A control engineering approach for designing an optimized treatment plan for fibromyalgia

Abstract

Control engineering offers a systematic and efficient means for optimizing the effectiveness of behavioral interventions. In this paper, we present an approach to develop dynamical models and subsequently, hybrid model predictive control schemes for assigning optimal dosages of naltrexone as treatment for a chronic pain condition known as fibromyalgia. We apply system identification techniques to develop models from daily diary reports completed by participants of a naltrexone intervention trial. The dynamic model serves as the basis for applying model predictive control as a decision algorithm for automated dosage selection of naltrexone in the face of the external disturbances. The categorical/discrete nature of the dosage assignment creates a need for hybrid model predictive control (HMPC) schemes. Simulation results that include conditions of significant plant-model mismatch demonstrate the performance and applicability of hybrid predictive control for optimized adaptive interventions for fibromyalgia treatment involving naltrexone. Index Termssystem identification; hybrid model predictive control; fibromyalgia; optimized behavioral interventions

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

“…3, we show selected variables associated with the naltrexone intervention for a participant from the pilot study; illustrating our proposed methodology by analyzing data from this participant is the focus of this paper; however, Deshpande [36] documents this analysis for all 40 participants associated with the combined pilot and full study. The plot shows the primary inputs (i.e., drug and placebo) and its resulting effect on outcomes such as FM symptoms and sleep.…”

Hybrid model predictive control for optimizing gestational weight gain behavioral interventions

“…(9) shows the new PBC inflow to the energy intake TPB (EI-TPB) model under the influence of n intervention components, (9) where I i is the dosage of intervention components; k 3i correspond to intervention gains. The expressions for the other inflows to the TPB models with the intervention effect are obtained similarly.…”

“…In an adaptive intervention, dosages of intervention components are assigned to participants based on values of tailoring variables. The use of dynamical systems and control engineering methods to optimize adaptive behavioral interventions is described in [8], [9].…”

A Dynamical Systems Approach to Understanding Self-Regulation in Smoking Cessation Behavior Change

“…Such an approach is appealing given control engineering's emphasis on systematic optimization, meaning algorithms could be designed to specify intervention dosage adjustments that support fast quit success within practical constraints (e.g., medication dose toxicity and resource management issues). Moreover, the algorithmic nature of the dynamical systems approach supports rapid treatment adaptation, as would be required by effective ecological momentary interventions that optimally leverage mobile technologies and control engineering principles (Deshpande et al, 2011;Dong et al, 2013;Nandola & Rivera, 2013;Rivera et al, 2007). In summary, this article introduces an engineering approach to analysis of ILD that describes self-regulation in the smoking cessation process more comprehensively than existing analytic approaches.…”

“…The result includes novel descriptions of time-varying predictors of substance use (Chandra, Scharf, & Shiffman, 2011;Galea, Hall, & Kaplan, 2009;Samanta, 2011;Shiyko, Lanza, Tan, Li, & Shiffman, 2012;Timms, Rivera, Collins, & Piper, 2012), pain management (Deshpande, Nandola, Rivera, & Younger, 2011), and disease transmission within populations (Bhadra et al, 2011;Ionides et al, 2006). Increased availability of ILD also allows use of an engineering analytical approach to study self-regulation within smoking (Rivera 2012;Timms et al, 2012).…”