Trial reportNicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco2014
A dynamical systems approach to understanding self-regulation in smoking cessation behavior change.
Trial report in Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
20 citing papers in PubMed, 39 citations in OpenAlex.
- Adaptive step goals and rewards: a longitudinal growth model of daily steps for a smartphone-based walking intervention.Journal of behavioral medicine · 2018Trial
- Rapid Innovation of Precision Psychiatry Interventions Using Dynamical Systems Modeling and Ecological Quasi-Experiments: Protocol for a Proof-of-Concept Study on Eating Disorders.JMIR research protocols · 2026Article
- Data-Driven Mobile Health: System Identification and Hybrid Model Predictive Control to Deliver Personalized Physical Activity Interventions.IEEE open journal of control systems · 2025Article
- Ask Less, Learn More: Adapting Ecological Momentary Assessment Survey Length by Modeling Question-Answer Information Gain.Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies · 2024Article
- An Agile Digital Platform to Support Population Health-A Case Study of a Digital Platform to Support Patients with Delirium Using IoT, NLP, and AI.International journal of environmental research and public health · 2021Article
- A BAYESIAN TIME-VARYING EFFECT MODEL FOR BEHAVIORAL MHEALTH DATA.The annals of applied statistics · 2020Article
- Intensive longitudinal methods for studying the role of self-regulation strategies in substance use behavior change.Current addiction reports · 2020Article
- Development of a Control-Oriented Model of Social Cognitive Theory for Optimized mHealth Behavioral Interventions.IEEE transactions on control systems technology : a publication of the IEEE Control Systems Society · 2020Article
- Advancing Analytic Approaches to Address Key Questions in Mechanisms of Behavior Change Research.Journal of studies on alcohol and drugs · 2018Article
- Evaluating the effect of smoking cessation treatment on a complex dynamical system.Drug and alcohol dependence · 2017Article
- Hybrid Statistical and Mechanistic Mathematical Model Guides Mobile Health Intervention for Chronic Pain.Journal of computational biology : a journal of computational molecular cell biology · 2017Article
- Control Engineering Methods for the Design of Robust Behavioral Treatments.IEEE transactions on control systems technology : a publication of the IEEE Control Systems Society · 2017Article
- A control systems engineering approach for adaptive behavioral interventions: illustration with a fibromyalgia intervention.Translational behavioral medicine · 2014Article
- A personalized and control systems engineering conceptual approach to target childhood anxiety in the contexts of cultural diversity.Journal of clinical child and adolescent psychology : the official journal for the Society of Clinical Child and Adolescent Psychology, American Psychological Association, Division 53 · 2014Article
- Ecological Momentary Assessment and Alcohol Use Disorder Treatment.Alcohol research : current reviews · 2014Review
- Background for Real-Time Monitoring and Intervention Related to Alcohol Use.Alcohol research : current reviews · 2014Review
- Continuous-Time System Identification of a Smoking Cessation Intervention.International journal of control · 2014Article
- The Promises and Pitfalls of Digital Technology in Its Application to Alcohol Treatment.Alcohol research : current reviews · 2014Review
- Nicotine-Cadmium Interaction Alters Exploratory Motor Function and Increased Anxiety in Adult Male Mice.Journal of neurodegenerative diseases · 2014Article
- Alcohol research and eHealth technology.Alcohol research : current reviews · 2014Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 4 institutions in 1 country.
Funding
Abstract
introductionSelf-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.
methodsDynamical systems modeling is a mature engineering methodology that can represent smoking cessation as a self-regulation process. This article shows how a dynamical systems approach effectively captures the reciprocal relationship between day-to-day changes in craving and smoking. Models are estimated using ILD from a smoking cessation randomized clinical trial.
resultsA system of low-order differential equations is presented that models cessation as a self-regulatory process. It explains 87.32% and 89.16% of the variance observed in craving and smoking levels, respectively, for an active treatment group and 62.25% and 84.12% of the variance in a control group. The models quantify the initial increase and subsequent gradual decrease in craving occurring postquit as well as the dramatic quit-induced smoking reduction and postquit smoking resumption observed in both groups. Comparing the estimated parameters for the group models suggests that active treatment facilitates craving reduction and slows postquit smoking resumption.
conclusionsThis article illustrates that dynamical systems modeling can effectively leverage ILD in order to understand self-regulation within smoking cessation. Such models quantify group-level dynamic responses in smoking cessation and can inform the development of more effective interventions in the future.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.