Evaluating the effect of smoking cessation treatment on a complex dynamical system

Abstract

Objective To understand the dynamic relations among tobacco withdrawal symptoms to inform the development of effective smoking cessation treatments. Dynamical system models from control engineering are introduced and utilized to evaluate complex treatment effects. We demonstrate how dynamical models can be used to examine how distinct withdrawal-related processes are related over time and how treatment influences these relations. Method Intensive longitudinal data from a randomized placebo-controlled smoking cessation trial (N=1504) are used to estimate a dynamical model of withdrawal-related processes including momentary craving, negative affect, quitting self-efficacy, and cessation fatigue for each of six treatment conditions (nicotine patch, nicotine lozenge, bupropion, patch + lozenge, bupropion + lozenge, and placebo). Results Estimation and simulation results show that (1) withdrawal measurements are interrelated over time, (2) nicotine patch + nicotine lozenge showed reduced cessation fatigue and enhanced self-efficacy in the long-term while bupropion + nicotine lozenge was more effective at reducing negative affect and craving, and (3) although nicotine patch + nicotine lozenge had a better initial effect on cessation fatigue and self-efficacy, nicotine lozenge had a stronger effect on negative affect and nicotine patch had a stronger impact on craving. Conclusions This approach can be used to provide new evidence illustrating (a) the total impact of treatment conditions (via steady state values) and (b) the total initial impact (via rate of initial change values) on smoking-related outcomes for separate treatment conditions, noting that the conditions that produce the largest change may be different than the conditions that produce the fastest change.

Longer duration of smoking abstinence is associated with waning cessation fatigue

“…This study also provides insights about factors associated with cessation fatigue which may provide clues about how cessation fatigue influences risk of relapse. In general, the findings from this population-based study on correlates of cessation fatigue correspond to those from studies based on convenience samples of smokers and those recruited for treatment studies (Bekiroglu et al, 2017;Heckman et al, 2018;Liu et al, 2013;Mathew et al, 2017;Piper et al, 2011). In this study, we found several sociodemographic variables that predicted cessation fatigue.…”

“…A second investigation found that cessation fatigue increased over the first two weeks of a quit attempt, but this could be dampened by using stop smoking medications (Liu et al, 2013). A third study simulated cessation fatigue trajectories over two months, and estimated which medications were most effective for mitigating fatigue (Bekiroglu, Russell, Lagoa, Lanza, & Piper, 2017). Nicotine patch produced the fastest initial rate of change, whereas combination nicotine replacement therapy (patch + lozenge) produced the greatest long-term reductions in cessation fatigue.…”

Dynamic models of stress-smoking responses based on high-frequency sensor data

“…Simulations by Lagoa et al revealed that treatment outcomes could be improved by delivering real-time interventions when and where needed (33). Bekiroglu et al also represented the relations between tobacco withdrawalrelated processes over time using dynamical models (34). These works demonstrated the feasibility of applying system identification tools to model problems involving smoking.…”

Persuasive Health

“…However, in comparison to work within the social sciences on behavioural change theories, this field remains in its infancy. Dynamical models of smoking cessation [4], alcohol consumption [24] and physical activity in response to text message micro-interventions [49,50] have been developed.…”