The Journal of School Nursing2021, Vol. 37(6) 470–479© The Author(s) 2020Article reuse guidelines:sagepub.com/journals-permissionsDOI: 10.1177/1059840519901161journals.sagepub.com/home/jsn
Multidimensional causes of insufficient sleep among adolescents are not fully explored, particularly, the association between substance use and sleep duration. The 2017 Youth Risk Behavior Surveillance System (YRBSS) of high school students (N = 14,638; 51% female, 14–18 years old) was used to examine the association between substance use, namely cigarette, electronic vapor, alcohol, and marijuana use in the past 30 days, and insufficient sleep (<8 hr) using logistic regression analyses. Findings suggest that the use of alcohol (adjusted odds ratio [AOR], 1.42; 95% CI [1.22, 1.65]) and marijuana (AOR, 1.19; 95% CI [1.00, 1.41]) was significantly associated with having insufficient hours of sleep adjusting for age, sex, race, and computer use ≥ 3 hr per day. Moreover, interaction effects were examined for sex and age, which revealed that the association between marijuana use and having insufficient sleep was stronger for male and younger students. It highlights the potential value of sleep assessment among adolescent substance users.
adolescent, sleep, tobacco use, alcohol drinking, marijuana use, substance use, electronic vapor use, school nurse
Alcohol, marijuana, and tobacco are the most commonly used substances by adolescents and pose a serious public health threat (Center for Disease Control and Prevention [CDC], 2019a). Although there has been a steady decline of alcohol and tobacco use among high school students, there has been a significant rise in the prevalence of electronic vapor use (i.e., nicotine, marijuana, and flavoring) among this population (Johnston et al., 2018). While a recent survey reports 8.2%, 18.6%, and 30.2% of alcohol use within the past month among 8th (13–14 years old), 10th (15–16 years old), and 12th grade (17–18 years old) students, respectively (Johnston et al., 2018), more than one in four (25%) high school students reported using a tobacco product vastly driven by an increased use of electronic (e-cigarette) vapor products (CDC, 2019b). Concurrently, substance use and misuse place teens at risk of consequences such as a decline in academic performance (Grant et al., 2012), polysubstance use (Grant et al., 2010), violence (Hall et al., 2016), accidents (Marshall, 2014), overdose (Hall et al., 2016), and self-harm (Shain & Committee on Adolescence, 2016). Particularly, these behaviors impose direct cognitive and mental health consequences that may increase the risk of poor or lack of sleep during a time period that is critical to adolescent development (Beebe, 2011). The purpose of this study is to examine the relationship between substance use and sleep duration among adolescents.
Having an adequate amount of sleep for adolescents is important as it serves to restore and rejuvenate, retain information, solve problems, make decisions, and learn new skills (National Sleep Foundation, 2017). However, it is reported that about 70% of high school students do not obtain enough sleep on school nights (National Center for Chronic Disease Prevention and Health Promotion [NCCDPHP], 2018). This is a major public health concern as having insufficient sleep is associated with negative physical and mental health outcomes, increased risk of injuries, and behavioral problems, including substance use and abuse (Owens & Weiss, 2017). Hence, substance use and sleep disturbances may function as a negative feedback loop (Fucito et al., 2015; Haynie et al., 2017; Patte et al., 2018), and a clear understanding of this association among adolescents is needed. Given that substance use behaviors intertwined with a lack of sleep exacerbates the health risk in adolescents and threatens the safety of the school and public health (Owens & Adolescent Sleep Working Group & Committee on Adolescence, 2014), it is important to examine the associations between multiple substance use behaviors and insufficient sleep.
Considerable efforts to reduce and prevent high school students’ insufficient hours of sleep during school nights are based on identifiable understanding of risk factors—including concurrent behaviors—that are associated with increased risks of having insufficient sleep. Research has established some evidence on the association between adolescent substance use and sleep (Kwon et al., 2019). A recent systematic review examined 13 studies that explored this relationship published within the past 10 years for this population. For example, the use of marijuana and tobacco was significantly associated with greater sleep problems among adolescents (Zhabenko et al., 2016). In addition, teens with more consumption of alcohol experienced later sleep schedules and more sleep on weekends compared with weekdays (Singleton & Wolfson, 2009), while those who used e-cigarettes tended to have shorter total sleep time on weekends (Dunbar et al., 2017) and had increased complaints related to sleep (e.g., bad dreams, sleeping restlessly, or falling asleep during the daytime; Riehm et al., 2019) compared to the nonusers. The association between sleep and substance use has been an interest among researchers for a long period of time, but relatively limited studies exist for adolescents. In light of these findings, there is still a dearth of empirical research examining various types of commonly used substances regarding the association to adolescent sleep using a nationally representative sample of high school students. Likewise, there is a lack of studies examining the moderating effect of sex and age on the association between substance use and sleep, which may help to provide additional insights into such an association. Furthermore, studies exploring the relationship between substances and sleep health among adolescents in the context of school health or school nursing remains relatively sparse.
Accordingly, we address the following research questions: (1) whether students who report having used substances (cigarette, alcohol, electronic vapor, marijuana) are more likely to report insufficient hours of sleep compared to students who do not, after controlling for age, sex, race, and computer use of more than 3 hr, and (2) whether the use of substances and their associations to having insufficient sleep differed by age and sex. Given what the limited body of research addressing these questions has revealed, we hypothesize that (1) students who report having used substances (cigarette, alcohol, electronic vapor, marijuana) will be more likely to report insufficient hours of sleep, and that these associations will remain significant even after adjusting for covariates, and (2) these associations will be stronger among older male adolescent substance users. We examined these hypotheses, using data from the 2017 Youth Risk Behavior Surveillance System (YRBSS).
The 2017 YRBSS of high school students was used to examine the association between substance use (i.e., cigarette, electronic vapor, alcohol, and marijuana use in the past 30 days) and insufficient sleep (<8 hr per school night; CDC, 2018) for the reporting period of September 2016 to December 2017. The YRBSS was developed to monitor various categories of health risk–taking behaviors among youth and young adults (Kann et al., 2018). In brief, YRBSS is a publicly available dataset developed by the CDC where a threestage cluster sample design was employed to produce a nationally representative sample of U.S. high school students in the 50 states including the District of Columbia (Kann et al., 2018). Schools were selected systematically with probability proportional to size using a random start and thus 192 schools were sampled (Kann et al., 2018). Counties were the primary sampling units (PSUs) followed by selections of schools within each PSU and classrooms within the selected schools (Kann et al., 2018). Samples were weighted to the general census for youths typically of age 14–18 years. According to the weighted data, 50.7% of the students were female. Approximately 53.5% of them were White, 13.4% were Black, 22.8% were Hispanic, and 10.3% were American Indian or Alaska Native, Asian, Native Hawaiian or other Pacific Islander, or multiple races (non-Hispanic; Kann et al., 2018). It included national (public and private schools), state, territorial, tribal government, and local school-based surveys of representative samples of 9th through 12th grade students. There were 27.3%, 25.6%, 23.9%, and 23.0% in 9th, 10th, 11th, and 12th grade, respectively (Kann et al., 2018). Parental permission was obtained prior to the administration of the survey (CDC, 2014). The surveys were designed to have student participants remain anonymous, participate voluntarily, and allow students to skip any questions that they did not wish to answer. During one class period, students completed selfadministered surveys and recorded their responses directly on a computer-scannable booklet or answer sheet. The 2017 national student sample size included 14,956 respondents. The overall response rate was 60% (school 75%; student 81%). An institutional review board at the CDC approved the national YRBSS (Kann et al., 2018). Details on the methodology of the 2017 YRBSS are described elsewhere (Kann et al., 2018).
Missing data imputations. The YRBSS 2017 national student sample size included 14,765 respondents after including the variables of interest for this study such as demographic characteristics, self-reported sleep duration on a typical school night, and selected substance use behaviors. However, there were sizable percentages of missing data in the variables such as lack of sleep (19.9%), age (12.32%), computer use for more than 3 hr (6.24%), cigarette use (2.30%), electronic vapor product use (12.82%), and alcohol use (12.01%). Multiple imputation based on multivariate normal distribution and expectation maximization (EM) algorithm was implemented to impute missing data. The final sample used for analysis constituted 14,638 adolescents. There are some discrepancies in the sample sizes used for descriptive statistics, correlation matrix, and logistic regression analysis. The sample size used for descriptive statistics represents the weighted sample size without conducting the missing data imputation. However, we conducted subsequent analysis using multiple imputed data in order to allow for as much information as possible.
Outcome variable. Hours of sleep on an average school night was used as an outcome variable. Hours of sleep was assessed by the question, “On an average school night, how many hours of sleep do you get?” Response options were as follows: “4 or less hours,” “5 hr,” “6 hr,” “7 hr,” “8 hr,” “9 hr,” and “10 or more hours.” Responses were dichotomized into insufficient sleep (<8 hr of sleep) and sufficient sleep (≥8 hr of sleep). Some sleep researchers and experts have defined 8 hr or less (McKnight-Eily et al., 2011; Meldrum & Restivo, 2014; Stea et al., 2014) or 7 hr or less (Eaton et al., 2010), while others have used 6 hr or less per night to demonstrate sleep deprivation (Angold et al., 2012; Daly et al., 2015; Roberts & Duong, 2014). Despite the variability in defining what constitutes insufficient sleep among older adolescents, this study uses less than 8 hr on a typical school night as the cutoff in accordance with the most recent national guidelines (CDC, 2018; Hirshkowitz et al., 2015). Having sufficient sleep was used as a reference in the analysis.
Independent variables. Current substance use behaviors, defined as use within the last 30 days, were assessed using the following questions: (1) On how many days did you have at least one drink of alcohol? (2) On how many days did you smoke cigarettes? (3) On how many days did you use electronic vapor? (4) On how many days did you use marijuana? Students who reported using the substance on one or more days within the last 30 days were identified as current users of the assessed substance (Daly et al., 2015). For alcohol, electronic vapor, and marijuana use variables, binary variables (yes/no) were used due to convergence issues in these variables in the multiple imputation process, while a continuous variable was used for cigarette smoking.
Controlling variables. The covariates included adolescents’ age, sex, race, and computer use of 3 or more hours per day. The cutoff of computer use of 3 or more hours was selected based on the dichotomous variable suggested by the YRBSS, which is also consistent with the over the 2-hr limit screen use recommended by the American Academy of Pediatrics (Council on Communications and Media, 2013). The age variable was centered since it was used for the interaction with the composite variable denoting substance use. As for the race, three dummy variables indicating Black, Hispanic, and other racial groups were used, while non-Hispanic White was used as a reference group. A binary variable was used to denote whether or not the student used a computer over 3 hr a day (yes/no).
Descriptive statistics along with a correlation matrix were provided for the background information of this study. A series of logistic regression analysis was conducted to examine the relationships between substance use behaviors and insufficient sleep. The study used the logistic regression approach in order to first explore the associations between all four substance use variables which were the current use of cigarettes, electronic vapor, alcohol drinking, and marijuana on insufficient sleep without the interaction effects of sex and age (Model 1) and then with the interaction effect of sex (Model 2) and age (Model 3). The correlations among all substance use variables were examined to check the presence of multicollinearity before these variables were entered into the models. Age- and sex-specific moderations were examined further with the aid of visualization by using the multiply imputed data. All analyses including multiple imputation were performed using STATA Version 13. The p values of less than .05 were considered statistically significant.
The description of demographic characteristics and each substance use is summarized in Table 1. As shown, more than half (51.5%) of the respondents in the sample were females and identified as non-Hispanic Whites (57.9%). The mean age at the time of the survey was approximately 16 years. About 43% of the sample reported playing video or computer games or using a computer for nonschool-related tasks for more than 3 hr on an average school day. The mean hours of sleep on an average school night were 7 hr and appeared as a bell-shaped curve across all age groups, with only about one quarter of high school students getting the recommended 8 hr of sleep per night. The percentages of students who reported one or more times of cigarette use, electronic vapor product use, alcohol drinking, and marijuana use in the past month were 8.7%, 11.6%, 26.3%, and 16.4%, respectively.
Table 2 presents the correlations among the variables used in this study. Results indicated that all of the substance use variables—smoking cigarettes, electronic vapor use, alcohol drinking, and marijuana use—were significantly associated with having insufficient sleep, with the correlation coefficients ranging from .02 (smoking cigarettes) to .07 (alcohol drinking and marijuana use). Among the demographic and other factors, older age, female, White and other racial groups, and using a computer ≥3 hr per day were significantly associated with having insufficient sleep.
Table 3 presents cigarette smoking, electronic vapor use, alcohol drinking, and marijuana use relative to having insufficient sleep per school night, adjusted for age, sex, race, and using a computer ≥3 hr per day. Results indicated that the uses of alcohol and marijuana were significantly associated with having insufficient hours of sleep, whereas cigarette smoking and electronic vapor uses were not. Namely, the students who reported the use of alcohol and marijuana had higher odds of having insufficient sleep on school nights (adjusted odds ratio [AOR], 1.42; 95% CI [1.22, 1.65] for alcohol drinking; AOR, 1.19; 95% CI [1.00, 1.41] for marijuana use [Model 1]).
Model 2 in Table 3 includes the interaction effect of student sex to examine whether sex might play a moderating role in the relationship between substance use variables and insufficient sleep. While both alcohol and marijuana uses still remained significantly associated with having insufficient sleep, the significant interaction effect of sex existed only with the association between marijuana use and insufficient sleep (AOR, 0.66, 95% CI [0.47, 0.92]), differing across male and female students.
Likewise, Model 3 in Table 3 includes an interaction effect of student age in the association between substance use variables and insufficient sleep. Results indicated that there was a significant interaction effect of student age in the relationship between marijuana use and insufficient sleep (AOR, 0.80, 95% CI [0.66, 0.96]), signifying that the relationship between marijuana use and insufficient sleep differed across varied age levels.
Visualization of moderation effects. The moderating effect or interaction effect signifies that the relationship between a predictor and an outcome does not remain the same across different levels of a potential moderator. In our example, student sex was used as a potential moderator to see whether the relationship between marijuana use (predictor) and insufficient sleep (outcome) would differ across male and female students. If there was a significant moderation effect of student sex, the overall relationship between marijuana use and insufficient sleep would not be the same for male and female students. If the moderating effect was significant and positive, then the relationship between marijuana use and insufficient sleep would be more so for female students than males (since male was used as a reference group). Figures with brief descriptions are provided to demonstrate the interaction effects of student sex and age with insufficient sleep. As shown in Figure 1, the association between marijuana use and insufficient sleep was stronger for male than for female students. In other words, the decrease in the hours of sleep among male students from not using marijuana to using marijuana was greater than the decrease in the hours of sleep among female peers, although the average insufficient sleep was slightly higher for female students (M = 2.16 for male; M = 2.20 for female).
Similarly, the interaction effect of student age is shown in Figure 2. Of note, prior to generating the age interaction graph, the age variable was collapsed into a binary variable with the age 16 as a cutoff point. The overall result indicated that older students had greater insufficient hours of sleep than younger students. However, younger students suffered more from insufficient sleep when using marijuana, whereas older students had similar levels of insufficient sleep regardless of the marijuana use.
This study expands on previous research by examining the association between substance use (current use of cigarettes, electronic vapor, alcohol, and marijuana) and insufficient sleep among U.S. high school students and identifies moderating effects of sex and age in these associations. Our findings indicate that only alcohol and marijuana use were associated with having insufficient sleep after controlling for age, sex, race, and computer use, while all of the substance use variables were significantly correlated with insufficient sleep. Moreover, significant interaction effects existed only in the association between marijuana use and insufficient sleep revealing this association to be stronger for male and for younger students.
The finding about alcohol use and having insufficient sleep was consistent with previous studies (Haynie et al., 2017; Singleton & Wolfson, 2009), and our study extends these studies by testing the interaction effects by age and sex. A previous cross-sectional study reported that adolescents who used alcohol were more likely to have insufficient sleep than nonusers, although their study was based on less than 8.5 hr of sleep on school nights deemed as having insufficient sleep (Reichenberger et al., 2016). Similarly, the alcohol use, specifically binge drinking, was significantly associated with sleep disturbances (i.e., trouble falling asleep, trouble staying asleep, and snoring/sleep apnea) among emerging adults in another study (Popovici & French, 2013). It has been reported that alcohol reduces the sleep onset latency and wake after sleep onset in the first half of sleep; however, in the latter part of sleep, a stimulating effect occurs in an attempt to eliminate alcohol from the body resulting in sleep fragmentation (Ebrahim et al., 2013). Interestingly, no interaction effect in the associations between alcohol use and sleep by age or sex among high school students was found in our study. Thus, the current findings indicate the interference that alcohol use has with shorter sleep duration specifically, and addresses a need for effective screening for both alcohol use and sleep, regardless of sex and age among students.
Although the data on the effects of marijuana use on adolescents’ sleep are scarce, there is evidence showing a positive association between marijuana use and sleep disturbance (Jacobus et al., 2009; Kwon et al., 2019). Particularly, in our results, the impact of marijuana use on insufficient sleep was stronger for boys than girls, and for younger than older students, which to our knowledge, is the first study to examine the moderation effect on such associations for adolescents. The findings from our study showed that younger high school students were more detrimentally influenced by their marijuana use in terms of having a lack of sleep than older students. In terms of sleep, it is reported that more mature teenagers are slower to fall asleep even after being awake for an extended period than younger teenagers (Hagenauer et al., 2009), which may indicate that older high school students are able to better tolerate longer waking episodes but, to the degree in which the use of marijuana affect sleep differently for younger and older high school students is yet to be elucidated. In addition, this finding indicates that the physical impact of marijuana on sleep may be stronger among boys than girls. Research has shown that there are sex differences in the effect of marijuana on sleep: Men were more likely to have insomnia with vivid dreams than women among marijuana users (Cuttler et al., 2016). This moderation effect found in this study may be related to such sex differences on the effect of marijuana use. However, since this is the first study to explore the moderation effect of sex on the association between marijuana use and sleep disturbance among high school students to our knowledge, further research needs to be conducted to explore mechanisms and contextual factors.
Tobacco use has been associated with insomnia and sleep fragmentation such as increased sleep latency, and decreased sleep efficiency and total sleep time (Deleanu et al., 2016). However, our findings indicated that neither cigarette nor electronic vapor use was associated with having insufficient sleep. This finding contrasts previous studies in which tobacco use was associated with having shorter sleep duration among adolescents (Dunbar et al., 2017; Pasch et al., 2012; Reichenberger et al., 2016). Potential reasons for our finding—indicating no association with sleep—can be explained by current trends in the exponential decrease in regular tobacco use among adolescents, while studies that report on electronic vapor use and sleep are lacking. Another explanation is that the impact of computer use on adolescents’ sleep was controlled for in our analysis to examine the influence of substance use on sleep while previous studies did not. The use of computer and electronic devices has shown to have a consistent and significant impact on sleep, especially in adolescents (Hale & Guan, 2015; Hysing et al., 2015).
It is important to note that the relationship between substance use and insufficient sleep appears as bidirectional, portraying a vicious cycle of increased risk of the worsening of health outcomes in adolescence (McGlinchey & Harvey, 2015). Studies have also demonstrated a cross-sectional association between sleep disturbances and increased risks of substance use among adolescents (Hasler et al., 2015; Paiva et al., 2016). Moreover, when sleeping is not prioritized during the weekdays, there is a greater variability of hours of sleep in their weekday–weekend bedtimes. This in turn may lead adolescents to experience greater differences in their sleep patterns and circadian rhythms between weekdays and weekends, hence, having insufficient hours of sleep. Furthermore, this may contribute to a compromise in the adolescents’ decision-making skills including their substance use (Pasch et al., 2012). Hence, both substance use and sleep disturbances before the full maturation of the brain appear detrimental and further warrant clinical implications for screening for sleep and providing appropriate interventions.
The main limitation of this study includes the cross-sectional and observational design of the study that precludes determination of the temporal link between substance use and insufficient sleep. Utilizing longitudinal design may shed light on the causal association and the potential reversibility of the consequences. Another limitation is that the measurement of the variables, including electronic vapor product use, marijuana use, and alcohol use, was categorized into the binary items due to a technical issue if used as continuous variables. Thus, the current substance use was defined with a broad range of days from one to more than one day within the last 30 days, which needs to be noted in interpreting the findings. Thirdly, although the information on puberty status was not provided in the YRBSS data, it has a significant impact on sleep and needs to be controlled for (Carskadon et al., 1993). Fourthly, the sleep variable in this study only measures the duration (hours of sleep on school nights), which may not fully capture the sleep pattern of adolescence. Adolescents tend to oversleep during weekends in order to “catch up” on their lack of sleep (Sun et al., 2019), and current data may not fully account for hours of sleep on weekends or sleep phase delays. Finally, although the cluster sampling design used in this study allows for the sampling frame of a large nationally representative data to be generated in the most cost-effective manner, there exists potential limitations to reflect unique characteristics among diverse subgroups. Future studies may need to explore these associations with the multiple sleep measures on different dimensions of sleep health with a diverse population of high school students.
In spite of these limitations, the study has strengths and adds important knowledge on the association between substance use and insufficient sleep among adolescents. This study used the recent large, nationally representative data, with a systematic sampling procedure, allowing for more generalizable findings with regard to the association between substance use and sleep among adolescents. Moreover, the study includes the use of electronic vapor, which was included in the YRBSS data collection for the first time. To our knowledge, this study is one of the first to test the use of electronic vapor products, which is the most rapidly increasing substance used among adolescents and insufficient sleep among adolescents using nationally representative samples.
School nurses and health-care professionals who work for adolescent mental health, such as counselors, clinicians, therapists, and social workers in schools and community settings should assess students who are substance users for the presence of sleep disturbances. This study indicates the importance of screening for insufficient sleep among adolescents who report marijuana and alcohol use, especially for boys and adolescents at the stage of entering high school. Individual sleep assessment, such as a one- or two-week sleep diary in addition to screening for sleep problems and daytime sleepiness may be utilized for nurses to assess students’ sleep issues. School nurses play an important role in recognizing, preventing, intervening, and addressing students’ health risk–taking behaviors such as substance use and unhealthy sleep duration and patterns which lead to consequences thereafter. It may also be vital for them to screen for sleep problems in the early stage of students’ substance use upon detection. During school-based cessation and substance abuse prevention programs, the importance of sleep hygiene and the consequences of sleep deprivation may need to be addressed. Moreover, school administrators may need to provide support for the offering of wellness programs that integrate sleep health education and substance use prevention.
Substance use and sleep behaviors have been studied extensively in adults (Colrain et al., 2014; Conroy & Arnedt, 2014); however, these associations in adolescence were not explored much before. Hence, continued research is warranted to monitor patterns of sleep among varied youth substance users. Further evaluation of the mechanisms for these associations and development of effective interventions tailored specially for adolescents in a school setting are expected to reduce substance use and improve sleep, anticipated long-term sleep deprivation and consequences that result from it, as well as improve overall school and public health.
The authors wish to thank Dr. Seong Won Han who assisted in the proofreading of the initial study protocol.
M. Kwon contributed to the design of the manuscript. All authors contributed to data acquisition and interpretation. M. Kwon and Y. Seo drafted the manuscript. All authors gave final approval on the text and agreed to be accountable for all aspects of work, ensuring integrity and accuracy.
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
The author(s) received no financial support for the research, authorship, and/or publication of this article.
Misol Kwon, BS, RN https://orcid.org/0000-0002-9608-2754 Yu-Ping Chang, PhD, RN, FGSA, FAAN, FIAAN https://orcid.org/0000-0003-2328-6876
Angold, A., Erkanli, A., Copeland, W., Goodman, R., Fisher, P. W., & Costello, E. J. (2012). Psychiatric diagnostic interviews for children and adolescents: A comparative study. Journal of American Academy of Child & Adolescent Psychiatry, 51, 506–517. https://doi.org/10.1016/j.jaac.2012.02.020
Beebe, D. W. (2011). Cognitive, behavioral, and functional consequences of inadequate sleep in children and adolescents. Pediatric Clinics, 58, 649–665. https://doi.org/10.1016/j.pcl.2011.03.002
Carskadon, M. A., Vieira, C., & Acebo, C. (1993). Association between puberty and delayed phase preference. Sleep, 16, 258–262. https://doi.org/10.1093/sleep/16.3.258
Center for Disease Control and Prevention. (2014). Youth Risk Behavior Surveillance Systems (YRBSS): A guide to conducting your own youth risk behavior survey. Center for Disease Control and Prevention website. https://www.cdc.gov/healthyyouth/data/yrbs/faq.htm
Center for Disease Control and Prevention. (2018). CDC features: Sleep in middle and high school students. Center for Disease Control and Prevention website. https://www.cdc.gov/features/students-sleep/index.html
Center for Disease Control and Prevention. (2019a). CDC features: Teen substance use and risks. Center for Disease Control and Prevention website. https://www.cdc.gov/features/teen-substance-use/index.html
Center for Disease Control and Prevention. (2019b). Vital signs: Tobacco use by youth is rising. https://www.cdc.gov/vitalsigns/youth-tobacco-use/index.html
Colrain, I. M., Nicholas, C. L., & Baker, F. C. (2014). Alcohol and the sleeping brain. Handbook of Clinical Neurology, 125, 415–431. https://doi.org/10.1016/B978-0-444-62619-6.00024-0
Conroy, D. A., & Arnedt, J. T. (2014). Sleep and substance use disorders: An update. Current Psychiatry Reports, 16, 487. https://doi.org/10.1007/s11920-014-0487-3
Council on Communications and Media. (2013). Children, adolescents, and the media. Pediatrics, 132, 958–961. https://doi.org/10.1542/peds.2013-2656
Cuttler, C., Mischley, L. K., & Sexton, M. (2016). Sex differences in cannabis use and effects: A cross-sectional survey of cannabis users. Cannabis and Cannabinoid Research, 1, 166–175. https://doi.org/10.1089/can.2016.0010
Daly, B. P., Jameson, J. P., Patterson, F., McCurdy, M., Kirk, A., & Michael, K. D. (2015). Sleep duration, mental health, and substance use among rural adolescents: Developmental correlates. Journal of Rural Mental Health, 39, 108–122. https://doi.org/10.1037/rmh0000033
Deleanu, O. C., Pocora, D., Mihălcuţă, S., Ulmeanu, R., Zaharie, A. M., & Mihălţan, F. D. (2016). Influence of smoking on sleep and obstructive sleep apnea syndrome. Pneumologia, 65, 28–35.
Dunbar, M. S., Tucker, J. S., Ewing, B. A., Pedersen, E. R., Miles, J. N., Shih, R. A., & D’amico, E. J. (2017). Frequency of e-cigarette use, health status, and risk and protective health behaviors in adolescents. Journal of Addiction Medicine, 11, 55–62. https://doi.org/10.1097/ADM.0000000000000272
Eaton, D. K., Kann, L., Kinchen, S., Shanklin, S., Ross, J., Hawkins, J., Harris, W. A., Lowry, R., McManus, T., Chyen, D., Lim, C., Whittle, L., Brener, N. D., & Wechsler, H. (2010). Youth risk behavior surveillance—United States, 2009. Morbidity and Mortality Weekly Report: Surveillance Summaries, 59, 1–142.
Ebrahim, I. O., Shapiro, C. M., Williams, A. J., & Fenwick, P. B. (2013). Alcohol and sleep I: Effects on normal sleep. Alcoholism: Clinical and Experimental Research, 37, 539–549. https://doi.org/10.1111/acer.12006
Fucito, L. M., DeMartini, K. S., Hanrahan, T. H., Whittemore, R., Yaggi, H. K., & Redeker, N. S. (2015). Perceptions of heavydrinking college students about a sleep and alcohol health intervention. Behavioral Sleep Medicine, 13, 395–411. https://doi.org/10.1080/15402002.2014.919919
Grant, J. D., Lynskey, M. T., Scherrer, J. F., Agrawal, A., Heath, A. C., & Bucholz, K. K. (2010). A Cotwin-control analysis of drug use and abuse/dependence risk associated with early-onset cannabis use. Addictive Behaviors, 35, 35–41. https://doi.org/10.1016/j.addbeh.2009.08.006
Grant, J. D., Scherrer, J. F., Lynskey, M. T., Agrawal, A., Duncan, A. E., Haber, J. R., Heath, A. C., & Bucholz, K. K. (2012). Associations of alcohol, nicotine, cannabis, and drug use/dependence with educational attainment: Evidence from Cotwin-control analyses. Alcoholism: Clinical & Experimental Research, 36, 1412–1420. https://doi.org/10.1111/j.1530-0277.2012.01752.x
Hagenauer, M. H., Perryman, J. I., Lee, T. M., & Carskadon, M. A. (2009). Adolescent changes in the homeostatic and circadian regulation of sleep. Developmental Neuroscience, 31, 276–284. https://doi.org/10.1159/000216538
Hale, L., & Guan, S. (2015). Screen time and sleep among schoolaged children and adolescents: A systematic literature review. Sleep Medicine Reviews, 21, 50–58. https://doi.org/10.1016/j.smrv.2014.07.007
Hall, W. D., Patton, G., Stockings, E., Weier, M., Lynskey, M., Morley, K. I., & Degenhardt, L. (2016). Why young people’s substance use matters for global health. Lancet Psychiatry, 3, 265–279. https://doi.org/10.1016/S2215-0366(16)00013-4
Hasler, B. P., Soehner, A. M., & Clark, D. B. (2015). Sleep and circadian contributions to adolescent alcohol use disorder. Alcohol, 49, 377–387. https://doi.org/10.1016/j.alcohol.2014.06.010
Haynie, D. L., Lewin, D., Luk, J. W., Lipsky, L. M., O’Brien, F., Iannotti, R. J., Liu, D., & Simons-Morton, B. G. (2017). Beyond sleep duration: Bidirectional associations among chronotype, social jetlag, and drinking behaviors in a longitudinal sample of US high school students. Sleep, 41, 1–14. https://doi.org/10.1093/sleep/zsx202
Hirshkowitz, M., Whiton, K., Albert, S. M., Alessi, C., Bruni, O., DonCarlos, L., Hazen, N., Herman, J., Katz, E., Kheirandish- Gozal, L., Neubauer, D. N., O’Donnell, A. E., Ohayon, M., Peever, J., Rawding, R., Sachdeva, R. C., Setters, B., Vitiello, M. V., Ware, J. C., & Neubauer, D. N. (2015). National Sleep Foundation’s sleep time duration recommendations: Methodology and results summary. Sleep Health, 1, 40–43. https://doi.org/10.1016/j.sleh.2014.12.010
Hysing, M., Pallesen, S., Stormark, K. M., Jakobsen, R., Lundervold, A. J., & Sivertsen, B. (2015). Sleep and use of electronic devices in adolescence: Results from a large population-based study. BMJ Open, 5, e006748. https://doi.org/10.1136/bmjopen-2014-006748
Jacobus, J., Bava, S., Cohen-Zion, M., Mahmood, O., & Tapert, S. F. (2009). Functional consequences of marijuana use in adolescents. Pharmacology Biochemistry and Behavior, 92, 559–565. https://doi.org/10.1016/j.pbb.2009.04.001
Johnston, L. D., Miech, R. A., O’Malley, P. M., Bachman, J. G., Schulenberg, J. E., & Patrick, M. E. (2018). Monitoring the future national survey results on drug use, 1975-2018: Overview, key findings on adolescent drug use. Institute for Social Research, University of Michigan. http://hdl.handle.net/2027.42/148123
Kann, L., McManus, T., Harris, W. A., Shanklin, S. L., Flint, K. H., Queen, B., Lowry, R., Chyen, D., Whittle, L., Thornton, J., Lim, C., Bradford, D., Yamakawa, Y., Leon, M., Brener, N., & Lim, C. (2018). Youth risk behavior surveillance—United States, 2017. Morbidity and Mortality Weekly Report: Surveillance Summaries, 67, 1–114. https://doi.org/10.15585/mmwr.ss6708a1
Kwon, M., Park, E., & Dickerson, S. S. (2019). Adolescent substance use and its association to sleep disturbances: A systematic review. Sleep Health, 5, 382–394. https://doi.org/10.1016/j.sleh.2019.06.001
Marshall, E. J. (2014). Adolescent alcohol use: Risks and consequences. Alcohol and Alcoholism, 49, 160–164. https://doi.org/10.1093/alcalc/agt180
McGlinchey, E. L., & Harvey, A. G. (2015). Risk behaviors and negative health outcomes for adolescents with late bedtimes. Journal of Youth and Adolescence, 44, 478–488. https://doi.org/10.1007/s10964-014-0110-2
McKnight-Eily, L. R., Eaton, D. K., Lowry, R., Croft, J. B., Presley-Cantrell, L., & Perry, G. S. (2011). Relationships between hours of sleep and health-risk behaviors in US adolescent students. Preventive Medicine, 53, 271–273. https://doi.org/10.1016/j.ypmed.2011.06.020
Meldrum, R. C., & Restivo, E. (2014). The behavioral and health consequences of sleep deprivation among US high school students: Relative deprivation matters. Preventive Medicine, 63, 24–28. https://doi.org/10.1016/j.ypmed.2014.03.006
National Center for Chronic Disease Prevention and Health Promotion. (2018, March). Do you children get enough sleep? Centers for Disease Control and Prevention website. https://www.cdc.gov/chronicdisease/resources/infographic/children-sleep.htm
National Sleep Foundation. (2017). Why do we need sleep? National Sleep Foundation website. https://www.sleepfoundation.org/articles/why-do-we-need-sleep
Owens, J., Adolescent Sleep Working Group, & Committee on Adolescence. (2014). Insufficient sleep in adolescents and young adults: An update on causes and consequences. Pediatrics, 134, e921–e932. https://doi.org/10.1542/peds.2014-1696
Owens, J. A., & Weiss, M. R. (2017). Insufficient sleep in adolescents: Causes and consequences. Minerva Pediatrica, 69, 326–336. https://doi.org/10.23736/S0026-4946.17.04914-3
Paiva, T., Gaspar, T., & Matos, M. G. (2016). Mutual relations between sleep deprivation, sleep stealers and risk behaviours in adolescents. Sleep Science, 9, 7–13. https://doi.org/10.1016/j.slsci.2016.02.176
Pasch, K. E., Latimer, L. A., Cance, J. D., Moe, S. G., & Lytle, L. A. (2012). Longitudinal bi-directional relationships between sleep and youth substance use. Journal of Youth and Adolescence, 41, 1184–1196. https://doi.org/10.1007/s10964-012-9784-5
Patte, K. A., Qian, W., & Leatherdale, S. T. (2018). Modifiable predictors of insufficient sleep durations: A longitudinal analysis of youth in the COMPASS study. Preventive Medicine, 106, 164–170. https://doi.org/10.1016/j.ypmed.2017.10.035
Popovici, I., & French, M. T. (2013). Binge drinking and sleep problems among young adults. Drug and Alcohol Dependence, 132, 207–215. https://doi.org/10.1016/j.drugalcdep.2013.02.001
Reichenberger, D. A., Hilmert, C. J., Irish, L. A., Secor-Turner, M., & Randall, B. A. (2016). Associations between sleep and health-risk behaviors in a rural adolescent population. Journal of Pediatric Health Care, 30, 317–322. https://doi.org/10.1016/j.pedhc.2015.08.003
Riehm, K. E., Rojo-Wissar, D. M., Feder, K. A., Mojtabai, R., Spira, A. P., Thrul, J., & Crum, R. M. (2019). E-cigarette use and sleeprelated complaints among youth. Journal of Adolescence, 76, 48–54. https://doi.org/10.1016/j.adolescence.2019.08.009
Roberts, R. E., & Duong, H. T. (2014). The prospective association between sleep deprivation and depression among adolescents. Sleep, 37, 239–244. https://doi.org/10.5665/sleep.3388
Shain, B., & Committee on Adolescence. (2016). Suicide and suicide attempts in adolescents. Pediatrics, 138, e20161420. https://doi.org/10.1542/peds.2016-1420
Singleton, R. A., Jr., & Wolfson, A. R. (2009). Alcohol consumption, sleep, and academic performance among college students. Journal of Studies on Alcohol & Drugs, 70, 355–363. https://doi.org/10.15288/jsad.2009.70.355
Stea, T. H., Knutsen, T., & Torstveit, M. K. (2014). Association between short time in bed, health-risk behaviors and poor academic achievement among Norwegian adolescents. Sleep Medicine, 15, 666–671. https://doi.org/10.1016/j.sleep.2014.01.019
Sun, W., Ling, J., Zhu, X., Lee, T. M. C., & Li, S. X. (2019). Associations of weekday-to-weekend sleep differences with academic performance and health-related outcomes in schoolage children and youths. Sleep Medicine Reviews, 46, 27–53. https://doi.org/10.1016/j.smrv.2019.04.003
Zhabenko, O., Austic, E., Conroy, D. A., Ehrlich, P., Singh, V., Epstein-Ngo, Q., Cunningham, R. M., & Walton, M. A. (2016). Substance use as a risk factor for sleep problems among adolescents presenting to the emergency department. Journal of Addiction Medicine, 10, 331–338. https://doi.org/10.1097/adm.0000000000000243
Misol Kwon, BS, RN, is a doctoral student at the SUNY University at Buffalo School of Nursing, New York, NY, USA.
Young S. Seo is a doctoral student at the Department of Counseling, School and Educational Psychology, Graduate School of Education, SUNY University at Buffalo, New York, NY, USA.
Eunhee Park, PhD, RN, is an assistant professor at the SUNY University at Buffalo School of Nursing, New York, NY.
Yu-Ping Chang, PhD, RN, FGSA, FAAN, FIAAN, is an associate dean for research and scholarship, the Richard E. Garman Endowed Professor, and an associate professor at SUNY University at Buffalo School of Nursing, New York, NY, USA.
1 School of Nursing, University at Buffalo, The State University of New York, NY, USA
2 Department of Counseling, School, Educational Psychology, Graduate School of Education, University at Buffalo, The State University of New York, NY, USA
Corresponding Author:Misol Kwon, BS, RN, School of Nursing, University at Buffalo, The State University of New York, 3435 Main Street, 301 Wende Hall, Buffalo, NY 14214, USA.Email: misolkwo@buffalo.edu
