Department of Psychology, West Virginia University, Morgantown, West Virginia, USA
Division of Social and Behavioral Science/Health Administration and Policy, University of Nevada Reno, Reno, Nevada, USA.
Tob Control. 2020 Nov;29(6):644-651. doi: 10.1136/tobaccocontrol-2019-055193. Epub 2019 Nov 4.
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.
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.
In all models tested, longer puff durations and higher liquid nicotine concentrations were associated significantly with increased nicotine delivery (p<0.05). For e-cigarette users only, higher presession nicotine concentration was associated significantly with increased nicotine delivery (p<0.05).
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.
电子烟（e-cigarette）有效输送尼古丁的能力可能取决于设备、液体和使用者的特点。之前的一些研究已经检验了这些特点中的一些（例如，液体尼古丁浓度和抽吸模式），而其他特点则没有（例如，尼古丁依赖和人口统计学特征）。本二次分析的目的是使用相对较大的样本量，检查这些特点是否可作为电子烟尼古丁输送的预测指标。
合并了四项研究，其中电子烟有经验的使用者（n=63；89%为男性；75%为白人）和电子烟无经验的吸烟人群（n=67；66%为男性；54%为白人）从一款电子烟（~7.3 瓦特）中抽吸 10 口，该电子烟装有尼古丁浓度为 18、25 或 36mg/ml 的液体。因此，在所有研究中，保持设备的电池/雾化器类型和功率水平以及抽吸次数和抽吸间隔的模式参数不变。使用前后采集血液样本，并测量抽吸模式。进行了三个一般线性模型来预测血浆尼古丁浓度（使用前后的增加量）：（1）仅电子烟使用者，（2）仅吸烟者，（3）两组合并。预测变量包括抽吸持续时间、抽吸量、液体尼古丁浓度、使用前的血浆尼古丁浓度、尼古丁依赖评分（仅吸烟者）、性别和种族。
在所有测试的模型中，抽吸持续时间较长和液体尼古丁浓度较高与尼古丁输送量的增加显著相关（p<0.05）。对于仅电子烟使用者，较高的使用前尼古丁浓度与尼古丁输送量的增加显著相关（p<0.05）。
在监管机构试图平衡电子烟的安全性和有效性时，抽吸持续时间和液体尼古丁浓度可能是需要考虑的更重要因素。这些发现应在考虑到输出功率相对较低的设备的情况下进行解释，而这一变量在本研究中未被研究，但也可能直接与产品监管相关。
