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

Overview of attention for article published in Translational Behavioral Medicine, September 2014
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Title
A control systems engineering approach for adaptive behavioral interventions: illustration with a fibromyalgia intervention
Published in
Translational Behavioral Medicine, September 2014
DOI 10.1007/s13142-014-0282-z
Pubmed ID
Authors

Sunil Deshpande, Daniel E. Rivera, Jarred W. Younger, Naresh N. Nandola

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.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 52 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 2%
United States 1 2%
Unknown 50 96%

Demographic breakdown

Readers by professional status Count As %
Student > Master 8 15%
Student > Bachelor 7 13%
Student > Ph. D. Student 5 10%
Student > Doctoral Student 4 8%
Researcher 4 8%
Other 11 21%
Unknown 13 25%
Readers by discipline Count As %
Psychology 10 19%
Medicine and Dentistry 8 15%
Nursing and Health Professions 6 12%
Engineering 4 8%
Computer Science 2 4%
Other 7 13%
Unknown 15 29%