Continuous-Time System Identification of a Smoking Cessation Intervention.
Cigarette smoking is a major global public health issue and the leading cause of preventable death in the United States. Toward a goal of designing better smoking cessation treatments, system identification techniques are applied to intervention data to describe smoking cessation as a process of behavior change. System identification problems that draw from two modeling paradigms in quantitative psychology (statistical mediation and self-regulation) are considered, consisting of a series of continuous-time estimation problems. A continuous-time dynamic modeling approach is employed to describe the response of craving and smoking rates during a quit attempt, as captured in data from a smoking cessation clinical trial. The use of continuous-time models provide benefits of parsimony, ease of interpretation, and the opportunity to work with uneven or missing data.
Control Systems Engineering Laboratory, Mail Stop 875001, Tempe, AZ 85287-5001 ; Biological Design Program, Arizona State University, Mail Stop 875001, Tempe, AZ 85287-5001.
Kevin P
KP
Timms
Control Systems Engineering Laboratory, Mail Stop 875001, Tempe, AZ 85287-5001 ; School for Engineering of Matter, Transport, and Energy, Arizona State University, Mail Stop 876106, Tempe, AZ 85287-6106.
Daniel E
DE
Rivera
The Methodology Center & Department of Human Development and Family Studies, The Pennsylvania State University, 204 E. Calder Way, Suite 400, State College, PA 16801.
Linda M
LM
Collins
Department of Medicine & Center for Tobacco Research and Intervention, University of Wisconsin-Madison, 1930 Monroe Street, Suite 200, Madison, WI 53711.
Megan E
ME
Piper
behavioral science; continuous-time identification; self-regulation; smoking cessation; statistical mediation