Continuous-time system identification of a smoking cessation intervention

Abstract

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 behaviour change. System identification problems that draw from two modelling paradigms in quantitative psychology (statistical mediation and self-regulation) are considered, consisting of a series of continuous-time estimation problems. A continuous-time dynamic modelling 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.

Keywords:

• continuous-time identification
• behavioural science
• statistical mediation
• smoking cessation
• self-regulation

Acknowledgements

The content is solely the responsibility of the authors and does not necessarily represent the views of the NIH.

Additional information

Funding

This work was supported by an award from the American Heart Association, a National Research Service Award from the National Institute on Drug Abuse at the National Institutes of 950 Health [grant number F31 DA035035], and the Office of Behavioural and Social Sciences Research and the National Institute on Drug Abuse at the National Institutes of Health [grant number K25 DA021173], [grant number R21 DA024266], [grant number P50 DA10075].