Literature DB >> 28868531

A Control Theory Model of Smoking.

Georgiy Bobashev1, John Holloway2, Eric Solano3, Boris Gutkin4.   

Abstract

We present a heuristic control theory model that describes smoking under restricted and unrestricted access to cigarettes. The model is based on the allostasis theory and uses a formal representation of a multiscale opponent process. The model simulates smoking behavior of an individual and produces both short-term ("loading up" after not smoking for a while) and long-term smoking patterns (e.g., gradual transition from a few cigarettes to one pack a day). By introducing a formal representation of withdrawal- and craving-like processes, the model produces gradual increases over time in withdrawal- and craving-like signals associated with abstinence and shows that after 3 months of abstinence, craving disappears. The model was programmed as a computer application allowing users to select simulation scenarios. The application links images of brain regions that are activated during the binge/intoxication, withdrawal, or craving with corresponding simulated states. The model was calibrated to represent smoking patterns described in peer-reviewed literature; however, it is generic enough to be adapted to other drugs, including cocaine and opioids. Although the model does not mechanistically describe specific neurobiological processes, it can be useful in prevention and treatment practices as an illustration of drug-using behaviors and expected dynamics of withdrawal and craving during abstinence.

Entities:  

Year:  2017        PMID: 28868531      PMCID: PMC5578474          DOI: 10.3768/rtipress.2017.op.0040.1706

Source DB:  PubMed          Journal:  Methods Rep RTI Press


  25 in total

Review 1.  Current models of nicotine dependence: what is known and what is needed to advance understanding of tobacco etiology among youth.

Authors:  W G Shadel; S Shiffman; R Niaura; M Nichter; D B Abrams
Journal:  Drug Alcohol Depend       Date:  2000-05-01       Impact factor: 4.492

2.  Self-reported abstinence effects in the first month after smoking cessation.

Authors:  M M Ward; G E Swan; L M Jack
Journal:  Addict Behav       Date:  2001 May-Jun       Impact factor: 3.913

3.  Addiction as a computational process gone awry.

Authors:  A David Redish
Journal:  Science       Date:  2004-12-10       Impact factor: 47.728

4.  A neurocomputational hypothesis for nicotine addiction.

Authors:  Boris S Gutkin; Stanislas Dehaene; Jean-Pierre Changeux
Journal:  Proc Natl Acad Sci U S A       Date:  2006-01-13       Impact factor: 11.205

5.  An opponent-process theory of motivation. I. Temporal dynamics of affect.

Authors:  R L Solomon; J D Corbit
Journal:  Psychol Rev       Date:  1974-03       Impact factor: 8.934

Review 6.  The scientific case that nicotine is addictive.

Authors:  I P Stolerman; M J Jarvis
Journal:  Psychopharmacology (Berl)       Date:  1995-01       Impact factor: 4.530

7.  The simulation of addiction: pharmacological and neurocomputational models of drug self-administration.

Authors:  Serge H Ahmed; Georgiy Bobashev; Boris S Gutkin
Journal:  Drug Alcohol Depend       Date:  2007-10-08       Impact factor: 4.492

8.  Progression to daily smoking: is there a gender difference among cessation treatment seekers?

Authors:  Elissa D Thorner; Maria Jaszyna-Gasior; David H Epstein; Eric T Moolchan
Journal:  Subst Use Misuse       Date:  2007       Impact factor: 2.164

9.  Comprehensive mathematical modeling in drug addiction sciences.

Authors:  Georgiy Bobashev; Elizabeth Costenbader; Boris Gutkin
Journal:  Drug Alcohol Depend       Date:  2007-06-15       Impact factor: 4.492

Review 10.  A computational hypothesis for allostasis: delineation of substance dependence, conventional therapies, and alternative treatments.

Authors:  Yariv Z Levy; Dino J Levy; Andrew G Barto; Jerrold S Meyer
Journal:  Front Psychiatry       Date:  2013-12-19       Impact factor: 4.157

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  2 in total

1.  Towards a Smart Smoking Cessation App: A 1D-CNN Model Predicting Smoking Events.

Authors:  Maryam Abo-Tabik; Nicholas Costen; John Darby; Yael Benn
Journal:  Sensors (Basel)       Date:  2020-02-17       Impact factor: 3.576

2.  Are Machine Learning Methods the Future for Smoking Cessation Apps?

Authors:  Maryam Abo-Tabik; Yael Benn; Nicholas Costen
Journal:  Sensors (Basel)       Date:  2021-06-22       Impact factor: 3.576

  2 in total

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