Predicting smoking cessation. Who will quit with and without the nicotine patch

Kenford, Susan L.; Fiore, Michael C.; Jorenby, Douglas E.; Smith, Stevens S.; Wetter, David W.; Baker, Timothy B.

doi:10.1001/jama.271.8.589

Cited by 326 publications

(285 citation statements)

References 17 publications

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“…Numerous risk factors for relapse have been identified (e.g., see Garvey et al, 1992;Doherty et al, 1995;Shiffman et al, 1996b;Ockene et al, 2000;Piasecki et al, 2000;Piasecki et al, 2003), and developing treatments to effectively target these many factors remain a high research priority. In early abstinence, one of the most reliable predictors of relapse is the occurrence a single smoking lapse, which is generally defined as smoking at least a puff of a cigarette (Marlatt and George 1984;Brandon et al, 1990;Kenford et al, 1994). Because as much as 95 % of smokers who experience a lapse will progress to relapse (Garvey et al, 1992;Kenford et al, 1994), the first lapse has been theorized to represent the transition from abstinence to regular smoking; yet, the mechanisms mediating this progression remain unclear (Shiffman et al, 1996b;Shiffman et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

“…In early abstinence, one of the most reliable predictors of relapse is the occurrence a single smoking lapse, which is generally defined as smoking at least a puff of a cigarette (Marlatt and George 1984;Brandon et al, 1990;Kenford et al, 1994). Because as much as 95 % of smokers who experience a lapse will progress to relapse (Garvey et al, 1992;Kenford et al, 1994), the first lapse has been theorized to represent the transition from abstinence to regular smoking; yet, the mechanisms mediating this progression remain unclear (Shiffman et al, 1996b;Shiffman et al, 2006). Characterizing the factors that predispose individuals to a smoking lapse may aid in the development of treatments targeted to prevent a lapse and, by proxy, preclude the progression from smoking lapse to relapse.…”

Section: Introductionmentioning

confidence: 99%

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Predictors of smoking lapse in a human laboratory paradigm

et al. 2014

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Rationale During a smoking quit attempt, a single smoking lapse is highly predictive of future relapse. While several risk factors for a smoking lapse have been identified during clinical trials, a laboratory model of lapse was until recently unavailable and, therefore, it is unclear whether these characteristics also convey risk for lapse in a laboratory environment. Objectives The primary study goal was to examine whether real-world risk factors of lapse are also predictive of smoking behavior in a laboratory model of smoking lapse. Methods After overnight abstinence, 77 smokers completed the McKee smoking lapse task, in which they were presented with the choice of smoking or delaying in exchange for monetary reinforcement. Primary outcome measures were the latency to initiate smoking behavior and the number of cigarettes smoked during the lapse. Several baseline measures of smoking behavior, mood, and individual traits were examined as predictive factors. Results Craving to relieve the discomfort of withdrawal, withdrawal severity, and tension level were negatively predictive of latency to smoke. In contrast, average number of cigarettes smoked per day, withdrawal severity, level of nicotine dependence, craving for the positive effects of smoking, and craving to relieve the discomfort of withdrawal were positively predictive of number of cigarettes smoked. Conclusions The results suggest that real-world risk factors for smoking lapse are also predictive of smoking behavior in a laboratory model of lapse. Future studies using the McKee lapse task should account for between subject differences in the unique factors that independently predict each outcome measure.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predictors of smoking lapse in a human laboratory paradigm

et al. 2014

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“…Si bien se midieron algunas variables que pudieran asociarse con mayor o menor probabilidad de presentar recaída 8,9 (tiempo de uso de la TRN, grado de dependencia a la nicotina previo al cese, edad, género, raza y nivel educacional), el modelo explicativo de recaídas es sin duda de mayor complejidad y algunos factores de importancia no fueron incluidos (presencia de trastornos del ánimo y otras adicciones, estrés, apoyo social y niveles de motivación para dejar de fumar) 10,11 y no se explicita la forma en que se midió dependencia. Otro aspecto relacionado a la aplicabilidad de esta intervención tiene que ver con el tiempo de uso de la TRN: sólo un tercio de los usuarios en las fases 1 y 2 la utilizó durante el período recomendado (8 semanas).…”

Section: Comentarios Acerca De La Aplicabilidadunclassified

Análisis crítico de un artículo: terapia de reemplazo de la nicotina, efectividad en un escenario real

2013

Rev. méd. Chile

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“…Smoking lapses, which nearly always result in relapse (Kenford et al, 1994), frequently occur in situations that provoke stress and/or involve the presence of smoking-related cues or activities (Shiffman, Paty, Gnys, Kassel, & Hickcox, 1996). Cessation counseling helps smokers identify high-risk situations and provides them with strategies that can be invoked when those situations occur (e.g., relaxation, avoidance, and distraction).…”

Section: Introductionmentioning

confidence: 99%

I Am Your Smartphone, and I Know You Are About to Smoke: The Application of Mobile Sensing and Computing Approaches to Smoking Research and Treatment

McClernon

Choudhury

2013

Nicotine & Tobacco Research

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Much is known about the immediate and predictive antecedents of smoking lapse, which include situations (e.g., presence of other smokers), activities (e.g., alcohol consumption), and contexts (e.g., outside). This commentary suggests smartphone-based systems could be used to infer these predictive antecedents in real time and provide the smoker with just-in-time intervention. The smartphone of today is equipped with an array of sensors, including GPS, cameras, light sensors, barometers, accelerometers, and so forth, that provide information regarding physical location, human movement, ambient sounds, and visual imagery. We propose that libraries of algorithms to infer these antecedents can be developed and then incorporated into diverse mobile research and personalized treatment applications. While a number of challenges to the development and implementation of such applications are recognized, our field benefits from a database of known antecedents to a problem behavior, and further research and development in this exciting area are warranted.

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Predicting smoking cessation. Who will quit with and without the nicotine patch

Cited by 326 publications

References 17 publications

Predictors of smoking lapse in a human laboratory paradigm

Predictors of smoking lapse in a human laboratory paradigm

Análisis crítico de un artículo: terapia de reemplazo de la nicotina, efectividad en un escenario real

I Am Your Smartphone, and I Know You Are About to Smoke: The Application of Mobile Sensing and Computing Approaches to Smoking Research and Treatment

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