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A Novel Engineering Approach to Modelling and Optimizing Smoking Cessation Interventions

Abstract Cigarette smoking remains a major global public health issue. This is partially due to the chronic and relapsing nature of tobacco use, which contributes to the approximately 90% quit attempt failure rate. The recent rise in mobile technologies has led to an increased ability to frequently measure smoking behaviors and related constructs over time, i.e., obtain intensive longitudinal data (ILD). Dynamical systems modeling and system identification methods from engineering offer a means to leverage ILD in order to better model dynamic smoking behaviors. In this dissertation, two sets of dynamical systems models are estimated using ILD from a smoking cessation clinical trial: one set describes cessation as a craving-mediated process; a second... (more)
Created Date 2014
Contributor Timms, Kevin Patrick (Author) / Rivera, Daniel E (Advisor) / Frakes, David (Committee member) / Nielsen, David R (Committee member) / Arizona State University (Publisher)
Subject Engineering / Behavioral sciences / Biomedical engineering / Adaptive behavioral interventions / Continuous-time modeling / Hybrid Model Predictive Control / Self-regulation / Smoking cessation / Statistical mediation
Type Doctoral Dissertation
Extent 276 pages
Language English
Reuse Permissions All Rights Reserved
Note Doctoral Dissertation Bioengineering 2014
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS

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Description Dissertation/Thesis