A control systems engineering approach for adaptive behavioral interventions: illustration with a fibromyalgia intervention

The term adaptive intervention has been used in behavioral medicine to describe operationalized and individually tailored strategies for prevention and treatment of chronic, relapsing disorders. Control systems engineering offers an attractive means for designing and implementing adaptive behavioral interventions that feature intensive measurement and frequent decision-making over time. This is illustrated in this paper for the case of a low-dose naltrexone treatment intervention for fibromyalgia. System identification methods from engineering are used to estimate dynamical models from daily diary reports completed by participants. These dynamical models then form part of a model predictive control algorithm which systematically decides on treatment dosages based on measurements obtained under real-life conditions involving noise, disturbances, and uncertainty. The effectiveness and implications of this approach for behavioral interventions (in general) and pain treatment (in particular) are demonstrated using informative simulations.

[1]  Marie Löf,et al.  Measuring Physical Activity in a Cardiac Rehabilitation Population Using a Smartphone-Based Questionnaire , 2013, Journal of medical Internet research.

[2]  W. Velicer.  Applying Idiographic Research Methods: Two Examples , 2010 .

[3]  Daniel E Rivera,et al.  Control systems engineering for optimizing a prenatal weight gain intervention to regulate infant birth weight. , 2014, American journal of public health.

[4]  Kevin P. Timms,et al.  Continuous-time system identification of a smoking cessation intervention , 2014, Int. J. Control.

[5]  Daniel E. Rivera,et al.  Optimized Behavioral Interventions: What Does System Identification and Control Engineering Have to Offer? , 2012 .

[6]  F. Collins,et al.  The path to personalized medicine. , 2010, The New England journal of medicine.

[7]  Daniel E. Rivera,et al.  Model Predictive Control for Tactical Decision-Making in Semiconductor Manufacturing Supply Chain Management , 2008, IEEE Transactions on Control Systems Technology.

[8]  D. Rivera,et al.  Please Scroll down for Article Mathematical and Computer Modelling of Dynamical Systems a Dynamical Model for Describing Behavioural Interventions for Weight Loss and Body Composition Change a Dynamical Model for Describing Behavioural Interventions for Weight Loss and Body Composition Change , 2022 .

[9]  Daniel E. Davison,et al.  A control-theory reward-based approach to behavior modification in the presence of social-norm pressure and conformity pressure , 2012, 2012 American Control Conference (ACC).

[10]  Kevin P. Timms,et al.  A Hybrid Model Predictive Control strategy for optimizing a smoking cessation intervention , 2014, 2014 American Control Conference.

[11]  D. Rivera,et al.  Using engineering control principles to inform the design of adaptive interventions: a conceptual introduction. , 2007, Drug and alcohol dependence.

[12]  G. A. Mccain,et al.  Toward an integrated understanding of fibromyalgia syndrome. I. Medical and pathophysiological aspects , 1991, PAIN.

[13]  Daniel E. Rivera,et al.  An Improved Formulation of Hybrid Model Predictive Control With Application to Production-Inventory Systems , 2013, IEEE Transactions on Control Systems Technology.

[14]  D. Rivera,et al.  A Personalized and Control Systems Engineering Conceptual Approach to Target Childhood Anxiety in the Contexts of Cultural Diversity , 2014, Journal of clinical child and adolescent psychology : the official journal for the Society of Clinical Child and Adolescent Psychology, American Psychological Association, Division 53.

[15]  Jay H. Lee,et al.  Tuning of model predictive controllers for robust performance , 1994 .

[16]  Daniel E. Rivera,et al.  Towards Patient-Friendly Input Signal Design for Optimized Pain Treatment Interventions , 2012 .

[17]  Peter R. Giacobbi,et al.  Exploring Behavioral Markers of Long-Term Physical Activity Maintenance , 2013, Health education & behavior : the official publication of the Society for Public Health Education.

[18]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

[19]  N. Schork,et al.  The n-of-1 clinical trial: the ultimate strategy for individualizing medicine? , 2011, Personalized medicine.

[20]  E.F. Camacho,et al.  A Risk-based Model Predictive Control Approach to Adaptive Interventions in Behavioral Health , 2006, Proceedings of the 45th IEEE Conference on Decision and Control.

[21]  Daniel E. Rivera,et al.  A control engineering approach for designing an optimized treatment plan for fibromyalgia , 2011, Proceedings of the 2011 American Control Conference.

[22]  Daniel E. Rivera,et al.  Optimal input signal design for data-centric estimation methods , 2013, 2013 American Control Conference.

[23]  Lennart Ljung,et al.  System identification (2nd ed.): theory for the user , 1999 .

[24]  B. Buckingham,et al.  Closed-loop control in type 1 diabetes. , 2016, The lancet. Diabetes & endocrinology.

[25]  Susan A. Murphy,et al.  A Conceptual Framework for Adaptive Preventive Interventions , 2004, Prevention Science.

[26]  S. Murphy,et al.  Dynamic Treatment Regimes. , 2014, Annual review of statistics and its application.

[27]  F. Wolfe,et al.  The American College of Rheumatology Preliminary Diagnostic Criteria for Fibromyalgia and Measurement of Symptom Severity , 2010, Arthritis care & research.

[28]  S. Joe Qin,et al.  A survey of industrial model predictive control technology , 2003 .

[29]  Sunil Deshpande,et al.  Optimal Input Signal Design for Data-Centric Identification and Control with Applications to Behavioral Health and Medicine , 2014 .

[30]  Peter C. Young,et al.  The advantages of directly identifying continuous-time transfer function models in practical applications , 2014, Int. J. Control.

[31]  Sean Mackey,et al.  Fibromyalgia symptoms are reduced by low-dose naltrexone: a pilot study. , 2009, Pain medicine.

[32]  Darrell M. Wilson,et al.  A Closed-Loop Artificial Pancreas Using Model Predictive Control and a Sliding Meal Size Estimator , 2009, Journal of diabetes science and technology.

[33]  Alex Simpkins,et al.  System Identification: Theory for the User, 2nd Edition (Ljung, L.; 1999) [On the Shelf] , 2012, IEEE Robotics & Automation Magazine.

[34]  Runze Li,et al.  Functional data analysis for dynamical system identification of behavioral processes. , 2014, Psychological methods.

[35]  Babatunde A. Ogunnaike,et al.  Process Dynamics, Modeling, and Control , 1994 .

[36]  Daniel E. Rivera,et al.  A dynamical systems model of Social Cognitive Theory , 2014, 2014 American Control Conference.

[37]  S. Dib-Hajj,et al.  Effects of ranolazine on wild-type and mutant hNav1.7 channels and on DRG neuron excitability , 2010, Molecular pain.

[38]  Diana M. Thomas,et al.  Hybrid model predictive control for optimizing gestational weight gain behavioral interventions , 2013, 2013 American Control Conference.

[39]  P. Tugwell,et al.  The American College of Rheumatology 1990 Criteria for the Classification of Fibromyalgia. Report of the Multicenter Criteria Committee. , 1990, Arthritis and rheumatism.

[40]  Eyal Dassau,et al.  Closed-Loop Control of Artificial Pancreatic $\beta$ -Cell in Type 1 Diabetes Mellitus Using Model Predictive Iterative Learning Control , 2010, IEEE Transactions on Biomedical Engineering.

[41]  W. Nilsen,et al.  Moving Behavioral Theories into the 21st Century: Technological Advancements for Improving Quality of Life , 2013, IEEE Pulse.

[42]  Ian Postlethwaite,et al.  Multivariable Feedback Control: Analysis and Design , 1996 .

[43]  Predrag V. Klasnja,et al.  Mind the theoretical gap: interpreting, using, and developing behavioral theory in HCI research , 2013, CHI.

[44]  Oliver Mason,et al.  The rôle of control and system theory in systems biology , 2008, Annu. Rev. Control..

[45]  Ryan Zurakowski,et al.  A model predictive control based scheduling method for HIV therapy. , 2006, Journal of theoretical biology.

[46]  Peter C. M. Molenaar,et al.  The New Person-Specific Paradigm in Psychology , 2009 .

[47]  Jesse Dallery,et al.  Single-Case Experimental Designs to Evaluate Novel Technology-Based Health Interventions , 2013, Journal of medical Internet research.

[48]  Daniel E. Rivera,et al.  Hybrid model predictive control for sequential decision policies in adaptive behavioral interventions , 2014, 2014 American Control Conference.

[49]  Daniel J Clauw,et al.  The role of the central nervous system in the generation and maintenance of chronic pain in rheumatoid arthritis, osteoarthritis and fibromyalgia , 2011, Arthritis research & therapy.

[50]  Sean Mackey,et al.  Low-dose naltrexone for the treatment of fibromyalgia: findings of a small, randomized, double-blind, placebo-controlled, counterbalanced, crossover trial assessing daily pain levels. , 2013, Arthritis and rheumatism.

[51]  Megan E. Piper,et al.  A dynamical systems approach to understanding self-regulation in smoking cessation behavior change. , 2013, Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco.

[52]  Katsuhiko Ogata,et al.  Modern Control Engineering , 1970 .

[53]  Brian Milne,et al.  Ultra-low dose naltrexone attenuates chronic morphine-induced gliosis in rats , 2010, Molecular pain.

[54]  M. E. Boyle.  Single Case Experimental Designs: Strategies for Studying Behavior Change , 1983 .

[55]  Michel Gevers,et al.  Identification of multi-input systems: variance analysis and input design issues , 2006, Autom..

[56]  W. Nilsen,et al.  Health behavior models in the age of mobile interventions: are our theories up to the task? , 2011, Translational behavioral medicine.

[57]  Serge Perrot,et al.  Fibromyalgia syndrome: a relevant recent construction of an ancient condition? , 2008, Current opinion in supportive and palliative care.

[58]  Diana M. Thomas,et al.  A dynamical systems model for improving gestational weight gain behavioral interventions , 2012, 2012 American Control Conference (ACC).

[59]  Susan A Murphy,et al.  Customizing treatment to the patient: adaptive treatment strategies. , 2007, Drug and alcohol dependence.

[60]  M. Hoagland,et al.  Feedback Systems An Introduction for Scientists and Engineers SECOND EDITION , 2015 .