Control systems engineering for optimizing behavioral mhealth interventions

Daniel Rivera, César A. Martín, Kevin P. Timms, Sunil Deshpande, Naresh N. Nandola, Eric B. Hekler

Research output: Chapter in Book/Report/Conference proceedingChapter

4 Scopus citations

Abstract

Control systems engineering is a broad-based field that examines how system variables can be adjusted over time to improve important process outcomes. In recent years, control engineering approaches have been proposed as the basis for modeling and optimizing personalized, timevarying interventions in behav- ioral health. This chapter describes how control systems engineering principles, particularly system identification and model predictive control, can be applied to serve as dynamic modeling methods and optimal decision policies, respectively, for intensively adaptive interventions in behavioral mHealth applications. The role that behavioral theory plays in determining model structure and enabling semi- physical system identification is explained. The combined system identification-model predictive control strategy is illustrated with examples of interventions for fibromyalgia, smoking cessation, and enhancing physical activity.

Original languageEnglish (US)
Title of host publicationMobile Health
Subtitle of host publicationSensors, Analytic Methods, and Applications
PublisherSpringer International Publishing
Pages455-493
Number of pages39
ISBN (Electronic)9783319513942
ISBN (Print)9783319513935
DOIs
StatePublished - Jul 12 2017

ASJC Scopus subject areas

  • Medicine(all)
  • Computer Science(all)

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    Rivera, D., Martín, C. A., Timms, K. P., Deshpande, S., Nandola, N. N., & Hekler, E. B. (2017). Control systems engineering for optimizing behavioral mhealth interventions. In Mobile Health: Sensors, Analytic Methods, and Applications (pp. 455-493). Springer International Publishing. https://doi.org/10.1007/978-3-319-51394-2_24