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. Author manuscript; available in PMC: 2025 Feb 8.
Published in final edited form as: Subst Use Misuse. 2024 Feb 8;59(4):608–615. doi: 10.1080/10826084.2023.2294962

Indirect Effects of Body Mass Index and Sweet Taste Responsiveness on E-Cigarette Dependence: The Role of E-Cigarette Motives

Tyler B Mason 1,2, Alayna P Tackett 1,2, Adam M Leventhal 1,2
PMCID: PMC10922687  NIHMSID: NIHMS1956438  PMID: 38149796

Abstract

Recent research has shown obesity to be associated with e-cigarette use and appeal, but models have yet to examine how weight status may be related to e-cigarette dependence among e-cigarette users. To increase our understanding of pathways from body mass index (BMI) to e-cigarette dependence, the present cross-sectional observational study investigated a model in which BMI, sweet taste responsiveness, and the interaction of BMI and sweet taste responsiveness are associated with e-cigarette dependence indirectly via seven conceptually-distinct motives for e-cigarette use. Data from several e-cigarette clinical laboratory research studies were pooled and analyzed; only current e-cigarette users were included in the analyses (N=330). Structural equation modeling was used to analyze the hypothesized model. BMI was positively associated with lower social/environmental goad motives and higher weight control motives, and BMI × sweet taste interaction terms found that sweet taste responsiveness strengthened the association of BMI and weight control motives. BMI was not directly or indirectly associated with e-cigarette dependence nor was there a bivariate association between BMI and e-cigarette dependence. Sweet taste responsiveness was positively associated with greater affiliative attachment motives, cognitive enhancement motives, cue exposure-associative process motives, weight control motives, and affect enhancement motives. Sweet taste responsiveness was bivariately associated with e-cigarette dependence and mediation paths show indirect relations to e-cigarette dependence via three of the seven motives. The findings suggest that sweet taste responsiveness, opposed to BMI, is associated with a wider range of e-cigarette use motives and indirectly relates to e-cigarette dependence via several e-cigarette use motives.

Keywords: obesity, weight status, e-cigarettes, sweet taste, motives, dependence

Use of tobacco products, including e-cigarettes, has been shown to be elevated among individuals with obesity (Delk et al. 2018; Lanza et al. 2017), and e-cigarette product appeal differs by weight status (Mason and Leventhal 2021a; Morean and Wedel 2017). With regard to combustible cigarettes, there is evidence that individuals with obesity smoke more cigarettes and have higher dependence scores compared to their normal weight smoking counterparts (Carreras-Torres et al. 2018; Rupprecht et al. 2015). Evidence also suggests that concomitant obesity and tobacco product use may be more detrimental to health compared to either alone (Chiolero et al. 2008; Vurbic et al. 2015). While research on specific effects of e-cigarettes on the health of adults as a function of weight status has not been examined, individuals with obesity could be more susceptible to the negative health effects of e-cigarettes, such as respiratory effects (Bhatta and Glantz 2020; Mafort et al. 2016). Given this, it is important to empirically test models linking body mass index (BMI) and e-cigarette dependence to contribute to novel theory development and preventive strategies for e-cigarette use and dependence. However, little research has studied the role of BMI or weight status in e-cigarette dependence.

Behavioral motivation and learning theories have been used to understand individual differences in e-cigarette dependence. In general, motivation is what drives an individual to engage in a behavior and is influenced by learned experiences and one’s environment and social contexts (Reeve 2015). Relatedly, expectancy theory is a behavioral learning theory that suggests people engage in behaviors based on their expectation of various benefits or rewards from the behavior (Kirsch 1997). Based on these theories, motives for e-cigarette use may stem from intrinsic factors, such as expectancies, and extrinsic factors, such as social and environmental factors (Reeve 2015).

Research on motives for tobacco product use have evolved from the study of motives for use of traditional combustible cigarettes. Piper and colleagues (2004) developed a now well-established model of smoking-related motives, which is assessed with the Wisconsin Inventory of Smoking Dependence Motives (WSDM). The WSDM proposed several types of motives for cigarette use including affiliative attachment (i.e., socio-emotional attachment to cigarettes), cognitive enhancement (i.e., smoking to improve cognition), cue exposure-associative processes (i.e., smoking due to cue exposure and subsequent desire to smoke), social-environmental goads (i.e., smoking due to social and environmental contexts), taste (i.e., enjoyment of the taste of cigarettes), weight control (i.e., smoking to control weight and appetite), and affect enhancement (i.e., smoking to improve mood). Of which, each is positively associated with measures of cigarette dependence (Piasecki et al. 2010; Piper et al. 2004; Smith et al. 2010). The WSDM was adapted to measure e-cigarette motives, and similar findings have been elucidated in relation to e-cigarette dependence (Piper et al. 2020).

Most studies on BMI or weight status and smoking-related motives focus on smoking to control weight and appetite, primarily due to the appetite-suppressant effects of nicotine (Audrain-McGovern and Benowitz 2011). Consistently, elevated BMI has been positively associated with weight control motives for combustible cigarette use and e-cigarette use (Adams et al. 2011; Mason and Lewis 2021b; Morean and Wedel 2017). In addition to BMI, sweet taste responsiveness (i.e., difficulty resisting sweet foods and consuming sweet foods for mood regulation) is a separate but overlapping construct to BMI that may be important to understanding e-cigarette motives and dependence. Although elevated among individuals with higher BMI, sweet taste responsiveness is reported by individuals across the weight spectrum and is associated with increased unhealthy food consumption and weight gain (Berthoud & Zheng, 2012; Goodman et al., 2018; Tan & Tucker, 2019). Because e-cigarettes are available in sweet flavors and sweet flavors are the most appealing and used flavors (Leventhal et al. 2019; Zare et al. 2018), individuals higher in sweet taste responsiveness may be more likely to develop certain e-cigarette motives and become dependent to e-cigarettes (Mead et al. 2019). While associations between sweet taste responsiveness and e-cigarettes have seldom been studied, one previous study found a positive association between higher sweet taste responsiveness and e-cigarette weight control motives and an interaction between sweet taste responsiveness and BMI, with sweet taste responsiveness strengthening the association between BMI and e-cigarette weight control motives (Mason and Leventhal 2021b). However, there is an overall paucity of research studying associations of BMI and sweet taste responsiveness with other types of smoking-related motives and e-cigarette dependence.

Given the current limited evidence, the purpose of the current study was to examine a model in which BMI, sweet taste responsiveness, and the interaction of BMI and sweet taste responsiveness are associated with e-cigarette dependence indirectly via e-cigarette motives (see Figure 1). It was hypothesized that BMI, sweet taste responsiveness, and the interaction of BMI and sweet taste responsiveness would be associated with e-cigarette motives, and e-cigarette motives would be associated with e-cigarette dependence. Specifically, it was hypothesized that elevated BMI and sweet taste responsiveness would be associated with greater e-cigarette weight control motives, and in turn, e-cigarette weight control motives would be associated with greater e-cigarette dependence. The other direct and indirect effects among BMI, sweet taste responsiveness, e-cigarette motives, and dependence were exploratory given the limited literature.

Figure 1.

Figure 1.

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Hypothesized model of associations among body mass index, sweet taste responsiveness, e-cigarette motives, and e-cigarette dependence.

Method

Participants and Procedures

The current paper used a pooled data set of four laboratory studies of current tobacco product users in Los Angeles County (Leventhal et al., 2019; 2021; Han et al., 2023). One study included 18–35-year-old current e-cigarette users (N=100), and the other three studies included adults who either currently used e-cigarettes or only used combustible cigarettes but were interested in trying e-cigarettes (Ns= 119,121, and 125). Across studies, only current e-cigarettes users completed measures of e-cigarette motives and dependence. As such, participants who did not report being a current e-cigarette user were excluded. The total analytic sample included 330 adults who used e-cigarettes. Each individual study was approved by the University of Southern California Institutional Review Board. Eligible and interested participants (assessed via phone screen) completed a study visit where they provided written informed consent. In all studies, participants completed baseline survey questionnaires and an experimental e-cigarette product appeal paradigm. Only baseline self-report questionnaire data were used in the present analyses.

Measures

Demographics.

Demographics and vaping history questionnaires were used to measure age, gender, race/ethnicity, and self-reported height and weight.

Body mass index (BMI).

BMI was calculated using the standard BMI formula (Garrow and Webster 1985).

Sweet taste responsiveness.

The Sweet Taste Questionnaire (STQ; Kampov-Polevoy et al. 2006) measured individual difference in sweet taste responsiveness including rewarding reactions to eating sweets, craving for sweets, eating sweets for mood regulation, and degree of control over eating sweet foods. Participants responded to 12 items using a 7-point Likert scale ranging from 1 (strongly disagree) to 7 (strongly agree). Items were averaged to create a total score with higher scores indicating greater sweet taste responsiveness. The Cronbach’s alpha in the current study was .89.

E-cigarette motives.

The Wisconsin Inventory of Smoking Dependence Motives for e-cigarettes (e-WISDM; Piper et al. 2020) was used to assess various motivations for e-cigarette use. Secondary dependence motives subscales were used, including affiliative attachment, cognitive enhancement, cue exposure-associative processes, social-environmental goads, taste, weight control, and affect enhancement. Each subscale had three items. Participants responded to items on a scale from 1 (not true of me at all) to 7 (extremely true of me), and averages were taken using the items for each subscale with higher scores indicating greater motives. Previous research has shown adequate psychometric properties of the e-WISDM (Piper et al. 2020). The Subscale Cronbach’s alphas were adequate and ranged from .77–.95

E-cigarette product characteristics and nicotine dependence.

E-cigarette product use characteristics was assessed via self-reported questionnaires about e-liquid flavor preference (e.g., fruit, mint, menthol), and the type of e-cigarette device (e.g., rechargeable, adjustable voltage). The 10-item Penn State E-Cigarette Dependence Index (PSECDI; Foulds et al. 2015) was used to assess e-cigarette dependence. The items assess various aspects of e-cigarette dependence including use frequency, latency until first use, difficulty quitting, experience of craving, withdrawal symptoms), waking at night to use, and recent strength of urges to use. The PSECDI has been shown to have high internal consistency and relate to measures of heavy e-cigarette use, maintenance of use, and other measures of dependence (Piper et al. 2020).

Statistical Analyses

Descriptive statistics were calculated to examine weight status differences in e-cigarette product characteristics, and correlations were used to examine bivariate relations among study variables. BMI and sweet taste responsiveness were grand mean centered, and an interaction term was created between the two variables. Structural equation modeling (SEM) with Mplus 7.31 (Muthén and Muthén 2015) was used to analyze the hypothesized model (see Figure 1). Missing data were handled through full information maximum likelihood, which estimates a likelihood function for each person based on the available data so all data is used. (Enders and Bandalos 2001). The following indices were used as guidelines in evaluating model fit: comparative fit index (CFI) ≥ .95, Tucker-Lewis index (TLI) ≥ .95, root mean square error of approximation (RMSEA) ≤ .06, and standardized root mean square residual (SRMR) ≤ .08 (Hu and Bentler 1999). Bootstrapping with 5000 bootstrap resamples was used to test all direct and indirect effects. The bootstrap approach generally produces preferable standard errors for testing of effects (Preacher and Hayes 2008). Significance testing was done using 95% bias-corrected (BC) confidence intervals (CIs) generated from 5,000 bootstrap samples for both direct and indirect effects. If the confidence interval did not include 0, then it was significant. Simple slope analyses and plots were run for significant interactions.

Results

Data Availability and Descriptives

Among the 330 participants, 96.67% had complete data and only 0.33% of total data was missing. The gender of the samples was 38.5% female, 60.9% male, and 0.6% other. The mean age of the sample was 29.98 (SD=10.86; Range:18–73). Weight status categories were 46.5% normal weight (BMI<25), 28.6% overweight (BMI≥25 and <30), and 24.9% obesity (BMI≥ 30). Table 1 displays differences in e-cigarette product use characteristics by weight status. Individuals with obesity had a greater likelihood for e-cigarette preference of candy, fruit, and chocolate/other dessert. Bivariate correlations among study variables are displayed in Table 2. BMI was not associated with e-cigarette dependence. However, BMI was positively associated with sweet taste responsiveness and was negatively associated with social/environment goads. Sweet taste responsiveness was positively associated with e-cigarette dependence and all e-cigarette motives, except social/environment goads and taste. All e-cigarette motives were positively correlated with one another and with e-cigarette dependence.

Table 1.

Frequencies of E-Cigarette Product Use Characteristics by Weight Status

Total (N=329) Normal Weight (n=153) Overweight (n=94) Obesity (n=82) Test of Group Differences
Device type
 Tank, yes 33.7% 32.7% 28.7% 41.5% χ2=3.23, p=.19
 Cannister, yes 8.5% 8.5% 7.4% 9.8% χ2=0.30, p=.86
 Disposables, yes 28.0% 26.1% 30.9% 28.0% χ2=0.64, p=.73
E-Cigarette Flavor Used
 Tobacco, yes 19.8% 19.0% 25.5% 14.6% χ2=3.40, p=.18
 Menthol, yes 38.9% 42.5% 34.0% 37.8% χ2=1.80, p=.41
 Mint, yes 33.7% 32.0% 38.3% 31.7% χ2=1.23, p=.54
 Clove, yes 1.2% 1.3% 2.1% 0.0% χ2=1.67, p=.43
 Spice, yes 3.3% 3.3% 3.2% 3.7% χ2=0.04, p=.98
 Candy, yes 37.4% 35.9% 28.7% 50.0% χ2=8.72, p=.01
 Fruit, yes 73.9% 74.5% 63.8% 84.1% χ2=9.42, p=.01
 Chocolate/other sweets, yes 28.9% 20.9% 33.0% 39.0% χ2=9.60, p=.01
 Alcohol, yes 2.4% 2.6% 2.1% 2.4% χ2=0.06, p=.97
Rechargeable device, yes 81.3% 79.1% 84.9% 81.5% χ2=1.31, p=.52
Button to control voltage, yes 38.7% 40.1% 37.6% 37.0% χ2=0.27, p=.87
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Note. n=1 had missing data for BMI and was excluded from these analyses; SD=standard deviation.

Table 2.

Descriptive Statistics among Study Variables

M SD 2 3 4 5 6 7 8 9 10
1. BMI 26.81 6.50 .15** .11 −.04 .01 −.11* .03 .19** −.03 .06
2. Sweet taste responsiveness motives 2.99 1.34 − .27** .24** .23** .08 .10 .32** .28** .13*
3. Affiliative attachment motives 2.03 1.48 - .51** .52** .24** .27** .47** .59** .41**
4. Cognitive enhancement motives 3.47 1.86 - .52** .18** .35** .38** .68** .41**
5. Cue exposure-associative process motives 3.11 1.59 - .40** .38** .40** .66** .41**
6. Social/environmental goad motives 3.23 1.87 - .27** .20** .25** .19**
7. Taste motives 5.38 1.49 - .17** .47** .22**
8. Weight control motives 1.82 1.32 - .42** .19**
9. Affect enhancement motives 3.56 1.69 - .47**
10. E-cigarette dependence 8.56 3.12 -
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Note. BMI=body mass index.

**

p<.01

*

p< .05

Structural Equation Modeling

The structural model with standardized path estimates is displayed in Figure 2. The hypothesized structural model estimated with maximum likelihood estimation demonstrated good model fit, χ2(2)=1.59, p=.45, CFI=1.00, TLI=1.00, RMSEA=<.001, and SRMR=.01. The model explained 9% of the variance in affiliative attachment motives, 6% of the variance in cognitive enhancement motives, 5% of the variance in cue exposure-associative process motives, 3% of the variance in social-environmental goad motives, 1% of the variance in taste motives, 16% of the variance in weight control motives, 9% of the variance in affect enhancement motives, and 28% of the variance in e-cigarette dependence.

Figure 2.

Figure 2.

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Structural model of associations among body mass index (BMI), sweet taste responsiveness, e-cigarette motives, and e-cigarette dependence with standardized path coefficients. Significance was established by bootstrapped confidence intervals that did not include 0. Solid lines indicate significant pathways, whereas dashed lines indicate non-significant pathways; path estimates are not displayed for non-significant paths. Correlational paths were included between all motives but are not depicted. Also, directional paths were included from BMI, sweet taste responsiveness, and their interaction to e-cigarette dependence; these were non-significant and are not depicted.

Direct effect tests.

Model path effect estimates with BC confidence intervals are reported in Table 3. BMI was positively associated with lower social/environmental goad motives and higher weight control motives. Sweet taste responsiveness was positively associated with greater affiliative attachment motives, cognitive enhancement motives, cue exposure-associative process motives, weight control motives, and affect enhancement motives. The interaction of BMI and sweet taste responsiveness was positively associated with weight control motives; see Figure 3 for the interaction plot. Simple slope analyses showed that BMI was positively associated with higher weight control motives at mean (Estimate=0.03, p=.01) and one SD above the mean (Estimate=0.07, p<.001) levels of sweet taste responsiveness, and BMI was unrelated to weight control motives at low levels of sweet taste responsiveness (Estimate=−0.01, p=.28). Greater affiliative attachment motives, cognitive enhancement motives, and affect enhancement motives were associated with higher e-cigarette dependence.

Table 3.

Model Path Estimates with Bootstrapped SEs and CIs

Path β B SE 95% BC CI
BMI→Affiliative attachment 0.07 0.02 0.01 [−0.01, 0.04]
BMI→Cognitive enhancement −0.07 −0.02 0.02 [−0.05, 0.01]
BMI→Cue exposure-associative processes −0.04 −0.01 0.01 [−0.04, 0.01]
BMI→Social/environmental goads −0.13 −0.04 0.01 [−0.07,−0.01]
BMI→Taste 0.02 0.01 0.01 [−0.02, 0.03]
BMI→Weight control 0.13 0.03 0.01 [0.01, 0.05]
BMI→Affect enhancement −0.08 −0.02 0.01 [−0.05, 0.01]
BMI→E-cigarette dependence 0.08 0.04 0.03 [−0.01, 0.10]
STP→Affiliative attachment 0.26 0.28 0.07 [0.15, 0.41]
STP→Cognitive enhancement 0.25 0.34 0.08 [0.13, 0.40]
STP→Cue exposure-associative processes 0.23 0.27 0.07 [0.13, 0.40]
STP→Social/environmental goads 0.10 0.13 0.05 [−0.02, 0.29]
STP→Taste 0.10 0.11 0.06 [−0.01, 0.23]
STP→Weight control 0.29 0.29 0.05 [0.19, 0.39]
STP→Affect enhancement 0.29 0.36 0.07 [0.22, 0.49]
STP→E-cigarette dependence −0.02 −0.04 0.12 [−0.26, 0.19]
BMI×STP→Affiliative attachment 0.11 0.01 0.01 [−0.002, 0.04]
BMI×STP→Cognitive enhancement 0.02 0.01 0.01 [−0.02, 0.03]
BMI×STP→Cue exposure-associative processes 0.06 0.01 0.01 [−0.01, 0.04]
BMI×STP→Social/environmental goads 0.06 0.01 0.01 [−0.01, 0.04]
BMI×STP→Taste 0.03 0.01 0.01 [−0.01, 0.02]
BMI×STP→Weight control 0.20 0.03 0.01 [0.01, 0.04]
BMI×STP→Affect enhancement 0.10 0.02 0.01 [−0.004, 0.05]
BMI×STP→E-cigarette dependence −0.05 −0.02 0.02 [−0.05, 0.02]
Affiliative attachment→E-cigarette dependence 0.18 0.39 0.16 [0.06, 0.70]
Cognitive enhancement→E-cigarette dependence 0.15 0.25 0.12 [0.02, 0.48]
Cue exposure-associative processes→E-cigarette dependence 0.13 0.26 0.15 [−0.04, 0.54]
Social/environmental goads→E-cigarette dependence 0.04 0.07 0.09 [−0.11, 0.25]
Taste→E-cigarette dependence −0.04 −0.09 0.11 [−0.30, 0.14]
Weight control→E-cigarette dependence −0.09 −0.22 0.14 [−0.48, 0.07]
Affect enhancement→E-cigarette dependence 0.23 0.43 0.16 [0.11, 0.73]
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Note. 95% bias-corrected (BC) confidence intervals (CIs) that do not include 0 are significant. Simple slope analyses and plots were run for significant interactions. BMI=body mass index; STP=sweet taste responsiveness.

Figure 3.

Figure 3.

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Interaction between body mass index (BMI) and sweet taste responsiveness in relation to e-cigarette weight control motives plotted at one standard deviation below the mean (−1 SD), mean, and one standard deviation above the mean (+1 SD) levels of sweet taste responsiveness. BMI and sweet taste responsiveness were centered with a mean of 0.

Indirect effects tests.

Indirect effect tests were run for paths in which direct effects were significant, and tests of indirect effects revealed several indirect pathways to e-cigarette dependence. Sweet taste responsiveness was associated with e-cigarette dependence via affiliative attachment motives, Estimate=0.11, SE=0.06, 95% BC CI: [0.02, 0.24]. Sweet taste responsiveness was associated with e-cigarette dependence via cognitive enhancement motives, Estimate=0.09, SE=0.05, 95% BC CI: [0.01, 0.19]. Sweet taste responsiveness was associated with e-cigarette dependence via affect enhancement motives, Estimate=0.15, SE=0.07, 95% BC CI: [0.05, 0.30].

Discussion

The present study tested a model in which BMI and sweet taste responsiveness were associated with e-cigarette dependence via e-cigarette motives. This expands upon prior research which has primarily focused on weight status differences in e-cigarette use and weight control motives for tobacco product use (e.g., Bennett and Pokhrel 2018; Bloom et al. 2019; Delk et al. 2018; Lanza et al. 2017; Morean and Wedel 2017). Analyses showed no indirect associations from BMI to e-cigarette dependence, but indirect associations were found from sweet taste responsiveness to e-cigarette dependence via affiliative attachment, cognitive enhancement, and affect enhancement motives. This supports expectancy theory (Kirsch, 1997) as a relevant framework for understanding mechanisms linking sweet taste responsiveness and e-cigarette dependence. In addition, weight status differences in e-cigarette product use characteristics were examined, with obesity being associated with greater likelihood of using sweet flavored e-liquids.

Consistent with previous research (Fahey et al. 2021; Mason and Leventhal 2021b; Morean and Wedel 2017), results showed the importance of BMI and the interaction between BMI and sweet taste responsiveness in relation to higher weight control motives. Specifically, higher sweet taste responsiveness strengthened the association between BMI and weight control motives. BMI was also associated with lower social/environmental goad motives. However, the main effect of BMI and the interaction between BMI and sweet taste responsiveness were not related to any other e-cigarette motives or e-cigarette dependence. Because e-cigarettes are available in sweet flavors, individuals with elevated BMI who have difficulty regulating their sweet intake may be motivated to use e-cigarettes for appetite or weight control, with e-cigarettes possibly being a substitute for sweet foods. This is consistent with findings from the present study of higher sweet flavored e-liquid use in adults with obesity.

Compared to BMI, elevated sweet taste responsiveness was related to a greater array of e-cigarette motives, although effect sizes were small-to-medium. First, greater sweet taste responsiveness was associated with higher affect enhancement motives. Similar to how sweet food can improve mood in individuals with elevated emotional eating and internalizing symptoms (Kampov-Polevoy et al. 2006; Mason et al. 2020), individuals with sweet taste responsiveness may use e-cigarettes to improve their mood states. Second, greater sweet taste responsiveness was associated with higher cognitive enhancement motives. While sweet foods typically have a detrimental effect on cognition (Ginieis et al., 2018), people with greater sweet taste responsiveness may try to enhance their cognitive states with vaping. Also, given that individuals with elevated sweet taste responsiveness may have difficulty controlling their intake of sweet foods, when individuals with elevated sweet taste responsiveness attempt to control their sweet food intake, they may turn to e-cigarettes opposed to food to improve mood and cognition. Third, greater sweet taste responsiveness was associated with higher cue exposure-associative process motives. Like how people with eating-related concerns are more susceptible to eating in response to food cues (Boswell & Kober, 2016), individuals with elevated sweet taste responsiveness may be more susceptible to vaping in response to sweet food cues in e-cigarette advertisements and packaging. Fourth, greater sweet taste responsiveness was associated with higher affiliative attachment motives. Research has shown that individuals with eating-related concerns (such as difficulty controlling food intake) may present with a variety of different interpersonal and social difficulties, such as increased social anxiety, lack of social support, and negative social interactions, which can lead to turning to food for comfort (Arcelus et al, 2013). Those who vape and have eating-related concerns may also turn to vaping for these social needs.

Interestingly, neither BMI nor sweet taste responsiveness were related to taste motives. It is possible that taste motives may be elevated among most e-cigarette users, given availability in highly palatable flavors. This is consistent with research on the high appeal of flavored e-cigarettes (Leventhal et al. 2019; 2021) and data from the current study showing the highest mean for taste motives compared to other motives.

Of all motives, greater affiliative attachment, cognitive enhancement, and affect enhancement motives were independently associated with increased e-cigarette dependence. This suggests that emotional and cognitive regulatory motives are most critically related to dependence. Affect regulation models have consistently been used to explain substance use behaviors (Bradley 2003), and findings from the present analyses support affect and cognitive regulation as the most salient motives that may be associated with e-cigarette dependence.

Weight control motives were unrelated to e-cigarette dependence, and thus, no motives linked BMI to e-cigarette dependence. Therefore, in the current study, there were no direct or indirect links from BMI to e-cigarette dependence. Although, it is possible that weight control motives could be linked to other negative e-cigarette outcomes that were not explored in this study, such as e-cigarette cessation outcomes or longitudinal e-cigarette use patterns. Further, weight control motives could be linked to non-tobacco-related outcomes, such as eating disorder psychopathology (Mason et al. 2021).

There are some study limitations. This study was cross-sectional and causal inferences cannot be made from the data; it is plausible that associations between variables in the study reflect bi-directional or reverse direction associations. While the cross-sectional methodology was useful for exploring relations between BMI, sweet taste responsiveness, e-cigarette motives, and e-cigarette dependence, future longitudinal research is warranted. BMI was assessed with self-report height and weight, which may be subject to reporting biases. Future studies should measure height and weight in the laboratory to obtain more accurate measurements of BMI. The R2 values for motives were small-to-medium, suggesting the need to examine other variables in combination with BMI and sweet taste responsiveness as potential mediators and moderators. Finally, psychosocial experiences related to BMI (e.g., weight stigma, internalized weight stigma, body image) were not measured and thus could not be included in the model. Future research should integrate these psychosocial measures into models of BMI to better understand the relationships between e-cigarette characteristics (e.g., type of device, nicotine strength) and dependence.

In conclusion, BMI was directly and indirectly unrelated to e-cigarette dependence, but BMI was associated with weight control motives. Yet, sweet taste responsiveness was indirectly associated with e-cigarette dependence through several motives. Thus, the results underscore sweet taste responsiveness as a potential salient factor associated with e-cigarette outcomes. More research on longitudinal associations will be needed to understand the causal mechanisms by which sweet taste responsiveness may contribute to the continued use of e-cigarette use and how this impacts nicotine dependence.

Role of Funding Sources:

This project was supported in part by grants K01DK124435 from the National Institute of Diabetes and Digestive and Kidney Diseases Award Number (NIDDK), U54CA180905 from the National Cancer Institute (NCI) and Food and Drug Administration (FDA), and K23DA045081, K01DA040043, and K24DA048160 from the National Institute on Drug Abuse (NIDA). The content is solely the responsibility of the authors and does not necessarily represent the official views of NIDDK, NCI, FDA, or NIDA. The funding agencies had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Footnotes

Conflicts of Interest: The authors have no conflicts of interest.

Data availability statement.

Data is available through contacting the corresponding author

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

Among the 330 participants, 96.67% had complete data and only 0.33% of total data was missing. The gender of the samples was 38.5% female, 60.9% male, and 0.6% other. The mean age of the sample was 29.98 (SD=10.86; Range:18–73). Weight status categories were 46.5% normal weight (BMI<25), 28.6% overweight (BMI≥25 and <30), and 24.9% obesity (BMI≥ 30). Table 1 displays differences in e-cigarette product use characteristics by weight status. Individuals with obesity had a greater likelihood for e-cigarette preference of candy, fruit, and chocolate/other dessert. Bivariate correlations among study variables are displayed in Table 2. BMI was not associated with e-cigarette dependence. However, BMI was positively associated with sweet taste responsiveness and was negatively associated with social/environment goads. Sweet taste responsiveness was positively associated with e-cigarette dependence and all e-cigarette motives, except social/environment goads and taste. All e-cigarette motives were positively correlated with one another and with e-cigarette dependence.

Table 1.

Frequencies of E-Cigarette Product Use Characteristics by Weight Status

Total (N=329) Normal Weight (n=153) Overweight (n=94) Obesity (n=82) Test of Group Differences
Device type
 Tank, yes 33.7% 32.7% 28.7% 41.5% χ2=3.23, p=.19
 Cannister, yes 8.5% 8.5% 7.4% 9.8% χ2=0.30, p=.86
 Disposables, yes 28.0% 26.1% 30.9% 28.0% χ2=0.64, p=.73
E-Cigarette Flavor Used
 Tobacco, yes 19.8% 19.0% 25.5% 14.6% χ2=3.40, p=.18
 Menthol, yes 38.9% 42.5% 34.0% 37.8% χ2=1.80, p=.41
 Mint, yes 33.7% 32.0% 38.3% 31.7% χ2=1.23, p=.54
 Clove, yes 1.2% 1.3% 2.1% 0.0% χ2=1.67, p=.43
 Spice, yes 3.3% 3.3% 3.2% 3.7% χ2=0.04, p=.98
 Candy, yes 37.4% 35.9% 28.7% 50.0% χ2=8.72, p=.01
 Fruit, yes 73.9% 74.5% 63.8% 84.1% χ2=9.42, p=.01
 Chocolate/other sweets, yes 28.9% 20.9% 33.0% 39.0% χ2=9.60, p=.01
 Alcohol, yes 2.4% 2.6% 2.1% 2.4% χ2=0.06, p=.97
Rechargeable device, yes 81.3% 79.1% 84.9% 81.5% χ2=1.31, p=.52
Button to control voltage, yes 38.7% 40.1% 37.6% 37.0% χ2=0.27, p=.87
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Note. n=1 had missing data for BMI and was excluded from these analyses; SD=standard deviation.

Table 2.

Descriptive Statistics among Study Variables

M SD 2 3 4 5 6 7 8 9 10
1. BMI 26.81 6.50 .15** .11 −.04 .01 −.11* .03 .19** −.03 .06
2. Sweet taste responsiveness motives 2.99 1.34 − .27** .24** .23** .08 .10 .32** .28** .13*
3. Affiliative attachment motives 2.03 1.48 - .51** .52** .24** .27** .47** .59** .41**
4. Cognitive enhancement motives 3.47 1.86 - .52** .18** .35** .38** .68** .41**
5. Cue exposure-associative process motives 3.11 1.59 - .40** .38** .40** .66** .41**
6. Social/environmental goad motives 3.23 1.87 - .27** .20** .25** .19**
7. Taste motives 5.38 1.49 - .17** .47** .22**
8. Weight control motives 1.82 1.32 - .42** .19**
9. Affect enhancement motives 3.56 1.69 - .47**
10. E-cigarette dependence 8.56 3.12 -
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Note. BMI=body mass index.

**

p<.01

*

p< .05

Data is available through contacting the corresponding author

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