A Hybrid Model Predictive Control strategy for optimizing a smoking cessation intervention

Kevin P. Timms, Daniel Rivera, Megan E. Piper, Linda M. Collins

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Citations (Scopus)

Abstract

The chronic, relapsing nature of tobacco use represents a major challenge in smoking cessation treatment. Recently, novel intervention paradigms have emerged that seek to adjust treatments over time in order to meet a patient's changing needs. This article demonstrates that Hybrid Model Predictive Control (HMPC) offers an appealing framework for designing these optimized, time-varying smoking cessation interventions. HMPC is a particularly appropriate approach as it recognizes that intervention doses must be assigned in predetermined, discrete units while retaining receding-horizon, constraint-handling, and combined feedback and feedforward capabilities. Specifically, an intervention algorithm is developed here in which counseling and two pharmacotherapies are manipulated to reduce daily smoking and craving levels. The potential usefulness of such an intervention is illustrated through simulated treatment of a quit attempt in a hypothetical patient, which highlights that prioritizing reduction in craving over total daily smoking levels significantly reduces craving levels, suppresses relapse, and successfully rejects time-varying disturbances such as stress, all while adhering to several practical operational constraints and resource use considerations.

Original languageEnglish (US)
Title of host publicationProceedings of the American Control Conference
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2389-2394
Number of pages6
ISBN (Print)9781479932726
DOIs
StatePublished - 2014
Event2014 American Control Conference, ACC 2014 - Portland, OR, United States
Duration: Jun 4 2014Jun 6 2014

Other

Other2014 American Control Conference, ACC 2014
CountryUnited States
CityPortland, OR
Period6/4/146/6/14

Fingerprint

Model predictive control
Drug therapy
Tobacco
Feedback

Keywords

  • Biomedical
  • Emerging control applications
  • Predictive control for linear systems

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Cite this

Timms, K. P., Rivera, D., Piper, M. E., & Collins, L. M. (2014). A Hybrid Model Predictive Control strategy for optimizing a smoking cessation intervention. In Proceedings of the American Control Conference (pp. 2389-2394). [6859466] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ACC.2014.6859466

A Hybrid Model Predictive Control strategy for optimizing a smoking cessation intervention. / Timms, Kevin P.; Rivera, Daniel; Piper, Megan E.; Collins, Linda M.

Proceedings of the American Control Conference. Institute of Electrical and Electronics Engineers Inc., 2014. p. 2389-2394 6859466.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Timms, KP, Rivera, D, Piper, ME & Collins, LM 2014, A Hybrid Model Predictive Control strategy for optimizing a smoking cessation intervention. in Proceedings of the American Control Conference., 6859466, Institute of Electrical and Electronics Engineers Inc., pp. 2389-2394, 2014 American Control Conference, ACC 2014, Portland, OR, United States, 6/4/14. https://doi.org/10.1109/ACC.2014.6859466
Timms KP, Rivera D, Piper ME, Collins LM. A Hybrid Model Predictive Control strategy for optimizing a smoking cessation intervention. In Proceedings of the American Control Conference. Institute of Electrical and Electronics Engineers Inc. 2014. p. 2389-2394. 6859466 https://doi.org/10.1109/ACC.2014.6859466
Timms, Kevin P. ; Rivera, Daniel ; Piper, Megan E. ; Collins, Linda M. / A Hybrid Model Predictive Control strategy for optimizing a smoking cessation intervention. Proceedings of the American Control Conference. Institute of Electrical and Electronics Engineers Inc., 2014. pp. 2389-2394
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