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Sleep patterns among adolescents are reported to be less than optimal; according to the Na-tional Sleep Foundation,1 sleep duration is

less than 8 hours per night for nearly half of teen-age adolescents. Duration decreases as adoles-cents get older, likely due to the fact that as youth grow older, they go to bed later as they may experi-ence delayed sleep phase syndrome,2 but they still need to wake up early for school. The time it takes to fall asleep (sleep onset latency) is 30 minutes or more for more than one-fourth of adolescents.1 A study of over 4000 youth found that one-fourth of adolescents experienced at least one symptom of insomnia, including problems initiating sleep, dif-ficulty maintaining sleep, and awakening prior to wake-up times.3

Outcomes of poor sleep have been studied ex-tensively, indicating various negative consequenc-es including psychological, behavioral and health problems among adolescents. These outcomes

result in decreased academic performance,4 anxi-ety, and depressive symptoms,5 as well as weight gains.6 National survey data also indicate signifi-cant levels of sleepiness, crankiness, and irritabil-ity, not being able to stay awake during school, de-pressive mood, and increased caffeine intake.1 Ad-ditionally, symptoms of chronic insomnia among youth have been associated with increased odds of poor somatic functioning (ie, low perceived health, physical limitations, negative impact of illness), and problems at home, with peers, and at school.3 Further, adolescents reporting insomnia are more likely to report depressive symptoms, suicidal ide-ation and attempts, as well as development of de-pression as adults.7 Despite this knowledge, few studies have explored predictors of sleep behavior,8

which is needed to develop effective approaches for improving sleep.

There are a host of factors experienced during adolescence that may lead to poor sleep. The peri-od of adolescence is characterized by rapid psycho-logical, sociological, and biological development,9,10

each of which has the potential to promote stress and depressive symptoms. Research shows that adolescents with high scores on depressive mood (feeling unhappy, tense and worrying) are more likely to take longer to fall asleep and get insuf-ficient sleep.1 Results of a cross-sectional study of over 3000 Chinese middle and high school stu-dents indicated that those reporting depressive symptoms were nearly 2.5 times more likely to ex-

Selena T. Nguyen-Rodriguez, Assistant Professor, California State University Long Beach, Department of Health Science, Long Beach, CA. Nadra E. Lisha, Senior Statistician, Univer-sity of California, San Francisco, Division of General Internal Medicine, San Francisco, CA. Donna Spruijt-Metz, Adjunct Associate Professor, Ping Sun, Assistant Professor, Louise A. Rohrbach, Associate Professor and Steve Sussman, Profes-sor, University of Southern California, Department of Preven-tive Medicine, Los Angeles, CA. Correspondence Dr Nguyen-Rodriguez; selena.nguyen-rodri-guez@csulb.edu

Coping Mediates the Effects of Depressive Symptoms on Sleep Problems

Selena T. Nguyen-Rodriguez, PhD, MPH; Nadra E. Lisha, PhD; Donna Spruijt-Metz, PhD, MFA; Ping Sun, PhD; Louise A. Rohrbach, PhD; Steve Sussman, PhD

Objectives: To explore the relationships of perceived stress and depressive symp-toms to sleep problems, testing for me-diation by adaptive and maladaptive cop-ing strategies. Methods: Alternative high school students (N = 1676) completed self-report surveys. Cross-sectional data were analyzed via Preacher and Hayes’ procedures for multiple mediation. Re-sults: The positive relationship between depressive symptoms and sleep prob-lems was mediated partially by anger coping (positively related to sleep prob-lems). The positive relationship between

perceived stress and sleep problems was not mediated by coping strategies. Con-clusions: Findings provide information on psychological factors that may lead to poor sleep outcomes, and are useful for developing health promotion inter-ventions to impact lifelong health be-haviors.

Key words: Transactional Model of Stress and Coping; perceived stress; de-pressive symptoms; sleep problems; cop-ing; alternative high school

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perience sleep problems.11 However, longitudinal research on psychosocial health and sleep behav-ior has focused mostly on outcomes of sleep prob-lems versus its precursors;8 thus, little is known about the psychological factors that lead to poor sleep outcomes. One longitudinal study using a birth cohort of New Zealand children reported that those who had depression and anxiety were at high risk of insomnia in adulthood.12

Extant literature indicates reciprocal effects be-tween negative affect and sleep problems. As noted above, it has been found that sleep problems are precursors to negative psychological health out-comes, with recent findings supporting this di-rection of association. Based on longitudinal and treatment studies, results of a meta-analysis of the relationship between depression and sleep in adolescence found stronger support for sleep prob-lems being predictive of depression than for de-pressive symptoms predicting sleep problems.13 A systematic review and meta-analysis of longitudi-nal research among youth concluded that healthy sleep and reducing negative coping strategies was a protective factor for depression.14 A randomized control trial with adolescents found that sleep ex-tension led to reduced depressive symptoms.

Additional studies have explicity tested the di-rection of association, with findings supporting a bi-directional relationship. Shanahan et al15 found that sleep disturbance both predicts and is predict-ed by anxiety and depression in a sample of rural urban adolescents. Another prospective communi-ty-based study of metropolitan youth found similar results, where sleep deprivation predicted major depression and symptoms at follow-up, whereas major depression at baseline also predicted sleep deprivation.16 Thus, additional research that iden-tifies mechanisms through which these variables impact one another may help to shed light on the complexities of these associations.

Alternative (continuation) schools are defined as ones that “are designed to address the needs of students that typically cannot be met in regu-lar schools. The students who attend alterna-tive schools and programs are typically at risk of educational failure (as indicated by poor grades, truancy, disruptive behavior, pregnancy, or simi-lar factors associated with temporary or perma-nent withdrawal from school).”17(p. 1) Comparison of health behaviors of a national sample of regular high school students revealed that among continu-ation high school students, prevalence of risk be-haviors, such as fighting, alcohol use, tobacco use, risky sexual activity, and insufficient physical ac-tivity, was higher than for their regular high school counterparts.18 Because these alternative high school students are more likely to engage in less healthy behavior, they represent a unique popula-tion to study.

The core constructs of the Transactional Model of Stress and Coping (TMSC)19,20 include primary appraisal (evaluation of whether the stressor is

significant), secondary appraisal (assessment of the controllability of the stressor), coping efforts (strategies used to deal with primary and second-ary appraisal), and coping outcomes (adaptation to the stressor in relation to emotional well-being, functional status, or health behaviors). The mod-el describes problem-focused (eg, problem solv-ing and planning) coping strategies as those that are directed at changing the stressful situation, while emotion-focused (eg, seeking social support and venting) coping strategies are those that fo-cus on changing one’s feelings about the stress-ful situation. The TMSC holds that each type of coping effort may be adaptive or maladaptive. This model posits that appraisals of stress are related to health behavior outcomes, and that these relation-ships are mediated by coping efforts.19,20 Thus, this theoretical model serves as an appropriate guide to examining the influence of psychological factors on poor sleep outcomes, and supports the exploration of coping strategies as mediators to further under-standing those relationships.

In short, adolescents are prone to experience stress, depressive symptoms, and sleep problems (ie, trouble falling asleep and staying asleep). De-veloping positive health habits during this life pe-riod has implications for adult health; therefore, identification of psychological factors that influ-ence sleep problems to improve sleep may have lifelong benefits. Thus, the current study aims to identify psychological predictors of sleep problems, and coping strategies that may influence those re-lationships. Using the TMSC to guide the analytical design, depressive symptoms and perceived stress represent the stressors that lead to primary and secondary appraisal, sleep problems are the cop-ing outcome, and coping efforts will include adap-tive and maladaptive coping strategies. Findings from this research will add important information on psychological influences on poor sleep to cur-rent extant literature.

Based on review of the literature, this is one of only a few studies (eg, Morin et al21) to examine the mediating influence of coping strategies on the relationship between psychological factors and sleep problems, and the first among alternative high school students. It was hypothesized that de-pressive symptoms and perceived stress would be significant predictors of sleep problems. Further, it was anticipated that coping strategies would me-diate those relationships, with adaptive strategies showing an inverse relationship, while maladap-tive strategies would be positively associated with sleep problems.

METHODSParticipants

Data used in the present study were collected at baseline from participants in a larger efficacy test of a substance abuse prevention program.22 The participants were continuation (alternative) high school students from 24 schools in 4 South-

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ern California counties. Participants under age 18 were asked for signed parental consent and per-sonal assent. Signed informed consent was ob-tained from those over 18. Of the 1694 consented participants, 1676 (98.9%) students completed the pretest survey.

ProceduresStudents were administered a closed-ended,

self-report questionnaire at baseline. If a student was absent during a data collection day, an ab-sentee packet was left with instructions. Two sepa-rate versions of the questionnaire were randomly distributed. Surveys began with core demographic and behavioral items, and rotated 2 ensuing sec-tions on psychosocial correlates of substance use. The surveys took approximately 20 to 30 minutes to complete.

MeasuresDemographics. Demographic information was

collected on age (in years), sex, and ethnicity (cod-ed as Latino/Hispanic, African American/ Black, White/Caucasian, American Indian/Native Ameri-can, mixed or other).

Sleep problems. Sleep problems were the pri-mary dependent variable. Two items that assessed problems with initiating and maintaining sleep, which are used routinely to assess history of sleep problems,23 were used to measure sleep problems. These items were: “How often do you have trouble falling asleep?” and “Do you generally have trouble staying asleep at night?”24 Items were measured on an ordinal scale of measurement. The item regard-ing falling asleep was recoded to the same response options for trouble staying asleep, including yes, sometimes, and no (originally, Never, Sometimes, About half of the time, Usually, Always ). The sum of the 2 items was used as a continuous measure of sleep problems, with a higher score indicating more frequent problems; this estimate has been shown previously to have good concurrent validity.25

Stress. Four items taken from Cohen’s Perceived Stress Scale26 were chosen to measure stress in the past month. This short version of the scale exhib-ited good internal consistency in the current study (Cronbach’s alpha = 0.87), and has demonstrated psychometric quality in previous research by the principal investigator.27,28 Response options ranged from 0 = never to 5 = very often. An example item includes: “In the last month I have felt I couldn’t control the important things in my life.”

Depressive symptoms. The Center for Epide-miologic Studies Depression scale (CES-D)29,30 has been used in a multitude of studies to measure depression. A 5-item subset of the CES-D that has been used frequently in other studies of ado-lescents31-33 was used here. Items included: “I had trouble shaking off sad feelings,” “I felt depressed,” “I felt sad,” “I thought that my life had been a fail-ure,” and “I felt lonely,” (1 = 1 day or less to 4 = 6-7 days). The depressive symptoms construct showed

high internal consistency (Cronbach’s alpha = 0.90). The mean of all 5 items was used as a con-tinuous measure of depressive symptoms.

Coping strategies. The coping measure includ-ed items that were adapted originally from Wills,34 and utilized in previous research.28 The items ask what participants do when they have problems at school or home, with response options rating the frequency of each possible behavior, ranging from Never to Always, on a 5-point scale. The 12-item measure is comprised of 4 subscales, each com-posed of 3 items: decision-making coping (eg, “I think hard about what steps to take;” Cronbach’s alpha = 0.851), fantasy coping (eg, “I daydream about better times;” Cronbach’s alpha = 0.705), anger coping (eg, “I yell and scream at someone;” Cronbach’s alpha = 0.787), and social support seeking from parents (eg, “I get sympathy and un-derstanding from my parents;” Cronbach’s alpha = 0.873). Decision-making coping and social support seeking are considered adaptive types of coping strategies (decision-making would be a problem-focused effort, whereas seeking social support is viewed as emotion-focused), and daydreaming cop-ing and anger coping are considered maladaptive (emotion-focused) types of coping strategies.

Statistical Analysis

SPSS v.21 was used to conduct statistical analy-ses for the study. Descriptive statistics were cal-culated to present demographic data. Preliminary analyses of potential demographic covariates in-dicated that sex was related to coping strategies; thus, sex was controlled for in the models. Primary analyses employed procedures recommended by Preacher and Hayes,35 conducting multiple media-tion analysis using bias-corrected bootstrapping to obtain 99% confidence intervals for indirect ef-fects (using 1000 bootstrap resamples). Multiple mediation allows for the assessment of the unique ability of each mediator (ie, controlling for all oth-er mediators) to mediate the relationship between the independent variable (depressive symptoms / perceived stress) and the dependent variable (sleep problems). Alpha was set at .05 for statistical sig-nificance; any confidence interval including zero indicated a relationship that was not statistically significant. Effect sizes are reported for overall me-diation effects (using the ratio of the indirect effect to the total effect, PM) as well as individual betas (using the correlation coefficient, r).

RESULTSCharacteristics of the sample are reported in

Table 1. The sample was of diverse ethnic back-ground, with the majority being Latino and male. There was little variation in age (range: 16.06-17.32 years). In addition, 49.3% of participants lived with both parents (N = 814), 4.8% currently lived with their boyfriend or girlfriend (N = 80), 6.2% were parents (N = 103) and 19.9% had a job (N = 330).

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Depressive SymptomsMultiple mediation analyses, controlling for

sex, revealed a statistically significant positive relationship (total effect) between depressive symptoms and sleep problems (ß =.3267, SE = .0349, p < .0001, r = .2478; see Table 2). Further, Anger Coping (ß =.2558, SE = .0289, p < .0001, r = .0956) and Fantasy Coping (ß = .3106, SE = .0251, p < .0001, r = .0613) also showed posi-tive associations with sleep problems. Seeking Parental Social Support (ß = -.1746, SE = .0438, p = .0001, r = .0507) was negatively associated. Results also showed that depressive symptoms were positively associated with Anger Coping (ß = .1221, SE = .0344, p = .0004, r = .2227) and Fan-tasy Coping (ß = .0616, SE = .0272, p = .0234, r = .2104).

Bootstrap results (Table 2) revealed that the unique indirect effect through Anger Coping was statistically significant (ß = .0308, SE = .0099, 99% CI: .0082, .0673). The relationship between depressive symptoms and sleep problems, control-ling for all mediators (direct effect), was attenuated from the total effect (change in beta = .0586), but remained statistically significant (ß = .2681, SE = .0363, p < .0001, r = .1977), where the ratio of the indirect effect to the total effect36indicates that coping strategies mediate approximately 18% of the total effect of depressive symptoms on sleep problems (PM = .1794).

Perceived StressResults showed a statistically significant posi-

tive relationship between perceived stress and sleep problems, controlling for sex (ß =.3125, SE = .0266, p < .0001, r = .3036; total effect; Table 3). Additionally, perceived stress was positively asso-ciated with Anger Coping (ß = .3355, SE = .0212, p < .0001, r = .3772), and Fantasy Coping (ß = .3222, SE = .0283, p < .0001, r = .2816), but negatively associated with Seeking Parental Social Support (ß = -.0934, SE = .0340, p = .0062, r = .0705). In the multiple mediation model of perceived stress and sleep problems, none of the proposed media-tors were significantly related to the dependent variable. Similarly, as seen in Table 3, the total indirect effects for all mediators, as a group, was not statistically significant (ß = .0366, SE = .0140, 99% CI: -.0012, .0748), as was the case for the indirect effect of each individual mediator. These findings indicate that, although the beta for per-ceived stress is attenuated with inclusion of me-diators, bootstrap results indicate this change in beta is not statistically significant; therefore, it is concluded that these specific coping strategies do not mediate the relationship between perceived stress and sleep problems.

DISCUSSIONOur study aimed to test relationships among de-

pressive symptoms, perceived stress, coping, and

Table 1Selected Characteristics of the Study Sample (N = 1676)

Percent (N) Mean SDEthnicity African American 4.95 (83) Asian 2.98 (50) Latino 62.41 (1,046) Mixed 12.95 (217) Other 6.03 (101) White 10.68 (179)Female sex 42.24 (708)Age 16.78 0.93Sleep Problems 1.44 1.06Perceived Stress 2.87 1.08Depressive Symptoms 1.70 0.86Decision-making Coping 3.44 1.00Social Support Seeking Coping 2.78 1.30Anger Coping 2.26 0.91Fantasy Coping 3.40 1.12Note.Ranges: Age 16-17, Sleep Problems 0-4, Coping 1-5

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sleep problems. As hypothesized, positive asso-ciations were found, where depressive symptoms and perceived stress predicted sleep problems; this is in line with previous findings (eg, Hall et al37). Further analyses revealed that only the re-lationship between depressive and sleep problems was partially mediated by a specific coping strat-egy, partially supporting hypotheses. It appears that higher anger coping, a maladaptive strategy, partially mediates the relationship between de-pressive symptoms and sleep problems. This in-dicates that when these youth experience depres-sive symptoms, they are more likely to use anger as a coping mechanism, which leads to increased sleep problems. This is an important finding re-lated to treatment—as part of broad, multi-faceted

interventions, one facet that teaches adolescents to deal with their depressive symptoms with relax-ation or distraction strategies may help ameliorate frequency of sleep problems. As mentioned above, coping mediated approximately 18% of the total effect of depressive symptoms on sleep problems; however, it should be noted that the effect size, for the direct relationship between anger coping on sleep problems was r = .0956, which represents a small practical effect.

The hypothesis that coping strategies would me-diate the relationship between perceived stress and sleep problems was not supported. Although, perceived stress was significantly related to 3 of the coping strategies, anger, fantasy, and support seeking did not mediate this relationship. It is pos-

Table 3Relationship between Perceived Stress and Insomnia Symptoms

Insomnia Symptomsβ SE p-value 99% CI

Perceived stress (total effect) 0.3125 0.0266 < .0001 --Perceived stress (direct effect) 0.2759 0.0296 < .0001 --Male sex -0.0348 0.0571 .5426 --Bootstrap results of multiple mediation model, controlling for all mediatorsTotal indirect effect of all mediators 0.0366 0.0140 -- -0.0012, 0.0748Anger 0.0172 0.0123 -- -0.0136, 0.0522Seeking parental support 0.0040 0.0027 -- -0.0012, 0.0147Decision making 0.0002 0.0013 -- -0.0034, 0.0060Fantasy 0.0152 0.0093 -- -0.0068, 0.0429Note.Model: F (6, 1363) = 27.0293, p < .0001, Adjusted R2 = 0.1024

Table 2Relationship between Depressive Symptoms and Insomnia Symptoms

Insomnia Symptomsβ SE p-value 99% CI

Depressive symptoms (total effect) 0.3267 0.0349 < .0001 --Depressive symptoms (direct effect) 0.2681 0.0363 < .0001 --Male sex -0.0145 0.0579 .8024 --Bootstrap results of multiple mediation model, controlling for all mediatorsTotal indirect effect of all mediators 0.0587 0.0122 -- 0.0281, 0.0953Anger 0.0308 0.0009 -- 0.0082, 0.0673Seeking parental support 0.0074 0.0047 -- -0.0036, 0.0221Decision making 0.0009 0.0020 -- -0.0039, 0.0115Fantasy 0.0196 0.0092 -- -0.0037, 0.0469Note.Model: F (6, 1356) = 21.2389, p < .0001, Adjusted R2 = 0.0819

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sible that the physiological stress response may be the driving force through which perceived stress impacts sleep, for example cortisol has been shown to be associated with insomnia symptoms.38 Thus, when the hypothalamic-pituitary-adrenal (HPA) axis is activated by stress, this excitatory response may be responsible for sleep problems. Therefore, the mechanisms by which perceived stress im-pacts stress might be physiological, whereas the pathway to sleep disturbance from depressive symptoms may be impacted more readily by emo-tion-focused coping efforts. These pathways must be explored further to gain a more in-depth un-derstanding of the differential impact of coping for various psychological factors on sleep outcomes.

Socio-environmental factors not measured in this study also could be responsible for the differ-ent findings for depressive symptoms vs perceived stress. A primary factor involved in sleep out-comes is that of sleep hygiene; these factors may be impacted differentially by depression or stress or may exacerbate either, having varied effects on sleep behavior. Future research would benefit from including sleep hygiene in models of sleep behav-ior to tease out the effects of associated environ-mental factors. Social influence (peer and family) and delayed sleep-phase shift experienced during adolescence also must be considered, as they both can impact emotional, behavioral, and physiologi-cal processes. Studies that include socio-environ-mental factors as part of their assessment likely would provide more holistic understanding of this phenomenon.

One coping strategy, decision-making, was not found to be associated with perceived stress, de-pressive symptoms, or any of the coping strategies. The TMSC holds that problem management strate-gies are most adaptive when stressors are modi-fiable, and emotional regulation efforts are most adaptive when the stressor cannot be modified. Depending on the nature of the stressors, partici-pants may not have felt they would be able to im-pact those factors, making the use of problem-fo-cused strategies ineffective. The same may be said for perceived sense of control over sleep problems, especially if students had tried unsuccessfully to deal with sleep problems in the past. Considering that the decision-making coping items had to do with exploring options to act on a stressor, it is possible that this may be irrelevant to dealing with perceived stress and depressive and sleep prob-lems for this sample.

Because little is known about predictors of sleep outcomes,8 results can begin to inform the litera-ture regarding the relationship between psycholog-ical factors and sleep problems. In evaluating the effect sizes for the relationship between depressive symptoms (r = .1977; small direct effect) and sleep problems, and between perceived stress (r = .3036; medium total effect) and sleep problems, we can conclude that these represent meaningful effects, beyond statistical significance. Due to the complex

nature of sleep, there are many factors that inde-pendently influence and interact with various oth-er factors to impact sleep outcomes; so, it makes sense that depressive symptoms and perceived stress explain about 10% of the variance in sleep problems. Thus, this study sheds light on one as-pect, psychological influence, an important piece of the puzzle. More importantly, it identifies modi-fiable risk factors on which intervention efforts can be focused.

Further, considering the target population, it makes sense that there may be various other fac-tors that impact sleep for alternative high school students that are likely to engage in a myriad of other risk factors that can impact their sleep be-haviors. This again underscores the important in-fluence of environment, as the unhealthy behav-iors engaged in by these at-risk youth often are dictated by contextual factors, and thus, are not a matter of choice. Nonetheless, it seems that de-velopment and use of adaptive coping strategies would stand to improve health outcomes, regard-less of the causes of risk behavior. Further re-search is needed to learn about these relationships in the general population of high school students.

The findings of this paper should be evaluated in light of limitations. The use of regression analy-ses assumes that depressive symptoms and per-ceived stress are predictive of sleep problems, ie, these psychological factors lead to sleep outcomes. Thus, the principal limitation of the current analy-sis is the cross-sectional design, as current find-ings cannot establish direction of association and significant associations cannot be assumed to be causal – longitudinal research is sorely needed to identify factors that lead to poor sleep. Still, this study is one of few to explore these relationships (eg, Morin et al21), and the first among alternative high school youth, an at-risk population, providing a first step towards novel research that can inform interventions to improve sleep.

As in all survey research, the results are subject to possible biases; however, the methods and mea-sures used have been validated and have shown reliable results.39 For example, to reduce partici-pant burden, shorter versions of validated scales were employed, which could have introduced mea-surement error; however, internal consistency scores for the shortened scales indicated good reli-ability. Further, studies have shown child reports of sleep behavior to be more accurate than parent report.40,41 Replication of findings using objective measures of sleep such as actigraphy or polysom-nography would enhance the validity of findings, although less feasible and cost-effective in large-scale studies like this one. Because data were col-lected from a population of alternative high school students, the results are not generalizable outside of this group.

Lastly, this school-based study employed nested data, for which multilevel regression analyses are recommended to address the violation of indepen-

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dence; however, the analytic strategies that were used do not allow for multi-level procedures. Stud-ies show that the school-level ICCs for the majority of health behaviors are less than 0.05, indicating low degree of dependence in observations;42 thus, data with low ICCs are likely not to be correlat-ed enough to bias estimates. The value for sleep problems was ICC = .0033 in the present study; therefore the lack of multi-level modeling was likely inconsequential, and the assumption of in-dependence was met. Further, assessment of the remaining statistical assumptions of regression indicates that multicollinearity, homoscedasticity, independent errors, normality of residuals, and linearity were not violated, supporting generaliz-ability of the model.

The present study aimed to provide findings that would serve as an initial step towards developing an understanding of the influence of psychological factors on sleep problems in adolescents. Notably, depression, stress, and coping strategies are modi-fiable risk factors for poor sleep among adolescents, and thus, may serve as targets in multi-component interventions that seek to improve sleep quality and duration. Future studies that employ a longi-tudinal approach in a broader sample of youth are recommended. By clearly identifying the precur-sors of sleep problems, comprehensive interven-tions can be designed to reduce sleep difficulties among this age group. Targeting youth is essen-tial, as health behaviors developed during adoles-cence tend to track into adulthood. In light of the many consequences of poor sleep, programs that are able to reduce sleep difficulties have significant and far-reaching public health benefits. Sleep has been identified as the third pillar of health, next to diet and physical activity; as such, health promo-tion efforts must address the prevalence of sleep problems to impact population health.

Human Subjects StatementAll study procedures were approved by the Uni-

versity of Southern California’s Institutional Re-view Board (protocol number: HS-07-00601; ap-proval date: 6/12/2013).

Conflict of Interest StatementThe authors do not have any conflicts of interest

to disclose.

AcknowledgementsThis research was supported a grant from the

National Institute on Drug Abuse #DA020138. The contents of this paper are solely the responsibility of the authors and do not necessarily reflect the views of the Institute.

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