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Anxiety Sensitivity and Emotion Dysregulation in Dual and Exclusive E-Cigarette Users.

Alison C McLeishTanya SmitLorra GareyMichael J Zvolensky PhD
Published in: Substance use & misuse (2021)
Background: E-cigarette use has increased exponentially over the past decade, particularly among current combustible cigarette users. Preliminary evidence has demonstrated differences across exclusive and dual e-cigarette/combustible cigarette users, such that dual users represent a more clinically severe group. Yet, the extent to which these groups differ on critical transdiagnostic risk factors that may promote and maintain nicotine dependence, such as anxiety sensitivity and emotional dysregulation has yet to be systematically studied. The purpose of the current study was to examine differences between exclusive e-cigarette users and dual e-cigarette and combustible cigarette users in anxiety sensitivity and both global emotion regulation difficulties as well as within specific emotion regulation difficulty domains. Methods: Participants were 192 exclusive e-cigarette users and 315 dual e-cigarette and combustible cigarette users (52.1% female, Mage = 34.8 years, SD = 11.42). Results: Compared to exclusive e-cigarette users, dual users reported higher anxiety sensitivity, overall emotion regulation difficulties, and specific emotion regulation difficulties related to impulse control and lack of access to effective emotion regulation strategies. There were no group differences in emotion regulation difficulties related to emotional clarity, ability to engage in goal-directed behavior when upset, and emotional non-acceptance. Conclusion: The current data suggest that dual users are an at-risk group in terms of emotional processes. Dual users would likely benefit from targeted intervention efforts focused on reducing anxiety sensitivity and improving emotion regulation in order to reduce nicotine consumption and/or promote cessation.
Keyphrases
  • smoking cessation
  • risk factors
  • randomized controlled trial
  • sleep quality
  • machine learning
  • early onset
  • drug delivery
  • big data
  • artificial intelligence
  • drug induced