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Factors Associated with Adherence to Transdermal Nicotine Patches within a Smoking Cessation Effectiveness Trial‡
Published online by Cambridge University Press: 09 March 2017
Abstract
Introduction: Adherence to transdermal nicotine patches, one of the most popular and effective treatments for nicotine dependence, remains very low and is a strong predictor of cessation rates.
Aims: This study examined individual factors related to adherence as well as differences over time between adherent (>85% of daily patch use) and non-adherent participants (<85% of daily patch use).
Methods: We analysed data from 440 participants who received 8 weeks of 21 mg transdermal nicotine and four behavioural counselling sessions within an effectiveness trial that examined the effects of long-term treatment. Multiple logistical regression assessed baseline variables associated with patch adherence and generalised estimating equations (GEE) were used to evaluate changes in craving and withdrawal, depressive and anxiety symptoms, substitute and complementary reinforcers, and side effects between participants who were or were not adherent.
Results: Adherence to patch use was strongly associated with smoking cessation at week 8 (p < 0.05). In a logistic regression model, being female, living with a child or children, and higher self-reported anxiety symptoms were predictive of lower patch adherence (p < 0.05). In the GEE analysis, adherence was significantly associated with a greater reduction in craving, a greater engagement in substitute reinforcers, and a greater decrease in complementary reinforcers over time (p < 0.05).
Conclusions: Difficulties adhering to transdermal nicotine patches may be related to psychiatric comorbidity, difficulty managing nicotine craving, and challenges with engaging in substitute reinforcers and reducing exposure to complementary reinforcers. These constructs may serve as targets for interventions designed to increase treatment adherence.
Trial registration: ClinicalTrials.gov Identifier: NCT01047527
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- Copyright © The Author(s) 2017
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