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. 2017 May;25(3):979-990.
doi: 10.1109/TCST.2016.2580661. Epub 2016 Jun 28.

Control Engineering Methods for the Design of Robust Behavioral Treatments

Korkut Bekiroglu  1 , Constantino Lagoa  1 , Suzan A Murphy  2 , Stephanie T Lanza  3
Affiliations

Control Engineering Methods for the Design of Robust Behavioral Treatments

Korkut Bekiroglu et al. IEEE Trans Control Syst Technol. 2017 May.

Abstract

In this paper, a robust control approach is used to address the problem of adaptive behavioral treatment design. Human behavior (e.g., smoking and exercise) and reactions to treatment are complex and depend on many unmeasurable external stimuli, some of which are unknown. Thus, it is crucial to model human behavior over many subject responses. We propose a simple (low order) uncertain affine model subject to uncertainties whose response covers the most probable behavioral responses. The proposed model contains two different types of uncertainties: uncertainty of the dynamics and external perturbations that patients face in their daily life. Once the uncertain model is defined, we demonstrate how least absolute shrinkage and selection operator (lasso) can be used as an identification tool. The lasso algorithm provides a way to directly estimate a model subject to sparse perturbations. With this estimated model, a robust control algorithm is developed, where one relies on the special structure of the uncertainty to develop efficient optimization algorithms. This paper concludes by using the proposed algorithm in a numerical experiment that simulates treatment for the urge to smoke.

Keywords: Adaptive treatment design; adaptive-robust intervention; behavioral treatment design; min–max structured robust optimization; receding horizon control.

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Figures

Fig. 1
Fig. 1
Adaptive intervention algorithm.
Fig. 2
Fig. 2
Performance of adaptive intensive intervention.
Fig. 3
Fig. 3
Smoking urge, negative affect, and self-efficacy under adaptive intervention.
Fig. 4
Fig. 4
Sparse disturbance.
Fig. 5
Fig. 5
Adaptive and full treatment intervention results.
See this image and copyright information in PMC

References

    1. Bierman KL, Nix RL, Maples JJ, Murphy SA. Examining clinical judgment in an adaptive intervention design: The fast track program. J Consulting Clin Psychol. 2006;74(3):468–481. - PMC - PubMed
    1. Rivera DE. Optimized behavioral interventions: What does system identification and control engineering have to offer?. Proc. 16th IFAC Symp. Syst. Identificat; 2012; pp. 882–893.
    1. Timms KP, Rivera DE, Collins LM, Piper ME. Continuous-time system identification of a smoking cessation intervention. Int J Control. 2014;87(7):1423–1437. - PMC - PubMed
    1. Navarro-Barrientos J-E, Rivera DE, Collins LM. A dynamical model for describing behavioural interventions for weight loss and body composition change. Math Comput Model Dyn Syst. 2011 Jan;17:183–203. - PMC - PubMed
    1. Dong Y, et al. A dynamical systems model for improving gestational weight gain behavioral interventions. Proc. Amer. Control Conf. (ACC); 2012; pp. 4059–4064. - PMC - PubMed

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