A Novel Engineering Approach to Modeling and Optimizing Smoking Cessation Interventions: American Heart Association Fellowship for Kevin P Timms

Project: Research project

Project Details

Description

A Novel Engineering Approach to Modeling and Optimizing Smoking Cessation Interventions: American Heart Association Fellowship for Kevin P Timms American Heart Association Fellowship for Kevin P. Timms Cigarette smoking remains the leading cause of preventable death in the United States, and 35% of the deaths attributed to cigarette smoking in the U.S. annually are cardiovascular in nature. The deleterious effects of smoking on cardiovascular health take several forms: smoking is closely related to stroke, coronary heart disease, myocardial infarction, abdominal aortic aneurysms, and more. In recent years, decreases in smoking rates have stalled, with approximately 20% of adults in the U.S. continuing to smoke. Smokings continued significance as a public health issue is due in part to the limited success of cessation therapies, for which maximum success rates are around 35%. Clearly, smoking cessation interventions need to be better understood, and the factors affecting their efficacy must be rigorously characterized in order to improve such interventions. Traditionally, behavioral scientists characterize smoking cessation interventions with structural equation models (SEMs). These models, however, only quantify an interventions net effect on smoking behavior(s). Developed by engineers primarily for application to industrial settings, dynamical modeling and system identification techniques can be applied to intensive longitudinal data (ILD), behavioral data collected over short time intervals, to characterize the dynamic effects of a smoking cessation intervention: dynamical models characterize the shape, periodicity, time-dependency of the behavioral response to an intervention, and more. We hypothesize that dynamical modeling and system identification principles can be used to produce more comprehensive descriptions of the process of smoking cessation, and how therapeutic interventions and inter-individual variability affect this process. Such descriptions involve dynamical model development and validation with ILD from a clinical trial studying bupropion, an anti-depressant, and counseling as cessation treatments. These rich descriptions can then be used in conjunction with control systems engineering principles to design smoking cessation interventions of increased efficacy despite resource limitations.
StatusFinished
Effective start/end date7/1/1212/31/13

Funding

  • American Heart Association: Western States Affiliate: $50,000.00

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