INTRODUCTION
E-cigarette use, or electronic vaporizer use, is currently the most popular form of nicotine consumption among adolescents in the U.S.1–3 In 2015, roughly 16% of U.S. 12th graders indicated e-cigarette use during the past 30 days, while 11.4% indicated traditional cigarette smoking.3 Despite marketing campaigns claiming that e-cigarettes provide a safer platform to deliver nicotine, very little is known regarding both the short- and long-term health implications of e-cigarette use within the U.S. population.4
Several recent longitudinal studies of adolescents and young adults have found that individuals who used e-cigarettes at baseline assessments had higher odds of engaging in traditional cigarette smoking roughly 12 months later.5–6 These findings are concerning given that e-cigarettes may have the potential to appeal to individuals who would otherwise never engage in any type of smoking behaviors. For instance, a recent nationally representative sample of adolescents in the U.S. found that among e-cigarette users, more than half indicated inhaling just flavoring in their most recent use, while less than a quarter of e-cigarette users indicated inhaling nicotine.7 Although inhaling nicotine-free e-liquid could be considered a safer alternative when engaging in e-cigarette use, a recent regional study found that 34.1% of adolescent e-cigarette users did not know their e-liquid nicotine concentration.8 Despite these studies showing a large percentage of adolescents using nicotine-free flavoring, a large segment of these e-cigarette users are unaware of the nicotine content. Problematically, e-cigarettes could inadvertently expose adolescents to nicotine and may increase subsequent initiation rates of traditional cigarette smoking among nonsmokers.9
While the health risks associated with traditional cigarette smoking are well documented among adolescents,10 the association between adolescent health and e-cigarette use remains unclear. For instance, levels of physical activity and traditional cigarette smoking are inversely related in adolescent and adult populations.11 Moreover, adolescents who participate in sports have been found to be at a lower risk of traditional cigarette smoking when compared to their nonparticipating peers.12–13 These findings demonstrate that adolescents who regularly engage in physical activity, like adolescents who participate in competitive sports,14 typically abstain from traditional cigarette smoking.
The inverse association between involvement in sport and cigarette smoking among adolescents may be due to an effort to simply maximize aerobic endurance in order to maintain a high level of performance.15–17 Additionally, adolescents may also abstain from cigarette smoking in order to cultivate important relationships among both adults and peers who are connected to these activities (e.g., cigarette smoking is not acceptable behavior for athletes).18–20 Despite the overall positive impact sport participation may have on lowering cigarette smoking among adolecents,12–13 some research has found that adolescent athletes who participate in certain sports may be at a greater or lower risk for cigarette smoking.16 In particular, a recent national study found that adolescents who participate in high-contact sports (e.g., football) were at the greatest risk for cigarette smoking, while adolescents who participate in non-contact sports (e.g., cross-country) were at the lowest risk for cigarette smoking.16
While the bifurcated effect found between participation in high- and non-contact sports on cigarette smoking could be due to either variation in aerobic demands placed on these athletes15–17 or the normative culture within these sports,16,21 the results clearly suggest a distinct pattern based on the level of contact or risk embedded within the sport.16,22 Unfortunately, it has yet to be determined if the same pattern among adolescent athletes exists with e-cigarette use. Given the marketing of a healthier, or safer form of smoking promoted by e-cigarette campaigns,9,23–25 it may be possible that certain low risk groups (i.e., athletes or athletes who participate in non-contact sports) may use this product because it is perceived to be much safer than traditional cigarette smoking. To date, no study has assessed the association between involvement in competitive sports and e-cigarette use among adolescents in the U.S. In order to fill this gap in our current knowledge of potential correlates of e-cigarette use, this study will explore the association between involvement in competitive sport participation, e-cigarette use and traditional cigarette smoking among a nationally representative sample of 12th grade students (modal age of 18).
METHODS
The present study uses cross-sectional data from the 2014 and 2015 Monitoring the Future (analyses were conducted in 2016).3 Based on a three-stage sampling procedure, MTF has surveyed nationally representative samples of approximately 15,000 U.S. high school seniors each year since 1975, with a response rate of 82% in 2014 and 83% in 2015. The project design and sampling methods are described in detail elsewhere.3 This study was approved by the institutional review board at the University of Michigan.
Sample
Measures for past-30 day e-cigarette use were added to several forms in 2014. Accordingly, data for the current analysis included 12th graders who were randomly assigned to complete form 5 given that questions on both e-cigarette use and competitive sport participation were only provided on this particular form. The analytic sample included 4,450 (weighted n=4,453) 12th grade respondents, 49.0% boys (51.0% girls). The racial/ethnic distribution was 13.2% Black, 17.1% Hispanic, 51.2% White, and 18.4% other race. Refer to Table 1 for additional sample characteristics.
Table 1.
Sample characteristics (n = 4,450)
| MI (10 imputations) | Listwise Deletion | % missing | |||
|---|---|---|---|---|---|
| % | SE | % | SE | ||
| Control Variables | |||||
| Boys (ref.) | 49.0% | 0.009 | 47.1% | 0.010 | 5.4% |
| Girls | 51.0% | 0.009 | 52.9% | 0.010 | |
| White (ref.) | 51.2% | 0.009 | 58.5% | 0.010 | 0.0% |
| Black | 13.2% | 0.006 | 10.8% | 0.007 | |
| Hispanic | 17.1% | 0.007 | 16.0% | 0.007 | |
| Other Race | 18.4% | 0.007 | 14.7% | 0.007 | |
| Exercise less than every day (ref.) | 62.9% | 0.009 | 62.2% | 0.010 | 2.4% |
| Exercise every day | 37.1% | 0.009 | 37.8% | 0.010 | |
| Did not cut class (ref.) | 71.1% | 0.009 | 72.2% | 0.009 | 9.0% |
| Cut class | 28.9% | 0.009 | 27.8% | 0.009 | |
| Has an average grade of B− or higher (ref.) | 84.1% | 0.007 | 85.1% | 0.007 | 5.6% |
| Has an average grade of C+ or lower | 15.9% | 0.007 | 14.9% | 0.007 | |
| Goes out at most 2 nights a week (ref.) | 61.8% | 0.009 | 61.2% | 0.013 | 6.9% |
| Goes out 3 or more times a week | 38.2% | 0.009 | 38.8% | 0.010 | |
| Does not have a job (ref.) | 40.8% | 0.009 | 39.7% | 0.010 | 6.7% |
| Works 1 to 20 hours a week | 38.1% | 0.009 | 40.5% | 0.010 | |
| Works 21 or more hours a week | 21.1% | 0.008 | 19.9% | 0.008 | |
| Both parents have less than a BA (ref.) | 49.8% | 0.009 | 47.9% | 0.010 | 4.3% |
| At least one parent has a BA | 50.2% | 0.009 | 52.1% | 0.010 | |
| Respondent lives in a Non MSA (ref.) | 19.9% | 0.007 | 21.4% | 0.008 | 0.0% |
| Respondent lives in a MSA | 49.6% | 0.009 | 48.7% | 0.010 | |
| Respondent lives in a Large MSA | 30.5% | 0.008 | 29.9% | 0.010 | |
| Respondent lives in the Northeast (ref.) | 18.3% | 0.006 | 16.8% | 0.007 | 0.0% |
| Respondent lives in the Midwest | 20.3% | 0.007 | 21.9% | 0.008 | |
| Respondent lives in the South | 39.1% | 0.009 | 37.6% | 0.010 | |
| Respondent lives in the West | 22.2% | 0.008 | 23.7% | 0.009 | |
| No past 30 day alcohol use (ref.) | 64.5% | 0.009 | 63.8% | 0.010 | 7.7% |
| past 30 day alcohol use | 35.5% | 0.009 | 36.2% | 0.010 | |
| No past 30 day marijuana use (ref.) | 78.0% | 0.007 | 79.0% | 0.008 | 5.4% |
| Past 30 day marijuana use | 22.0% | 0.007 | 21.0% | 0.008 | |
% = percent; SE = standard error; MI = multiple imputation; ref. = reference group in logistic regression models.
Measures
Traditional cigarette smoking and e-cigarette use was based on several questions that asked respondents to report on past 30-day traditional cigarette smoking (“How frequently have you smoked cigarettes”) and past 30-day e-cigarette use (“on how many occasions have you used electronic cigarettes”). Response options ranged from “Not at all” to “Two packs or more per day” and “0 days” to “20–30 days”. For the purposes of this study, these questions were treated as dichotomous variables. Moreover, these measures were combined to create several categories that identified the following types of past 30 day traditional cigarette smoking and e-cigarette use: traditional cigarette smoking only, e-cigarette use only, and dual traditional cigarette smoking and e-cigarette use. Finally, it should also be noted that two additional outcomes were constructed using a measure of lifetime traditional cigarette smoking and the measure for past 30 day e-cigarette use: past 30 day e-cigarette use only without a history of lifetime traditional cigarette smoking and past 30 day e-cigarette use only with a previous history of lifetime traditional cigarette smoking.
Participation in competitive sports was the key set of independent variables used in the analyses. Participation in competitive sports was measured by asking respondents “In which competitive sports (if any) did you participate in during the LAST 12 MONTHS? Include school, community, and other organized sports. (Mark all that apply).” The competitive sports that respondents were able to select included baseball/softball, basketball, cheerleading, crew, cross-country, equestrian, field hockey, football, golf, gymnastics, ice hockey, lacrosse, soccer, swimming and diving, tennis, track and field, volleyball, water polo, weightlifting, wrestling, and ‘other’ sports. This variable was recoded into both a two category variable and a four category variable for the analyses (refer to Table 2). In order to adequately assess differences based on different types of sports, the analyses included the ten most popular sports for boys and ten most popular sports for girls.26 Accordingly, thirteen different competitive sports that adolescents commonly participate in were included in the analyses along with an additional variable that collapsed the other sports that were measured. See Table 2 for these sample characteristics
Table 2.
Descriptive statistics for the major independent and dependent variables (n = 4,450)
| MI (10 imputations) | Listwise Deletion | % missing | |||
|---|---|---|---|---|---|
| % | SE | % | SE | ||
| Independent Variables of Interest (Past Year) | |||||
| Participates in Competitive Sports | |||||
| Does not participate in sport (ref.) | 35.3% | 0.009 | 34.9% | 0.010 | 12.8% |
| Participates in at least one sport | 64.7% | 0.009 | 65.1% | 0.010 | |
| Number of Different Sports | |||||
| Participates in one sport | 30.8% | 0.009 | 30.6% | 0.010 | 12.8% |
| Participates in two sports | 17.7% | 0.007 | 17.9% | 0.008 | |
| Participates in three + sports | 15.9% | 0.007 | 16.5% | 0.008 | |
| Different Types of Sports | |||||
| Baseball/Softball | 11.7% | 0.006 | 11.8% | 0.007 | 12.8% |
| Basketball | 17.4% | 0.007 | 17.0% | 0.008 | |
| Cheerleading | 4.8% | 0.004 | 5.0% | 0.005 | |
| Cross Country | 4.7% | 0.004 | 4.6% | 0.004 | |
| Football | 12.9% | 0.006 | 12.5% | 0.007 | |
| Golf | 4.9% | 0.004 | 5.0% | 0.005 | |
| Lacrosse | 2.6% | 0.003 | 2.5% | 0.003 | |
| Soccer | 11.9% | 0.006 | 11.5% | 0.006 | |
| Swimming and Diving | 5.7% | 0.004 | 6.0% | 0.005 | |
| Tennis | 5.5% | 0.004 | 5.5% | 0.004 | |
| Track and Field | 11.4% | 0.006 | 12.1% | 0.007 | |
| Volleyball | 8.0% | 0.005 | 8.6% | 0.006 | |
| Wrestling | 3.8% | 0.003 | 3.3% | 0.004 | |
| Other Sport | 23.1% | 0.008 | 23.6% | 0.008 | |
| Dependent Variables (Past 30 Days) | |||||
| Cigarette Smoking | 12.1% | 0.006 | 11.2% | 0.007 | 3.6% |
| E-Cigarette Use | 18.0% | 0.007 | 17.8% | 0.008 | 11.1% |
| Dual Cigarette and E-Cigarette Use | 7.2% | 0.005 | 6.8% | 0.005 | 13.0% |
| Cigarette Smoking Only | 4.7% | 0.004 | 4.4% | 0.004 | 13.0% |
| E-Cigarette Use Only | 10.8% | 0.006 | 11.0% | 0.007 | 13.0% |
| E-Cigarette Use Only (without a history of lifetime cigarette smoking) | 4.9% | 0.004 | 4.7% | 0.004 | 13.0% |
| E-Cigarette Use Only (previous history of lifetime cigarette smoking) | 6.2% | 0.005 | 6.2% | 0.005 | 13.0% |
% = percent; SE = standard error; MI = multiple imputation; ref. = reference group in logistic regression models.
Control variables were also included in the analyses to account for potentially confounding factors that are known to be associated with cigarette smoking, e-cigarette use and other substance use within the MTF sample.3 Please refer to Table 1 for more details on these control variables.3
Analysis
Descriptive statistics were provided to examine the association between competitive sport participation and the measures for past 30-day traditional cigarette smoking and e-cigarette use. Multiple logistic regression was used to examine the odds of past 30-day traditional cigarette smoking and e-cigarette use among the different measures for competitive sport participation when controlling for potentially confounding factors.
For the analyses, STATA 14.0 was the software used to estimate the models outlined above (Version 14.0; StataCorp LP, College Station, Texas). All logistic regression models provide adjusted odds ratios (AOR) and 95% confidence intervals (95% CI) while controlling for the potentially confounding factors outlined above. All analyses used the weights provided by the MTF to account for the probability of selection into the sample. Given missing data within the MTF sample, multiple imputation was used to impute missing observations (see Table 1 and 2 for more details).27 Finally, it should be noted that the models (i.e., Table 4) examining the differences across the 14 sports only consider values statistically significant at a .01 alpha level due to multiple comparisons being made within each outcome of interest.
Table 4.
Multiple logistic regression models assessing the association between different types of sport participation and past 30 day cigarette/e-cigarette use.
| Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| n = 4453 weighted/n = 4450 unweighted | Cigarette smoking | E-Cigarette use | Dual use | Cigarette smoking only | E-Cigarette use only | ||||||||||
| Past year participation in sports | % | AOR | (95% CI) | % | AOR | (95% CI) | % | AOR | (95% CI) | % | AOR | (95% CI) | % | AOR | (95% CI) |
| Baseball/Softball | 10.8% | 0.78 | (.483,1.26) | 22.7% | 1.36 | (.974,1.90) | 7.2% | 0.88 | (.500,1.56) | 4.0% | 0.88 | (.443,1.73) | 16.6% | 1.74** | (1.20,2.51) |
| Basketball | 9.2% | 0.75 | (.496,1.15) | 17.1% | 0.90 | (.627,1.28) | 5.3% | 0.70 | (.428,1.13) | 3.9% | 0.93 | (.508,1.71) | 11.9% | 1.03 | (.673,1.56) |
| Cheerleading | 7.1% | 0.72 | (.367,1.42) | 12.5% | 0.84 | (.482,1.47) | 4.5% | 0.85 | (.282,2.53) | 2.6% | 0.53 | (.177,1.61) | 8.6% | 0.89 | (.472,1.69) |
| Cross Country | 8.6% | 0.84 | (.404,1.74) | 14.9% | 0.80 | (.448,1.41) | 5.0% | 0.78 | (.333,1.84) | 3.0% | 0.82 | (.316,2.13) | 9.4% | 0.78 | (.430,1.40) |
| Football | 13.5% | 0.81 | (.526,1.25) | 22.8% | 0.84 | (.571,1.22) | 8.4% | 0.79 | (.475,1.31) | 5.1% | 1.05 | (.528,2.08) | 14.6% | 0.96 | (.649,1.43) |
| Golf | 6.6% | 0.36 | (.157,.825) | 19.0% | 0.81 | (.505,1.29) | 4.2% | 0.41 | (.163,1.01) | 1.5% | 0.29 | (.084,1.01) | 15.1% | 1.23 | (.716,2.12) |
| Lacrosse | 20.0% | 1.53 | (.706,3.30) | 34.6% | 1.77 | (1.06,2.96) | 11.9% | 1.31 | (.559,3.08) | 7.8% | 1.75 | (.532,5.78) | 21.0% | 1.54 | (.867,2.75) |
| Soccer | 6.2% | 0.46** | (.274,.776) | 14.6% | 0.70 | (.491,1.003) | 3.1% | 0.37** | (.181,.753) | 2.9% | 0.71 | (.397,1.28) | 11.5% | 1.04 | (.700,1.55) |
| Swimming and Diving | 9.4% | 0.78 | (.422,1.44) | 19.1% | 0.99 | (.621,1.57) | 7.4% | 1.13 | (.578,2.20) | 1.8% | 0.41 | (.138,1.24) | 11.3% | 0.85 | (.526,1.39) |
| Tennis | 9.5% | 0.98 | (.484,1.96) | 17.6% | 0.99 | (.636,1.55) | 5.4% | 0.85 | (.418,1.74) | 3.1% | 1.00 | (.306,3.24) | 10.2% | 0.86 | (.480,1.52) |
| Track and Field | 7.8% | 0.81 | (.507,1.31) | 14.7% | 0.99 | (.671,1.46) | 4.1% | 0.69 | (.354,1.34) | 3.9% | 1.19 | (.609,2.32) | 10.9% | 1.21 | (.789,1.86) |
| Volleyball | 10.7% | 1.20 | (.704,2.05) | 15.0% | 0.87 | (.567,1.34) | 5.8% | 1.90 | (.578,2.06) | 5.3% | 1.36 | (.729,2.55) | 9.4% | 0.78 | (.464,1.30) |
| Wrestling | 24.4% | 2.91*** | (1.75,4.85) | 33.7% | 2.14** | (1.29,3.53) | 15.8% | 2.44** | (1.34,4.47) | 7.9% | 2.02 | (.862,4.73) | 18.6% | 1.53 | (.872,2.68) |
| Other Sport | 11.8% | 1.07 | (.776,1.48) | 19.7% | 1.14 | (.871,1.49) | 7.6% | 1.15 | (.789,1,62) | 4.2% | 1.00 | (.645,1.54) | 12.3% | 1.12 | (.840,1.49) |
p<.01**, p<.001***; % = percent (prevalence); AOR = adjusted odds ratio; 95% CI = 95% confidence interval
Note that the reference group for a specific sport (e.g., baseball) are all respondents who indicated not participating in that particular sport (e.g., baseball) during the past year.
All models control for sex, race, exercise frequency, cutting class, average grade in school, time away from home at night, work status, parental education, urbanicity (e.g., does respondent live in a MSA), region (e.g., does respondent live in the Northeast), past 30-day alcohol use, and past 30-day marijuana use. Please refer to Table 1 for more details on these control variables.
Results for E-cigarette use only/with no history of cigarette smoking and E-cigarette use only/with history of cigarette smoking can be provided upon request. Note that only baseball/softball reached statistical significance for E-cigarette use only/with no history of cigarette smoking (AOR = 2.26 95% CI = 1.37,3.71).
RESULTS
Table 2 shows that e-cigarette use (18.0%) was more prevalent during the past 30 days than traditional cigarette smoking (12.1%). Past 30 day use of e-cigarettes only (10.8%) was the most common pattern followed by dual use of traditional cigarettes and e-cigarettes (7.2%) and traditional cigarette smoking only (4.7%). Moreover, roughly 5% of respondents indicated past 30 day e-cigarette use only without a history of lifetime traditional cigarette smoking while roughly 6% of respondents indicated past 30 day e-cigarette use only with a previous history of lifetime traditional cigarette smoking. With respect to competitive sport participation, the majority of respondents indicated participating in competitive sport during the past year (64.7%), with 30.8% of respondents participating in one competitive sport, 17.7% participating in two sports, and 15.9% participating in three or more sports. Table 2 also provides the percent of respondents indicating participation in thirteen of the most popular competitive sports for boys and girls.
Table 3 provides the results of the logistic regression examining the adjusted odds of past 30 day traditional cigarette smoking and e-cigarette use among adolescents involved in at least one competitive sport. The analyses show that respondents who participated in at least one competitive sport had lower odds of past 30 day traditional cigarette smoking and lower odds of dual use of traditional cigarettes and e-cigarettes when compared to respondents who did not participate in sports during the past year. No differences were found between participants and non-participants with respect to past 30 day e-cigarette use, past 30 day cigarette smoking only, past 30 day e-cigarette use only, past 30 day e-cigarette use only with no history of traditional cigarette smoking (results not shown), and past 30 day e-cigarette use only with a previous history of traditional cigarette smoking (results not shown). It should also be highlighted that when the number of different competitive sports that adolescents participated in during the past year was used in the logistic regression models, only adolescents who participated in three or more sports had significantly lower odds of past 30 day cigarette smoking and past 30 day dual cigarette smoking and e-cigarette use when compared to their peers who did not participate in sports during the past year. No statistically significant differences were found between the number of different sports and the other outcomes for past 30 day traditional cigarette smoking and e-cigarette use.
Table 3.
Multiple logistic regression models assessing the association between sport participation and past 30 day cigarette/e-cigarette use.
| n = 4453 weighted/n = 4450 unweighted | Model 1 | Model 2 | Model 3 | Model 4 | Model 5 |
|---|---|---|---|---|---|
| Cigarette smoking | E-Cigarette use | Dual use | Cigarette smoking only | E-Cigarette use only | |
| Past year participation in sports | % AOR (95% CI) | % AOR (95% CI) | % AOR (95% CI) | % AOR (95% CI) | % AOR (95% CI) |
| Does not participate in sport | 14.7% Reference | 18.2% Reference | 8.7% Reference | 5.7% Reference | 9.4% Reference |
| Participates in at least one sport | 10.6% 0.73 (.538,.973) | 17.8% 0.97 (.754,1.24) | 6.2% 0.66 (.438,.982) | 4.2% 0.98 (.643,1.50) | 11.5% 1.29 (.934,1.78) |
| Model 6 | Model 7 | Model 8 | Model 9 | Model 10 | |
| Cigarette smoking | E-Cigarette use | Dual use | Cigarette smoking only | E-Cigarette use only | |
| Past year participation in sports | % AOR (95% CI) | % AOR (95% CI) | % AOR (95% CI) | % AOR (95% CI) | % AOR (95% CI) |
| Does not participate in sport | 14.7% Reference | 18.2% Reference | 8.8% Reference | 5.7% Reference | 9.5% Reference |
| Participates in one sport | 11.3% 0.88 (.619,1.24) | 17.2% 1.00 (.769,1.30) | 6.5% 0.80 (.538,1.18) | 4.9% 1.13 (.669,1.91) | 11.0% 1.24 (.884,1.75) |
| Participates in two sports | 9.9% 0.62 (.389,1.01) | 17.2% 0.92 (.651,1.29) | 5.9% 0.57 (.322,1.03) | 3.8% 0.85 (.462,1.56) | 11.5% 1.29 (.856,1.96) |
| Participates in three or more sports | 9.9% 0.55 (.332,.909) | 19.4% 0.94 (.674,1.32) | 6.4% 0.53 (.303,.918) | 3.6% 0.79 (.372,1.69) | 13.4% 1.47 (.954,2.26) |
Note: Boldface indicates statistical significance (p<0.05); % = percent (prevalence); AOR = adjusted odds ratio; 95% CI = 95% confidence interval; Reference = reference group.
All models control for sex, race, exercise frequency, cutting class, average grade in school, time away from home at night, work status, parental education, urbanicity (e.g., does respondent live in a MSA), region (e.g., does respondent live in the Northeast), past 30-day alcohol use, and past 30-day marijuana use. Please refer to Table 1 for more details on these control variables.
Results for E-cigarette use only/with no history of cigarette smoking and E-cigarette use only/with history of cigarette smoking can be provided upon request.
Table 4 provides the results of the logistic regression examining the adjusted odds of past 30 day traditional cigarette smoking and e-cigarette use among adolescents involved in different types of sports. Adolescents who participated in wrestling had higher odds of past 30 day traditional cigarette smoking, past 30 day e-cigarette use, and past 30 day dual traditional cigarette smoking and e-cigarette use when compared to their peers who did not participate in this sport during the past year. Additionally, adolescents who participated in baseball/softball had higher odds of past 30 day e-cigarette use only and past 30 day e-cigarette use only with no history of traditional cigarette smoking when compared to adolescents who did not participate in this sport during the past year.
The analyses in Table 4 also shows one sport that reduced the odds of both past 30 day traditional cigarette smoking and e-cigarette use. Adolescents who participated in soccer had lower odds of past 30 day traditional cigarette smoking and past 30 day dual use when compared to adolescents who did not participate in these sports during the past year.
DISCUSSION
This study was the first to assess the association between competitive sport participation and different patterns of past 30-day traditional cigarette smoking and e-cigarette use. Overall, adolescents who participated in competitive sport, particularly those involved in three or more sports during the past year, were less likely to engage in past 30 day traditional cigarette smoking and past 30 day dual traditional cigarette smoking and e-cigarette use when compared to their nonparticipating peers. However, no association was found between competitive sport participation and past 30 day e-cigarette use. While the findings support previous research that adolescent athletes are at a lower risk of cigarette smoking,12–13 the same protective influence of participation in sport was not found with respect to e-cigarette use. The lack of a negative association between e-cigarette use and involvement in competitive sports among 12th graders raises concerns regarding the use of e-cigarettes as a possible gateway to traditional cigarette smoking and other tobacco use among healthy segments of the adolescent population. Traditional cigarette smoking is linked to lower levels of physical activity among adolescents and young adults,11 and the use of e-cigarettes may erode barriers that may have protected healthier adolescents from using traditional cigarette smoking.
With respect to the analyses examining traditional cigarette smoking and e-cigarette use across different sports, participation in soccer was found to significantly lower the odds of traditional cigarette smoking when compared to the other types of sports. However, participation in wrestling was associated with a greater risk of traditional cigarette smoking, e-cigarette use, and dual use when compared to their peers involved in other types of sports. These findings are consistent with another study that found that adolescents who participated in non-contact sports (i.e., physical contact is officially prohibited) were less likely to engage in traditional cigarette smoking while adolescents who participated in high-contact sports (i.e., hitting is officially sanctioned) were more likely to smoke cigarettes.16 One possible explanation would suggest that non-contact sports may reduce the risk to engage in tobacco use due to these sports requiring more aerobic endurance than what is typically found in high contact sports that require higher levels of anaerobic effort.15–17 Another possible explanation is that sensation-seeking “riskier” adolescents, who are more prone to engage in cigarette smoking, may self-select into high-contact sports while risk-averse adolescents, who are less likely to smoke traditional cigarettes, may choose safer sports that have minimal contact and a lower possibility of severe injury.16,28 Finally, the influence of the cultural views and socioeconomic status of the athletes within certain sport may likely play a significant role with respect to viewing cigarette smoking or e-cigarette use as acceptable or trendy.21, 29–30
The analyses also revealed that participation in baseball/softball was associated with a greater risk of certain patterns of e-cigarette use. In particular, participants in baseball/softball had higher odds of recent e-cigarette use with no history of traditional cigarette smoking. While it is difficult to determine in the current study as to why adolescents involved in these sports are moving toward e-cigarette use, these sports do tend to require more anaerobic effort. In other words, participants in baseball/softball may perceive that their performance will not be drastically impaired by using e-cigarettes and view them as a safer alternative to traditional cigarette smoking. Unfortunately, these adolescents who would otherwise never engage in traditional cigarette smoking may be at risk to engage in this behavior in the future due to their current exposure to e-cigarettes.
Limitations
The analyses for the current study rely on cross-sectional data and cannot determine causal patterns with respect to sport participation and different patterns of traditional cigarette and e-cigarette use. Future studies must assess how involvement in certain types of sport influences patterns of traditional cigarette and e-cigarette use as adolescents move into young adulthood. Second, the measures for tobacco use were constrained to traditional cigarette smoking and e-cigarette use. Measures of smokeless tobacco were not included on the MTF form used in the current study. Studies have shown that smokeless tobacco use is greater among adolescent athletes and could be linked to normative practices within certain sports that view smokeless tobacco use as an acceptable behavior.31–34 Given the findings from these studies, future studies need to assess if athletes moved from smokeless tobacco use to e-cigarette use or traditional cigarette smoking, or vice versa. Finally, the results from this study only focus on older adolescents (i.e., 12th graders). Younger athletes in elementary and middle school may reveal different patterns with respect to traditional cigarette and e-cigarette use that future studies should take into consideration. Despite these limitations, the MTF study is the only large-scale nationally representative survey to have questions on e-cigarette use and measures of different types of sports that adolescents commonly participate in within their school or community.
CONCLUSIONS
The results of this study provide several important findings that aid in better understanding the association between e-cigarette use, traditional cigarette smoking and competitive sport participation. First, it appears that participation in multiple sports is a modest protective factor from traditional cigarette smoking and dual use of traditional cigarettes and e-cigarettes. Second, involvement in certain sports like soccer may be more likely to reduce the risk of traditional cigarette smoking, while other sports like wrestling may increase the likelihood of engaging in both traditional cigarette smoking and e-cigarette use. Finally, adolescents involved in baseball/softball had the highest rates of e-cigarette use without a history of traditional cigarette smoking. This is potentially problematic given that this behavior may eventually lower healthy levels of physical activity or may serve as a gateway to other forms of nicotine use that are known to cause serious health problems such as traditional cigarette smoking and smokeless tobacco. Greater effort should be directed at examining the association between participation in certain types of competitive sports and e-cigarette using longitudinal data to gain a better understanding of the short- and long-term effects of e-cigarette use on various health related outcomes.
Acknowledgments
Funding Source: The development of this article was supported by research grants L40DA042452, R01CA203809, R01DA031160 and R01DA036541 from the National Cancer Institute and National Institute on Drug Abuse, National Institutes of Health. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Cancer Institute, National Institute on Drug Abuse or the National Institutes of Health.
Footnotes
Financial Disclosure: The authors have no financial relationships relevant to this article to disclose.
Conflict of Interest: The authors have no potential conflicts of interest relevant to this article to disclose.
Contributors Statement:
Phil Veliz: Dr. Veliz conceptualized and designed the study, drafted the initial manuscript, conducted the analyses, and approved the final manuscript as submitted.
Sean Esteban McCabe: Dr. McCabe helped draft the initial manuscript, interpret results, and approved the final manuscript as submitted.
Vita V. McCabe: Dr. McCabe interpreted data, critically reviewed the manuscript with a focus on the significance of the study for clinical practice, and approved the final manuscript as submitted.
Carol J. Boyd: Dr. Boyd helped conceptualize the study, critically reviewed the manuscript, and approved the final manuscript as submitted.
All authors approved the final manuscript as submitted and agree to be accountable for all aspects of the work.
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