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. Author manuscript; available in PMC: 2020 Oct 1.
Published in final edited form as: J Adolesc. 2019 Aug 20;76:48–54. doi: 10.1016/j.adolescence.2019.08.009

E-cigarette use and sleep-related complaints among youth

Kira E Riehm a, Darlynn M Rojo-Wissar a, Kenneth A Feder a, Ramin Mojtabai a,b, Adam P Spira a,b,c, Johannes Thrul a, Rosa M Crum a,b,d,e
PMCID: PMC6803098  NIHMSID: NIHMS1538321  PMID: 31442814

Abstract

Introduction:

E-cigarette use is highly prevalent among adolescents. However, little research has examined the relationship between e-cigarette use and sleep-related complaints in this population. The objective of this study was to assess whether exclusive e-cigarette, exclusive combusted cigarette, and dual-product use are associated with sleep-related complaints among adolescents.

Methods:

Participants were 9,588 U.S. adolescents from the Population Assessment of Tobacco and Health Study, a nationally representative cohort, followed from 2013 through 2015. Using logistic regression, we examined the cross-sectional association between past-year e-cigarette, combusted cigarette, or dual-product use and past-year sleep-related complaints (bad dreams, sleeping restlessly, or falling asleep during the day), both measured at Wave 2. We controlled for Wave 1 demographic characteristics, emotional and behavioral health, and prior history of e-cigarette use, combusted cigarette use, and sleep-related complaints.

Results:

In unadjusted analyses, e-cigarette, combusted cigarette, and dual-product use were significantly associated with greater odds of sleep-related complaints, compared to use of neither product (e-cigarettes: OR=1.61, 95% CI 1.34–1.94; combusted cigarettes: OR=1.62, 95% CI 1.26–2.09; dual-product use: OR=2.00, 95% CI 1.63–2.46). Associations between e-cigarette and dual-product use and sleep-related complaints remained significant in fully adjusted analyses (e-cigarettes: aOR=1.29, 95% CI 1.05–1.59; dual-product use: aOR=1.57, 95% CI 1.24–1.99), whereas associations with combusted cigarette use were significant in all models except the fully adjusted model (aOR=1.30, 95% CI 0.98–1.71).

Conclusions:

E-cigarette and dual-product use are significantly associated with greater odds of reporting sleep-related complaints among adolescents. Future research should evaluate whether this association may be causal.

Keywords: sleep problems, smoking, e-cigarettes, adolescents

INTRODUCTION

In 2018, approximately 20.8% of U.S. high school students reported e-cigarette use in the past 30 days (Cullen, 2018). Although e-cigarettes can be used without nicotine, up to half of the population of youth who use e-cigarettes report using e-liquids that contain nicotine (Goldenson, Leventhal, Stone, McConnell, & Barrington-Trimis, 2017). E-cigarette use increases the odds of subsequent combusted cigarette use by approximately three and half times compared to non-e-cigarette users (Soneji, Barrington-Trimis, Wills, & et al., 2017), and this is especially likely when e-liquids with nicotine are used (Goldenson et al., 2017). With the recent emergence of “pod” e-cigarettes, including JUUL, that deliver high levels of nicotine with few unpleasant side effects (Barrington-Trimis & Leventhal, 2018), e-cigarette use among adolescents continues to be a prominent public health issue.

Although the long-term health outcomes of e-cigarette use are poorly understood, there is ample evidence supporting short-term risks of e-cigarette use, which include increased heart rate, cough, and wheeze (St. Helen & Eaton, 2018; Wang, Ho, Leung, & Lam, 2016). Another potential adverse health outcome is disrupted sleep. Short sleep duration and poor quality sleep are highly prevalent among adolescents, especially in late adolescence; for example, up to 75% of 12th grade students report less than eight hours of sleep per night (Foundation, 2006; Owens, 2014). Socioemotional correlates of insufficient sleep include depressive and anxious symptoms, substance use, risk-taking behaviors, loneliness, poor self-esteem, and suicidal ideation (Chaput et al., 2016; Shochat, Cohen-Zion, & Tzischinsky, 2014). Disrupted sleep and combusted cigarette use have a well-documented association among adolescents (Bartel, Gradisar, & Williamson, 2015; Costa & Esteves, 2018). This relationship appears to be bidirectional, with cigarette use predicting greater sleep disturbance longitudinally, and as well as the reverse (Pasch, Latimer, Cance, Moe, & Lytle, 2012; Patten, Choi, Gillin, & Pierce, 2000). Cigarette use in adolescence is also associated with sleep disruptions in adulthood (Mathers, Toumbourou, Catalano, Williams, & Patton, 2006). Nicotine is thought to be a mechanism linking combusted cigarette use with sleep disturbances though stimulation of the release of neurotransmitters that modulate the sleep-wake cycle, and because nighttime acute withdrawal can cause unpleasant sleep-disrupting physiological symptoms (Costa & Esteves, 2018). Respiratory problems may also mediate this association, as they are known to be associated with cigarette use and may lead to sleep problems (Merianos, Jandarov, & Mahabee-Gittens, 2018).

E-cigarettes are now the most commonly used tobacco product among middle and high school students (Jamal et al., 2017), and their use may have implications for adolescent health, including sleep health. However, few studies have examined the association between e-cigarette use and sleep, with only one study to our knowledge in adolescents (Dunbar et al., 2017), and the results of these studies have been mixed. A recent systematic review and meta-analysis identified insomnia as one of the most prevalent adverse events following e-cigarette use in adults who were trying to quit smoking using e-cigarettes as an aid (Liu et al., 2018). One study of adolescents found that e-cigarette users report shorter weekend sleep duration compared to non-users, but did not find evidence for shorter weekday sleep duration or poorer sleep quality (Dunbar et al., 2017). Given the negative health, academic, and psychosocial outcomes associated with insufficient sleep during adolescence (Owens, 2014), more evidence is needed to improve our understanding of the potential impact of e-cigarette use on sleep disruptions among youth.

The current study uses two waves of the Population Assessment of Tobacco and Health (PATH) Study to examine the cross-sectional association of adolescent sleep-related complaints with three mutually exclusive smoking behaviors: e-cigarette use only; combusted cigarette use only; and dual-product use. Our study seeks to replicate and build upon existing evidence (Dunbar et al., 2017) by examining this association in a nationally-representative sample of U.S. adolescents with adjustment for a wide range of potential confounders. We hypothesized that adolescents reporting exclusive e-cigarette, exclusive combusted cigarette, or dual-product use would all have a higher likelihood of reporting sleep-related complaints relative to adolescents reporting no use of either product.

METHODS

Participants

The study sample was drawn from the public-use files of Waves 1 and 2 of the PATH Study, a nationally representative, prospective cohort study of adolescents (ages 12 to 17) living in the U.S. (Hyland et al., 2017). The PATH Study investigators used multi-stage stratified sampling to obtain a sample of households from which up to two individuals ages 12–17 were randomly selected to be interviewed. Selected households were sent an advance letter and brochure with information about the survey, and participants were allowed to ask questions prior to data collection. After obtaining parent permission and youth assent, youth were interviewed using audio computer-assisted self-interviewing. Youth received $25 following participation as a token of appreciation. Wave 1 data were collected from September 2013 to December 2014. Participants were re-interviewed one year later during Wave 2 from October 2014 to October 2015 (US Department of Health and Human Services, 2017).

The weighted response rate for youth at baseline was 78.4%. The weighted retention rate at follow-up was 88.4%. A total of 10,081 youth completed both PATH Study waves. Our study sample excluded 493 youth (4.9%) because they had missing data on at least one variable required for the analysis; the remaining 9,588 adolescents comprised the analytic sample. This study did not constitute human subjects research, because data were publicly available and de-identified.

Measures

Sleep-related Complaints (Outcome, Wave 2).

The outcome of interest was past-year sleep-related complaints measured by a single item at Wave 2. Adolescents were asked, “When was the last time that you had significant problems with sleep trouble, such as bad dreams, sleeping restlessly, or falling asleep during the day?” Sleep-related complaints were considered present if the respondent selected either “in the past month” or “two to twelve months” from the response options, which yielded a dichotomous variable of past-year sleep-related complaints versus no past-year sleep-related complaints. This item was drawn from the internalizing subscale of the Global Appraisal of Individual Needs – Short Screener (GAIN-SS) (Dennis, Chan, & Funk, 2006), which is a screening measure intended to identify those likely to have a mental disorder and assess symptom severity. It has previously been validated in adolescents (McDonell, Comtois, Voss, Morgan, & Ries, 2009).

Combusted Cigarette, E-cigarette, and Dual-Product Use (Exposure, Wave 2).

Participants were grouped in four mutually-exclusive exposure categories based on their self-reported past-year e-cigarette or combusted cigarette use at Wave 2: 1) exclusive e-cigarette use; 2) exclusive combusted cigarette use; 3) dual-product use; or 4) no use of either product. E-cigarette use was assessed by first asking adolescents “Which of the following electronic nicotine products have you ever used?” and presenting a list of products. Those who selected “E-cigarette (including vape pens and personal vaporizers)” were then asked, “When was the last time you used an e-cigarette, even one or two times?” Adolescents were considered to have used e-cigarettes if they selected a response that fell within the past year. Combustion cigarette use was assessed by asking “In the past 12 months, have you smoked a cigarette, even one or two puffs?” Adolescents were categorized as dual-product users if criteria for past-year use were met for both e-cigarettes and combusted cigarettes.

Other Covariates (Wave 1).

Covariates were selected on the basis of existing literature pertaining to the relationship between sleep and substance use (Pasch et al., 2012), in addition to three reviews that summarized correlates and outcomes of, and well as risk factors for, insufficient sleep (Chaput et al., 2016; Owens, 2014; Shochat et al., 2014). These potential confounders, all measured at Wave 1, included: demographic characteristics (age, sex, race, parent education level); prior history of the exposure or outcome variables, including earlier lifetime e-cigarette use or combusted cigarette use; or a history of sleep-related complaints; and history of emotional and behavioral health (depressive symptoms, anxiety symptoms, marijuana use, alcohol use, and social media use). Covariates were measured at Wave 1 instead of Wave 2 to ensure we were not adjusting for mediating variables, which may produce biased regression results (Schisterman, Cole, & Platt, 2009).

Demographics.

Age (12–14, 15–17), sex (female, male), and race (White only, Black only, other [American Indian or Alaska Native, Asian Indian, Chinese, Filipino, Japanese, Korean, Vietnamese, other Asian, Native Hawaiian, Guamanian or Chamorro, Samoan, and other Pacific Islander]) were measured via adolescent self-report, whereas parent educational attainment (less than high school, high school or equivalent, some college or associates degree, bachelor’s degree, advanced degree) was measured via parent self-report. Missing values for age, sex, and race were pre-imputed by the PATH investigators and provided in the publicly available dataset, as detailed in the user guide (US Department of Health and Human Services, 2017); we used the imputed variables to minimize missing data.

Prior History of Sleep-related Complaints and Cigarette Use.

For lifetime e-cigarette use (yes / no), adolescents were considered lifetime users if they responded “yes” to the question, “Have you ever used an e-cigarette, such as NJOY, Blu, or Smoking Everywhere, even one or two times?” For lifetime combusted cigarette use, adolescents were considered lifetime users if they responded “yes” to the question, “Have you ever tried cigarette smoking, even one or two puffs?”

For sleep-related complaints (yes / no), we used the same item as our outcome measure (see above), except that lifetime sleep-related complaints were considered present if the respondent selected “in the past month”, “two to twelve months”, or “over a year ago” from the response options.

Emotional and Behavioral Health.

For lifetime depressive symptoms (yes / no), adolescents were asked, “When was the last time that you had significant problems with feeling very trapped, lonely, sad, blue, depressed, or hopeless about the future?” For lifetime anxiety symptoms (yes / no), adolescents were asked, “When was the last time that you had significant problems with feeling very anxious, nervous, tense, scared, panicked, or like something bad was going to happen?” Adolescents who selected “in the past month”, “two to twelve months”, or “over a year ago” for either of these items were considered to have lifetime depressive or anxiety symptoms. These items were drawn from the internalizing subscale of the GAIN-SS (see above) (Dennis et al., 2006). For lifetime marijuana use (yes / no), adolescents were considered to have used marijuana if they responded “yes” to the question, “Have you ever used marijuana, hash, THC, grass, pot or weed?” or if they responded “yes” to the question, “Have you ever smoked part or all of a cigar, cigarillo or filtered cigar with marijuana in it?” For lifetime alcohol use (yes / no), adolescents were considered to have used alcohol if they responded “yes” to the question, “Have you ever used alcohol at all, including sips of someone’s drink or your own drink?” For social media use, adolescents who reported having a social media account were asked, “How often do you visit your Facebook, Google Plus, MySpace, Twitter, or other social networking account?” The response options were “several times a day,” “daily,” “weekly,” or “monthly or less,” or “never,” and we retained these categories for our analyses.

Statistical Analysis

Logistic regression was used to examine the cross-sectional association of past-year sleep-related complaints at Wave 2 with past-year e-cigarette, combusted cigarette, and dual-product use at Wave 2. Four successive models were estimated, each adjusting for additional potential confounders.

Model 1 – Unadjusted.

We first estimated an unadjusted regression model to examine the crude association between the exposure variable and sleep-related complaints in isolation.

Model 2 – Demographic Adjustment.

We then estimated a model adjusted for demographic variables (sex, age, race, and parent education level).

Model 3 – Past Exposure and Outcome Adjustment.

We then estimated a model further adjusted for prior history of e-cigarette use, combusted cigarette use, and sleep-related complaints, all as reported at Wave 1.

Model 4 – Emotional and Behavioral Health Adjustment.

Finally, we estimated a model further adjusted for lifetime depressive symptoms, anxiety symptoms, marijuana use, alcohol use, and social media use, all as reported at Wave 1.

Post-hoc analyses using Model 4 compared the odds of sleep-related complaints among dual users to the odds among each group of exclusive users to determine if dual use was associated with a higher odds of sleep-related complaints than exclusive use. We also used Model 4 to generate predicted probabilities of past-year sleep-related complaints, conditional on reporting e-cigarette use, combusted cigarette use, dual-product use, or no use of either product, and with all other covariates fixed at their population average value.

All analyses were weighted to account for the complex survey design, and are considered representative of the population of 12- to 17-year-old adolescents living in the United States in 2013–2014. Standard errors were estimated using the Wave 2 replicate weights provided in the PATH dataset, which were constructed using balanced repeated replication (Fay’s method). Statistical significance was assessed at the p<0.05 level. All analyses were conducted using Stata Version 14 (StataCorp Inc., College Station, TX).

RESULTS

Sample Characteristics

At Wave 2, a total of 4,670 adolescents (49.0%) reported past-year sleep-related complaints. A total of 588 (6.3%) adolescents reported past-year exclusive e-cigarette use, 294 (3.0%) reported past-year exclusive combusted cigarette use, and 474 (5.0%) reported past-year dual-product use. The remaining 8,232 (85.7%) adolescents reported no use of either product (Table 1).

Table 1.

Characteristics associated with past-year sleep complaints among U.S. youth in the PATH Study, 2013–2015.

Predictor Complete sample
(n=9588)		 Prevalence of past-year sleep complaintsa
(n=4670)		 Model 1 Model 2 Model 3 Model 4
n (%) n (%) OR (95% CI) OR (95% CI) OR (95% CI) aOR (95% CI)
Cigarette usea
  Neither product 8232 (85.7%) 3854 (47.1%) ref. ref. ref. ref.
  E-cigarettes only 588 (6.3%) 345 (58.9%) 1.61 (1.34,1.94) 1.60 (1.32,1.94) 1.43 (1.16,1.77) 1.29 (1.05,1.59)
  Combusted cigarettes only 294 (3.0%) 174 (59.4%) 1.62 (1.26,2.09) 1.57 (1.24,2.00) 1.43 (1.10,1.85) 1.30 (0.98,1.71)
  Dual use 474 (5.0%) 297 (64.1%) 2.00 (1.63,2.46) 1.84 (1.51,2.24) 1.68 (1.34,2.11) 1.57 (1.24,1.99)
Sexb
  Female 4674 (48.7%) 2767 (59.2%) – ref. ref. ref.
  Male 4914 (51.3%) 1903 (39.4%) – 0.44 (0.41,0.48) 0.49 (0.45,0.53) 0.54 (0.49,0.58)
Raceb
  White only 6653 (70.9%) 3331 (50.5%) – ref. ref. re f.
  Black only 1451 (14.9%) 640 (44.8%) – 0.81 (0.72,0.92) 0.88 (0.77,1.00) 0.90 (0.79,1.03)
  Other 1484 (14.2%) 699 (46.3%) – 0.84 (0.74,0.96) 0.85 (0.74,0.99) 0.84 (0.72,0.97)
Educationb
  Less than high school 1950 (17.4%) 857 (44.0%) – re f. ref. ref.
  High school or equivalent 1777 (17.7%) 882 (49.8%) – 1.23 (1.06,1.43) 1.18 (1.01,1.37) 1.13 (0.96,1.32)
  Some college or associates degree 3064 (31.9%) 1573 (52.0%) – 1.36 (1.19,1.55) 1.25 (1.08,1.44) 1.19 (1.02,1.38)
  Bachelor’s degree 1819 (21.0%) 879 (48.0%) – 1.17 (1.02,1.34) 1.11 (0.96,1.27) 1.04 (0.90,1.21)
  Advanced degree 978 (12.0%) 479 (49.2%) – 1.29 (1.08,1.55) 1.24 (1.02,1.50) 1.15 (0.94,1.40)
Ageb
  12 to 14 5858 (60.3%) 2738 (46.5%) – ref. ref. ref.
  15 to 17 3730 (39.7%) 1932 (52.9%) – 1.22 (1.11,1.34) 1.19 (1.08,1.31) 1.10 (1.00,1.22)
Lifetime combusted cigarette useb
  No 8628 (90.2%) 4127 (48.2%) – – ref. ref.
  Yes 960 (9.8%) 543 (57.0%) – – 1.00 (0.81,1.23) 0.89 (0.72,1.11)
Lifetime e-cigarette useb
  No 8802 (91.8%) 4240 (48.5%) – – ref. ref.
  Yes 786 (8.2%) 430 (55.0%) – – 0.89 (0.70,1.13) 0.84 (0.65,1.08)
Lifetime sleep complaintsb
  No 3923 (40.9%) 1076 (27.7%) – – ref. ref.
  Yes 5665 (59.1%) 3594 (63.8%) – – 4.23 (3.80,4.71) 2.66 (2.39,2.97)
Lifetime depressive symptomsb
  No 4608 (47.8%) 1510 (32.8%) – – – ref.
  Yes 4980 (52.2%) 3160 (63.9%) – – – 1.72 (1.53,1.93)
Lifetime anxiety symptomsb
  No 3886 (40.3%) 1180 (30.7%) – – – ref.
  Yes 5702 (59.7%) 3490 (61.4%) – – – 1.54 (1.36,1.74)
Lifetime marijuana useb
  No 8620 (90.2%) 4113 (48.0%) – – – ref.
  Yes 968 (9.8%) 557 (58.4%) – – – 0.93 (0.76,1.15)
Lifetime alcohol useb
  No 6414 (66.1%) 2780 (43.4%) – – – ref.
  Yes 3174 (33.9%) 1890 (60.1%) – – – 1.28 (1.14,1.43)
Frequency of social media useb
  Never 2005 (20.7%) 792 (39.8%) – – – ref.
  Monthly or less 874 (9.0%) 383 (44.8%) – – – 1.08 (0.90,1.30)
  Weekly 1176 (12.3%) 597 (51.0%) – – – 1.21 (1.02,1.44)
  Daily 2627 (27.5%) 1293 (49.4%) – – – 1.05 (0.89,1. 22)
  Several times a day 2906 (30.5%) 1605 (55.5%) – – – 1.11 (0.95,1.30)
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Notes:

a

Measured at Wave 2.

b

Measured at Wave 1. All percents are weighted to be representative of adolescents ages 12–17 using the Wave 2 replicate weights. Bold font indicates statistical significance at the p<0.05 level. Model 1 is unadjusted; Model 2 adjusts for demographic variables; Model 3 further adjusts for past exposure and outcome; and Model 4 further adjusts for behavioral health variables. OR = odds ratio; aOR = adjusted odds ratio

Association of E-cigarettes and Combustion Cigarettes with Sleep Complaints

In Model 1 (Table 1), e-cigarette, combusted cigarette, and dual-product use were significantly associated with increased odds of self-reported sleep-related complaints, relative to no use of either product (e-cigarettes: odds ratio [OR]=1.61, 95% confidence interval [CI] 1.34–1.94; combusted cigarettes: OR=1.62, 95% CI 1.26–2.09; dual-product use: OR=2.00, 95% CI 1.63–2.46). In Model 2 (adjusting for demographic variables) and Model 3 (further adjustment for past exposure and outcome), associations were slightly attenuated, but remained statistically significant. Finally, in Model 4 (further adjustment for emotional and behavioral health), the association between sleep-related complaints and e-cigarette and dual-product use remained statistically significant (e-cigarettes: adjusted odds ratio [aOR]=1.29, 95% CI 1.05–1.59; dual-product use: aOR=1.57, 95% CI 1.24–1.99), but the association between sleep-related complaints and combusted cigarette use did not (aOR=1.30, 95% CI 0.98–1.71). Post hoc analyses indicated that the odds of sleep-related complaints were not significantly higher in dual-product users compared to exclusive e-cigarette users (aOR=1.22, 95% CI 0.92–1.61) or compared to exclusive combusted cigarette users (aOR=1.21, 95% CI 0.91–1.62). With all covariates fixed at their population average value, the probabilities of reporting sleep-related complaints were 48.1% (95% CI 47.1–49.1) for users of neither product, 53.4% (95% CI 49.3–57.4) for exclusive e-cigarette users, 53.4% (95% CI 48.1–58.8) for exclusive combusted cigarette users, and 57.3% (95% CI 53.0–61.7) for dual-product users, based on Model 4.

Additionally, in Model 4, parent education level of “some college or associates degree,” older age, lifetime sleep-related complaints, lifetime depressive symptoms, lifetime anxiety symptoms, lifetime alcohol use, and frequency of social media use of “weekly” were significantly associated with higher odds of sleep-related complaints. Male adolescents had lower odds of reporting sleep-related complaints compared to female adolescents. Adolescents in the “other” race category had a lower odds of reporting sleep-related complaints compared to white adolescents.

DISCUSSION

The objective of the current study was to assess whether exclusive e-cigarette, exclusive combusted cigarette, and dual-product use were associated with sleep-related complaints among adolescents. Using data from adolescents in the PATH Study, we found that adolescents who reported past-year e-cigarette and dual-product use had 29% and 57% higher odds of reporting sleep-related complaints, respectively, compared to no use of either product. These associations were robust to adjustments for a wide range of demographic and behavioral characteristics, in addition to a prior history of sleep problems, e-cigarette use, and combusted cigarette use. On the other hand, adolescents who reported past-year combusted cigarette use had 30% higher odds of reporting sleep-related complaints; this association was statistically significant in all models, except for the fully adjusted model that controlled for emotional and behavioral health.

Prior research has found that use of a wide range of substances, including alcohol, marijuana, and combusted cigarettes, is associated with increased risk of disturbed sleep (Pasch et al., 2012). Our findings extend this literature by suggesting that a similar association exists between e-cigarette use and sleep-related complaints. The association between combusted cigarette use and sleep-related complaints did not reach statistical significance after adjustment for depressive and anxious symptoms, other substance use, and social media use. This may be due to the relatively small number of exclusive combusted cigarette users in our study, rather than a true null association. The odds ratios for reporting sleep-related complaints were comparable for exclusive e-cigarette and exclusive combusted cigarette users (1.29 and 1.30 respectively), and combusted cigarette use has been associated with sleep problems in multiple past studies with greater statistical power (Bartel et al., 2015; Bellatorre, Choi, Lewin, Haynie, & Simons-Morton, 2017; Boakye et al., 2018). Dual product users had the greatest odds of sleep-related complaints of any group, although this association was not significantly different from the associations of exclusive use. Interestingly, the prevalences of e-cigarette and combusted cigarette use in our study (11.3% and 8.0%, respectively, for past 12-month use) were lower than those obtained in other nationally-representative surveys. For example, in the 2015 National Youth Tobacco Survey, 21.3% and 11.6% of middle and high school students reported past 30-day use of e-cigarettes and combusted cigarettes, respectively (Singh et al., 2016).

There are several potential explanations for our findings. In adults, insomnia is a known adverse outcome associated with e-cigarette use (Liu et al., 2018), and our findings imply that this may extend to adolescent populations. Exposure to nicotine stimulates the release of several neurotransmitters that modulate the sleep-wake cycle, which may lead to changes in sleep architecture (Costa & Esteves, 2018; Zhang, Samet, Caffo, & Punjabi, 2006). Similarly, sleep disruptions are a prominent symptom of nicotine withdrawal (Ogeil & Phillips, 2015). Estimates of the proportion of current adolescent e-cigarette users that use e-liquids containing nicotine vary widely, with studies reporting figures between 16–52% (Goldenson et al., 2017; Institute for Social Research (University of Michigan), 2017). However, even these upper estimates may understate the current true extent of the problem, given that only a third of adolescents actually know that all JUUL cartridges contain nicotine (Willett et al., 2018). Alternatively, adolescents experiencing sleep disruptions may choose to use e-cigarettes as a coping tool in order to “self-medicate” (Bellatorre et al., 2017). Attention problems, mood dysregulation, and poor decision-making are common consequences of insufficient sleep (Beebe, 2011); e-cigarette use, particularly with e-liquids containing nicotine, may offset these unpleasant symptoms in the short-term (Bellatorre et al., 2017). Because we examined a cross-sectional association, these proposed mechanisms are speculative and should be more closely studied in future research using longitudinal designs to establish temporality. Although the PATH Study uses a longitudinal design, we chose to examine a cross-sectional association because we hypothesized that exposure to nicotine is a proximal, rather than distal, risk factor for sleep-related complaints; PATH Study waves are conducted at year-long intervals, which is not consistent with this proposed mechanism of action. However, we note that, since our models adjusted for both a prior history of sleep problems, and a prior history of e-cigarette and combusted cigarette use, at the very least we think it is likely that initiation of e-cigarette use is associated with new onset sleep problems.

Our findings have important public health implications. This is true both because insufficient and poor-quality sleep among adolescents is associated with poor psychological health, increased risk-taking, and heightened risk for obesity (Chaput et al., 2016; Owens, 2014), and because current e-cigarette use among high school students increased from 1.5% in 2011 to 20.8% in 2018 (Cullen, 2018), suggesting this may be a growing cause of sleep problems. Although we cannot be certain about the direction of our results, given the existing evidence that nicotine use causes sleep disturbances (Costa & Esteves, 2018), our findings suggest preventing the initiation of e-cigarette use and intervening to stop existing use may reduce sleep-related complaints among adolescents. Conversely, if sleep problems are causing e-cigarette initiation, then teaching adolescents proper sleep hygiene and stress management techniques to improve sleep may help reduce the risk of e-cigarette use. Existing sleep hygiene interventions have observed reductions in body mass index, improved mental health, and sustained academic performance among participants (Tan, Healey, Gray, & Galland, 2012; Wing et al., 2015; Wolfson, Harkins, Johnson, & Marco, 2015); spillover effects of these interventions may be observed on substance use outcomes. Pushing school start times to be later, which is associated with longer sleep duration among adolescents, may achieve similar results (Dunster et al., 2018). Finally, we should note that, in the present study, almost half of adolescents reported past-year sleep-related complaints. There is an urgent need to better understand the predictors of sleep disturbances and reduce the burden of these problems in this population.

This study has several limitations. First, our exposure was any use of products in the past-year. This broad exposure may obscure different degrees of use, for example differences between experimenters and frequent users (Amato, Boyle, & Levy, 2016). The PATH Study assesses the frequency of e-cigarette and combusted cigarette use only among past 30-day users, and there were too few adolescents who used these products with high enough frequency in the 30 days prior to their interview to study frequency or quantity of use with any precision. Future studies should consider extending our findings by measuring e-cigarette use with greater granularity, for example, the number of days where e-cigarettes were used in the past month, number of puffs per vaping episode, and nicotine level of e-liquids used while vaping. Second, there was only a single, unvalidated item available to assess sleep-related complaints. The lack of specificity in this item may be a source of non-differential measurement error, which would have biased our results toward the null (Wacholder, Hartge, Lubin, & Dosemeci, 1995). Under this scenario, the true association between e-cigarette use and sleep-related complaints would be stronger than we observed in the present study. Sleep health can be measured using a variety of subjective (i.e., sleep diaries, self-report questionnaires) and objective measures (i.e., polysomnography, actigraphy) (Ji & Liu, 2016; Krystal & Edinger, 2008). Associations with e-cigarette and combusted cigarette use may differ based on the selected measure of sleep. Third, the cross-sectional nature of our study did not allow us to infer the direction of causality in our results. Fourth, the observed findings may reflect an unmeasured, common cause of both our exposure and outcome, such as shared genetic risk factors (Gibson, Munafò, Taylor, & Treur, 2018). Despite these limitations, the present study explored an important question in a nationally-representative sample of adolescents, and our results provide a starting point for future research.

In summary, we found that sleep-related complaints were more common among adolescents who reported past-year exclusive e-cigarette and dual-product use compared to adolescents who used neither product. Future studies should utilize more detailed and better-validated measures of sleep-related complaints and consider implementation of longitudinal designs to evaluate the direction of these associations and whether they may be causal.

Footnotes

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