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. Author manuscript; available in PMC: 2026 Jan 1.
Published in final edited form as: Behav Med. 2024 May 6;51(1):73–84. doi: 10.1080/08964289.2024.2347226

Associations of Impulsivity and Risky Decision-Making with E-cigarette-Related Outcomes Among Adolescents with Congenital Heart Disease: Variable- and Person-Oriented Approaches

Kristen R Fox a,*, Joseph R Rausch a,b, Victoria R Grant a, Amy K Ferketich c, Judith A Groner d, Vidu Garg b,e,f, Clifford L Cua e, Jamie L Jackson a,b
PMCID: PMC11538366  NIHMSID: NIHMS1992894  PMID: 38706412

Abstract

Adolescents with congenital heart disease (CHD) have elevated risk for acquired cardiovascular complications, increasing their vulnerability to e-cigarette-related health harms. Impulsivity and risky decision-making have been associated with adolescent substance use, but the relationships between these factors and e-cigarette-related outcomes among cardiovascular at-risk adolescents with CHD are unknown. This cross-sectional study aimed to (a) determine the associations of impulsivity and risky decision-making with e-cigarette-related outcomes (i.e., susceptibility, ever use, perceptions of harm and addictiveness) via variable-oriented analysis (logistic regression), (b) identify groups of adolescents with similar profiles of impulsivity and risky decision-making via exploratory person-oriented analysis (latent profile analysis; LPA), and (c) examine differences on e-cigarette-related outcomes between profile groups. Adolescents aged 12 to 18 years with CHD (N = 98) completed a survey assessing impulsivity facets (Short UPPS-P) and e-cigarette-related outcomes and were administered a risky decision-making task (Iowa Gambling Task, Version 2; IGT2). In variable-oriented analyses, impulsivity facets (negative urgency, positive urgency, lack of premeditation) but not risky decision-making were associated with e-cigarette susceptibility and ever use. The exploratory LPA identified two groups with similar patterns of responding on the Short UPPS-P and IGT2 labeled “Low Impulsivity” and “High Impulsivity,” which were primarily characterized by significant differences in negative and positive urgency. Adolescents in the High Impulsivity group had increased odds of e-cigarette susceptibility but not ever use compared to the Low Impulsivity group. This work indicates that strategies to prevent e-cigarette use among adolescents with CHD may be enhanced by addressing impulsivity, particularly negative and positive urgency.

Keywords: adolescents, congenital heart disease, e-cigarettes, impulsivity, risky decision-making

Introduction

The youth vaping crisis is a significant public health burden, with one in 10 U.S. adolescents currently using e-cigarettes1 and an additional one in three susceptible to future use.2 Susceptible adolescents, who report curiosity about or willingness to use e-cigarettes, are up to four times more likely to initiate vaping in the future than non-susceptible adolescents,3 possibly due to having lower perceptions of e-cigarette harm and addictiveness.2,4 These risk perceptions, and consequent e-cigarette susceptibility and use, may be driven by impulsivity5 and risky decision-making that is characteristic of adolescence.6,7

Longitudinal research has demonstrated that impulsivity is associated with all phases of substance use, including initiation,8 making impulsivity and associated constructs (e.g., risky decision-making) an important consideration for preventing adolescent vaping. Adolescents with high impulsivity may have both increased sensitivity to substance-related stimuli and decreased sensitivity to potential negative consequences of substance-related behavior.8 Thus, through these potential mechanisms, higher impulsivity may confer upon adolescents a greater risk for susceptibility to or use of e-cigarettes, as well as lower perceptions of harm or addictiveness.

While impulsivity and risky decision-making are both related to executive function,9 a collection of cognitive processes including inhibitory control, planning, and problem-solving,10 they are distinct constructs11 with a small but robust association.12 Impulsivity is conceptualized as a multidimensional personality trait and is measured by self-report,13 whereas risky decision-making refers to performance on tasks that resemble real-world contingencies.14 Impulsivity is associated with multiple e-cigarette outcomes among adolescents, including uptake.15 Risky decision-making pertaining to adolescent vaping has received less attention, but adolescents with a history of recent cigarette use demonstrate riskier decision-making than adolescents who have never used cigarettes.16 In addition, risky decision-making has predicted return to smoking among treatment-seeking adults who smoke cigarettes.17

Research demonstrating the associations of impulsivity and risky decision-making with e-cigarette-related outcomes has primarily focused on adolescents from school-based samples. Work among adolescents with medical complexities is lacking. This gap is notable because e-cigarette use is associated with adverse short- (e.g., increased arterial stiffness, elevated blood pressure)18,19 and long- (e.g., conventional combustible cigarette use)20 term cardiovascular outcomes. These consequences are particularly concerning for adolescents born with structural abnormalities of the heart or its major blood vessels, a condition known as congenital heart disease (CHD) that affects approximately one in 100 live births.21 Adolescents with CHD have elevated risk for acquired cardiovascular disease22 and thus may be acutely vulnerable to e-cigarette-related harm.23 However, despite heightened potential for negative health outcomes, our prior work has shown that nearly one-third of adolescents with CHD endorse susceptibility to future or continued use of e-cigarettes and 15% report a history of e-cigarette use.24

Given that e-cigarette susceptibility is prevalent among adolescents with CHD, research examining factors associated with e-cigarette-related outcomes is vital for the long-term health of this at-risk population. Impulsivity and risky decision-making may be particularly important considerations for adolescents with CHD. Adolescents with CHD experience greater difficulties with executive function compared to healthy peers.25 These difficulties, possibly due to structural abnormalities of the brain,26 genetic alterations,27 or surgical intervention,28 may hinder the ability to avoid risky health behaviors.29 E-cigarette use is associated with executive function difficulties among healthy youth.30 However, despite greater executive function difficulties and increased medical vulnerability, the relationship between impulsivity, risky decision-making, and e-cigarette outcomes among adolescents with CHD is unknown. Therefore, examining associations of impulsivity and risky decision-making with e-cigarette-related outcomes may yield information that could inform tailored strategies to prevent vaping and ultimately its potential cardiovascular harms for adolescents with CHD.

Prior work examining associations between impulsivity, risky decision-making, and adolescent tobacco use has predominantly utilized variable-oriented analytic approaches, and research that also employs person-oriented approaches is lacking. Variable-oriented approaches, such as regression, are based on the premise that the population is homogenous and associations between variables are similar among all members of the population. Variable-oriented approaches may obscure differing findings that are present among smaller subgroups of the population and disregard interrelations between variables that may relate to outcomes.

In contrast, person-oriented approaches, such as latent profile analysis (LPA), assume that the population is heterogenous and that the associations between independent and dependent variables differ across homogeneous subgroups within a heterogenous population. Person-oriented approaches also take into account interrelations among variables. This consideration may be particularly important for the analysis of multidimensional constructs, such as impulsivity. In addition, variable- and person-oriented approaches have been shown to yield complementary findings for adolescent substance use.31 Thus, examining the associations of impulsivity and risky decision-making with e-cigarette-related outcomes via both variable- and person-oriented approaches may provide a comprehensive understanding of these relationships.

The primary goals of the present study were to (a) determine the associations of self-reported impulsivity and task-based risky decision-making with e-cigarette-related outcomes (i.e., susceptibility, ever use, perceptions of harm and addictiveness) via variable-oriented analysis, (b) identify groups of adolescents with similar profiles of impulsivity and risky decision-making via exploratory person-oriented analysis, and (c) examine differences on e-cigarette-related outcomes between profile groups.

Methods

Procedure

Potential participants were identified from outpatient cardiology clinic rosters at a Midwestern pediatric hospital and were screened for eligibility via medical record review. Adolescents were eligible if they were between the ages of 12 and 18 years and had a diagnosis of CHD. Adolescents were excluded if English was not their preferred language, they had a genetic syndrome or other non-CHD-related medical condition with multiorgan involvement, or they had cognitive impairments limiting their ability to complete study measures. A convenience sample of adolescents was invited for participation based on clinic schedules and researcher availability. Parents/guardians and participants 18 years of age provided informed consent. Participants < 18 years of age provided informed assent. More detail about recruitment procedures has been described elsewhere.24 Participants completed (a) an online survey of impulsivity and e-cigarette-related outcomes either at home or the hospital and (b) were administered a risky decision-making task at the hospital. During the consent process, participants were told that their survey responses and risky decision-making task results were confidential and would not be released to their parent/guardian or become part of their medical records. Participants were recruited and data were collected between April 2021 and April 2022. The study protocol was approved by the site’s Institutional Review Board (STUDY00001155).

Measures

Impulsivity

The Short Urgency, Premeditation, Perseverance, Sensation Seeking, Positive Urgency Impulsive Behavior Scale (Short UPPS-P)13 measured impulsive behavior according to five facets: 1) negative urgency (impulsivity under negative affective states), 2) positive urgency (impulsivity under positive affective states), 3) (lack of) perseverance (inability to focus), 4) (lack of) premeditation (acting without thinking), and 5) sensation seeking (seeking novel and thrilling experiences). The 20 items are rated on a 1 (“Agree strongly”) to 4 (“Disagree strongly”) scale. Higher mean scores for each facet reflect greater impulsivity.

Internal consistency reliability in the good range (i.e., 0.8 ≤ α < 0.9) was observed for negative urgency (α = .79), positive urgency (α = .83), and (lack of) premeditation (α = .80). Internal consistency reliability was lower for (lack of) perseverance (α = .62) and sensation seeking (α = .62) but similar to internal consistency reliability for these facets that has been observed for the Short UPPS-P in work among youth (e.g., α = 0.58 – 0.79).5 Deleting individual items that have low correlations with their respective total scale scores can address lower internal consistency reliability,32 but this procedure did not improve the coefficients for (lack of) perseverance or sensation seeking. Thus, all items for these subscales were retained. The five-factor structure of the Short UPPS-P has been validated among adolescents.33

Risky decision-making

The Iowa Gambling Task™, Version 2 (IGT2)14 assessed risky decision-making. The IGT2 is a brief, computer-administered task that creates conflict between immediate reward and delayed punishment. Participants start with $2,000 in play money and select cards with corresponding rewards and losses from two advantageous (i.e., small reward but net gain over time) and two disadvantageous (i.e., high reward but net loss over time) decks with the goal of maximizing profits over 100 trials. A net total raw score is calculated by subtracting the number of disadvantageous selections from the number of advantageous selections. Higher scores indicate less risky decision-making. T-scores for the net total raw score ≥ 45 are considered nonimpaired, while T-scores 40–44 and 0–39 are considered below average and impaired, respectively. The IGT2 was administered according to standard procedures.14

E-cigarette-related outcomes

The Expanded Susceptibility to Smoke Index34 was modified to assess susceptibility to future/continued use of e-cigarettes. Participants responding “Definitely not” to 4 items such as, “Have you ever been curious about using an e-cigarette?,” were classified as “Not susceptible.” Consistent with other work,3 participants endorsing any other response (“Probably not”; “Probably yes”; “Definitely yes”) to ≥ one item were classified as “Susceptible.” The Expanded Susceptibility to Smoke Index has been similarly modified for e-cigarettes in other work and has predicted future e-cigarette use.3 The item, “Have you ever tried an e-cigarette, even once or twice?” (Yes/No), from the National Youth Tobacco Survey (NYTS)35 measured ever use of e-cigarettes. Assessing ever use is important because adolescents who endorse using e-cigarettes, “even once or twice,” are more likely to begin smoking conventional cigarettes in the future.36

Given that e-cigarette susceptibility is a psychological indicator of vaping risk while ever use is a behavioral outcome, participants’ susceptibility and ever use statuses were determined independently from each other. That is, each participant was assigned a status for both susceptibility and ever use. Thus, participants determined to be “Susceptible” may also be positive for ever use if they reported trying e-cigarettes, for example. In the present study, 41.9% of “susceptible” adolescents reported ever use, and of 86.7% of adolescents who reported ever use were “susceptible.” Frequency of e-cigarette use was not assessed.

Perceptions of harm and addictiveness were measured in accordance with the NYTS. For harm, participants were asked, “How much do you think people harm themselves when they use e-cigarettes some days but not every day?” Responses were rated 1 (“No harm”) to 4 (“A lot of harm”). For addictiveness, participants were asked, “Do you believe that e-cigarettes are less, equally, or more addictive than cigarettes?” Response choices were “Less addictive,” “Equally addictive,” “More addictive,” “I have never heard of e-cigarettes,” and “I don’t know enough about these products.”

Demographic characteristics and covariates

Demographic characteristics, including gender, age, race, and ethnicity were self-reported. Severity of CHD was abstracted from medical records and was classified as simple (e.g., atrial septal defect), moderate (e.g., bicuspid aortic valve), or complex (e.g., hypoplastic left heart syndrome) according to clinical guidelines.37 Household use of any tobacco product (e.g., conventional cigarettes, smokeless tobacco, e-cigarettes) was obtained by participant report. Family income was estimated from U.S. census tract data.38

Statistical analysis

Data were examined for missingness prior to statistical analysis. For the Short UPPS-P impulsivity facets, mean scores were calculated if at least three of the four subscale items were available. Seven participants were missing one subscale item for one (n = 5) or two (n = 2) impulsivity facets, and their mean scores were prorated accordingly. Two participants did not respond to one item on the modified Expanded Susceptibility to Smoke Index, and their e-cigarette susceptibility status could not be determined. The number of participants with valid data for each variable is shown in Figure 1.

Figure 1.

Figure 1.

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Participant flow diagram.

Descriptive statistics characterized the demographic and clinical profile of the study sample and described impulsivity, risky decision-making, and e-cigarette-related outcomes. Covariates (i.e., age, gender, family income, household tobacco use, and CHD severity) used in adjusted analyses were determined a priori based on prior research demonstrating associations between these sociodemographic characteristics and e-cigarette susceptibility, use, and perceptions.2,39,40 CHD severity was also included as a covariate because cognitive and executive function differs by defect type,25 potentially impacting impulsivity and decision-making. One participant (1.0%) reported non-binary gender and is included in unadjusted analyses but is not included in adjusted analyses due to the small cell size for non-binary gender. For the IGT2, net total raw scores were used in analyses; T-scores are reported for descriptive purposes.

For variable-oriented analyses, logistic regressions determined the associations between individual impulsivity facets and risky decision-making with e-cigarette-related outcomes. Each logistic regression was performed with the continuous variables (a) standardized and (b) unstandardized. Odds ratios (ORs) reflect the odds of e-cigarette susceptibility or ever use for every one standard deviation increase (for standardized ORs) or every one unit increase (for unstandardized ORs) in impulsivity or risky decision-making.

For person-oriented analyses, an exploratory latent profile analysis (LPA) was conducted to identify groups of participants with similar patterns of responding on the Short UPPS-P and performance on the IGT2. LPA is a person-centered mixture model that separates data from one presumably homogenous overall group into two to more distinct homogeneous subgroups (i.e., profiles) of an underlying latent variable.41 The LPA was exploratory, meaning that there were no expectations about the number, shape, or size of profiles. Despite the exploratory nature of the LPA, the selection of indicator variables (i.e., impulsivity facets, risky decision-making) was informed by prior work indicating a) associations between impulsivity and risky decision-making with e-cigarette and other tobacco-related outcomes among youth, b) that self-reported impulsivity and task-based risky decision-making are related but distinct, thereby providing complementary information to derive profiles, and c) the multidimensional structure of impulsivity, which may be aptly conceptualized and analyzed with person-oriented approaches.

Multiple models were estimated, and the following fit indices were examined to determine the optimal number of profiles: (a) Akaike Information Criteria (AIC), (b) Bayesian Information Criteria (BIC), (c) sample size-adjusted BIC, and (d) entropy. Information criteria (i.e., AIC, BIC, and adjusted BIC) are relative estimates of the information lost when a given model is used to represent the data. For these indices, the model with the lowest value provides the best fit. Entropy refers to the confidence of which participants have been successfully assigned to a subgroup. Entropy ≥ 0.80 is acceptable.42 The LPA was conducted with MPlus using full-information maximum likelihood that estimated missing data. Examination of Q-Q plots indicated the assumption that the indicator variables are normally distributed across profile groups was met. After the best profile solution was identified, t-tests were conducted to determine if impulsivity facets and risky decision-making differed by profile group. Logistic regression determined the associations between profile group and e-cigarette-related outcomes. ORs reflect the odds of e-cigarette susceptibility or ever use for one profile group compared to another.

Results

Participant characteristics and description of impulsivity, risky decision-making, and e-cigarette-related outcomes

As shown in Figure 1, 80.3% (n = 102) of those invited to participate in the study enrolled, and of those, 96.1% (n = 98) had valid data on at least one impulsivity, risky decision-making, or e-cigarette-related variable. The demographic and clinical characteristics of the sample have been described elsewhere.24 In brief, the sample was primarily male (57.1%; n = 56), had a mean age of 15.16 (SD = 1.93) years, and had CHD of moderate severity (60.2%; n = 59). Participants identified with one or more of the following races: American Indian or Native Alaskan (3.1%; n = 3), Asian (9.2%; n = 9), Black (17.3%; n = 17), Multiracial (3.1%; n = 3), White (73.5%; n = 72), and Other (1.0%; n = 1); 3.2% (n = 3) reported Hispanic or Latino ethnicity.

Descriptive statistics and intercorrelations for impulsivity and risky decision-making are presented in Table 1. Of note, no facet of impulsivity was correlated with risky decision-making. Nearly one-third of participants (31.5%; n = 29) demonstrated below average (16.3%; n = 15) or impaired (15.2%; n = 14) decision-making according to their performance on the IGT2 (MT-score = 48.87, SD = 10.52). Participants indicated belief that e-cigarettes are harmful (M = 3.45, SD = 0.63), with 94.9% (n = 93) reporting that e-cigarettes either cause “some” (43.9%; n = 43) or “a lot” of harm (51.0%; n = 50). Most (68.4%; n = 67) participants reported that e-cigarettes were equally (34.7%; n = 34) or more (33.7%; n = 33) addictive than conventional cigarettes; however, one participant (1.0%) reported that e-cigarettes were less addictive, and 3.1% (n = 3) and 27.6% (n = 27) reported that they had never heard of e-cigarettes or lacked adequate knowledge to assess their addictiveness, respectively. Participants who were unaware of or lacked knowledge of e-cigarettes were less likely to endorse susceptibility to (3.4% [n = 1] vs. 43.3% [n = 29]; p < .001) or ever use of (0.0% [n = 0] vs. 22.1% [n = 15]; p < .01) e-cigarettes than participants who provided an informative response to the perceived addictiveness item.

Table 1.

Descriptive statistics and bivariate correlations between impulsivity facets and risky decision-making.

M SD 1 2 3 4 5
1. Negative urgency 2.35 0.75
2. Positive urgency 2.12 0.73 .67*
3. (Lack of) premeditation 2.11 0.66 .42* .41*
4. (Lack of) perseverance 1.82 0.51 .13 .12 .44*
5. Sensation seeking 2.53 0.72 .19 .36* .17 −.04
6. Risky decision-makinga 3.67 25.56 −.05 .04 .06 .04 .17
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*

p < .001

a

IGT2 total net score.

Due to the limited variability observed for perceptions of harm and addictiveness (i.e., 95% believe they cause harm, 68% believe they are addictive), these outcomes were not analyzed as a function of impulsivity or risky decision-making. This decision was made due to concerns that the data could not be appropriately analyzed in a manner that could produce a potentially meaningful interpretation of the results. For example, it is uncertain whether an adolescent viewing e-cigarettes as more versus equally addictive than conventional cigarettes is a valuable distinction that may impact behavioral outcomes.

Associations of impulsivity and risky decision-making with e-cigarette susceptibility and ever use

Negative urgency, positive urgency, and (lack of) premeditation were associated with increased odds of being susceptible to e-cigarettes, as well as ever use of cigarettes (Table 2).

Table 2.

Associations between impulsivity and risky decision-making with e-cigarette susceptibility and ever use.

Susceptibility Ever Use
% Yes, 31.3%; n = 30 % Yes, 15.3%; n = 15a
Standardizedc OR [95% CI] Unstandardizedd OR [95% CI] p n Standardizedc OR [95% CI] Unstandardizedd OR [95% CI] p n
Negative urgency
Unadjusted 2.34 [1.40, 3.90] 3.10 [1.57, 6.10] <.01 95 1.86 [1.04, 3.31] 2.27 [1.05, 4.91] .04 97
Adjustedb 2.67 [1.47, 4.85] 3.69 [1.67, 8.16] <.01 90 2.30 [1.11, 4.75] 3.02 [1.15, 7.92] .03 92
Positive urgency
Unadjusted 2.45 [1.47, 4.09] 3.43 [1.70, 6.95] <.001 95 2.10 [1.19, 3.73] 2.79 [1.27, 6.13] .01 97
Adjustedb 2.83 [1.55, 5.14] 4.18 [1.83, 9.52] <.001 90 2.56 [1.33, 4.93] 3.65 [1.48, 9.00] .01 92
Sensation seeking
Unadjusted 1.36 [0.86, 2.15] 1.53 [0.81, 2.90] .19 95 1.46 [0.82, 2.60] 1.70 [0.76, 3.78] .20 97
Adjustedb 1.53 [0.90, 2.59] 1.81 [0.87, 3.78] .12 90 1.81 [0.92, 3.58] 2.29 [0.89, 5.93] .09 92
(Lack of) premeditation
Unadjusted 1.75 [1.10, 2.77] 2.34 [1.16, 4.72] .02 95 1.82 [1.04, 3.17] 2.49 [1.06, 5.81] .04 97
Adjustedb 2.53 [1.42, 4.52] 4.12 [1.71, 9.97] <.01 90 2.97 [1.40, 6.33] 5.26 [1.66, 16.66] .01 92
(Lack of) perseverance
Unadjusted 1.09 [0.71, 1.69] 1.19 [0.51, 2.79] .69 95 0.96 [0.55, 1.68] 0.93 [0.31, 2.74] .89 97
Adjustedb 1.15 [0.71, 1.86] 1.31 [0.51, 3.36] .57 90 0.95 [0.53, 1.69] 0.91 [0.29, 2.79] .86 92
Risky decision-making
Unadjusted 0.92 [0.59, 1.44] 1.00 [0.98, 1.01] .72 90 0.97 [0.56, 1.70] 1.00 [0.98, 1.02] .92 92
Adjustedb 0.97 [0.58, 1.61] 1.00 [0.98, 1.02] .90 86 0.90 [0.49, 1.64] 1.00 [0.97, 1.02] .72 88
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a

Of those who reported ever use, 26.7% (n=4) reported past 30 day use of e-cigarettes.

b

Adjusted for age, gender, family income, household tobacco use, and CHD severity.

c

Standardized odds ratios represent the odds of susceptibility/ever use for each standard deviation increase in impulsivity/risky decision-making.

d

Unstandized odds ratios represent the odds of susceptibility/ever use for each unit increase in impulsivity/risky decision-making.

Latent profiles of impulsivity and risky decision-making

Two of the four fit indices (i.e., BIC and entropy) indicated that a two-profile solution was the best fit to the data (Table 3). The AIC and adjusted BIC indicated that a three-profile solution was the best fit, but these indices may be prone to selecting models that are too complex43 or that overestimate the number of profiles,44 respectively. Thus, the two-profile solution was selected, and the profiles were labeled “High Impulsivity” and “Low Impulsivity.”

Table 3.

Fit statistics for latent profile solutions.

Fit Statistic
Number of profiles AIC BIC Adjusted BIC Entropy
1 1858.47 1889.49 1851.60 -
2 a 1797.29 1846.41 1786.41 0.80
3 1783.86 1851.07 1768.98 0.79
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a

Selected profile

As shown in Figure 2, the High Impulsivity group was characterized by greater negative urgency (p < .001, d = 2.02), positive urgency (p < .001, d = 2.55), sensation seeking (p < .001, d = 0.75), and (lack of) premeditation (p < .001, d = 1.00) compared to the Low Impulsivity group. (Lack of) perseverance (p = .07, d = 0.39) and risky decision-making (p = .36, d = 0.20) did not differ by group. Two-thirds (66.3%; n = 65) of participants were in the Low Impulsivity group, and one-third (33.7%; n = 33) were in the High Impulsivity group.

Figure 2.

Figure 2.

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Impulsivity facets (A) and risky decision-making (B) by LPA profile group.

Note. Vertical bars represent the standard error.

Association of latent profiles with e-cigarette susceptibility and ever use

Compared to participants in the Low Impulsivity group, those in the High Impulsivity group had over three times higher odds of being susceptible to e-cigarettes in the unadjusted model (OR = 3.29, 95% CI = 1.33, 8.14, p < .01; n = 96) and over five times higher odds of being susceptible to e-cigarettes in the adjusted model (OR = 5.19, 95% CI = 1.76, 15.28, p < .01, n = 91). Profile group was not associated with ever use of e-cigarettes in unadjusted (OR = 1.92, 95% CI = 0.63, 5.85, p = .25; n = 98) or adjusted (OR = 2.86, 95% CI = 0.81, 10.05, p = .10; n = 93) models.

Discussion

This study identified associations of self-reported impulsivity and task-based risky decision-making with e-cigarette susceptibility and ever use among cardiovascular at-risk adolescents with CHD. The primary finding was that impulsivity, particularly rash action during heightened emotional states (i.e., negative and positive urgency) is associated with e-cigarette susceptibility via both variable- and person- oriented analytic approaches. Further, adolescents with CHD believe e-cigarettes are harmful and addictive, but many are susceptible to future or continued vaping. Strategies that help adolescents with CHD regulate emotion and avoid impulsive behavior under heightened affect may be useful considerations for e-cigarette prevention.

In variable-oriented analyses, several impulsivity facets were related to e-cigarette susceptibility and ever use, with the urgency facets demonstrating the strongest associations. Likewise, in person-oriented analyses, the largest effect sizes for differences between the Low and High Impulsivity groups were for negative and positive urgency. These findings align with other work showing that negative and positive urgency are particularly important considerations for the onset and progression of adolescent substance use.45 In contrast, research among youth with asthma showed that impulsivity was not associated with e-cigarette susceptibility or current use, but the measure used to assess impulsive behavior did not capture affect-related impulsivity.46 The observed associations between urgency and e-cigarette susceptibility and ever use may be attributable to difficulties with emotion regulation47 or expectancies that vaping will reduce negative affect.48 Cyders and Smith49 proposed that rash action occurring during heightened emotional states can involve risky behaviors that provide immediate reinforcement. In turn, these risky behaviors are repeated, reducing opportunities for reinforcement from more adaptive behaviors and thus forming a reliance on risky behaviors during intense emotional states. Therefore, programs that enhance emotion regulation and promote adaptive coping may help adolescents with CHD avoid initiation and progression of e-cigarette use.

In terms of ever use of e-cigarettes, the variable-oriented analysis revealed that multiple facets of impulsivity were associated with ever use of e-cigarettes, but adolescents in the Low and High Impulsivity groups had similar odds of ever use. The relatively small number of adolescents who reported ever use (n = 15) may have limited the ability to detect significant associations between ever use of e-cigarettes and LPA group. It is also possible that impulsivity is more strongly related to e-cigarette susceptibility than use and thereby more of a consideration for preventing e-cigarette use among adolescents compared to reducing or stopping ongoing vaping. This idea is consistent with the suggestion that impulsivity and risk-taking contribute more to substance use initiation and progression versus continued use or addiction and return to use.50

In contrast to self-reported impulsivity, task-based risky decision-making measured by the IGT2 had no relation to susceptibility or ever use in regression models, nor did it differ between the High and Low Impulsivity profile groups identified by the LPA. While limited, prior work has found associations between risky decision-making and smoking-related outcomes among adolescents. Xiao and colleagues51 reported that IGT performance predicted cigarette smoking status and number of cigarettes smoked per day one year later. However, this finding was obtained only with a cognitive decision model scoring approach for the IGT, and the IGT net total, which is commonly reported in the literature and was used in the present study, was not related to smoking outcomes. Another study found that adolescents who smoked cigarettes within the past seven days performed worse on the IGT than non-smoking adolescents.16 It is possible that recent nicotine exposure impacts risky decision-making performance52 and that an association may not be detected when susceptibility and ever use are the outcomes of interest.

It is also notable that in the present study self-reported impulsivity, but not task-based risky decision-making, was associated with e-cigarette susceptibility and ever use. Balevich and colleagues53 also found that self-reported impulsivity was associated with smoking status among young adults but that behavioral measures, including the IGT, were not.53 Discrepancy between self-report and behavioral measures of purportedly similar constructs, including the UPPS-P and the IGT,54 has been identified in the literature.55 Indeed, in the present study, impulsivity and risky decision-making were not correlated. Differing developmental trajectories and neural processes may underlie impulsivity and risk-taking and may contribute to the observed differing associations with e-cigarette-related outcomes. Impulsivity declines with age during childhood and adolescence and is linked to prefrontal cortex development, whereas risk-taking increases during adolescence and is associated with subcortical systems involved in rewards.56

In the current research, nearly all participants (95%) believed that e-cigarettes cause at least some harm, and over two-thirds (68%) reported that e-cigarettes are equally or more addictive than conventional cigarettes. Adolescents with CHD in the current study appeared to endorse greater harm and addictiveness of e-cigarettes than healthy peers.57 Because of their heart condition, adolescents with CHD may perceive that e-cigarettes pose greater health harms for them relative to peers.58 It must also be recognized that 31% of adolescents with CHD reported that they lacked the knowledge to assess the relative addictiveness of e-cigarettes. This finding does not appear to be unique to this population, however, as over 40% of adolescents from school-based samples respond similarly to the addictiveness perception item.2

Despite most adolescents with CHD acknowledging the potential for e-cigarette-related harm and addiction, a considerable portion reported susceptibility to and/or ever use of e-cigarettes. Further, those who reported lacking knowledge about e-cigarettes were less likely to be susceptible e-cigarettes or to have ever vaped. These observations indicate that perceiving vaping as harmful and addictive may not deter adolescents with CHD from e-cigarettes. Therefore, intervention programs that primarily communicate the health risks of e-cigarettes may be redundant and insufficient for vaping prevention for adolescents with CHD.

Hence, this cardiovascular at-risk population may require special considerations for effective vaping prevention. A school-based vaping prevention program with educational (e.g., health consequences), social normative (e.g., expectations of peer use), and motivational (e.g., reasons for e-cigarette use) components has shown promise for reducing e-cigarette use among middle school students.59 However, rigorous empirical evaluation of adolescent e-cigarette prevention interventions is lacking in the literature, and existing prevention interventions have gaps in important content areas (e.g., impact on mental health).60 Further, school-based prevention strategies designed to target a broad audience of youth may not address the unique needs of adolescents with CHD. The present research indicates that incorporating strategies that address impulsivity, particularly negative and positive urgency, into e-cigarette interventions for adolescents with CHD may be beneficial. Several strategies for addressing urgency and improving emotion regulation have been suggested, including distress tolerance skills,61 mindfulness meditation, and working memory training.48 Mindfulness62 and working memory training63 have shown promise for adolescents with CHD. Moreover, because impulsivity has been associated with multiple maladaptive behaviors,64 strategies to reduce urgency may have broad health impact and be valuable additions to pediatric to adult CHD care transition programs.

The results of this research must be interpreted in the context of some limitations. First, the sample was obtained with convenience methods and represents adolescents actively engaged in cardiology follow-up at one pediatric hospital. Thus, findings may not generalize to the population of adolescents with CHD in terms of demographics, impulsivity and risky decision-making, and e-cigarette-related outcomes. Further, while similar to work in other pediatric specialty populations, the sample size was smaller than generally recommended for LPA. This limitation may compromise the ability to detect small but meaningful profiles with low memberships. It will be important for future work to attempt to replicate these findings in larger, multicenter samples that reflect the population of adolescents with CHD and that permit both exploratory and confirmatory analyses. Second, social desirability may have impacted self-report of e-cigarette-related outcomes, introducing potential bias. Adolescents with CHD may have underreported susceptibility to and ever use of e-cigarettes and overreported perceptions of harm and addictiveness, given their heart condition and that the research was conducted through a healthcare institution. Future research should consider including an objective assessment of nicotine exposure to corroborate adolescents’ self-reports. While the rigor of the present research is bolstered with the use of both self-report and behavioral measures, a single task of risky decision-making under ambiguity and uncertainty based on a gambling paradigm was used. Other behavioral measures of risky decision-making with different task types may exhibit different associations with e-cigarette-related outcomes.12 In addition, the present research examined only the associations between impulsivity and risky decision-making with susceptibility to and ever use of e-cigarettes. Other e-cigarette outcomes (e.g., current use, frequency of use) may have different relationships with impulsivity and risky decision-making. Furthermore, it is possible that participants classified as susceptible represent a broad range of e-cigarette use experience (i.e., no use to frequent use). However, in the absence of data on frequency of e-cigarette use, this cannot be ascertained for the present study.

Conclusions

Variable- and person-oriented analyses supplied generally similar results for the associations of impulsivity and risky decision-making with e-cigarette susceptibility and ever use. Both approaches underscored the strong associations between impulsivity, particularly negative and positive urgency, and susceptibility to e-cigarettes among adolescents with CHD. However, more work is needed to confirm the latent profiles of impulsivity and risky decision-making. Adolescents with CHD, despite commonly perceiving e-cigarettes as harmful and addictive, are at risk for future/continued use of e-cigarettes. Should the present findings be replicated, interventions that address negative and positive urgency should be considered to prevent e-cigarette use among the cardiovascular at-risk population of adolescents with CHD.

Funding:

This work was supported by the National Center for Advancing Translational Sciences under grant TL1TR002735 and funding from the Clinical and Translational Intramural Funding at Nationwide Children’s Hospital.

Footnotes

Declaration of Interest: The authors have no competing interests to declare.

Data Availability Statement:

De-identified data that support the findings of this study are available from the corresponding author, [KRF], upon reasonable request, subject to the provisions of a data use agreement, and as allowable by institutional IRB standards.

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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

De-identified data that support the findings of this study are available from the corresponding author, [KRF], upon reasonable request, subject to the provisions of a data use agreement, and as allowable by institutional IRB standards.

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