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 symptoms to sleep problems, testing for mediation by adaptive and maladaptive coping 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. Results: The positive relationship between depressive symptoms and sleep problems was mediated partially by anger coping (positively related to sleep problems). The positive relationship between
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leep patterns among adolescents are reported to be less than optimal; according to the National Sleep Foundation,1 sleep duration is less than 8 hours per night for nearly half of teenage adolescents. Duration decreases as adolescents get older, likely due to the fact that as youth grow older, they go to bed later as they may experience 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, difficulty maintaining sleep, and awakening prior to wake-up times.3 Outcomes of poor sleep have been studied extensively, indicating various negative consequences including psychological, behavioral and health problems among adolescents. These outcomes Selena T. Nguyen-Rodriguez, Assistant Professor, California State University Long Beach, Department of Health Science, Long Beach, CA. Nadra E. Lisha, Senior Statistician, University 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, Professor, University of Southern California, Department of Preventive Medicine, Los Angeles, CA. Correspondence Dr Nguyen-Rodriguez; [email protected]
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perceived stress and sleep problems was not mediated by coping strategies. Conclusions: Findings provide information on psychological factors that may lead to poor sleep outcomes, and are useful for developing health promotion interventions to impact lifelong health behaviors. Key words: Transactional Model of Stress and Coping; perceived stress; depressive symptoms; sleep problems; coping; alternative high school Am J Health Behav. 2015;39(2):183-190 DOI: http://dx.doi.org/10.5993/AJHB.39.2.4
result in decreased academic performance,4 anxiety, and depressive symptoms,5 as well as weight gains.6 National survey data also indicate significant levels of sleepiness, crankiness, and irritability, not being able to stay awake during school, depressive mood, and increased caffeine intake.1 Additionally, 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 ideation and attempts, as well as development of depression 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 period of adolescence is characterized by rapid psychological, 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 insufficient sleep.1 Results of a cross-sectional study of over 3000 Chinese middle and high school students indicated that those reporting depressive symptoms were nearly 2.5 times more likely to ex-
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Coping Mediates the Effects of Depressive Symptoms on Sleep Problems perience sleep problems.11 However, longitudinal research on psychosocial health and sleep behavior has focused mostly on outcomes of sleep problems 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 between negative affect and sleep problems. As noted above, it has been found that sleep problems are precursors to negative psychological health outcomes, with recent findings supporting this direction 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 problems being predictive of depression than for depressive symptoms predicting sleep problems.13 A systematic review and meta-analysis of longitudinal 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 extension led to reduced depressive symptoms. Additional studies have explicity tested the direction of association, with findings supporting a bi-directional relationship. Shanahan et al15 found that sleep disturbance both predicts and is predicted by anxiety and depression in a sample of rural urban adolescents. Another prospective community-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 identifies 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 regular schools. The students who attend alternative schools and programs are typically at risk of educational failure (as indicated by poor grades, truancy, disruptive behavior, pregnancy, or similar factors associated with temporary or permanent withdrawal from school).”17(p. 1) Comparison of health behaviors of a national sample of regular high school students revealed that among continuation high school students, prevalence of risk behaviors, such as fighting, alcohol use, tobacco use, risky sexual activity, and insufficient physical activity, 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 population 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
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significant), secondary appraisal (assessment of the controllability of the stressor), coping efforts (strategies used to deal with primary and secondary appraisal), and coping outcomes (adaptation to the stressor in relation to emotional well-being, functional status, or health behaviors). The model describes problem-focused (eg, problem solving 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 focus on changing one’s feelings about the stressful 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 relationships 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 understanding those relationships. In short, adolescents are prone to experience stress, depressive symptoms, and sleep problems (ie, trouble falling asleep and staying asleep). Developing positive health habits during this life period has implications for adult health; therefore, identification of psychological factors that influence 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 relationships. 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 coping outcome, and coping efforts will include adaptive and maladaptive coping strategies. Findings from this research will add important information on psychological influences on poor sleep to current 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 depressive symptoms and perceived stress would be significant predictors of sleep problems. Further, it was anticipated that coping strategies would mediate those relationships, with adaptive strategies showing an inverse relationship, while maladaptive strategies would be positively associated with sleep problems. METHODS Participants 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-
Nguyen-Rodriquez et al ern California counties. Participants under age 18 were asked for signed parental consent and personal assent. Signed informed consent was obtained from those over 18. Of the 1694 consented participants, 1676 (98.9%) students completed the pretest survey. Procedures Students were administered a closed-ended, self-report questionnaire at baseline. If a student was absent during a data collection day, an absentee packet was left with instructions. Two separate versions of the questionnaire were randomly distributed. Surveys began with core demographic and behavioral items, and rotated 2 ensuing sections on psychosocial correlates of substance use. The surveys took approximately 20 to 30 minutes to complete. Measures Demographics. Demographic information was collected on age (in years), sex, and ethnicity (coded as Latino/Hispanic, African American/ Black, White/Caucasian, American Indian/Native American, mixed or other). Sleep problems. Sleep problems were the primary 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 regarding 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 exhibited 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 Epidemiologic 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 adolescents31-33 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 failure,” and “I felt lonely,” (1 = 1 day or less to 4 = 6-7 days). The depressive symptoms construct showed
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high internal consistency (Cronbach’s alpha = 0.90). The mean of all 5 items was used as a continuous measure of depressive symptoms. Coping strategies. The coping measure included 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 composed 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 understanding 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 problemfocused effort, whereas seeking social support is viewed as emotion-focused), and daydreaming coping and anger coping are considered maladaptive (emotion-focused) types of coping strategies. Statistical Analysis SPSS v.21 was used to conduct statistical analyses for the study. Descriptive statistics were calculated to present demographic data. Preliminary analyses of potential demographic covariates indicated 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 mediation analysis using bias-corrected bootstrapping to obtain 99% confidence intervals for indirect effects (using 1000 bootstrap resamples). Multiple mediation allows for the assessment of the unique ability of each mediator (ie, controlling for all other 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 significance; any confidence interval including zero indicated a relationship that was not statistically significant. Effect sizes are reported for overall mediation effects (using the ratio of the indirect effect to the total effect, PM) as well as individual betas (using the correlation coefficient, r). RESULTS Characteristics of the sample are reported in Table 1. The sample was of diverse ethnic background, with the majority being Latino and male. There was little variation in age (range: 16.0617.32 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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Coping Mediates the Effects of Depressive Symptoms on Sleep Problems
Table 1 Selected Characteristics of the Study Sample (N = 1676) Percent (N)
Mean
SD
16.78
0.93
Sleep Problems
1.44
1.06
Perceived Stress
2.87
1.08
Depressive Symptoms
1.70
0.86
Decision-making Coping
3.44
1.00
Social Support Seeking Coping
2.78
1.30
Anger Coping
2.26
0.91
Fantasy Coping
3.40
1.12
Ethnicity 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 Age
42.24 (708)
Note. Ranges: Age 16-17, Sleep Problems 0-4, Coping 1-5
Depressive Symptoms Multiple 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 positive 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 Fantasy 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, controlling 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).
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Perceived Stress Results showed a statistically significant positive 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 associated 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 mediators 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 perceived stress is attenuated with inclusion of mediators, 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. DISCUSSION Our study aimed to test relationships among depressive symptoms, perceived stress, coping, and
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Table 2 Relationship between Depressive Symptoms and Insomnia Symptoms Insomnia Symptoms β
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 mediators Total indirect effect of all mediators
0.0587
0.0122
--
0.0281, 0.0953
Anger
0.0308
0.0009
--
0.0082, 0.0673
Seeking parental support
0.0074
0.0047
--
-0.0036, 0.0221
Decision making
0.0009
0.0020
--
-0.0039, 0.0115
Fantasy
0.0196
0.0092
--
-0.0037, 0.0469
Note. Model: F (6, 1356) = 21.2389, p < .0001, Adjusted R2 = 0.0819
sleep problems. As hypothesized, positive associations 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 relationship between depressive and sleep problems was partially mediated by a specific coping strategy, partially supporting hypotheses. It appears that higher anger coping, a maladaptive strategy, partially mediates the relationship between depressive symptoms and sleep problems. This indicates that when these youth experience depressive symptoms, they are more likely to use anger as a coping mechanism, which leads to increased sleep problems. This is an important finding related to treatment—as part of broad, multi-faceted
interventions, one facet that teaches adolescents to deal with their depressive symptoms with relaxation 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 mediate 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 3 Relationship between Perceived Stress and Insomnia Symptoms Insomnia Symptoms β
SE
p-value
99% CI
Perceived stress (total effect)
0.3125
0.0266
< .0001
--
Perceived stress (direct effect)
0.2759
0.0296
< .0001
--
-0.0348
0.0571
.5426
--
Male sex
Bootstrap results of multiple mediation model, controlling for all mediators Total indirect effect of all mediators
0.0366
0.0140
--
-0.0012, 0.0748
Anger
0.0172
0.0123
--
-0.0136, 0.0522
Seeking parental support
0.0040
0.0027
--
-0.0012, 0.0147
Decision making
0.0002
0.0013
--
-0.0034, 0.0060
Fantasy
0.0152
0.0093
--
-0.0068, 0.0429
Note. Model: F (6, 1363) = 27.0293, p < .0001, Adjusted R2 = 0.1024
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Coping Mediates the Effects of Depressive Symptoms on Sleep Problems 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 impacts stress might be physiological, whereas the pathway to sleep disturbance from depressive symptoms may be impacted more readily by emotion-focused coping efforts. These pathways must be explored further to gain a more in-depth understanding 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 different findings for depressive symptoms vs perceived stress. A primary factor involved in sleep outcomes 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 behavior to tease out the effects of associated environmental 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 physiological processes. Studies that include socio-environmental 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, depressive symptoms, or any of the coping strategies. The TMSC holds that problem management strategies are most adaptive when stressors are modifiable, and emotional regulation efforts are most adaptive when the stressor cannot be modified. Depending on the nature of the stressors, participants may not have felt they would be able to impact those factors, making the use of problem-focused 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 problems for this sample. Because little is known about predictors of sleep outcomes,8 results can begin to inform the literature regarding the relationship between psychological 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
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nature of sleep, there are many factors that independently influence and interact with various other 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 aspect, psychological influence, an important piece of the puzzle. More importantly, it identifies modifiable risk factors on which intervention efforts can be focused. Further, considering the target population, it makes sense that there may be various other factors 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 behaviors. This again underscores the important influence of environment, as the unhealthy behaviors 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 development and use of adaptive coping strategies would stand to improve health outcomes, regardless of the causes of risk behavior. Further research 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 analyses assumes that depressive symptoms and perceived stress are predictive of sleep problems, ie, these psychological factors lead to sleep outcomes. Thus, the principal limitation of the current analysis is the cross-sectional design, as current findings 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 measures used have been validated and have shown reliable results.39 For example, to reduce participant burden, shorter versions of validated scales were employed, which could have introduced measurement error; however, internal consistency scores for the shortened scales indicated good reliability. 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 polysomnography would enhance the validity of findings, although less feasible and cost-effective in largescale studies like this one. Because data were collected 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-
Nguyen-Rodriquez et al dence; however, the analytic strategies that were used do not allow for multi-level procedures. Studies 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 correlated 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 independence 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 generalizability 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 modifiable 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 longitudinal approach in a broader sample of youth are recommended. By clearly identifying the precursors of sleep problems, comprehensive interventions can be designed to reduce sleep difficulties among this age group. Targeting youth is essential, as health behaviors developed during adolescence 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 promotion efforts must address the prevalence of sleep problems to impact population health.
1. National Sleep Foundation. Teens and Sleep. Washington, DC; National Sleep Foundation; 2006. 2. Crowley SJ, Acebo C, Carskadon MA. Sleep, circadian rhythms, and delayed phase in adolescence. Sleep Med.
2007;8(6):602-612. 3. Roberts RE, Roberts CR, Duong HT. Chronic insomnia and its negative consequences for health and functioning of adolescents: a 12-month prospective study. J Adol Health. 2008;42(3):294-302. 4. Taras H, Potts-Datema W. Sleep and student performance at school. J School Health. 2005;75(7):248-254. 5. Alfano CA, Zakem AH, Costa NM, et al. Sleep problems and their relation to cognitive factors, anxiety, and depressive symptoms in children and adolescents. Depress Anxiety. 2009;26(6):503-512. 6. Snell EK, Adam EK, Duncan GJ. Sleep and the body mass index and overweight status of children and adolescents. Child Dev. 2007;78(1):309-323. 7. Roane BM, Taylor DJ. Adolescent insomnia as a risk factor for early adult depression and substance abuse. Sleep. 2008;31(10):1351-1356. 8. Wolfson AR. Adolescents and emerging adults’ sleep patterns: new developments. J Adol Health. 2010;46(2):124132. 9. Compas BE, Hinden BR, Gerhardt CA. Adolescent development: pathways and processes of risk and resilience. Ann Rev Psychol. 1995;46(1):265-293. 10. Petersen AC. Adolescent development. Annual Review of Psychology. 1988;39(1):583-607. 11. Guo L, Deng J, He Y, et al. Prevalence and correlates of sleep disturbance and depressive symptoms among Chinese adolescents: a cross-sectional survey study. BMJ Open. 2014;4(7):e005517. 12. Goldman-Mellor S, Gregory AM, Caspi A, et al. Mental health antecedents of early midlife insomnia: evidence from a four-decade longitudinal study. Sleep. 2014: in press. Available at: http://research.gold.ac.uk/id/ eprint/7347. Accessed September 26, 2104. 13. Lovato N, Gradisar M. A meta-analysis and model of the relationship between sleep and depression in adolescents: recommendations for future research and clinical practice. Sleep Med Rev. 2014. doi: 10.10161j.stnrv.12014.10103.10006. 14. Cairns KE, Hui Yap MB, Pilkington PD, Jorm AF. Risk and protective factors for depression that adolescents can modify: a systematic review and meta-analysis of longitudinal studies. J Affect Disord. 2014. doi: 10.1016/j. jad.2014.1008.1006. 15. Shanahan L, Copeland WE, Angold A, et al. Sleep problems predict and pre predicted by generalized anxiety/ depression and oppositional defiant disorder. J Am Acad Child Adol Psychiatry. 2014;53(5):550-558. 16. Roberts RE, Duong HT. The prospective association between sleep deprivation and depression among adolescents. Sleep. 2014;37(2):239-244. 17. Carver PR, Lewis L. Alternative Schools and Programs for Public School Students At Risk of Educational Failure: 2007–08 (NCES 2010–026). Washington, DC: US Department of Education, National Center for Education Statistics; 2010. 18. Grunbaum JA, Lowry R, Kann L. Prevalence of healthrelated behaviors among alternative high school students as compared with students attending regular high schools. J Adol Health. 2001;29(5):337-343. 19. Cohen F. Coping. In: Matarazzo JD, Weiss SM, Herd JA, Miller NE, Weiss SM, (Eds). Behavioral Health: A Handbook of Health Enhancement and Disease Prevention. New York, NY: Wiley; 1984:3-40. 20. Lazarus RS, Cohen JB. Environmental stress. In: Altman I, Wohlwill JF, (Eds). Human Behavior and Environment. Vol 2. New York, NY: Plenum; 1977:89-127. 21. Morin CM, Rodrigue S, Ivers H. Role of stress, arousal, and coping skills in primary insomnia. Psychosom Med. 2003;65(2):259-267. 22. Lisha NE, Sun P, Rohrbach L, et al. An evaluation of immediate outcomes and fidelity of a drug abuse prevention
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Human Subjects Statement All study procedures were approved by the University of Southern California’s Institutional Review Board (protocol number: HS-07-00601; approval date: 6/12/2013). Conflict of Interest Statement The authors do not have any conflicts of interest to disclose. Acknowledgements This 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. References
http://dx.doi.org/10.5993/AJHB.39.2.4
189
Coping Mediates the Effects of Depressive Symptoms on Sleep Problems program in continuation high schools: Project Towards No Drug Abuse (TND). J Drug Educ. 2012;42(1):33-57. 23. Sateia M, Doghramji K, Hauri P, Morin C. Evaluation of chronic insomnia. an American Academy of Sleep Medicine review. Sleep. 2000;23(2):243-308. 24. Gluck ME, Geliebter A, Satov T. Night eating syndrome is associated with depression, low self-esteem, reduced daytime hunger, and less weight loss in obese outpatients. Obes. 2001;9(4):264-267. 25. Katz DA, McHorney CA. The relationship between insomnia and health-related quality of life in patients with chronic illness. J Fam Pract. 2002;51(3):229-236. 26. Cohen S, Kamarck T, Mermelstein R. A global measure of perceived stress. J Health Soc Behav. 1983;24(4):385396. 27. Sussman S, Dent CW. One-year prospective prediction of drug use from stress-related variables. Subst Use Misuse. 2000;35(5):717-735. 28. Sussman S, Brannon BR, Dent CW, et al. Relations of coping effort, coping strategies, perceived stress, and cigarette smoking among adolescents. Subst Use Misuse. 1993;28(7):599-612. 29. Radloff LS. The CES-D Scale. Appl Psychol Meas. 1977;1(3):385-401. 30. Sussman S, Dent CW, Galaif ER. The correlates of substance abuse and dependence among adolescents at high risk for drug abuse. J Subst Abuse. 1997;9:241-255. 31. Crockett LJ, Randall BA, Shen YL, et al. Measurement equivalence of the center for epidemiological studies depression scale for Latino and Anglo adolescents: a national study. J Consult Clin Psychol. 2005;73(1):47-58. 32. Egede LE, Nietert PJ, Zheng D. Depression and all-cause and coronary heart disease mortality among adults with
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and without diabetes. Diabetes Care. 2005;28(6):13391345. 33. Hudson DB, Elek SM, Campbell-Grossman C. Depression, self-esteem, loneliness, and social support among adolescent mothers participating in the New Parents Project. Adolesc. 2000;35(139):445-453. 34. Wills TA. Stress and coping in early adolescence: relationships to substance use in urban school samples. Health Psychol. 1986;5(6):503-529. 35. Preacher KJ, Hayes AF. Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behav Res Methods. 2008;40(3):879-891. 36. MacKinnon DP, Dwyer JH. Estimating mediated effects in prevention studies. Eval Rev. 1993;17(2):144-158. 37. Hall M, Buysse DJ, Nowell PD, et al. Symptoms of stress and depression as correlates of sleep in primary insomnia. Psychosom Med. 2000;62(2):227-230. 38. Rodenbeck A, Huether G, Ruther E, Hajak G. Interactions between evening and nocturnal cortisol secretion and sleep parameters in patients with severe chronic primary insomnia. Neurosci Lett. 2002;324(2):159-163. 39. Crano WD, Brewer MB. Principles and Methods of Social Research. Mahwah, NJ: Lawrence Erlbaum; 2002. 40. Gruber R, Sadeh A, Raviv A. Sleep of school-age children: objective and subjective measures. Sleep Res. 1997;26:158. 41. Owens J, Spirito A, McGuinn M, Nobile C. Sleep habits and sleep disturbance in elementary school-aged children. J Dev Behav Pediatr. 2000;21(1):27-36. 42. Murray DM, Blitstein JL. Methods to reduce the impact of intraclass correlation in group-randomized trials. Eval Res. 2003;27(1):79-103.
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leep patterns among adolescents are reported to be less than optimal; according to the National Sleep Foundation,1 sleep duration is less than 8 hours per night for nearly half of teenage adolescents. Duration decreases as adolescents get older, likely due to the fact that as youth grow older, they go to bed later as they may experience 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, difficulty maintaining sleep, and awakening prior to wake-up times.3 Outcomes of poor sleep have been studied extensively, indicating various negative consequences including psychological, behavioral and health problems among adolescents. These outcomes Selena T. Nguyen-Rodriguez, Assistant Professor, California State University Long Beach, Department of Health Science, Long Beach, CA. Nadra E. Lisha, Senior Statistician, University 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, Professor, University of Southern California, Department of Preventive Medicine, Los Angeles, CA. Correspondence Dr Nguyen-Rodriguez; [email protected]
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perceived stress and sleep problems was not mediated by coping strategies. Conclusions: Findings provide information on psychological factors that may lead to poor sleep outcomes, and are useful for developing health promotion interventions to impact lifelong health behaviors. Key words: Transactional Model of Stress and Coping; perceived stress; depressive symptoms; sleep problems; coping; alternative high school Am J Health Behav. 2015;39(2):183-190 DOI: http://dx.doi.org/10.5993/AJHB.39.2.4
result in decreased academic performance,4 anxiety, and depressive symptoms,5 as well as weight gains.6 National survey data also indicate significant levels of sleepiness, crankiness, and irritability, not being able to stay awake during school, depressive mood, and increased caffeine intake.1 Additionally, 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 ideation and attempts, as well as development of depression 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 period of adolescence is characterized by rapid psychological, 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 insufficient sleep.1 Results of a cross-sectional study of over 3000 Chinese middle and high school students indicated that those reporting depressive symptoms were nearly 2.5 times more likely to ex-
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Coping Mediates the Effects of Depressive Symptoms on Sleep Problems perience sleep problems.11 However, longitudinal research on psychosocial health and sleep behavior has focused mostly on outcomes of sleep problems 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 between negative affect and sleep problems. As noted above, it has been found that sleep problems are precursors to negative psychological health outcomes, with recent findings supporting this direction 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 problems being predictive of depression than for depressive symptoms predicting sleep problems.13 A systematic review and meta-analysis of longitudinal 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 extension led to reduced depressive symptoms. Additional studies have explicity tested the direction of association, with findings supporting a bi-directional relationship. Shanahan et al15 found that sleep disturbance both predicts and is predicted by anxiety and depression in a sample of rural urban adolescents. Another prospective community-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 identifies 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 regular schools. The students who attend alternative schools and programs are typically at risk of educational failure (as indicated by poor grades, truancy, disruptive behavior, pregnancy, or similar factors associated with temporary or permanent withdrawal from school).”17(p. 1) Comparison of health behaviors of a national sample of regular high school students revealed that among continuation high school students, prevalence of risk behaviors, such as fighting, alcohol use, tobacco use, risky sexual activity, and insufficient physical activity, 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 population 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
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significant), secondary appraisal (assessment of the controllability of the stressor), coping efforts (strategies used to deal with primary and secondary appraisal), and coping outcomes (adaptation to the stressor in relation to emotional well-being, functional status, or health behaviors). The model describes problem-focused (eg, problem solving 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 focus on changing one’s feelings about the stressful 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 relationships 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 understanding those relationships. In short, adolescents are prone to experience stress, depressive symptoms, and sleep problems (ie, trouble falling asleep and staying asleep). Developing positive health habits during this life period has implications for adult health; therefore, identification of psychological factors that influence 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 relationships. 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 coping outcome, and coping efforts will include adaptive and maladaptive coping strategies. Findings from this research will add important information on psychological influences on poor sleep to current 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 depressive symptoms and perceived stress would be significant predictors of sleep problems. Further, it was anticipated that coping strategies would mediate those relationships, with adaptive strategies showing an inverse relationship, while maladaptive strategies would be positively associated with sleep problems. METHODS Participants 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-
Nguyen-Rodriquez et al ern California counties. Participants under age 18 were asked for signed parental consent and personal assent. Signed informed consent was obtained from those over 18. Of the 1694 consented participants, 1676 (98.9%) students completed the pretest survey. Procedures Students were administered a closed-ended, self-report questionnaire at baseline. If a student was absent during a data collection day, an absentee packet was left with instructions. Two separate versions of the questionnaire were randomly distributed. Surveys began with core demographic and behavioral items, and rotated 2 ensuing sections on psychosocial correlates of substance use. The surveys took approximately 20 to 30 minutes to complete. Measures Demographics. Demographic information was collected on age (in years), sex, and ethnicity (coded as Latino/Hispanic, African American/ Black, White/Caucasian, American Indian/Native American, mixed or other). Sleep problems. Sleep problems were the primary 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 regarding 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 exhibited 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 Epidemiologic 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 adolescents31-33 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 failure,” and “I felt lonely,” (1 = 1 day or less to 4 = 6-7 days). The depressive symptoms construct showed
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high internal consistency (Cronbach’s alpha = 0.90). The mean of all 5 items was used as a continuous measure of depressive symptoms. Coping strategies. The coping measure included 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 composed 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 understanding 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 problemfocused effort, whereas seeking social support is viewed as emotion-focused), and daydreaming coping and anger coping are considered maladaptive (emotion-focused) types of coping strategies. Statistical Analysis SPSS v.21 was used to conduct statistical analyses for the study. Descriptive statistics were calculated to present demographic data. Preliminary analyses of potential demographic covariates indicated 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 mediation analysis using bias-corrected bootstrapping to obtain 99% confidence intervals for indirect effects (using 1000 bootstrap resamples). Multiple mediation allows for the assessment of the unique ability of each mediator (ie, controlling for all other 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 significance; any confidence interval including zero indicated a relationship that was not statistically significant. Effect sizes are reported for overall mediation effects (using the ratio of the indirect effect to the total effect, PM) as well as individual betas (using the correlation coefficient, r). RESULTS Characteristics of the sample are reported in Table 1. The sample was of diverse ethnic background, with the majority being Latino and male. There was little variation in age (range: 16.0617.32 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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Coping Mediates the Effects of Depressive Symptoms on Sleep Problems
Table 1 Selected Characteristics of the Study Sample (N = 1676) Percent (N)
Mean
SD
16.78
0.93
Sleep Problems
1.44
1.06
Perceived Stress
2.87
1.08
Depressive Symptoms
1.70
0.86
Decision-making Coping
3.44
1.00
Social Support Seeking Coping
2.78
1.30
Anger Coping
2.26
0.91
Fantasy Coping
3.40
1.12
Ethnicity 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 Age
42.24 (708)
Note. Ranges: Age 16-17, Sleep Problems 0-4, Coping 1-5
Depressive Symptoms Multiple 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 positive 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 Fantasy 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, controlling 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).
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Perceived Stress Results showed a statistically significant positive 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 associated 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 mediators 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 perceived stress is attenuated with inclusion of mediators, 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. DISCUSSION Our study aimed to test relationships among depressive symptoms, perceived stress, coping, and
Nguyen-Rodriquez et al
Table 2 Relationship between Depressive Symptoms and Insomnia Symptoms Insomnia Symptoms β
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 mediators Total indirect effect of all mediators
0.0587
0.0122
--
0.0281, 0.0953
Anger
0.0308
0.0009
--
0.0082, 0.0673
Seeking parental support
0.0074
0.0047
--
-0.0036, 0.0221
Decision making
0.0009
0.0020
--
-0.0039, 0.0115
Fantasy
0.0196
0.0092
--
-0.0037, 0.0469
Note. Model: F (6, 1356) = 21.2389, p < .0001, Adjusted R2 = 0.0819
sleep problems. As hypothesized, positive associations 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 relationship between depressive and sleep problems was partially mediated by a specific coping strategy, partially supporting hypotheses. It appears that higher anger coping, a maladaptive strategy, partially mediates the relationship between depressive symptoms and sleep problems. This indicates that when these youth experience depressive symptoms, they are more likely to use anger as a coping mechanism, which leads to increased sleep problems. This is an important finding related to treatment—as part of broad, multi-faceted
interventions, one facet that teaches adolescents to deal with their depressive symptoms with relaxation 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 mediate 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 3 Relationship between Perceived Stress and Insomnia Symptoms Insomnia Symptoms β
SE
p-value
99% CI
Perceived stress (total effect)
0.3125
0.0266
< .0001
--
Perceived stress (direct effect)
0.2759
0.0296
< .0001
--
-0.0348
0.0571
.5426
--
Male sex
Bootstrap results of multiple mediation model, controlling for all mediators Total indirect effect of all mediators
0.0366
0.0140
--
-0.0012, 0.0748
Anger
0.0172
0.0123
--
-0.0136, 0.0522
Seeking parental support
0.0040
0.0027
--
-0.0012, 0.0147
Decision making
0.0002
0.0013
--
-0.0034, 0.0060
Fantasy
0.0152
0.0093
--
-0.0068, 0.0429
Note. Model: F (6, 1363) = 27.0293, p < .0001, Adjusted R2 = 0.1024
Am J Health Behav.™ 2015;39(2):183-190
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Coping Mediates the Effects of Depressive Symptoms on Sleep Problems 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 impacts stress might be physiological, whereas the pathway to sleep disturbance from depressive symptoms may be impacted more readily by emotion-focused coping efforts. These pathways must be explored further to gain a more in-depth understanding 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 different findings for depressive symptoms vs perceived stress. A primary factor involved in sleep outcomes 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 behavior to tease out the effects of associated environmental 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 physiological processes. Studies that include socio-environmental 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, depressive symptoms, or any of the coping strategies. The TMSC holds that problem management strategies are most adaptive when stressors are modifiable, and emotional regulation efforts are most adaptive when the stressor cannot be modified. Depending on the nature of the stressors, participants may not have felt they would be able to impact those factors, making the use of problem-focused 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 problems for this sample. Because little is known about predictors of sleep outcomes,8 results can begin to inform the literature regarding the relationship between psychological 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
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nature of sleep, there are many factors that independently influence and interact with various other 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 aspect, psychological influence, an important piece of the puzzle. More importantly, it identifies modifiable risk factors on which intervention efforts can be focused. Further, considering the target population, it makes sense that there may be various other factors 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 behaviors. This again underscores the important influence of environment, as the unhealthy behaviors 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 development and use of adaptive coping strategies would stand to improve health outcomes, regardless of the causes of risk behavior. Further research 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 analyses assumes that depressive symptoms and perceived stress are predictive of sleep problems, ie, these psychological factors lead to sleep outcomes. Thus, the principal limitation of the current analysis is the cross-sectional design, as current findings 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 measures used have been validated and have shown reliable results.39 For example, to reduce participant burden, shorter versions of validated scales were employed, which could have introduced measurement error; however, internal consistency scores for the shortened scales indicated good reliability. 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 polysomnography would enhance the validity of findings, although less feasible and cost-effective in largescale studies like this one. Because data were collected 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-
Nguyen-Rodriquez et al dence; however, the analytic strategies that were used do not allow for multi-level procedures. Studies 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 correlated 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 independence 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 generalizability 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 modifiable 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 longitudinal approach in a broader sample of youth are recommended. By clearly identifying the precursors of sleep problems, comprehensive interventions can be designed to reduce sleep difficulties among this age group. Targeting youth is essential, as health behaviors developed during adolescence 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 promotion efforts must address the prevalence of sleep problems to impact population health.
1. National Sleep Foundation. Teens and Sleep. Washington, DC; National Sleep Foundation; 2006. 2. Crowley SJ, Acebo C, Carskadon MA. Sleep, circadian rhythms, and delayed phase in adolescence. Sleep Med.
2007;8(6):602-612. 3. Roberts RE, Roberts CR, Duong HT. Chronic insomnia and its negative consequences for health and functioning of adolescents: a 12-month prospective study. J Adol Health. 2008;42(3):294-302. 4. Taras H, Potts-Datema W. Sleep and student performance at school. J School Health. 2005;75(7):248-254. 5. Alfano CA, Zakem AH, Costa NM, et al. Sleep problems and their relation to cognitive factors, anxiety, and depressive symptoms in children and adolescents. Depress Anxiety. 2009;26(6):503-512. 6. Snell EK, Adam EK, Duncan GJ. Sleep and the body mass index and overweight status of children and adolescents. Child Dev. 2007;78(1):309-323. 7. Roane BM, Taylor DJ. Adolescent insomnia as a risk factor for early adult depression and substance abuse. Sleep. 2008;31(10):1351-1356. 8. Wolfson AR. Adolescents and emerging adults’ sleep patterns: new developments. J Adol Health. 2010;46(2):124132. 9. Compas BE, Hinden BR, Gerhardt CA. Adolescent development: pathways and processes of risk and resilience. Ann Rev Psychol. 1995;46(1):265-293. 10. Petersen AC. Adolescent development. Annual Review of Psychology. 1988;39(1):583-607. 11. Guo L, Deng J, He Y, et al. Prevalence and correlates of sleep disturbance and depressive symptoms among Chinese adolescents: a cross-sectional survey study. BMJ Open. 2014;4(7):e005517. 12. Goldman-Mellor S, Gregory AM, Caspi A, et al. Mental health antecedents of early midlife insomnia: evidence from a four-decade longitudinal study. Sleep. 2014: in press. Available at: http://research.gold.ac.uk/id/ eprint/7347. Accessed September 26, 2104. 13. Lovato N, Gradisar M. A meta-analysis and model of the relationship between sleep and depression in adolescents: recommendations for future research and clinical practice. Sleep Med Rev. 2014. doi: 10.10161j.stnrv.12014.10103.10006. 14. Cairns KE, Hui Yap MB, Pilkington PD, Jorm AF. Risk and protective factors for depression that adolescents can modify: a systematic review and meta-analysis of longitudinal studies. J Affect Disord. 2014. doi: 10.1016/j. jad.2014.1008.1006. 15. Shanahan L, Copeland WE, Angold A, et al. Sleep problems predict and pre predicted by generalized anxiety/ depression and oppositional defiant disorder. J Am Acad Child Adol Psychiatry. 2014;53(5):550-558. 16. Roberts RE, Duong HT. The prospective association between sleep deprivation and depression among adolescents. Sleep. 2014;37(2):239-244. 17. Carver PR, Lewis L. Alternative Schools and Programs for Public School Students At Risk of Educational Failure: 2007–08 (NCES 2010–026). Washington, DC: US Department of Education, National Center for Education Statistics; 2010. 18. Grunbaum JA, Lowry R, Kann L. Prevalence of healthrelated behaviors among alternative high school students as compared with students attending regular high schools. J Adol Health. 2001;29(5):337-343. 19. Cohen F. Coping. In: Matarazzo JD, Weiss SM, Herd JA, Miller NE, Weiss SM, (Eds). Behavioral Health: A Handbook of Health Enhancement and Disease Prevention. New York, NY: Wiley; 1984:3-40. 20. Lazarus RS, Cohen JB. Environmental stress. In: Altman I, Wohlwill JF, (Eds). Human Behavior and Environment. Vol 2. New York, NY: Plenum; 1977:89-127. 21. Morin CM, Rodrigue S, Ivers H. Role of stress, arousal, and coping skills in primary insomnia. Psychosom Med. 2003;65(2):259-267. 22. Lisha NE, Sun P, Rohrbach L, et al. An evaluation of immediate outcomes and fidelity of a drug abuse prevention
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DOI:
Human Subjects Statement All study procedures were approved by the University of Southern California’s Institutional Review Board (protocol number: HS-07-00601; approval date: 6/12/2013). Conflict of Interest Statement The authors do not have any conflicts of interest to disclose. Acknowledgements This 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. References
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189
Coping Mediates the Effects of Depressive Symptoms on Sleep Problems program in continuation high schools: Project Towards No Drug Abuse (TND). J Drug Educ. 2012;42(1):33-57. 23. Sateia M, Doghramji K, Hauri P, Morin C. Evaluation of chronic insomnia. an American Academy of Sleep Medicine review. Sleep. 2000;23(2):243-308. 24. Gluck ME, Geliebter A, Satov T. Night eating syndrome is associated with depression, low self-esteem, reduced daytime hunger, and less weight loss in obese outpatients. Obes. 2001;9(4):264-267. 25. Katz DA, McHorney CA. The relationship between insomnia and health-related quality of life in patients with chronic illness. J Fam Pract. 2002;51(3):229-236. 26. Cohen S, Kamarck T, Mermelstein R. A global measure of perceived stress. J Health Soc Behav. 1983;24(4):385396. 27. Sussman S, Dent CW. One-year prospective prediction of drug use from stress-related variables. Subst Use Misuse. 2000;35(5):717-735. 28. Sussman S, Brannon BR, Dent CW, et al. Relations of coping effort, coping strategies, perceived stress, and cigarette smoking among adolescents. Subst Use Misuse. 1993;28(7):599-612. 29. Radloff LS. The CES-D Scale. Appl Psychol Meas. 1977;1(3):385-401. 30. Sussman S, Dent CW, Galaif ER. The correlates of substance abuse and dependence among adolescents at high risk for drug abuse. J Subst Abuse. 1997;9:241-255. 31. Crockett LJ, Randall BA, Shen YL, et al. Measurement equivalence of the center for epidemiological studies depression scale for Latino and Anglo adolescents: a national study. J Consult Clin Psychol. 2005;73(1):47-58. 32. Egede LE, Nietert PJ, Zheng D. Depression and all-cause and coronary heart disease mortality among adults with
190
and without diabetes. Diabetes Care. 2005;28(6):13391345. 33. Hudson DB, Elek SM, Campbell-Grossman C. Depression, self-esteem, loneliness, and social support among adolescent mothers participating in the New Parents Project. Adolesc. 2000;35(139):445-453. 34. Wills TA. Stress and coping in early adolescence: relationships to substance use in urban school samples. Health Psychol. 1986;5(6):503-529. 35. Preacher KJ, Hayes AF. Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behav Res Methods. 2008;40(3):879-891. 36. MacKinnon DP, Dwyer JH. Estimating mediated effects in prevention studies. Eval Rev. 1993;17(2):144-158. 37. Hall M, Buysse DJ, Nowell PD, et al. Symptoms of stress and depression as correlates of sleep in primary insomnia. Psychosom Med. 2000;62(2):227-230. 38. Rodenbeck A, Huether G, Ruther E, Hajak G. Interactions between evening and nocturnal cortisol secretion and sleep parameters in patients with severe chronic primary insomnia. Neurosci Lett. 2002;324(2):159-163. 39. Crano WD, Brewer MB. Principles and Methods of Social Research. Mahwah, NJ: Lawrence Erlbaum; 2002. 40. Gruber R, Sadeh A, Raviv A. Sleep of school-age children: objective and subjective measures. Sleep Res. 1997;26:158. 41. Owens J, Spirito A, McGuinn M, Nobile C. Sleep habits and sleep disturbance in elementary school-aged children. J Dev Behav Pediatr. 2000;21(1):27-36. 42. Murray DM, Blitstein JL. Methods to reduce the impact of intraclass correlation in group-randomized trials. Eval Res. 2003;27(1):79-103.
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