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. Author manuscript; available in PMC: 2023 Aug 30.
Published in final edited form as: Subst Use Misuse. 2022 Aug 30;57(12):1797–1807. doi: 10.1080/10826084.2022.2115848

Electronic Cigarette Use Intentions Mediate the Association between Low Self-Control and Future Use by Internalizing Symptoms

Benjelene D Sutherland a, Matthew T Sutherland a,b, Elisa M Trucco a,b,c,d
PMCID: PMC9560985  NIHMSID: NIHMS1833047  PMID: 36041007

Abstract

Background:

Adolescent electronic (e-)cigarette use intentions are related to initiation. Low self-control is also a risk factor for early stages of substance use. Yet, the impact of low self-control on use through intentions may vary across individuals; depression and anxiety may affect this association.

Methods:

A sample of 200 adolescents who completed waves 1 and 2 of an ongoing longitudinal study were assessed. We hypothesized that high internalizing symptoms would moderate the indirect effect of low self-control on actual e-cigarette use through e-cigarette use intentions.

Results:

The mediation pathway was significant at high levels of internalizing symptoms, but not at low or moderate levels.

Conclusions:

Specifically, those with low self-control and high internalizing symptomatology endorsed the highest e-cigarette use intentions and were more likely to subsequently use e-cigarettes. Youth low in self-control and high in depression and anxiety might be at increased risk to initiate e-cigarette use compared to youth high in self-control and high in internalizing symptomatology.

Keywords: e-cigarettes, adolescence, depression, anxiety, low self-control

1. Introduction

The use of combustible cigarettes has decreased among adolescents (Curran, Burk, Pitt, & Middleman, 2018) but the increase in electronic (e-)cigarette use is alarming. The appeal of e-cigarette use among adolescents stems, in part, from the perception that e-cigarettes are less harmful, concealable, and more socially acceptable than combustible cigarettes (Kong, Bold, et al., 2019; McKeganey, Barnard, & Russell, 2017; Pénzes, Foley, Balázs, & Urbán, 2016; Romijnders, Osch, Vries, & Talhout, 2018). Moreover, the available flavors, the ability to perform vape tricks, and e-cigarette’s potential anxiolytic effects all appeal to adolescents (Kong, Bold, et al., 2019; Kong, LaVallee, Rams, Ramamurthi, & Krishnan-Sarin, 2019; Meernik, Baker, Kowitt, Ranney, & Goldstein, 2019). However, the long-term negative health effects of vaping remain unclear. Nicotine (found in most e-cigarettes), is addictive and can impact the brain structure and function (Sutherland et al., 2016), as well as working memory and attention (Grundey et al., 2015; Sutherland, Ross, Shakleya, Huestis, & Stein, 2011). Further, vaping increases the risk of subsequent cigarette use (Johnston, O’Malley, Miech, Bachman, & Schulenberg, 2016). One study found that approximately 30% of 14-year-old adolescents who used e-cigarettes transitioned to combustible cigarette use within 6 months of e-cigarette use initiation compared to only 8% initiating combustible cigarette use within 6 months among non-e-cigarette users (Leventhal et al., 2015). Yet, factors influencing early stages of e-cigarette use (i.e., future intentions to use) remain unclear. A greater understanding of risk factors promoting onset could help inform prevention programming, especially among Hispanic/Latinx populations (Barrington-Trimis et al., 2019). For example, Lanza, Russell, and Braymiller (2017) demonstrated earlier e-cigarette use onset for Hispanic/Latinx adolescents relative to White or Black adolescents (between ages 12–14). Likewise, the prevalence of ever using e-cigarettes among youth between 2014 and 2017 was greater for Hispanic/Latinx than for Whites (26% vs. 23.9%; (Odani, Armour, & Agaku, 2018).

Self-control reflects the voluntary action of inhibiting impulses and placing more value on long-term goals despite conflicting urges associated with instant gratification (Duckworth & Steinberg, 2015). Low self-control and related concepts, (e.g., impulsivity; Duckworth & Steinberg, 2015; Kortesoja et al., 2020) may contribute to vulnerability to substance use (SU) whereas high self-control may contribute to protection against SU (e.g., delay of gratification; Wills, Ainette, Stoolmiller, Gibbons, & Shinar, 2008; Wills & Dishion, 2004; Wills, Windle, & Cleary, 1998). Wills and colleagues (1998; 2008) demonstrated that individuals with low self-control are more likely to use substances, such as tobacco, and to indicate using substances as a (maladaptive) coping mechanism to manage stressors. Individuals with low self-control are more prone to seek instant gratification without considering possible consequences (Daly, Egan, Quigley, Delaney, & Baumeister, 2016; Meldrum, Trucco, Cope, Zucker, & Heitzeg, 2018). This instant gratification is associated with impulsigenic processes, such as reward sensitivity and sensation seeking (Duckworth & Steinberg, 2015). From a neurobiological perspective, adolescents are at increased risk for engaging in SU given the differential development of two brain systems contributing to decision-making (Casey, Getz, & Galvan, 2008; Steinberg, 2008). Brain regions related to the socioemotional system (e.g., the striatum), which contributes to the pursuit of rewarding experiences, develop more rapidly. Whereas brain regions related to the cognitive control system (e.g., the prefrontal cortex), which contributes to impulse regulation and self-control, develop more gradually (Shulman et al., 2016). Reduced inhibitory control, or low self-control, possibly render some individuals more vulnerable to riskier decision-making, including the decision to engage in SU (Fergusson, Boden, & Hordwood, 2013; Hussong, Jones, Stein, Baucom, & Boeding, 2011; Meldrum et al., 2018). Indeed, Riggs and Pentz (2016), found a negative association between inhibitory control and e-cigarette use among adolescents. Further, Intravia, Vito, and Rocheleau (2022) found that young adults with low self-control were more likely to perceive vaping as a safer tobacco alternative and best option for quitting, and that it did not pose health risks. Since low self-control is a risk factor for adolescent e-cigarette use (Hoffmann, 2021), it is important to elucidate processes which link low self-control to actual use.

Intentions to use e-cigarettes could be one factor linking low self-control and e-cigarette use, as SU intentions are a strong predictor of future initiation (Trucco, Colder, Bowker, & Wieczorek, 2011; Trucco, Colder, & Wieczorek, 2011). Prior work has shown that SU intentions reflect early cognitions supportive of use that have significant predictive utility for actual use, especially when rates of prevalence are low (Trucco, Cristello, & Sutherland, 2021). Indeed, beliefs can have an impact on intentions to engage in a behavior, and as intentions originate from reasoning and planning this could lead to an action; including the decision to initiate SU (Andrews, Hampson, Barckley, Gerrard, & Gibbons, 2008). Notably, a common adolescent belief linked with e-cigarette initiation is that use will help them to cope with negative emotions, such as anxiety and depression (Kong, Bold, et al., 2019). Therefore, it follows that the association between low self-control, e-cigarette use intentions, and subsequent e-cigarette use may differ based on levels of internalizing symptoms (e.g., depression and anxiety). As low self-control could render some teens vulnerable to riskier decision-making, they could be more likely to demonstrate intentions to use e-cigarettes if they believe vaping will help them to cope with internalizing symptomology.

Internalizing symptomology, such as elevated depression, is a risk factor for SU and is predictive of e-cigarette use during adolescence (Green et al., 2018; Hussong, Ennett, Cox, & Haroon, 2017; Leventhal et al., 2016; Trucco, Villafuerte, Hussong, Burmeister, & Zucker, 2018). Even though bidirectional effects may exist between depression and e-cigarette use among adolescents (Lee & Lee, 2019), prior work indicates that adolescent depression increases the likelihood of e-cigarette use. For example, Lechner and colleagues (2017) found that higher levels of depression predicted e-cigarette use onset among high school students. According to the self-medication hypothesis (Khantzian, 1985), some individuals are thought to use substances to cope with their depressive symptomatology (Weinstein & Mermelstein, 2013).

Like depression, adolescents with elevated anxiety appear to be at increased risk for SU (Hussong et al., 2017; Johnson et al., 2000; Patton et al., 1996; Turner, Mota, Bolton, & Sareen, 2018). Youth with elevated anxiety symptoms may use substances to cope. More specifically, prior work indicates that nicotine, found in most e-cigarettes, is perceived as relieving stress and anxiety (Kupferschmidt, Funk, Erb, & Le, 2010). However, findings linking anxiety and SU tend to be mixed (Hussong et al., 2017). Harm avoidance theory may explain this difference, which posits that some youth may worry about the harmful effects of SU and exacerbate anxiety symptoms (Wills et al., 1998). Only a few studies have investigated clinical symptomatology and e-cigarette use. This work indicates that adults with mental health conditions, including anxiety, were more likely to have tried e-cigarettes (14.8%) compared to those without clinical diagnoses (6.6%; Cummins, Zhu, Tedeschi, Gamst, & Myers, 2014). Testing whether similar associations exist among adolescents in the earliest stages of use could help inform prevention efforts.

Inconsistencies regarding the role of internalizing symptomatology as either a risk factor or a protective factor could vary depending on an individual’s self-control ability. A reasonable hypothesis is that individuals characterized by both low self-control and high depression (or anxiety) are at elevated risk for using e-cigarettes to cope with negative affect. These youth may perceive e-cigarette use as way to reduce negative affect without considering potentially negative consequences of use. In contrast, someone both high in self-control and depression (or anxiety) may expend more time reflecting on the possible negative outcomes of use and ultimately not consider e-cigarettes as an effective coping strategy. Yet, interactive effects between self-control and internalizing symptomatology have not been examined with respect to intentions to use e-cigarettes and actual use.

The current study examined the association between low self-control on future e-cigarette use through intentions to initiate use by levels of depression and anxiety symptomatology among high school freshmen and sophomores using a moderated mediation approach. Examination of the interaction between low self-control and high internalizing symptomatology on e-cigarette use intentions is important as adolescents characterized by both may be most at risk for the earliest stages of SU initiation; and therefore, more at risk for future onset consistent with prior work (Trucco, Colder, Bowker, et al., 2011; Trucco, Colder, & Wieczorek, 2011). Indeed, these youth may perceive e-cigarettes as an effective coping strategy for their symptomatology and demonstrate intentions to use, which in turn will predict actual e-cigarette use. Examining synergistic effects could also inform discrepancies found in prior work suggesting that in some cases internalizing symptoms are protective against SU (Colder et al., 2013; Zehe, Colder, Read, Wieczorek, & Lengua, 2013). Given prior work demonstrating a potential bias when self-reporting low self-control (Meldrum, Young, Burt, & Piquero, 2013) and to address issues with shared method variance (De los Reyes et al., 2012; Hunsley & Mash, 2007) both self- and caregiver-reports of self-control were considered as recommended in prior work (Meldrum et al., 2013). We note that neither reporter is deemed to be more valid, rather including multiple reports of self-control has value in offering multiple perspectives. We hypothesized that internalizing symptomatology would moderate the indirect effect of low self-control on later e-cigarette use through e-cigarette use intentions, such that high internalizing symptomatology would exacerbate the link between low self-control and greater intentions to use e-cigarettes within one year, which in turn would predict subsequent e-cigarette use.

2. Methods

2.1. Participants

The subsample was comprised of 221 adolescents (14–17 years old [mean=14.9 at wave 1], 51.7% female, 84.7% White) who completed waves 1 (W1) and 2 (W2; ~15 months apart) of a larger longitudinal study (N=264) examining risk and protective factors impacting e-cigarette use.1 The subsample did not differ from the larger study sample on demographic characteristics and most study variables. Differences were found in terms of self-reported self-control such that the subsample was characterized by greater self-control.2 Data collection for the first wave spanned March 2018 through December 2019, and for W2 from June 2019 to June 2021. Data was collected from freshmen and sophomores enrolled in South Florida high schools, as well as their caregivers. Given the region where the study was conducted and the aims of the larger study, most of the sample identified as Hispanic/Latinx (81.8%). Exclusion criteria for the larger study included: a learning disorder or intellectual disability, such as dyslexia, that could make it hard to understand and complete surveys, and other procedures of the larger study (i.e., magnetic resonance imaging [MRI] tasks, not discussed further herein), a physical disability or neurological diseases such as epilepsy or muscular dystrophy that could make it hard to use an iPad/tablet to complete surveys, a severe mental illness such as schizophrenia that could impact responses and appointment procedures, and not being fluent in English since MRI tasks and adolescent study procedures for the larger study were administrated in English. Moreover, as the larger longitudinal study needed to ensure that an appreciable number of adolescents went on to initiate some form of SU over the course of the study, adolescents needed to meet criteria for either high levels of personality factors associated with SU (i.e., impulsivity, sensation seeking; Woicik, Stewart, Pihl, & Conrod, 2009) or endorsement of a friend or sibling having tried a substance. The purpose was to identify adolescents who had a slightly higher likelihood of engaging in SU during their high school careers to be able to distinguish risk and protective factors linked to e-cigarette use onset. Notably, most high school students in the region screened at high risk; that is, only 2.6% (11) of adolescents did not meet the high-risk criterion, suggesting that this sample is likely representative of high school students in the region.

2.2. Procedures

South Florida public schools were contacted regarding their willingness to participate in recruitment events. Following recruitment, interested families completed an eligibility screen, and those meeting criteria were scheduled for their first in-person visit (W1) to complete questionnaires (see Sample Selection Flow Chart in supplementary materials for more detailed information). Once the consent/assent process was completed, adolescents and caregivers completed questionnaires in separate rooms to increase confidentiality. All questionnaires were administrated on an iPad using REDCap (Research Electronic Data Capture; Harris et al., 2019; Harris et al., 2009). Participants were compensated for their participation. Approximately 15 months after W1, participants completed W2 assessments consisting of similar procedures.3 Yet, due to COVID-19, a portion of the participants completed their questionnaires remotely via the Zoom platform. The Institutional Review Board at the university approved this study.

2.3. Measures

Youth Self Report (YSR).

Raw scores from the YSR of the Achenbach System of Empirical Behavioral Assessment (ASEBA; Achenbach & Rescorla, 2001b) were used to assess low self-control and depressive and anxiety symptoms at W1. Items were rated on a 3-point Likert scale (0=not true, 2=very true or often true). Low self-control was derived by taking the sum of 8 items (e.g., “I act without stopping to think”; Cronbach’s α=0.71) consistent with previous work (e.g., Hay & Forrest, 2006; Meldrum et al., 2018). Further, we quantified depressive symptoms using the withdrawn/depressed subscale, which was comprised of 8 items (e.g., “I’m unhappy, sad or depressed”; Cronbach’s α=0.71). We quantified anxiety symptoms using the anxious/depressed subscale, which was comprised of 13 items (e.g., “I am too fearful or anxious”; Cronbach’s α=0.82).

Child Behavior Checklist (CBCL).

Raw scores from the CBCL of the ASEBA (Achenbach & Rescorla, 2001a) were also used to assess adolescent’s low self-control at W1 based on caregiver-report. Items were also rated on a 3-point Likert scale. The low self-control subscale was derived by taking the sum of the same 8 items (Cronbach’s α=0.83).

Adolescent e-cigarette use.

Adolescent e-cigarette use intentions within one year were assessed at W1 using an item adapted from the Population Assessment of Tobacco and Health Survey (PATH; Hyland et al., 2016). Participants were asked to rate the likelihood of initiating e-cigarette use within one year using a 5-point Likert scale (1=I definitely will not, 5=I definitely will). Furthermore, actual e-cigarette use was assessed at W2 using one item adapted from PATH, “Since your last visit, on how many days did you use an Electronic Nicotine Delivery System (ENDS) product?.” Participants had the option to enter a value spanning 0 days to 455 days of use. In addition, lifetime e-cigarette use at W1 was used as a covariate. Adolescents were asked, “Have you ever used an ENDS product, such as NJOY, Blu, Smoking Everywhere, a vape pen, or a vape mod, even one or two times?” (0=No, 1=Yes).

Demographic information.

The following demographic variables were included as covariates: adolescent’s biological sex, age, and ethnicity.4

2.4. Data Analysis

Analyses were conducted using SAS version 9.4 (SAS Institute, 2002–2012). Descriptive statistics and correlations were calculated first. All study variables were normally distributed (skewness range=0.30–2.01, kurtosis range=−0.02–3.20) apart from e-cigarette use at W2 (skewness=5.37, kurtosis=31.95). Thus, a logarithm transformation approach was used to normalize the data consistent with recommendations (e.g., Kline, 2016). As expected, the skewness and kurtosis for e-cigarette use at W2 improved with the transformation (i.e., 2.46 and 5.01, respectively). For ease of interpretation, prior to conducting moderated mediation analyses, study variables were standardized. Moderated mediation models were conducted using ordinary least squares regression using Hayes (2019) PROCESS macro version 3.3 for SAS. Separate models were tested for depression and anxiety, as well as separate models for self- and caregiver-reported self-control for a total of four models while controlling for demographic characteristics and lifetime e-cigarette use. Of interest were four two-way interactions (self-reported Self-Control × Depression, caregiver-reported Self-Control × Depression, self-reported Self-Control × Anxiety, caregiver-reported Self-Control × Anxiety) and their effect on the mediated pathway of low self-control on subsequent e-cigarette use through e-cigarette use intentions. The conditional indirect effect of self-control on days of e-cigarette use through intentions to use e-cigarettes within one year was evaluated at different levels of internalizing symptoms (i.e., low, moderate and high) using percentile bootstrap confidence intervals. Significant interactions were probed using the recommended guidelines of Cohen and Cohen (1983) by assessing values of self-control corresponding to the mean (i.e., moderate) and one standard deviation above (i.e., high) and below (i.e., low) the mean. Lastly, an index of moderated mediation with a bootstrap confidence interval was estimated whereby a value outside of 0 indicates that the indirect effect depends on the moderator, and hence support for moderated mediation (Hayes, 2018).

3. Results

Table 1 presents descriptive information and correlations. Of particular interest, e-cigarette use was positively correlated with lifetime e-cigarette use, self-reported low self-control, and e-cigarette use intentions. E-cigarette use intentions were positively correlated with lifetime e-cigarette use, self- and caregiver-reported low self-control, depression, and anxiety. Self-reported low self-control was positively correlated with lifetime e-cigarette use, depression, anxiety, and caregiver-reported low self-control. Caregiver-reported low self-control was positively correlated with depression and anxiety. Depression and anxiety were positively correlated.

Table 1.

Means, Standard Deviations, and Correlations for Study Variables

M SD Correlations
Study Variables 1 2 3 4 5 6 7 8 9 10
1. Ethnicitya 0.84 0.36 −
2. Age 14.90 0.68 0.08 −
3. Sexb 0.49 0.50 −0.12* 0.02 −
4. Lifetime e-cigarette use (W1) 0.33 0.47 0.10 0.06 0.03 −
5. Depression (W1) 4.22 2.82 0.04 −0.13* −0.10 0.14* −
6. Anxiety (W1) 5.53 4.23 0.05 −0.02 −0.21*** 0.02 0.69*** −
7. Low Self-Control (SR; W1) 5.49 3.06 0.04 −0.03 −0.12* 0.29*** 0.51*** 0.49*** −
8. Low Self-Control (CR; W1) 2.76 3.06 −0.22*** 0.06 0.13* 0.09 0.14* 0.15* 0.32*** −
9. E-Cigarette Use Intentions (W1) 1.71 1.03 0.12* 0.04 −0.04 0.55*** 0.18** 0.16** 0.29*** 0.25*** −
10. Actual E-cigarette Use (W2)c 10.93 43.43 −0.03 0.07 0.02 0.18** 0.07 −0.00 0.16* 0.11 0.25*** −
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Note: SR = self-report; CR = caregiver-report;

*

p < 0.05;

**

p < 0.01;

***

p < 0.001

a

Hispanic No = 0, Yes = 1;

b

Female = 0, Male = 1;

c

Variable represents untransformed value

3.1. Moderated Mediation Models for Self-Reported Low Self-Control

The depression model accounted for approximately 35% of the variance in e-cigarette use intentions (see Figure 1). Self-control and depression were not significantly associated with intentions to use e-cigarettes in one year. Yet, the two-way interaction was statistically significant (see Table 2). The two-way interaction accounted for a 3% increase in R2. The simple slope of low self-control was significant at high depression (t[213]=2.9, p <0.01), but not at low (t[213]=−1.23, p=0.21) or moderate (t[213]=1.03, p=0.31) levels (see Figure 2). Findings indicated that adolescents endorsing low self-control and high levels of depressive symptomatology reported the highest e-cigarette use intentions. Moreover, findings indicated that adolescents endorsing high self-control and high levels of depressive symptomatology reported the lowest e-cigarette use intentions, potentially indicating protective effects. Additionally, the model accounted for approximately 25% of the variance in days of e-cigarette use at W2. The direct effect of self-control on days of e-cigarette use at W2 was not significant. However, intentions to use e-cigarettes within one year was a significant predictor of subsequent days of e-cigarette use (see Table 2). The index of moderated mediation did not contain zero (effect=0.10, 95% bias-corrected confidence interval [0.03, 0.17]). This indicates that the mediated effect of low self-control on days of e-cigarette use through e-cigarette use intentions is moderated by depression. Moreover, the conditional indirect effect of self-control on days of e-cigarette use through intentions to use e-cigarettes within one year was statistically significant at high levels of depression (effect=0.15, 95% CI [0.05, 0.26]), but not at low (effect=−0.07, 95% CI [−0.18, 0.05]) or moderate levels (effect=0.04, 95% CI [−0.03, 0.13]). Thus, consistent with study hypotheses, e-cigarette use intentions mediated the effect of self-control on days of e-cigarette use at W2 only at high levels of depression.

Figure 1.

Figure 1.

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Youth’s self-report of low self-control on days of e-cigarette use through e-cigarette use intentions in one year by depression. Solid lines represent significant paths, while dashed lines represent non-significant paths. Covariates are not depicted in the figure. **p < 0.01, *** p < 0.001.

Table 2.

Moderated Mediation Models for Self-Reported Low Self-Control

M Y
E-cigarette use intentions in one year Days of e-cigarette use
Depression Model Coefficient (95% CI) SE t Value Coefficient (95% CI) SE t Value
Intercept −0.03 (−0.19, 0.13) 0.08 −0.37 0.55 (0.34, 0.77) 0.11 5.04
Ethnicity 0.06 (−0.05, 0.17) 0.06 0.99 −0.07 (−0.23, 0.09) 0.08 −0.90
Age 0.01 (−0.10, 0.12) 0.06 0.20 0.11 (−0.05, 0.27) 0.08 1.36
Sex −0.12 (−0.34, 0.10) 0.11 −1.06 0.07 (−0.25, 0.39) 0.16 0.41
Lifetime e-cigarette use 0.52*** (0.40, 0.63) 0.06 8.94 0.06 (−0.13, 0.26) 0.10 0.67
Low Self-Control (SR) 0.08 (−0.05, 0.21) 0.07 1.23 0.12 (−0.05, 0.28) 0.08 1.39
Depression −0.00 (−0.14, 0.13) 0.07 −0.04 - - - -
Low Self-Control (SR) × Depression 0.17** (0.06, 0.28) 0.06 3.01 - - - -
E-cigarette use intentions - - - - 0.59*** (0.40, 0.78) 0.10 6.07
Model R2 R2 = 0.35; F(7, 213) = 16.10, p < 0.001 R2 = 0.25; F(6, 214) = 11.94, p < 0.001
ΔR2 with Interaction R2 = 0.03; F(1, 213) = 9.06, p < 0.01 - - - -
Anxiety Model
Intercept −0.02 (−0.18, 0.13) 0.08 −0.29 0.55 (0.34, 0.77) 0.11 5.04
Ethnicity 0.05 (−0.06, 0.16) 0.06 0.91 −0.07 (−0.23, 0.09) 0.08 −0.90
Age 0.02 (−0.09, 0.13) 0.06 0.31 0.11 (−0.05, 0.27) 0.08 1.36
Sex −0.11 (−0.33, 0.12) 0.11 −0.96 0.07 (−0.25, 0.39) 0.16 0.41
Lifetime e-cigarette use 0.52*** (0.40, 0.63) 0.06 8.87 0.06 (−0.13, 0.26) 0.10 0.67
Low Self-Control (SR) 0.07 (−0.06, 0.20) 0.07 1.09 0.12 (−0.05, 0.28) 0.08 1.39
Anxiety 0.03 (−0.11, 0.17) 0.07 0.44 - - - -
Low Self-Control (SR) × Anxiety 0.15** (0.04, 0.26) 0.06 2.65 - - - -
E-cigarette use intentions - - - - 0.59*** (0.40, 0.78) 0.10 6.07
Model R2 R2 = 0.35; F(7, 213) = 16.16, p < 0.001 R2 = 0.25; F(6, 214) = 11.94, p < 0.001
ΔR2 with Interaction R2 = 0.02; F(1, 213) = 7.02, p < 0.01 - - - -
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Note: SR = self-report;

*

p < 0.05,

**

p < 0.01,

***

p < 0.001

Figure 2.

Figure 2.

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Youth’s self-report of low self-control on intentions to use e-cigarettes in one year by depression; ** p < 0.01. High values = 1 SD below the mean, Moderate values = mean, Low values = 1 SD above the mean.

Similar to the depression model, the anxiety model accounted for approximately 35% of the variance in e-cigarette use intentions (see Figure 3). Self-control and anxiety were not significantly associated with intentions to use e-cigarettes within one year. Nevertheless, the two-way interaction was significant (see Table 2). The two-way interaction accounted for a 2% increase in R2. The simple slope of low self-control was significant at high anxiety (t[213]=2.46, p <0.05), but not at low (t[213]=−0.73, p=0.46) or moderate levels (t[213]=0.81, p=0.42) (see Figure 4). Findings indicated that adolescents endorsing low self-control and high levels of anxiety symptomatology reported the highest e-cigarette use intentions. Additionally, findings indicated that adolescents endorsing high self-control and high levels of anxiety reported the lowest e-cigarette use intentions. Moreover, the model accounted for approximately 25% of the variance in days of e-cigarette use at W2. The direct effect of self-control on days of e-cigarette use at W2 was not significant. Yet, intentions to use e-cigarettes was a significant predictor of days of e-cigarette use (see Table 2). The index of moderated mediation did not contain zero (effect=0.09, 95% bias-corrected confidence interval [0.013, 0.16]). This indicates that the mediated effect of low self-control on days of e-cigarette use through e-cigarette use intentions is moderated by anxiety. The conditional indirect effect of self-control on days of e-cigarette use through intentions to use e-cigarettes was statistically significant at high levels of anxiety symptoms (effect=0.14, 95% CI [0.02, 0.26]), but not at low (effect=−0.03, 95% CI [−0.12, 0.07]) or moderate levels (effect=0.03, 95% CI [−0.04, 0.12]). Thus, consistent with study hypotheses, intentions to use e-cigarettes mediated the effect of self-control on days of e-cigarette use at W2 only at high levels of anxiety.

Figure 3.

Figure 3.

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Youth’s self-report of low self-control on days of e-cigarette use through e-cigarette use intentions in one year by anxiety. Solid lines represent significant paths, while dashed lines represent non-significant paths. Covariates are not depicted in the figure. ** p < 0.01, *** p < 0.001.

Figure 4.

Figure 4.

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Youth’s self-report of low self-control on intentions to use e-cigarettes in one year by anxiety; ** p < 0.01. High values = 1 SD below the mean, Moderate values = mean, Low values = 1 SD above the mean.

3.2. Moderated Mediation Models for Caregiver-Reported Low Self-Control

The depression model accounted for approximately 36% of the variance in e-cigarette use intentions. The association between caregiver-reported low self-control on e-cigarette use intentions was significant; yet, depression and the two-way interaction were not significant (see Table 3). Furthermore, the model accounted for approximately 25% of the variance in days of e-cigarette use at W2. Intentions to use e-cigarettes was a significant predictor of days of e-cigarette use at W2. However, the direct effect of self-control on days of e-cigarette use at W2 and the index of moderated mediation were not significant (see Table 3). Moreover, the anxiety model accounted for approximately 36% of the variance in e-cigarette use intentions. The association between caregiver-reported low self-control on e-cigarette use intentions was significant; yet, anxiety and the two-way interaction were not significant. In addition, the model accounted for approximately 25% of the variance in days of e-cigarette use at W2. Although intentions to use e-cigarettes was a significant predictor of days of e-cigarette use at W2, the direct effect of self-control on days of e-cigarette use and the index of moderated mediation were not significant (see Table 3).

Table 3.

Moderated Mediation Models for Caregiver-Reported Low Self-Control

M Y
E-cigarette use intentions in one year Days of e-cigarette use
Depression Model Coefficient (95% CI) SE t Value Coefficient (95% CI) SE t Value
Intercept 0.08 (−0.06, 0.23) 0.08 1.11 0.58 (0.36, 0.80) 0.11 5.26
Ethnicity 0.08 (−0.03, 0.20) 0.06 1.48 −0.04 (−0.21, 0.12) 0.08 −0.53
Age 0.01 (−0.10, 0.12) 0.06 0.14 0.10 (−0.06, 0.26) 0.08 1.22
Sex −0.15 (−0.37, 0.07) 0.11 −1.36 0.01 (−0.31, 0.33) 0.16 0.06
Lifetime e-cigarette use 0.52*** (0.41, 0.63) 0.06 9.35 0.09 (−0.10, 0.28) 0.10 0.98
Low Self-Control (CR) 0.25*** (0.13, 0.37) 0.06 4.12 0.12 (−0.05, 0.29) 0.09 1.39
Depression 0.05 (−0.06, 0.16) 0.06 0.87 - - - -
Low Self-Control (CR) × Depression −0.10 (−0.22, 0.02) 0.06 −1.66 - - - -
E-cigarette use intentions - - - - 0.57*** (0.37, 0.76) 0.10 5.71
Model R2 R2 = 0.36; F(7, 213) = 17.45, p < 0.001 R2 = 0.25; F(6, 214) = 11.94, p < 0.001
ΔR2 with Interaction R2 = 0.01; F(1, 213) = 2.75, p = 0.10 - - - -
Anxiety Model
Intercept 0.07 (−0.08, 0.22) 0.08 0.89 0.58 (0.36, 0.80) 0.11 5.26
Ethnicity 0.09 (−0.02, 0.20) 0.06 1.60 −0.04 (−0.21, 0.12) 0.08 −0.53
Age 0.01 (−0.10, 0.12) 0.06 0.13 0.10 (−0.06, 0.26) 0.08 1.22
Sex −0.15 (−0.38, 0.08) 0.12 −1.30 0.01 (−0.31, 0.33) 0.16 0.06
Lifetime e-cigarette use 0.53*** (0.42, 0.64) 0.06 9.49 0.09 (−0.10, 0.28) 0.10 0.98
Low Self-Control (CR) 0.20*** (0.09, 0.32) 0.06 3.42 0.12 (−0.05, 0.29) 0.09 1.39
Anxiety 0.10 (−0.02, 0.21) 0.06 1.68 - - - -
Low Self-Control (CR) × Anxiety 0.01 (−0.10, 0.13) 0.06 0.21 - - - -
E-cigarette use intentions - - - - 0.57*** (0.37, 0.76) 0.10 5.71
Model R2 R2 = 0.36; F(7, 213) = 17.27, p < 0.001 R2 = 0.25; F(6, 214) = 11.94, p < 0.001
ΔR2 with Interaction R2 = 0.00; F(1, 213) = 0.04, p = 0.83 - - - -
Open in a new tab

Note: CR = caregiver report;

*

p < 0.05,

**

p < 0.01,

***

p < 0.001

4. Discussion

The purpose of this study was to elucidate factors associated with e-cigarette use among adolescents. Levels of internalizing symptomatology were considered in the indirect effect of self-control on days of e-cigarette use through e-cigarette use intentions within one year via moderated mediation analyses. There was evidence that high levels of depressive and anxious symptomatology moderated the effect of self-reported low self-control on days of e-cigarette use through intentions to use e-cigarettes, supporting moderated mediation. Additionally, when these significant interactions were probed, nuanced associations indicative of both protective effects and increased risk for intentions to initiate e-cigarettes emerged. Yet, the interaction between internalizing symptomatology and caregiver-reported low self-control was not significant.

Those with high depression and low self-control were more likely to report intentions to use e-cigarettes and at greater risk for subsequent e-cigarette use compared to those with low depression. Adolescents with low self-control and high levels of depressive symptomatology may be more vulnerable to e-cigarette use as they may be prone to risky decision-making. These youth may view e-cigarettes as a viable means to cope with their symptomatology. Those with high levels of self-control and high depression endorsed the lowest use intentions and may be less likely to use e-cigarettes in the future. We speculate that high self-control buffers against risk factors leading to SU. Individuals high in self-control are likely to possess greater: 1) forethought to prepare for high-risk situations, 2) problem-solving skills to derive alternative strategies to deal with stressors, and 3) emotional regulation to avoid rumination after an adverse event (Wills et al., 2008; Wills & Dishion, 2004). Thus, these adolescents may be able to adjust their emotions and avoid engaging in maladaptive behaviors, such as using e-cigarettes, even when experiencing depressive symptoms.

Those with low self-control and high anxiety levels endorsed high e-cigarette use intentions and in turn were more at risk for subsequent e-cigarette use. Yet, adolescents high in both self-control and anxiety endorsed low intentions and were less at risk for engaging in e-cigarette use, suggesting a protective effect. These findings are consistent with prior work examining reasons that internalizing problems (i.e., social anxiety) may either increase or decrease risk for SU (Nicholls, Staiger, Williams, Richardson, & Kambouropoulos, 2014). Specifically, individuals characterized by high social anxiety combined with low impulsivity endorsed lower levels of SU than individuals characterized by high social anxiety and high impulsivity. Moreover, the former group of individuals also reported lower levels of SU compared to a normative sample (low social anxiety and low impulsivity) of individuals (Nicholls et al., 2014). High self-control and anxiety may be linked to risk aversion. Harm avoidance theory posits that some individuals avoid behaviors like SU that may lead to punishment or harm (Wills et al., 1998). Adolescents high in both self-control and anxiety may worry about the adverse impact of using e-cigarettes or fear consequences if caught using. Therefore, such individuals may not view e-cigarettes as a viable option to address their anxiety. Youth high in self-control may also possess more effective coping skills to buffer against increased risk of using e-cigarettes (Hussong & Chassin, 2004; Wills, Sandy, Yaeger, Cleary, & Shinar, 2001).

Prior work has recommended assessing adolescent self-control across multiple reporters (Meldrum et al., 2013). Yet, it is unclear why synergistic effects were not significant in caregiver-reported models. Inconsistencies across reporters using the ASEBA are not uncommon (De los Reyes, 2013), especially with regard to self-control (Meldrum et al., 2013). Prior work suggests that self-reported assessments of self-control may be biased for youth who are low in self-control and therefore collateral reports are preferable (e.g., Wright, Caspi, Moffitt, & Silva, 1999). Yet, caregiver reports of self-control are also likely to be biased given parent’s own self-control (Meldrum et al., 2013). Moreover, given developmental differences occurring within the psychosocial milieu as youth transition to adolescence (i.e., increased time spent away from parents), accuracy of caregiver-reports may decrease (Achenbach, McConaughy, & Howell, 1987). Consistent with study findings, one study found that adolescents rated themselves, on average, as lower in self-control compared to mother-report (Meldrum et al., 2013). Prior work also shows that adolescent reports of self-control were strongly correlated with delinquency, while maternal reports were not (Meldrum et al., 2013). These findings are consistent with current results whereby adolescent report on self-control was positively correlated with prior e-cigarette use and future e-cigarette use, while caregiver-report was not.

4.1. Limitations

Findings of the current study provide an important contribution given that early risk factors for e-cigarette use remain unclear; however, the present study had limitations. First, the sample was comprised primarily of Hispanic/Latinx adolescents; thus, findings may not generalize to a more diverse sample. Some reports indicate that youth identifying as Hispanic/Latinx are at increased risk for SU and internalizing disorders (Anderson & Mayes, 2010; Lanza et al., 2017). Second, our results may not generalize to other developmental stages. For instance, prior work indicates that at younger ages internalizing problems (especially anxiety) may be protective against SU, but as SU becomes more normative, internalizing problems may increase risk (Colder et al., 2018; Trucco et al., 2018). Third, adolescents enrolled in the larger longitudinal study were characterized by factors associated with increased SU. Even though our sample may be likely representative of regional high school students, our sample may still exhibit slightly elevated rates in low self-control and internalizing symptomatology. Furthermore, data was only available across two timepoints. Even though the study outcome was assessed ~15 months later, self-control, internalizing symptoms and e-cigarette use intentions were assessed at the same timepoint. Future studies could include an additional timepoint to account for temporal precedence. Lastly, alternative models are also worth exploring. Namely, given prior work supporting bidirectional effects between self-control and internalizing symptoms (Situ, Li, Dou, & Wang, 2021), it is possible that processes are best reflected as mediational mechanisms in addition to moderation. Similarly, it is possible that internalizing has a more immediate impact on self-control (Murray et al., 2021), thus ecological momentary assessments may capture critical in the moment nuances.

4.2. Future directions

Our findings highlight potential factors linked with e-cigarette use among adolescents, such as low self-control and internalizing symptomatology. This information could be used to improve prevention programming. Moreover, as low self-control is conceptually similar to impulsivity, reward sensitivity, and sensation seeking (Duckworth & Steinberg, 2015), findings could inform programming associated with these factors. Promoting self-control, especially among adolescents with internalizing symptomatology, may have utility in reducing rates of e-cigarette use initiation. Studies have demonstrated positive outcomes following programs targeting increased self-control (i.e., setting behavioral goals), such as decreased alcohol use (Walters, 2000; Wills et al., 2008) and reduced depressive symptomatology (Rehm, Kaslow, & Rabin, 1987). Similarly, the Coping Power Program (CPP) has been shown to reduce SU through skills comprising problem-solving strategies, resisting peer influence, and promoting coping techniques when experiencing anxiety (Lochman & Wells, 2002). Adopting self-control trainings may also prove useful for e-cigarette prevention programs.

5. Conclusion

An association between low self-control, e-cigarette use intentions and subsequent e-cigarette use by levels of internalizing symptomatology was observed. Youth with low self-control and high internalizing symptoms endorsed elevated e-cigarette use intentions, which in turn predicted e-cigarette use. In contrast, high self-control and high internalizing symptoms indicated potential protective effects. Providing coping techniques to handle stressors, and promoting self-control especially among adolescents with internalizing symptomatology, may help reduce the onset of e-cigarette use.

Supplementary Material

1

Footnotes

1

The remaining 43 adolescents who were enrolled in W1, did not complete W2 due to difficulties with retention (i.e., scheduling conflicts, unreachable, not interested anymore, parent would not consent for child, adolescent was not interested, no time to complete procedures, unfortunate personal events).

2

A post-hoc model was estimated in Mplus using full information maximum likelihood (FIML) as an effort to determine whether missingness patterns found for self-reported low self-control impacted study findings. Findings for the full sample (N = 264) were consistent with the original models. Thus, missingness seemed to have a negligible effect on the results.

3

The larger project employed a 15-month follow-up to balance obtaining comprehensive information from adolescents and caregivers while attempting to minimize participant burden as a large portion of the original sample was also invited for an additional research visit to complete a magnetic resonance imaging scan (although this data is not the focus of the current study). While year-long retrospective reporting periods of substance use have been consistently employed by national studies (Johnston et al., 2022), prior work has suggested strong reliability among adolescents reporting substance use across a 2-year study period (Shillington & Clapp, 2000).

4

Given the high degree of overlap between ethnicity and race, only ethnicity was included in the final models. Yet, findings were similar when replacing ethnicity with race as a covariate.

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