Matching Items (2)
Description
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 set was reverse-engineered and describes a psychological self-regulation process in which smoking activity regulates craving levels. The estimated expressions suggest that self-regulation more accurately describes cessation behavior change, and that the psychological self-regulator resembles a proportional-with-filter controller. In contrast to current clinical practice, adaptive smoking cessation interventions seek to personalize cessation treatment over time. An intervention of this nature generally reflects a control system with feedback and feedforward components, suggesting its design could benefit from a control systems engineering perspective. An adaptive intervention is designed in this dissertation in the form of a Hybrid Model Predictive Control (HMPC) decision algorithm. This algorithm assigns counseling, bupropion, and nicotine lozenges each day to promote tracking of target smoking and craving levels. Demonstrated through a diverse series of simulations, this HMPC-based intervention can aid a successful cessation attempt. Objective function weights and three-degree-of-freedom tuning parameters can be sensibly selected to achieve intervention performance goals despite strict clinical and operational constraints. Such tuning largely affects the rate at which peak bupropion and lozenge dosages are assigned; total post-quit smoking levels, craving offset, and other performance metrics are consequently affected. Overall, the interconnected nature of the smoking and craving controlled variables facilitate the controller's robust decision-making capabilities, even despite the presence of noise or plant-model mismatch. Altogether, this dissertation lays the conceptual and computational groundwork for future efforts to utilize engineering concepts to further study smoking behaviors and to optimize smoking cessation interventions.
ContributorsTimms, Kevin Patrick (Author) / Rivera, Daniel E (Thesis advisor) / Frakes, David (Committee member) / Nielsen, David R (Committee member) / Arizona State University (Publisher)
Created2014
Description
When people pick up the phone to call a telephone quitline, they are taking an important step towards changing their smoking behavior. The current study investigated the role of a critical cognition in the cessation process--self-efficacy. Self-efficacy is thought to be influential in behavior change processes including those involved in the challenging process of stopping tobacco use. By applying basic principles of self-efficacy theory to smokers utilizing a telephone quitline, this study advanced our understanding of the nature of self-efficacy in a "real-world" cessation setting. Participants received between one and four intervention calls aimed at supporting them through their quit attempt. Concurrent with the initiation of this study, three items (confidence, stress, and urges) were added to the standard telephone protocol and assessed at each call. Two principal sets of hypotheses were tested using a combination of ANCOVAs and multiple regression analyses. The first set of hypotheses explored how self-efficacy and changes in self-efficacy within individuals were associated with cessation outcomes. Most research has found a positive linear relation between self-efficacy and quit outcomes, but this study tested the possibility that excessively high self-efficacy may actually reflect an overconfidence bias, and in some cases be negatively related to cessation outcomes. The second set of hypotheses addressed several smoking-related factors expected to affect self-efficacy. As predicted, higher baseline self-efficacy and increases in self-efficacy were associated with higher rates of quitting. However, contrary to predictions, there was no evidence that overconfidence led to diminished cessation success. Finally, as predicted, shorter duration of quit attempts, shorter time to relapse, and stronger urges all were associated with lower self-efficacy. In conclusion, understanding how self-efficacy and changes in self-efficacy affect and are affected by cessation outcomes is useful for informing both future research and current quitline intervention procedures.
ContributorsGoesling, Jenna (Author) / Barrera, Manuel (Thesis advisor) / Shiota, Lani (Committee member) / Enders, Craig (Committee member) / Presson, Clark (Committee member) / Arizona State University (Publisher)
Created2011