Your browser doesn't support javascript.
loading
What factors reliably predict electronic cigarette nicotine delivery?
Blank, Melissa D; Pearson, Jennifer; Cobb, Caroline O; Felicione, Nicholas J; Hiler, Marzena M; Spindle, Tory R; Breland, Alison.
Afiliação
  • Blank MD; Department of Psychology, West Virginia University, Morgantown, West Virginia, USA mdblank@mail.wvu.edu.
  • Pearson J; Division of Social and Behavioral Science/Health Administration and Policy, University of Nevada Reno, Reno, Nevada, USA.
  • Cobb CO; Department of Health, Behavior, and Society, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA.
  • Felicione NJ; Department of Psychology, Virginia Commonwealth University, Richmond, Virginia, USA.
  • Hiler MM; Department of Psychology, West Virginia University, Morgantown, West Virginia, USA.
  • Spindle TR; Department of Psychology, Virginia Commonwealth University, Richmond, Virginia, USA.
  • Breland A; Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, Baltimore, Maryland, USA.
Tob Control ; 29(6): 644-651, 2020 11.
Article em En | MEDLINE | ID: mdl-31685583
BACKGROUND: The ability of an electronic cigarette (e-cigarette) to deliver nicotine effectively may be dependent on features of the device, the liquid and the user. Some of these features have been examined in previous work (eg, liquid nicotine concentration and puff topography), while others have not (eg, nicotine dependence and demographic characteristics). The purpose of this secondary analysis is to examine such features as predictors of e-cigarette nicotine delivery using a relatively large sample. METHODS: Four studies were combined in which e-cigarette-experienced users (n=63; 89% men; 75% white) and e-cigarette-naïve cigarette smokers (n=67; 66% men; 54% white) took 10 puffs from an eGo-style e-cigarette (~7.3 watts) filled with liquid that had a nicotine concentration of 18, 25 or 36 mg/mL. Thus, held constant across all studies were device features of battery/cartomiser style and power level and the topography parameters of puff number and interpuff interval. Blood was sampled before and after use, and puff topography was measured. Three general linear models were conducted to predict plasma nicotine concentrations (pre-post increase) for: (1) e-cigarette users only, (2) smokers only and (3) both groups combined. Predictor variables included puff duration, puff volume, liquid nicotine concentration, presession plasma nicotine concentration, nicotine dependence score (smokers only), gender and race. RESULTS: In all models tested, longer puff durations and higher liquid nicotine concentrations were associated significantly with increased nicotine delivery (ps<0.05). For e-cigarette users only, higher presession nicotine concentration was associated significantly with increased nicotine delivery (p<0.05). CONCLUSIONS: Puff duration and liquid nicotine concentration may be among the more important factors to consider as regulators attempt to balance e-cigarette safety with efficacy. These findings should be interpreted in the context of devices with relatively low power output, a variable not studied here but likely also directly relevant to product regulation.
Assuntos
Palavras-chave

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Sistemas Eletrônicos de Liberação de Nicotina Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Female / Humans / Male Idioma: En Ano de publicação: 2020 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Sistemas Eletrônicos de Liberação de Nicotina Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Female / Humans / Male Idioma: En Ano de publicação: 2020 Tipo de documento: Article