A Dynamical Systems Approach to Understanding Self-Regulation in Smoking Cessation Behavior Change

Abstract

introduction: Self-regulation, a key component of the addiction process, has been challenging to model precisely in smoking cessation settings, largely due to the limitations of traditional methodological approaches in measuring behavior over time. However, increased availability of intensive longitudinal data (ILD) measured through ecological momentary assessment facilitates the novel use of an engineering modeling approach to better understand self-regulation.

A control systems engineering approach for adaptive behavioral interventions: illustration with a fibromyalgia intervention

“…The interested reader is directed to work using the theory of planned behavior (TPB) to develop a dynamic model for a behavioral intervention for weight loss [20]; meanwhile, the combination of TPB and selfregulation to model the dynamics of an adaptive intervention for gestational weight gain is explored in [21][22][23][24]. The use of self-regulation for modeling smoking cessation dynamics is explored in [25][26][27][28], while a dynamical model for social cognitive theory (SCT) in the context of improving physical activity interventions is described in [41].…”

“…Published work that examines aspects of the control systems engineering approach described in this paper in other behavioral health settings includes work on prevention of conduct disorder [8,[16][17][18], promotion of moderate-to-vigorous physical activity [19], general weight change and body composition [20], gestational weight gain [21][22][23][24], and smoking cessation [25][26][27][28].…”

“…(1) can be extended to behavioral settings and, in the experience of the authors, representative of a large class of dynamical system responses in behavioral medicine. This will be shown in the paper with respect to fibromyalgia, but models within the general class of second-order systems have been observed in the modeling of smoking cessation [25], human weight and body composition change [20], and physical activity [19]. Statistically sound, data-centric procedures will be presented to estimate and validate these models; the procedures will similarly indicate when it is necessary to use higher order structures.…”

Continuous-time system identification of a smoking cessation intervention

“…A continuous-time approach is particularly appealing in behavioral health settings (Timms et al 2012, Rivera 2012). Notably, for low order dynamics (which appears to be the case for many dynamic behaviors; Timms et al 2012, in press, Deshpande et al 2011, Rivera 2012), continuous-time models estimated from discrete-time data result in parsimonious expressions through which important dynamic features are more easily discerned. Consequently, insight into behavioral phenomena and intervention effects may be more easily interpretable with continuous-time models.…”

“…In the context of cigarette smoking, the concepts of statistical mediation and self-regulation have been of particular interest (Timms et al 2012, in press, McCarthy et al 2008b, Piper et al 2008, Velicer et al 1992, Walls and Rivera 2009). Statistical mediation is a modeling paradigm central to the social and behavioral sciences, describing a multivariate causal relationship in which an independent variable affects a mediator variable and an outcome variable, with the mediator also affecting the outcome (MacKinnon 2008).…”