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

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Translational Behavioral Medicine

An Erratum to this article was published on 24 October 2014

Abstract

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.

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Acknowledgments

Support for this work has been provided by the Office of Behavioral and Social Sciences Research (OBSSR) of the National Institutes of Health (NIH) and the National Institute on Drug Abuse (NIDA) through grants R21 DA024266 and K25 DA021173. The content is solely the responsibility of the authors and does not necessarily represent the official views of OBSSR, NIDA, or the NIH. J. W. Younger received support from the American Fibromyalgia Syndrome Association (AFSA). Insights provided by L. M. Collins and J. Trail of the Methodology Center, Penn State University during the conduct of this research are greatly appreciated.

Conflict of interest and adherence to ethical standards statement

The authors have no conflicts of interest to disclose. This paper presented a de-identified secondary data and simulation analysis of two previously executed clinical studies performed in accordance to ethical standards and protection for human subjects.

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Author notes

  1. Naresh N. Nandola Ph.D.

    Present address: ABB Corporate Research Center, Bangalore, India

Authors and Affiliations

  1. Control Systems Engineering Laboratory, School for Engineering of Matter, Transport, and Energy, Arizona State University, Tempe, AZ, 85287, USA

    Sunil Deshpande Ph.D., Daniel E. Rivera Ph.D. & Naresh N. Nandola Ph.D.

  2. Neuroinflammation, Pain and Fatigue Laboratory, Department of Psychology, University of Alabama at Birmingham, Birmingham, AL, 35294, USA

    Jarred W. Younger Ph.D.

Authors
  1. Sunil Deshpande Ph.D.
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  3. Jarred W. Younger Ph.D.
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  4. Naresh N. Nandola Ph.D.
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Corresponding author

Correspondence to Daniel E. Rivera Ph.D..

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Implications

Practice: Adaptive interventions based on control systems engineering principles represent a valuable practical approach for personalizing and optimizing treatment in behavioral interventions that feature intensive data collection and frequent decision-making.

Research: Dynamical systems and control engineering provide a powerful, broad-based methodological framework for modeling and decision-making in behavioral settings that can serve to benefit modern time-varying, adaptive interventions.

Policy: Adaptive, time-varying interventions based on control systems engineering can substantially improve individual treatment outcomes while lowering costs and reducing negative effects.

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Deshpande, S., Rivera, D.E., Younger, J.W. et al. A control systems engineering approach for adaptive behavioral interventions: illustration with a fibromyalgia intervention. Behav. Med. Pract. Policy Res. 4, 275–289 (2014). https://doi.org/10.1007/s13142-014-0282-z

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