Electronic nicotine delivery systems, which include electronic cigarettes (e-cigarettes), vape pens, vapes, vaporizers, e-hookah, e-pipes, and e-cigars, are a growing category of tobacco products. On the U.S. market since 2007,… Click to show full abstract
Electronic nicotine delivery systems, which include electronic cigarettes (e-cigarettes), vape pens, vapes, vaporizers, e-hookah, e-pipes, and e-cigars, are a growing category of tobacco products. On the U.S. market since 2007, past 30-day use of e-cigarettes has surpassed use of conventional cigarettes, with the rate of 11.3% among high school students (8.0% for cigarettes) (Jamal et al., 2017; Syamlal, King, and Mazurek, 2017). In 2016, 15.4% of adults aged 18 and over had ever used e-cigarettes, with 3.2% of adults currently using e-cigarettes. Among young adults, 18–24 years old, 23.5% have ever used an e-cigarette (QuickStats, 2017). E-cigarettes aerosolize “e-liquids,” which may or may not contain nicotine. They also contain flavorings, propylene glycol, vegetable glycerin, and numerous other ingredients. E-cigarettes come in a multitude of sweet and fruity flavors (Brown, Luo, Isabelle, & Pankow, 2014), as well as mint/menthol and tobacco flavors, to name a few. While the flavors are sometimes marketed using conventional descriptors such as cherry, grape, cupcake, tobacco, or coffee, often the flavor names are more nuanced, such as churros, Grandmaster or Thug Juice (Salter, 2015), making it difficult to discern the actual flavors contained within the product. E-cigarettes also come in a plethora of nicotine concentrations, ranging from 0 mg/mL to 40 mg/mL, or 4.0%. Further, e-cigarettes come in a wide range of device types and accompanying parts and components, from the 1st generation cig-a-likes that appear more like combustible cigarettes to disposable products to new later generation devices that include tanks and mods (BarringtonTrimis et al., 2017). The shapes and functions also vary as the device becomes more sophisticated. To add to the complication, the terms of these products have greatly varied as described earlier (e.g., ENDS, ecigarettes, vape pens, e-hookah). The different e-cigarette devices also vary in their efficiency in delivering nicotine to the bloodstream (Farsalinos et al., 2014), with the newer devices tending to be more efficient. In addition, users find it easier to use e-cigarettes in their homes or public space because the vapors disappear quickly without smoke or cigarette smells. The devices are usually ready-to-use instantly; therefore, users can easily vape a few puffs on various occasions and throughout the day, sometimes without realizing how much they actually vaped (Kim, Davis, Dohack, & Clark, 2017). As such, it has been a challenge to accurately measure e-cigarette use including frequency and quantity, level of exposure to nicotine and other toxins, and perceptions of risks associated with e-cigarettes. Given these complicated issues associated with e-cigarette products, there are specific challenges facing researchers who want to accurately measure levels of e-cigarette use, dependence and addiction, and knowledge, attitudes, and perceptions toward e-cigarettes. Simply modifying existing measures of conventional cigarette use, dependence, and knowledge, attitudes, and perceptions is insufficient. For example, there are no current standardized measures of e-cigarette use that consider: (a) different terminology that apply to users in different age groups (e.g., e-cigarettes for adults versus vape or vape pens for youth), (b) accurate descriptions of use patterns, including the number of puffs or use throughout the day; (c) measures of quantity or intensity of use; and (d) measures of nicotine or flavor consumption. Similarly, we have few validated and agreed upon measures of nicotine dependence for ecigarettes. While many investigators have adapted measures such as the Fagerström Test for Nicotine Dependence (Heatherton, Kozlowski, Frecker, & Fagerstrom, 1991; Pomerleau, Majchrezak, & Pomerleau, 1989) or the Hooked on Nicotine Checklist (DiFranza et al., 2002), we have virtually no data to determine whether such adapted measures correctly assess nicotine dependence for e-cigarettes. In addition, while there are studies showing a relationship between perceptions of risks
               
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