Abstract
Background:
E-cigarette device and liquid characteristics, such as electrical power output and liquid nicotine concentration, determine the rate at which nicotine is emitted from the e-cigarette (i.e., nicotine flux), and thus are likely to influence user nicotine dependence. We hypothesize that nicotine flux would be associated with the e-cigarette dependence scale (EDS) among pod-based and disposable e-cigarette products.
Methods:
Data were obtained from online panel participants between 18 and 65 years of age, who had indicated that they were either former or current e-cigarette users and resided within the United States (N=1036). To be included in these analyses, participants had to provide information regarding device type (pod-based or disposable), power (watts), and nicotine concentration (mg/mL), from which we could determine nicotine flux (μg/s) (N=666). To assess the relationship between nicotine flux and EDS, a series of multivariable linear regressions were conducted. Each model was separated by device type and adjusted for by age and past 30-day e-cigarette use.
Results:
Greater nicotine flux was associated with higher EDS scores among pod-based e-cigarette users (beta = 0.19, SE = 0.09, p-value = 0.043), but not users of disposable e-cigarettes. Neither power nor nicotine concentration were associated with EDS scores among users of either e-cigarette device type.
Conclusion:
Results support the hypothesis that nicotine flux is positively associated with nicotine dependence in a sample of current users of pod-based and disposable e-cigarettes.
INTRODUCTION
In recent years, electronic cigarette (e-cigarette) use has increased substantially. Most e-cigarettes are battery-powered and heat a liquid solution typically composed of propylene glycol (PG), vegetable glycerin (VG), flavorings, and nicotine that becomes an aerosol that users inhale (Breland et al., 2017; Grana et al., 2014). There is considerable heterogeneity across e-cigarette devices and liquids – especially in their ability to deliver nicotine to the user. While some e-cigarette device/liquid combinations deliver very little nicotine, others may exceed the nicotine delivery profiles of combustible cigarettes (Cobb et al., 2015). Earlier e-cigarette device/liquid combinations were ineffective in delivering nicotine (Bullen et al., 2010; Vansickel et al., 2010), while later combinations, including pod-based e-cigarettes, can deliver nicotine as efficiently as a cigarette (Hajek et al., 2020; Hiler et al., 2017; Wagener et al., 2017).
E-cigarette characteristics, such as power and liquid nicotine concentration, influence nicotine emission and delivery (Hiler et al., 2020; Talih et al., 2017). All other things being equal, increasing power allows more liquid to be aerosolized, and thus, greater aerosol production per puff (Talih et al., 2017). Studies using machines to generate puff behavior demonstrate that increasing liquid nicotine concentration also results in greater nicotine emission (Talih et al., 2015). Perhaps not surprisingly, nicotine delivery to users’ blood is higher when liquid nicotine concentration is increased (Dawkins et al., 2016; Hiler et al., 2017; Phillips-Waller et al., 2021). Device power and liquid nicotine concentration also interact, such that higher-powered devices (i.e., ≥70 W) can deliver nicotine as effectively as a combustible cigarette (i.e., ~80-100 mg/s) even when filled with low concentration liquids (i.e., <4 mg/ml nicotine) (Wagener et al., 2017). Nicotine flux (i.e., nicotine emitted per puff second; denoted as μg/s) is a metric that simultaneously accounts for device (i.e., battery voltage, coil resistance, coil diameter) and liquid (i.e., PG/VG ratio, nicotine concentration) characteristics that influence it (Eissenberg & Shihadeh, 2015; Talih et al., 2017). A mathematical model of e-cigarette nicotine flux explains >80% of the variability in nicotine emission rate (Talih et al., 2017), and thus, can be used to predict nicotine flux when it cannot be measured experimentally in the laboratory provided certain characteristics of the device and liquid are known.
Pod-based e-cigarettes became available to the United States market in 2015, with the introduction of JUUL (McKelvey et al., 2018). The JUUL device operates with ~4.08 watts maximal power output (Talih et al., 2019) and its “pods” are filled with a liquid that had higher nicotine concentration relative to other e-cigarette liquids available at the time (Spindle & Eissenberg, 2018). Product labels for JUUL indicate that pods contain 59 mg/mL of nicotine (McKelvey et al., 2018), though independent investigation reveals a mean concentration of 65-69 mg/ml (Talih et al., 2019, 2020). A recently published systematic review finds that the highly efficient nicotine delivery of pod-based e-cigarettes is associated with nicotine dependence (Lee et al., 2020). One study demonstrates that among adolescents who reported past-week e-cigarette use, pod-based e-cigarette users exhibited more signs of nicotine dependence, relative to those who did not use pod-based e-cigarettes (Boykan et al., 2019).
Research suggests that e-cigarette device and liquid characteristics influencing nicotine delivery are also likely to impact behavioral outcomes related to nicotine dependence. To date, a few small studies have evaluated measures of nicotine dependence in users of JUUL products (Nardone et al., 2019; Yingst et al., 2019, 2021). While one small-sample study (n=6) suggests that JUUL user dependence, as measured with the Penn State Electronic Cigarette Dependence Index (PSECDI), is greater than that reported by other e-cigarette users (Yingst et al., 2019), a follow up report (N=76) (Yingst et al., 2021) using the same measure suggests JUUL users report low (PSECDI score between 4 and 8) to medium (PSECDI score between 9 and 12) dependence. Other studies measuring nicotine dependence in JUUL users have reported user dependence levels to be less than cigarette smokers (Nardone et al., 2019; Yingst et al., 2021).
Outside of the literature focused on JUUL, studies investigating associations between power, nicotine concentration, and measures of nicotine dependence are even more limited (Etter & Eissenberg, 2015; Rudy et al., 2017), though a reasonable hypothesis is that e-cigarette device and liquid combinations that produce a greater nicotine emission rate (i.e., greater nicotine flux) produce greater e-cigarette dependence. The objective of this research was to determine how e-cigarette device and liquid characteristics, such as power, nicotine concentration, and nicotine flux, are associated with measures of nicotine dependence, as measured by the E-Cigarette Dependence Scale (EDS) (Morean et al., 2019). Based upon the literature, we hypothesized that: 1) nicotine flux would be predictive of EDS score, and 2) nicotine flux will be a better predictor of EDS score, relative to the effects of power and nicotine concentration alone - even after adjusting for demographic and user behavior characteristics. The reason for this being that nicotine flux calculations consider the joint effects of power and nicotine concentration.
METHODS
Participants.
Participants were former and current e-cigarette users, aged 18 to 65 years, residing in the United States, recruited for an anonymous online study through Qualtrics Panels (N=1036). Qualtrics panel participants are recruited from various sources, including website intercept recruitment, member referrals, email lists, gaming sites, customer loyalty web portals, and social media. Although sampling from a variety of sources helps to ensure that the overall sampling frame is not overly reliant on any particular segment of the population, online panel participants may not be representative of the United States population. Though, there is growing evidence to suggest that samples recruited through online panels may be as nationally representative as participants recruited using more traditional methods (Farrell & Petersen, 2010; Heen et al., 2014; Miller et al., 2020).
To be included in the current analyses, participants had to have provided information on the e-cigarette device that they had used the most regularly. Non-current e-cigarette users were asked to answer questions based upon their experiences when they used e-cigarettes in the past. This information was matched to an existing database of device electrical characteristics, including voltage and resistance to calculate device power, nicotine concentration, and nicotine flux. Given the difficulty in obtaining reliable data on modifiable e-cigarette devices such as box mods and vape pens, only users of pod-based and disposable e-cigarette devices were included in these analyses (N=666). To ensure fidelity and validity of participant responses, we checked and removed from the data any response duplicates, evidence of rapidly completing the survey, and implausible values on variables of interest. Participants received compensation in the form of Qualtrics credits, which are used to purchase gift cards or other items through the Qualtrics Panels portal. Researchers are not informed of the specific number of credits that participants receive. This survey was exempt from Institutional Review Board approval. Data were collected in January 2021.
Predictor variables.
Predictor variables included the e-cigarette characteristics of device type (pod-based, disposable), power (watts), liquid nicotine concentration (mg/mL), and nicotine flux (μg/s). Device type was derived from participant’s self-reported most regularly used device type (by selecting one of the following: cig-a-like, e-hookah, vape pen / eGo style device, rebuildable / mechanical mod or box mod, e-cigar, e-pipe, pod mod such as JUUL or other similar device, or disposable vape such as Posh, Puff Bar, Mojo or other similar device) and model/brand (by writing in a response to the question, “What is the name and/or brand of the e-cigarette / vaping device you have used the most regularly?”). Information was verified by research staff and matched to an existing database of device electrical characteristics including voltage and resistance as measured by a multimeter (Soule et al., under review). Device wattage was calculated using the formula Power (watts) = Voltage (volts)2 / Resistance (ohms). For devices where multimeter readings were unavailable, the research team conducted an internet search of manufacturer and third-party websites that display device specifications. Where device electrical characteristics could not be determined using previous measurement with a multimeter or via internet searches (e.g., for many disposables), devices were purchased and disassembled for measurement of internal electrical characteristics using a multimeter using a similar strategy as described elsewhere (Talih et al., 2019). To determine nicotine concentration, the research team conducted an internet search of manufacturer and third-party websites for these details, where self-report was not available. Using a combination of self-report measures, validated by either multimeter measurement or internet search results, nicotine flux was estimated, as reported elsewhere (Talih et al., 2017). Power, nicotine concentration, and nicotine flux were categorized into low, medium, and high levels, based upon calculated terciles of the set of unique devices reported by participants (i.e., devices reported by multiple participants were only counted once).
Outcome measures.
Outcome measures included participants’ E-Cigarette Dependence Scale (EDS) score. The EDS score was derived by calculating the mean of the following four items: 1) I find myself searching for my e-cigarette without thinking about it, 2) I drop everything to go out and get e-cigarettes or e-juice, 3) I vape more before going into a situation where vaping is not allowed, and 4) When I haven’t been able to vape for a few hours, the craving gets intolerable. Potential responses included: never (0), rarely (1), sometimes (2), often (3), and almost always (4). A greater EDS score is indicative of greater level of nicotine dependence. This 4-item measure has been previously associated with the 9-item PSECDI scale (r = 0.74, p-value of <0.001) (Morean et al., 2019). Daily e-cigarette use has also been found to be associated with greater e-cigarette dependence (t = −4.36, p-value <0.001) (Morean et al., 2019).
Demographic and user behavior characteristics.
Demographic characteristics were assessed via self-report, including age (reported in years and categorized into: 18-20 years, 21-24 years, 25-34 years, 35-65 years), gender (male, female), race (categorized into: Non-Hispanic White, Hispanic, Non-Hispanic Black, and Non-Hispanic Asian; other race/ethnicity groups were excluded from the current analysis due to small sample size), education (less than high school vs. high school diploma or greater), and income (less than $50,000 vs. $50,000 or greater). User behavior was measured by e-cigarette and cigarette use within the past 30 days (did not use within the past 30 days vs. used within the past 30 days).
Statistical analyses.
Descriptive statistics across sociodemographic variables and e-cigarette device and liquid characteristics are provided for the overall sample. Descriptive statistics are also separated by e-cigarette device type, which has been previously associated with other measures of nicotine dependence (Tackett et al., 2021). Chi-square tests were used to compare characteristics by device type at a significance level of p-value <0.05. Multivariable linear regression models were used to predict EDS score, controlling for covariates (age, past 30-day e-cigarette use). To make comparisons between models including power, nicotine concentration, and nicotine flux as predictors of nicotine dependence, separate models were run. Model fit was determined from R-square and p-values. All analyses were conducted using SAS 9.4 (SAS Institute; Cary, NC). Two additional sets of sensitivity analyses were conducted, in order to determine the effects of past 30 day use of e-cigarettes and cigarettes on EDS score. The first set of sensitivity analyses excluded non-current e-cigarette users, who reported not using e-cigarettes in the past 30 days. The second set of sensitivity analyses included past 30-day use of cigarettes as an additional predictor of EDS score to assess the potential role of dual use on EDS score.
RESULTS
Sample characteristics.
As shown in Table 1, nearly half (48.4%) of participants were female. Regarding age, 30.2% were 18-20 years, 31.7% were 21-24 years, 18.2% were 25-34 years, and 20.0% were 35-65 years. The majority of the sample was non-Hispanic White (54.3%), followed by Hispanic (20.7%), non-Hispanic Black (15.6%), and non-Hispanic Asian (9.4%). Additionally, 89.8% had indicated that they had earned a high school diploma or greater, and 48.7% indicated that they had earned an annual income of $50,000 or greater. Compared to the overall sample obtained from the online Qualtrics panel, this analytic sample did not differ in regard to age, race/ethnicity, income, education level, or cigarette use behavior.
Table 1.
Sample Characteristics for Full Sample and by E-Cigarette Device Type (Pod-Based and Disposable E-Cigarettes)
| Total Sample (N=666) N (%) |
Pod-Based Users Only (n=539) N (%) |
Disposable Users Only (n=127) N (%) |
p-value | |
|---|---|---|---|---|
| Gender | <0.001 | |||
| Male | 344 (51.7%) | 302 (56.0%) | 42 (33.1%) | |
| Female | 322 (48.4%) | 237 (44.0%) | 85 (66.9%) | |
| Age Category | <0.001 | |||
| 18 – 20 years | 201 (30.2%) | 130 (24.1%) | 71 (55.9%) | |
| 21 – 24 years | 211 (31.7%) | 175 (32.5%) | 36 (28.4%) | |
| 25 – 34 years | 121 (18.2%) | 114 (21.2%) | 7 (5.5%) | |
| 35 – 65 years | 133 (20.0%) | 120 (22.3%) | 13 (10.2%) | |
| Race | 0.499 | |||
| White, Non-Hispanic | 341 (54.3%) | 280 (54.8%) | 61 (52.1%) | |
| Hispanic | 130 (20.7%) | 81 (15.9%) | 17 (14.5%) | |
| Black, Non-Hispanic | 98 (15.6%) | 100 (19.6%) | 30 (25.6%) | |
| Asian, Non-Hispanic | 59 (9.4%) | 50 (9.8%) | 9 (7.7%) | |
| Education | 0.009 | |||
| Less than high school | 68 (10.2%) | 47 (8.7%) | 21 (16.5%) | |
| High school diploma or greater | 598 (89.8%) | 492 (91.3%) | 106 (83.5%) | |
| Income | 0.062 | |||
| Less than $50,000 | 327 (51.3%) | 260 (49.6%) | 67 (59.3%) | |
| $50,000 or greater | 310 (48.7%) | 264 (50.4%) | 46 (40.7%) | |
| Cigarette Use Behavior | 0.134 | |||
| Did not use within the past 30 days | 434 (65.2%) | 344 (63.8%) | 90 (70.9%) | |
| Used within the past 30 days | 232 (34.8%) | 195 (36.2%) | 37 (29.1%) | |
| E-Cigarette Use Behavior | 0.348 | |||
| Did not use within past 30 days | 88 (13.2%) | 68 (12.6%) | 20 (15.8%) | |
| Used within past 30 days | 578 (86.8%) | 471 (87.4%) | 107 (84.3%) | |
| E-Cigarette Device and Liguid Characteristics | ||||
| Power (watts, range: 4-16) | <0.001 | |||
| Low (range: 4.0 to <8.2) | 330 (49.6%) | 330 (61.2%) | 0 (0.0%) | |
| Medium (range: 8.2 to <9.3) | 127 (19.1%) | 0 (0.0%) | 127 (100.0%) | |
| High (range: 9.3 to 16.0) | 209 (31.4%) | 209 (38.8%) | 0 (0.0%) | |
| Nicotine Concentration (mg / mL, range : 25.0-86.9) | <0.001 | |||
| Low (range: 25 to <60) | 224 (33.6%) | 209 (38.8%) | 15 (11.8%) | |
| Medium (range: 60 to <83.4) | 340 (51.1%) | 330 (61.2%) | 10 (7.9%) | |
| High (range: 83.4 to 86.9) | 102 (15.3%) | 0 (0.0%) | 102 (80.3%) | |
| Nicotine Flux (nicotine emitted in μg/s) | <0.001 | |||
| Low (range: 56.3 to < 76.3) | 330 (49.6%) | 330 (61.2%) | 0 (0.0%) | |
| Medium (range: 76.3 to < 93.8) | 70 (10.5%) | 55 (10.2%) | 15 (11.8%) | |
| High (range: 93.8 to 144.6) | 266 (39.9%) | 154 (28.6%) | 154 (88.2%) |
Since no statistically significant differences were found across device type, nicotine flux, nicotine concentration, or power across current and non-current e-cigarette users, both current and non-current users were included in analyses. Current users made up 86.8% of the overall sample and reported using e-cigarettes 16.7 (SD = 10.2) days, on average, during the past 30 days. Although 6.5% of current users indicated that they did not regularly use e-cigarettes, 47.8% of current users had used e-cigarettes for a year, 40.0% had used e-cigarettes between a year and less than five years, and 5.7% had used e-cigarettes for five years or more.
E-cigarette device and liquid characteristics.
A total of 13 unique devices were included in the analyses (e.g., Aspire Breeze, blu, Hyde, JUUL, Mr. Fog, NJOY Ace, Posh, Puffbar, SMOK Nord, SMOK Novo, Suorin Air, Vaporesso, and Vuse Alto). Regarding device type, 80.9% reported that they used a pod-based e-cigarette and 19.1% used a disposable e-cigarette. For pod-based e-cigarette users, the top three most regularly used brands were: JUUL (60.6%), SMOK (13.4%), and Vuse (9.1%). The top three most regularly used brands among disposable e-cigarette users were: Puff Bar (55.8%), Hyde (6.1%), and Posh (6.1%). Power of e-cigarette devices used by the sample ranged between 4-16 watts, with almost half (49.6%) using low-powered devices (range: 4.0 to <8.2 watts). Nicotine concentration ranged from 25.0 to 86.9 mg/mL, with most (51.1%) using e-cigarette liquids with nicotine concentrations in the medium range (60 to <83.4 mg/mL). Nicotine flux ranged from 56.3 to 144.6 of nicotine emitted in μg/s), with almost half (49.6%) using a low nicotine flux device (range: 56.3 to <76.3 μg/s).
Differences in sociodemographic characteristics by device type.
As shown in Table 1, differences across age, power, nicotine concentration, nicotine flux (all p-values <0.0001), and education (p-value = 0.0089) were found by device type. A greater percentage of those aged 18-20 years used disposable e-cigarettes (55.9%), relative to other age groups. Among pod-based e-cigarette users, age was more evenly distributed. A greater percentage of pod users reported having a high school diploma or greater, relative to disposable e-cigarette users (91.3% vs. 83.5%).
Differences in e-cigarette device and liquid characteristics by device type.
Regarding power, all disposable e-cigarettes fell within the medium range (8.2 to <9.3 watts), while most users of pod-based e-cigarettes (61.2%) used a device in the low range (4.0 to <8.2 watts). The majority (61.2%) of pod-based e-cigarette users reported nicotine concentration levels in the medium range (60 to <83.4 mg/mL), while most (80.3%) disposable e-cigarette users had devices with high nicotine concentration levels (range: 83.4 to 86.9 mg/mL). Regarding nicotine flux, most (61.2%) users of pod-based e-cigarettes used a device in the low range (56.3 to <76.3 μg/s), while most (88.2%) users of disposable e-cigarettes used a device in the high range (93.8 to 144.6 μg/s), which greater than the nicotine flux of a combustible cigarette (~80-100 μg/s).
Statistical models predicting nicotine dependence.
As shown on Table 2, bivariate associations with EDS score were found for age category, e-cigarette device type, and past 30-day e-cigarette use. Age category (21-24 years: beta = 0.36, SE = 0.10, p-value < 0.001; age 25-34: beta = 0.52, SE = 0.11, p-value <0.001; 35-65 years: beta = 0.32, SE = 0.11, p-value = 0.0004) was positively associated with EDS score. Using pod-based e-cigarettes (beta = 0.19, SE = 0.10, p-value = 0.048) and using e-cigarettes in the past 30 days (beta = 0.90, SE = 0.11, p-value <0.001) were also positively associated with EDS score.
Table 2.
Bivariate Associations with E-Cigarette Dependence Scale (EDS) Scores
| F-value, p-value | Estimate | SE | p-value | |
|---|---|---|---|---|
| Gender (Reference = Female) | 0.66, 0.4186 | 0.06 | 0.08 | 0.4186 |
| Age Category (Reference = 18-20 years) | 8.44, <0.001 | |||
| 21 – 24 years | 0.36 | 0.10 | 0.0002 | |
| 25 – 34 years | 0.52 | 0.11 | <0.0001 | |
| 35 – 65 years | 0.32 | 0.11 | 0.0039 | |
| Race/Ethnicity (Reference = White, Non-Hispanic) | 1.64, 0.1800 | |||
| Hispanic | −0.04 | 0.11 | 0.7004 | |
| Black, Non-Hispanic | −0.20 | 0.10 | 0.0437 | |
| Asian, Non-Hispanic | 0.07 | 0.14 | 0.6217 | |
| Education (Reference = High School Diploma or greater) | 0.63, 0.4280 | |||
| Less than high school | −0.10 | 0.13 | 0.4280 | |
| Income (Reference = $50,000 or greater) | 0.00, 0.9621 | |||
| Less than $50,000 | 0.00 | 0.08 | 0.9621 | |
| E-Cigarette Device Type (Reference = Disposable) | 3.90, 0.0486 | |||
| Pod mod | 0.19 | 0.10 | 0.0486 | |
| Past 30-Day E-Cigarette Use (Reference = No) | 68.68, <0.0001 | |||
| Yes | 0.90 | 0.11 | <0.0001 |
These predictors were included in the multivariable linear models shown in Tables 3-5, which show nicotine flux, power, and nicotine concentration as the main predictors of EDS score, respectively. Across each of these multivariable linear models, age category and past 30-day e-cigarette use were associated with greater EDS score. For models including nicotine flux (Table 3), the age category 25-34 years was positively associated with EDS score among users of pod-based e-cigarettes (beta = 0.33, SE = 0.12, p-value = 0.001) and users of disposable e-cigarettes (beta = 0.81, SE = 0.38, p-value = 0.033). The age category 21-24 years was also positively associated with EDS score among users of pod-based e-cigarettes (beta = 0.26, SE = 0.11, p-value = 0.018). Past 30-day e-cigarette use was positively associated with greater EDS score among pod-based e-cigarette users (beta = 0.85, SE = 0.12, p-value <0.001) and disposable e-cigarette users (beta = 0.67, SE = 0.24, p-value = 0.006). Similar estimates for age category and past 30-day e-cigarette use were found for models predicting power (Table 4) and nicotine concentration (Table 5).
Table 3.
Predictive Models for E-cigarette Dependence Scale (EDS) Scores Including Nicotine Flux
| Model 1 (Overall Sample) β estimate, SE, p-value |
Model 2 (Pod-Based Users Only) β estimate, SE, p-value |
Model 3 (Disposable Users Only) β estimate, SE, p-value |
|
|---|---|---|---|
| Intercept | 0.49, 0.15, 0.001 | 0.65, 0.13, <0.001 | 1.09, 0.35, 0.002 |
| Nicotine Flux (μg/s) | |||
| Low (range: 56.3 to <76.3) | Reference | Reference | N/A |
| Medium (range: 76.3 to < 93.8) | 0.09, 0.13, 0.482 | −0.02, 0.14, 0.877 | Reference |
| High (range: 93.8 to 144.6) | 0.15, 0.09, 0.093 | 0.19, 0.09, 0.043 | −0.40, 0.27, 0.132 |
| Age Category | |||
| 18 – 20 years | Reference | Reference | Reference |
| 21 – 24 years | 0.28, 0.10, 0.003 | 0.26, 0.11, 0.018 | 0.37, 0.20 0.059 |
| 25 – 34 years | 0.37, 0.11, 0.001 | 0.33, 0.12, 0.007 | 0.81, 0.38, 0.033 |
| 35 – 65 years | 0.20, 0.11, 0.077 | 0.22, 0.12, 0.077 | 0.09, 0.29, 0.756 |
| E-cigarette Device Type | |||
| Pod mod | 0.17, 0.11, 0.131 | N/A | N/A |
| Disposable | Reference | N/A | N/A |
| E-cigarette Use Behavior | |||
| Did not use e-cigarettes in the past 30 days | Reference | Reference | Reference |
| Used e-cigarettes in past 30 days | 0.82, 0.11, <0.001 | 0.85, 0.12, <0.001 | 0.67, 0.24, 0.006 |
| Model Fit Statistics | |||
| F-value | 12.63 | 11.81 | 3.92 |
| R-Square | 0.118 | 0.118 | 0.139 |
| p-value | <0.001 | <0.001 | 0.003 |
Note. Model 1 includes the main effects of nicotine flux, age, e-cigarette device type, and past 30-day e-cigarette use. Models 2 and 3 are separate models for pod-based and disposable e-cigarette users (respectively), including the main effects of nicotine flux, age, and past 30-day e-cigarette use.
Table 5.
Predictive Models for E-cigarette Dependence Scale (EDS) Scores Including Nicotine Concentration
| Model 1 (Overall Sample) β estimate, SE, p-value |
Model 2 (Pod-Based Users Only) β estimate, SE, p-value |
Model 2 (Disposable Users Only) β estimate, SE, p-value |
|
|---|---|---|---|
| Intercept | 0.58, 0.13, <0.001 | 0.65, 0.13, <0.001 | 0.66, 0.22, 0.004 |
| Nicotine Concentration (mg/mL) | |||
| Low (range: 25 to <60) | 0.38, 0.21, 0.076 | 0.13, 0.08, 0.113 | 0.42, 0.27, 0.118 |
| Medium (range: 60 to <83.4) | 0.24, 0.21, 0.268 | Reference | 0.22, 0.32, 0.498 |
| High (range: 83.4 to 86.9) | Reference | N/A | Reference |
| Age Category | |||
| 18 – 20 years | Reference | Reference | Reference |
| 21 – 24 years | 0.28, 0.10, 0.003 | 0.25, 0.11, 0.023 | 0.37, 0.29, 0.061 |
| 25 – 34 years | 0.36, 0.11, 0.001 | 0.31, 0.12, 0.011 | 0.81, 0.38, 0.035 |
| 35 – 65 years | 0.19, 0.11, 0.086 | 0.18, 0.12, 0.127 | 0.09, 0.29. 0.769 |
| E-Cigarette Device Type | |||
| Pod mod | −0.15, 0.20, 0.436 | Reference | Reference |
| Disposable | Reference | N/A | N/A |
| E-cigarette Use Behavior | |||
| Did not use e-cigarettes in the past 30 days | Reference | Reference | Reference |
| Used e-cigarettes in past 30 days | 0.82, 0.11, <0.001 | 0.86, 0.12, <0.001 | 0.67, 0.24, 0.006 |
| Model Fit Statistics | |||
| F-value | 14.71 | 13.77 | 3.33 |
| R-Square | 0.118 | 0.114 | 0.143 |
| p-value | <0.001 | <0.001 | 0.005 |
Note. Model 1 includes the main effects of power, nicotine concentration, age, e-cigarette device type, and past 30-day e-cigarette use. Models 2 and 3 are separate models for users of pod-based and disposable e-cigarettes, including the main effects of nicotine concentration, age, and past 30-day e-cigarette use.
Table 4.
Predictive Models for E-cigarette Dependence Scale (EDS) Scores Including Power
| Model 1 (Overall Sample) β estimate, SE, p-value |
Model 2 (Pod-Based Users Only) β estimate, SE, p-value |
|
|---|---|---|
| Intercept | 0.66, 0.12, <0.001 | 0.65, 0.13, <0.001 |
| Power (watts) | ||
| Low (range: 4.0 to <8.2) | Reference | Reference |
| Medium (range: 8.2 to <9.3) | −0.02, 0.10, 0.812 | N/A |
| High (range: 9.3 to 16.0) | 0.14, 0.08, 0.106 | 0.13, 0.08, 0.113 |
| Age Category | ||
| 18 – 20 years | Reference | Reference |
| 21 – 24 years | 0.28, 0.10, 0.003 | 0.25, 0.11, 0.023 |
| 25 – 34 years | 0.36, 0.11, 0.001 | 0.31, 0.12, 0.011 |
| 35 – 65 years | 0.19, 0.11, 0.086 | 0.18, 0.12, 0.127 |
| E-cigarette Use Behavior | ||
| Did not use e-cigarettes in the past 30 days | Reference | Reference |
| Used e-cigarettes in past 30 days | 0.82, 0.11, <0.001 | 0.86, 0.12, <0.001 |
| Model Fit Statistics | ||
| F-value | 14.71 | 13.77 |
| R-Square | 0.118 | 0.114 |
| p-value | <0.0001 | <0.0001 |
Note. Model 1 includes the main effects of power, age, e-cigarette device type, and past 30-day e-cigarette use. Model 2 includes the same predictors as Model 1 but limited to users of pod-based e-cigarette devices. A model that was limited to users of disposable e-cigarette devices could not be run due to the lack of variability in wattage across devices in that product category.
As shown in Table 3, high nicotine flux was associated with greater EDS score relative to low nicotine flux (estimate = 0.19, SE = 0.09, p-value = 0.0433) among pod-based e-cigarette users. No such main effect was observed among disposable e-cigarette users (estimate = −0.40, SE = 0.27, p-value = 0.132).
As shown in Table 4, power was not associated with EDS score in the overall sample (beta = −0.02, SE = 010, p-value = 0.812 for medium vs low power; beta = 0.14, SE = 0.08, p-value = 0.106 for high vs. low power) or among pod users (beta = 0.03, SE = 0.08, p-value = 0.113 for high vs. low power). Due to limited variability in power settings among disposable e-cigarette devices (i.e., all disposable e-cigarettes had power values within the medium range of 8.2 to <9.3 watts; see Table 1), no results are presented.
As shown in Table 5, nicotine concentration was not associated with EDS score in the overall sample (beta = 0.38, SE = 0.21, p-value = 0.076 for high vs. low nicotine concentration; beta = 0.24, SE = 0.21, p-value = 0.268 for medium vs. low nicotine concentration), among pod users (beta = 0.13, SE = 0.08, p-value =0.113 for low vs. medium nicotine concentration), or among disposable e-cigarette users (beta = 0.42, SE = 0.27, p-value = 0.118 for low vs. high nicotine concentration; beta = 0.22, SE = 0.32, p-value = 0.498 for medium vs. high nicotine concentration).
Sensitivity analyses.
Results from the first set of sensitivity analyses demonstrated similar beta estimates and directions of effect for nicotine flux, power, and nicotine concentration on EDS scores. Excluding non-current users from these analyses resulted in a reduction of the variance explained for models predicting EDS scores. Specifically, models including only current e-cigarette users explained between 1.4% to 2.8% of the variance in EDS score, while models including non-current and current e-cigarette users explained between 11.4% to 14.3% of the variance in EDS score. Additionally, associations between nicotine flux and EDS scores did not achieve conventional levels of statistical significance. This is unsurprising, given the decrease in sample size (n=578) and corresponding reduction in statistical power to detect effects. Results from these sensitivity analyses suggest that e-cigarette use within the past 30 days is an important indicator of EDS scores.
Additional sensitivity analyses including the effect of past 30-day use of cigarettes were also conducted. These models accounted explained between 12.4% and 13.3% of the variance in EDS score. Like the first set of sensitivity analyses, these analyses demonstrated similar beta estimates and directions of effect for nicotine flux, power, and nicotine concentration on EDS scores. Significant associations were found between age (beta = 0.24, SE = 0.11, p-value = 0.027 for age 18 to 20, beta = 0.26, SE = 0.13, p-value = 0.038 for age 21 to 24), past 30-day use of cigarettes (beta = 0.83, SE = 0.12, p-value <0.001), and past 30-day use of e-cigarettes (beta = 0.21, SE = 0.10, p-value = 0.021) and EDS scores among pod-based e-cigarette users. Meanwhile, only past 30-day use of e-cigarettes (beta = 0.68, SE = 0.24, p-value = 0.005) was associated with EDS score among disposable e-cigarette users. In these models, associations between nicotine flux and EDS scores were not statistically significant. Results suggest that past 30-day use of cigarettes may be an important indicator for EDS among pod-based e-cigarette users.
DISCUSSION
This analysis sought to determine associations between e-cigarette device and liquid characteristics that may influence nicotine dependence, as measured using the E-cigarette Dependence Scale. Given the significant differences in the distribution of power, nicotine concentration, and nicotine flux across pod-based and disposable e-cigarette devices, separate models were run by device type. Generally, pod-based e-cigarettes had lower nicotine flux and nicotine concentration relative to disposable e-cigarettes. Power was concentrated in the medium range for disposables but varied across low and high levels for pod-based e-cigarettes. Only nicotine flux was found to be associated with EDS score among pod-based e-cigarette users, such that high nicotine flux was associated with greater EDS score. Age and past 30-day e-cigarette use were also associated with greater EDS score.
Given that higher nicotine flux is expected to be associated with greater nicotine dependence, we hypothesized that disposable e-cigarettes, which have a higher nicotine flux, would yield a positive association with nicotine dependence. However, results demonstrated a null relationship between nicotine flux and nicotine dependence among disposable e-cigarette users. Future research should investigate why this predicted relationship was not observed. One explanation for this finding is that there is a ceiling effect. This would suggest that nicotine flux increases with nicotine dependence to a certain point, at which the effects of nicotine flux become aversive.
An alternative explanation is that nicotine flux did not produce nicotine dependence in the way that we were specifically measuring it for this study, at least among users of disposable e-cigarettes. The validated, four-item dependence measure included items asking about searching for an e-cigarette without thinking about it, dropping everything to go out and get e-cigarettes or e-juice, vaping more before going into a situation where it is not allowed, and intolerable cravings. It is possible that some of the items included in this measure do not apply to users of disposable e-cigarettes in the same way they might for users of other e-cigarette device types. Indeed, research has demonstrated that e-cigarette dependence may present differently compared to other types of products (Kaplan et al., 2020; Soule et al., 2020). For example, it could be that disposable e-cigarette users may find stealth vaping easier to do and not endorse vaping more before going into a situation where it is not allowed. More research is needed to determine how e-cigarette device type may influence the endorsement of nicotine dependence items. To date, only one study (Tackett et al., 2021) has done this and found that JUUL, pod-based, and other e-cigarette users differentially endorse items related to vaping in places where you are not supposed to, feelings of addiction or craving, difficulties concentrating, and feeling more irritable and/or nervous, restless, or anxious when not being able to vape.
Other studies that have examined associations between e-cigarette device type and nicotine dependence have focused on the effect of JUUL and similar pod-based e-cigarettes (Boykan et al., 2019; Yingst et al., 2021). These studies have found associations between the use of pod-based e-cigarettes and multiple nicotine dependence measures, including the Penn State Electronic Cigarette Dependence Index (Foulds et al., 2015) and the e-cigarette Fagerstrom Test for Nicotine Dependence (Piper et al., 2020), in addition to the E-Cigarette Dependence Scale (Morean et al., 2019). Future studies may consider the inclusion of multiple measures of nicotine dependence to determine the behavioral mechanisms by which nicotine flux may influence nicotine dependence.
The present study should also be considered within the context of certain limitations. Study results may not generalize to other populations, such as those who are younger and those who are former or current users of other e-cigarette devices. For example, differential results might be found among users of box mods, which allow for greater customization in relation to power, nicotine concentration, and nicotine flux. We also did not account for potential co-use of other nicotine-containing products, aside from cigarettes, in these analyses. Results from these analyses suggest that past 30-day cigarette use may be an important predictor of EDS score among pod-based e-cigarette users. Future research is needed to explain why predictors of e-cigarette dependence differs according to device type. Additionally, future research should look towards investigating how associations between e-cigarette device and liquid characteristics and dependence may differ among dual and poly-nicotine users. Another limitation of these data is that we could not assess user behavior, such as puff duration and other topography measures that may influence nicotine delivery, and thus also influence nicotine dependence directly. Put simply, flux is the rate of nicotine emission, and nicotine dose inhaled is a function of flux and puff duration. Thus, if (for reasons unknown) users of high flux device/liquid combinations also take very short puffs, their overall nicotine exposure may be lower than users of other devices who take longer puffs. Since we did not ask whether respondents used free-base versus salt-based nicotine solutions in their e-cigarettes, we were unable to include the effect of these e-liquid compositions on nicotine dependence. Nonetheless, prior literature suggests that e-liquid compositions are directly related to device type and nicotine concentration (DeVito & Krishnan-Sarin, 2018; Son et al., 2018), and thus, nicotine flux (Eissenberg & Shihadeh, 2015; Shihadeh & Eissenberg, 2014). Future studies should investigate the effect of e-liquid compositions on nicotine dependence. Additionally, we were unable to verify self-reported product use. Finally, as this is a cross-sectional study, future studies will want to consider a longitudinal design to determine how associations between e-cigarette device and liquid characteristics and nicotine dependence change over time.
Despite these limitations, our study results have implications for public health and regulatory action. Given the great variability in e-cigarette characteristics influencing nicotine delivery, regulation based upon a single factor (e.g., liquid nicotine concentration) are unlikely to control nicotine exposure, and thus nicotine dependence. Results from our study demonstrate a significant main effect of nicotine flux and no main effect of power or nicotine concentration. Future studies should seek to replicate this result, and if replicated, cessation researchers and regulators should measure nicotine flux as an important variable in associations between e-cigarette device and liquid characteristics and nicotine dependence.
CONCLUSION
Results support the hypothesis that e-cigarette device and liquid characteristics are associated with nicotine dependence, such that nicotine flux is associated with a validated measure of nicotine dependence among current e-cigarette users. These preliminary findings should be explored in studies with larger samples of users of different e-cigarette device types that collect multiple measures of dependence. Future research will also need to evaluate changes in use and dependence in response to the implementation of e-cigarette regulations.
HIGHLIGHTS.
Power, nicotine concentration, and nicotine flux differ by device type.
Nicotine flux is predictive of dependence among pod-based users.
Age and past 30-day use were associated with nicotine dependence.
ACKNOWLEDGEMENTS
The authors thank the participants of this study for their contributions to this work.
FUNDING STATEMENT:
This research was supported by grant number R21CA239188 from the National Cancer Institute of the National Institutes of Health (NIH) and the Center for Tobacco Products of the U.S. Food and Drug Administration (FDA), and partial support was provided by NIH grants U54 GM104940 from the National Institute of General Medical Sciences, which funds the Louisiana Clinical and Translational Science Center. Dr. Eissenberg’s effort also is supported by grant number U54DA036105 from the National Institute on Drug Abuse of the NIH and the Center for Tobacco Products of FDA. The content is solely the responsibility of the authors and does not necessarily represent the official views of NIH or the FDA.
Abbreviations:
- EDS
E-cigarette Dependence Scale
Footnotes
Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
FINANCIAL DISCLOSURES / CONFLICTS OF INTEREST STATEMENT: Eric Soule, Corby Martin, Thomas Eissenberg, and Bernard Fuemmeler are named on a patent application for a smartphone app that determines electronic cigarette device and liquid characteristics. Thomas Eissenberg is a paid consultant in litigation against the tobacco and electronic cigarette industry and is named on a patent for a device that measures the puffing behavior of electronic cigarette users. All other authors declare no conflicts of interest.
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