. Psychology of Addictive Behaviors 23(1), 56–66 (2009)

    Article  Google Scholar 

  17. Hekler, E.B.: Just walk study. http://justwalkstudy.weebly.com/ (2015). [Online; accessed September-23-2015]

  18. King, A.C., Hekler, E.B., Grieco, L.A., Winter, S.J., Sheats, J.L., Buman, M.P., Banerjee, B., Robinson, T.N., Cirimele, J.: Harnessing different motivational frames via mobile phones to promote daily physical activity and reduce sedentary behavior in aging adults. PLoS ONE 8(4), e62,613 (2013)

    Article  Google Scholar 

  19. Klasnja, P., Hekler, E., Shiffman, S., Boruvka, A., Almirall, D., Tewari, A., Murphy, S.: Microrandomized trials: An experimental design for developing just-in-time adaptive interventions. Health Psychology 34(Suppl), 1220–1228 (2015)

    Article  Google Scholar 

  20. Lindskog, P., Ljung, L.: Tools for semiphysical modelling. International Journal of Adaptive Control and Signal Processing 9(6), 509–523 (1995)

    Article  MathSciNet  Google Scholar 

  21. Ljung, L.: System identification: theory for the user, 2nd edn. Prentice Hall PTR, Upper Saddle River, NJ (1999)

    MATH  Google Scholar 

  22. Martín, C.A.: A system identification and control engineering approach for optimizing mHealth behavioral interventions based on Social Cognitive Theory. Ph.D. thesis, Electrical Engineering, Arizona State University (2016)

    Google Scholar 

  23. Martín, C.A., Deshpande, S., Hekler, E.B., Rivera, D.E.: A system identification approach for improving behavioral interventions based on Social Cognitive Theory. In: Proceedings of the American Control Conference, pp. 5878–5883 (2015)

    Google Scholar 

  24. Martín, C.A., Rivera, D.E., Hekler, E.B.: A decision framework for an adaptive behavioral intervention for physical activity using hybrid model predictive control. In: Proceedings of the American Control Conference, pp. 3576–3581 (2016)

    Google Scholar 

  25. Martín, C.A., Rivera, D.E., Riley, W.T., Hekler, E.B., Buman, M.P., Adams, M.A., King, A.C.: A dynamical systems model of Social Cognitive Theory. In: Proceedings of the American Control Conference, pp. 2407–2412 (2014)

    Google Scholar 

  26. Mattiloi, T.M., Milne, B., Cahill, C.: Ultra-low dose naltrexone attenuates chronic morphine-induced gliosis in rats. Molecular Pain 6(22), 1–11 (2010)

    Google Scholar 

  27. Centers for Disease Control and Prevention: The Great American Smokeout (2011). URL http://www.cdc.gov/Features/GreatAmericanSmokeout/

  28. Centers for Disease Control and Prevention: Current cigarette smoking among adults in the United States (2015). URL http://www.cdc.gov/tobacco/data_statistics/fact_sheets/adult_data/cig_smoking/

  29. National Library of Medicine: Nicotine Gum (2013). URL https://www.nlm.nih.gov/medlineplus/druginfo/meds/a684056.html

  30. McCarthy, D.E., Piasecki, T.M., Lawrence, D.L., Jorenby, D.E., Shiffman, S., Fiore, M.C., Baker, T.B.: A randomized controlled clinical trial of bupropion SR and individual smoking cessation counseling. Nicotine and Tobacco Research 10(4), 717–729 (2008)

    Article  Google Scholar 

  31. McGinnis, J.M., Williams-Russo, P., Knickman, J.R.: The case for more active policy attention to health promotion. Health Affairs 21(2), 78–93 (2002)

    Article  Google Scholar 

  32. Morari, M., Zafiriou, E.: Robust Process Control. Prentice-Hall International (1989)

    Google Scholar 

  33. Nahum-Shani, I., Hekler, E.B., Spruijt-Metz, D.: Building health behavior models to guide the development of just-in-time adaptive interventions: A pragmatic framework. Health Psychology 34(suppl), 1209–1219 (2015)

    Article  Google Scholar 

  34. Nandola, N.N., Rivera, D.E.: An improved formulation of Hybrid Model Predictive Control with application to production-inventory systems. IEEE Transactions on Control Systems Technology 21(1), 121–135 (2013)

    Article  Google Scholar 

  35. Navarro-Barrientos, J.E., Rivera, D.E., Collins, L.M.: A dynamical model for describing behavioural interventions for weight loss and body composition change. Mathematical and Computer Modelling of Dynamical Systems 17(2), 183–203 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  36. Ogunnaike, B.A., Ray, W.H.: Process Dynamics, Modeling, and Control. Oxford University Press, New York (1994)

    Google Scholar 

  37. Piper, M.E., Smith, S.S., Schlam, T.R., Fiore, M.C., Jorenby, D.E., Fraser, D., Baker, T.B.: A randomized placebo-controlled clinical trial of 5 smoking cessation pharmacotherapies. Archives of General Psychiatry 66(11), 1253–1262 (2009)

    Article  Google Scholar 

  38. Riley, W.T., Martín, C.A., Rivera, D.E., Hekler, E.B., Adams, M.A., Buman, M.P., Pavel, M., King, A.C.: Development of a dynamical systems model of social cognitive theory. Translational Behavioral Medicine: Practice, Policy and Research (2015). DOI 10.1007/s13142-015-0356-6. URL http://link.springer.com/article/10.1007/s13142-015-0356-6. Published online: 09 November 2015

  39. Riley, W.T., Rivera, D.E., Atienza, A.A., Nilsen, W., Allison, S.M., Mermelstein, R.: Health behavior models in the age of mobile interventions: are our theories up to the task? Translational Behavioral Medicine 1(1), 53–71 (2011)

    Article  Google Scholar 

  40. Riley, W.T., Serrano, K.J., Nilsen, W., Atienza, A.A.: Mobile and wireless technologies in health behavior and the potential for intensively adaptive interventions. Current Opinion in Psychology 5, 67–71 (2015)

    Article  Google Scholar 

  41. Rivera, D.E.: Optimized behavioral interventions: what does system identification and control engineering have to offer? In: Proceedings of 16th IFAC Symposium on System Identification, pp. 882–893 (2012)

    Google Scholar 

  42. 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(Supplement 2), S31–S40 (2007)

    Google Scholar 

  43. Shiffman, S., Stone, A.A., Hufford, M.R.: Ecological momentary assessment. Annual Reviews in Clinical Psychology 18(4), 1–32 (2008)

    Article  Google Scholar 

  44. Stenman, A.: Model on demand: Algorithms, analysis and applications. Tech. rep., ISBN 91-7219-450-2. N. Bergman (1999)

    Google Scholar 

  45. Timms, K.P.: A novel engineering approach to modeling and optimizing smoking cessation interventions. Ph.D. thesis, Arizona State University (2014)

    Google Scholar 

  46. Timms, K.P., Rivera, D.E., Collins, L.M., Piper, M.E.: Control systems engineering for understanding and optimizing smoking cessation interventions. Proceedings of the 2013 American Control Conference pp. 1967–1972 (2013)

    Google Scholar 

  47. Timms, K.P., Rivera, D.E., Collins, L.M., Piper, M.E.: Continuous-time system identification of a smoking cessation intervention. International Journal of Control 87(7), 1423–1437 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  48. Timms, K.P., Rivera, D.E., Collins, L.M., Piper, M.E.: A dynamical systems approach to understanding self-regulation in smoking cessation behavior change. Nicotine and Tobacco Research 16(Suppl. 2), S159–S168 (2014)

    Google Scholar 

  49. Timms, K.P., Rivera, D.E., Piper, M.E., Collins, L.M.: A Hybrid Model Predictive Control strategy for optimizing a smoking cessation intervention. Proceedings of the 2014 American Control Conference pp. 2389–2394 (2014)

    Google Scholar 

  50. Tobacco Use and Dependence Guideline Panel: A clinical practice guideline for treating tobacco use and dependence: 2008 update. Tech. rep., U.S. Department of Health and Human Services, Rockville, MD (2008)

    Google Scholar 

  51. Treede, R.D., Rief, W., Barke, A., Aziz, Q., Bennett, M.I., Benoliel, R., et al.: A classification of chronic pain for ICD-11. Pain 156(6), 1003–1007 (2015)

    Google Scholar 

  52. Walls, T.A., Schafer, J.L.: Models for Intensive Longitudinal Data. Oxford University Press, Oxford, UK (2006)

    Book  MATH  Google Scholar 

  53. Warner, C., Shoaib, M.: How does bupropion work as a smoking cessation aid? Addiction Biology 10, 219–231 (2005)

    Article  Google Scholar 

  54. Wolfe, F., D, C., et al., F.M.: The American College of Rheumatology preliminary diagnostic criteria for fibromyalgia and measurement of symptom severity. Arthritis Care and Research 62, 600–610 (2010)

    Google Scholar 

  55. Younger, J., Mackey, S.: Fibromyalgia symptoms are reduced by low-dose naltrexone: A pilot study. Pain Medicine 10(4), 663–672 (2009)

    Article  Google Scholar 

  56. Younger, J., Noor, N., McCue, R., Mackey, S.: Low-dose naltrexone for the treatment of fibromyalgia: Findings of a small, randomized, double-blind, placebo-controlled, counterbalanced, crossover trial assessing daily pain levels. Arthritis & Rheumatism 65(2), 529–538 (2013)

    Article  Google Scholar 

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Acknowledgements

Authors Sunil Deshpande, Naresh Nandola, and Kevin Timms performed the work described in this chapter while holding positions at Arizona State University. Support from the US National Institutes of Health (NIH; grants R21 DA024266 and K25 DA021173) and the National Science Foundation (NSF; grant IIS-1449751) is gratefully acknowledged. Additional support has been received from the Piper Health Solutions Consortium at Arizona State University. The opinions expressed in this article are the authors’ own and do not necessarily reflect the views of NIH, NSF or the Virginia G. Piper Charitable Trust.

We acknowledge as well the collaboration with many behavioral scientists and methodologists who have helped to influence this work; among these is Linda M. Collins (Penn State, Methodology Center and Human Development and Family Studies), Susan A. Murphy (University of Michigan Dept. of Statistics), Jarred Younger (University of Alabama-Birmingham Dept. of Psychology), Megan Piper (Univ. of Wisconsin Dept. of Medicine), William Riley (NIH Office of Behavioral and Social Science Research), Matthew Buman (ASU School of Nutrition and Health Promotion) and Marc Adams (ASU School of Nutrition and Health Promotion).

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Authors and Affiliations

  1. Control Systems Engineering Laboratory, School for Engineering of Matter, Transport, and Energy, Arizona State University, Tempe, AZ, USA

    Daniel E. Rivera, César A. Martín & Kevin P. Timms

  2. Facultad de Ingeniería en Electricidad y Computación, Escuela Superior Politécnica del Litoral, ESPOL, Campus Gustavo Galindo Km 30.5 Vía Perimetral, P.O. Box 09-01-5863, Guayaquil, Ecuador

    César A. Martín

  3. John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA

    Sunil Deshpande

  4. ABB India Corporate Research Center, Bangalore, India

    Naresh N. Nandola

  5. Designing Health Laboratory, School of Nutrition and Health Promotion, Arizona State University, Tempe, AZ, USA

    Eric B. Hekler

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Correspondence to Daniel E. Rivera .

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Editors and Affiliations

  1. College of Computing, Georgia Institute of Technology, Atlanta, Georgia, USA

    James M. Rehg

  2. Department of Statistics, University of Michigan, Ann Arbor, Michigan, USA

    Susan A. Murphy

  3. Department of Computer Science, University of Memphis, Memphis, Tennessee, USA

    Santosh Kumar

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Rivera, D.E., Martín, C.A., Timms, K.P., Deshpande, S., Nandola, N.N., Hekler, E.B. (2017). Control Systems Engineering for Optimizing Behavioral mHealth Interventions. In: Rehg, J., Murphy, S., Kumar, S. (eds) Mobile Health. Springer, Cham. https://doi.org/10.1007/978-3-319-51394-2_24

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  • DOI: https://doi.org/10.1007/978-3-319-51394-2_24

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