Continuous-time system identification of a smoking cessation intervention

Kevin P. Timms, Daniel Rivera, Linda M. Collins, Megan E. Piper

Research output: Contribution to journalArticle

9 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)1423-1437
Number of pages15
JournalInternational Journal of Control
Volume87
Issue number7
DOIs
Publication statusPublished - Jul 3 2014

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Keywords

  • Behavioural science
  • Continuous-time identification
  • Self-regulation
  • Smoking cessation
  • Statistical mediation

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications

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