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doi: 10.1093/sleep/33.12.1531pmid: N/A
DELAYED SLEEP PHASE DISORDER (DSPD) OFTEN PRESENTS AS SLEEP-ONSET INSOMNIA AND/OR EXCESSIVE MORNING SLEEPINESS ASSOCIATED WITH the chronic inability to fall asleep typically until between 2 am to 4 am, and extreme difficulty waking up in time to meet social, school or professional obligations.12 DSPD is one of the most common of the circadian rhythm sleep disorders (CRSD), affecting an estimated 1.7% of the general population,3 6% to 16% of adolescents and young adults, and nearly 10% of those with chronic insomnia.4,5 Treatment of DSPD typically involves a multimodal approach using both behavioral and pharmacological treatments. Of the pharmacologic approaches, the most commonly used and recommended as a “guideline” by the American Academy of Sleep Medicine Clinical Parameters is appropriately timed melatonin administration.26 However, to date there are no large multicenter randomized controlled trials of melatonin for the treatment of DSPD and controversy exists on the effectiveness of melatonin for the treatment of secondary sleep disorders, including DSPD. For example, a widely cited meta-analysis published in the British Medical Journal, concluded that there was no evidence that melatonin was effective.7 So, why are there such discrepancies? The lack of efficacy could be due to multiple factors, including the lack of regulation of the various melatonin preparations, dose, timing of administration, and perhaps more even than expected, the wrong indication. This issue of SLEEPcontains the results of a small, but rigorous meta-analysis that supports the effectiveness of exogenous melatonin in advancing the rhythm of melatonin and sleep onset time, and improving sleep latency in children and adults.8 Importantly this study sheds light on the apparently contradictory signals regarding the efficacy of melatonin in the treatment of CRSDs. There are several features of this study that are particularly noteworthy, not the least of which is that it's the first meta-analysis of the effectiveness of exogenous melatonin for the treatment of DSPD in which circadian timing was assessed prior to treatment. The randomized placebo controlled trials (RCT) included in this meta-analysis had measures of the timing of sleep and wake parameters or endogenous melatonin. Furthermore, dim light melatonin (DLMO) onset from plasma or saliva was available in the majority of the studies (6 out of 9) included in the meta-analysis. Such markers of circadian timing can be quite valuable for differentiating patients with circadian delay and misalignment from those without. One of the major challenges in clinical practice is that patients without a delay in the phase of circadian rhythms can present with symptoms of DSPD, such as sleep onset insomnia and daytime sleepiness. Indeed, alterations in homeostatic regulation or behavioral factors can manifest with symptoms similar to those of seen in DSPD.9,10 Therefore, circadian based treatments, such as melatonin would not be expected to be effective in those without circadian rhythm disturbances. The increasing availability of objective measures of the sleep wake rhythm (actigraphy) and circadian timing (DLMO) can help meet this challenge. Indeed, DLMO testing has been shown to be a relatively simple and reliable tool for circadian phase typing in patients with DSPD.11 Another important feature of the van Geijlswijk et al. study is that the timing of exogenous melatonin administration was included in the assessment of effectiveness. This could explain the difference between the positive results of this meta-analysis and the negative results reported by Buscemi et al., in which circadian timing measures or the time of melatonin administration was not taken into account.7 As predicted by the phase response curve for melatonin,12 and confirmed in a clinical population with DSPD, the most effective time of administration is 5–6 hours prior to DLMO.13 The results from the van Geijlswijk et al. meta-analysis confirm that administration of melatonin prior to the DLMO (1.5 to 6.5 hours) was more effective in advancing circadian timing. It surely follows that because a primary role of endogenous melatonin in humans is to inform the brain and other tissues about time of day, its effectiveness in the treatment of CRSDs will largely depend on the appropriate timing of exposure. In addition to its phase re-setting properties, the generally modest hypnotic effect of exogenous melatonin is also dependent on circadian timing.14 Some of the most consistent data on the effectiveness of melatonin for the treatment of DSPD arise from pediatric studies, including children with ADHD.15 In the meta-analysis by van Geijlswijk et al., four of the nine studies were in children8 Despite melatonin's potential effectiveness, its use in children raises concern regarding the safety of long term treatment. Although melatonin at the low dose (1–5 mg) typically used in clinical practice appears to be generally safe, little is known about the long term effects of chronic use on health in children or adults. The study by van Geijlswijk et al. underscores the importance of assessing circadian timing prior to treatment to establish the diagnosis, and most importantly to specify the timing of treatment. Current diagnostic criteria for DSPD rely largely on the patient's self-reported sleep/wake time and symptoms of insomnia and excessive sleepiness. Tools such as actigraphy and salivary DLMO are becoming more readily available in the clinic and recent data support the clinical efficacy of DLMO testing in diagnosing DSPD.11 Utilization of more precise and direct measures of circadian timing is a necessary first step towards effective treatment. The widespread use of melatonin in clinical settings is in contrast to the surprisingly small number of studies (only 9) that met the inclusion criteria set forth in the van Geijlswijk et al. meta-analysis.8 Although there is consistent evidence from small clinical studies that when appropriately timed, melatonin is an effective treatment for DSPD1316 determining the optimal schedule and dose will require further study. Large multicenter RCTs are warranted to establish the dose (very low vs. low), the timing of administration and long term safety in children and adults. With the exciting advances in the field of circadian biology and the recognition of the importance of circadian timing for health and disease, this is the perfect time to move circadian based treatments from the research bench to the bedside. References 1. Weitzman ED , Czeisler CA , Coleman RM , et al. Delayed sleep phase syndrome . A chronobiological disorder with sleep-onset insomnia. Arch Gen Psychiatry 1981 ; 38 : 737 – 46 . WorldCat 2. American Academy of Sleep Medicine . International classification of sleep disorders: diagnostic and coding manual . 2nd ed. Westchester, IL : American Academy of Sleep Medicine , 2005 . COPAC 3. Schrader H , Bovim G , Sand T . The prevalence of delayed and advanced sleep phase syndromes . J Sleep Res 1993 ; 2 : 51 – 5 . Google Scholar Crossref Search ADS PubMed WorldCat 4. Pelayo RT , MJ . Govinski, P . Prevalence of delayed sleep phase syndrome among adolescents. Sleep Res. 1988 ; 17 : 392 . WorldCat 5. Regestein QR , Monk TH . Delayed sleep phase syndrome: a review of its clinical aspects . Am J Psychiatry 1995 ; 152 : 602 – 8 . Google Scholar Crossref Search ADS PubMed WorldCat 6. Morgenthaler TI , Lee-Chiong T , Alessi C , et al. Practice parameters for the clinical evaluation and treatment of circadian rhythm sleep disorders . An American Academy of Sleep Medicine report. Sleep 2007 ; 30 : 1445 – 59 . WorldCat 7. Buscemi N , Vandermeer B , Hooton N , et al. Efficacy and safety of exogenous melatonin for secondary sleep disorders and sleep disorders accompanying sleep restriction: meta-analysis . BMJ 2006 ; 332 : 385 – 93 . Google Scholar Crossref Search ADS PubMed WorldCat 8. van Geijlswijk IM , Korzilius HPLM , Smits MG . The use of exogenous melatonin in delayed sleep phase disorder: a meta-analysis . Sleep 2010 ; 33 : 1605 – 14 . Google Scholar PubMed WorldCat 9. Uchiyama M , Okawa M , Shibui K , et al. Poor compensatory function for sleep loss as a pathogenic factor in patients with delayed sleep phase syndrome . Sleep 2000 ; 23 : 553 – 8 . Google Scholar PubMed WorldCat 10. Watanabe T , Kajimura N , Kato M , et al. Sleep and circadian rhythm disturbances in patients with delayed sleep phase syndrome . Sleep 2003 ; 26 : 657 – 61 . Google Scholar PubMed WorldCat 11. Rahman SA , Kayumov L , Tchmoutina EA , Shapiro CM . Clinical efficacy of dim light melatonin onset testing in diagnosing delayed sleep phase syndrome . Sleep Med 2008 . 12. Lewy AL , Ahmed S , Jackson JML , Sack RL . Melatonin shifts human circadian rhythms according to a phase-response curve . Chronobiol Int 1992 ; 9 : 380 – 92 . Google Scholar Crossref Search ADS PubMed WorldCat 13. Mundey K , Benloucif S , Harsanyi K , Dubocovich ML , Zee PC . Phase-dependent treatment of delayed sleep phase syndrome with melatonin . Sleep 2005 ; 28 : 1271 – 8 . Google Scholar PubMed WorldCat 14. Wyatt JK , Dijk DJ , Ritz-de Cecco A , Ronda JM , Czeisler CA . Sleep-facilitating effect of exogenous melatonin in healthy young men and women is circadian-phase dependent . Sleep 2006 ; 29 : 609 – 18 . Google Scholar PubMed WorldCat 15. Buscemi N , Witmans M . What is the role of melatonin in the management of sleep disorders in children? Paediatr Child Health 2006 ; 11 : 517 – 9 . Google Scholar PubMed WorldCat 16. Nagtegaal JE , Kerkhof GA , Smits MG , Swart AC , Van Der Meer YG . Delayed sleep phase syndrome: a placebo-controlled cross-over study on the effects of melatonin administered five hours before the individual dim light melatonin onset . J Sleep Res 1998 ; 7 : 135 – 43 . Google Scholar Crossref Search ADS PubMed WorldCat
Dang-Vu, Thien Thanh; Schabus, Manuel; Desseilles, Martin; Sterpenich, Virginie; Bonjean, Maxime; Maquet, Pierre
doi: 10.1093/sleep/33.12.1589pmid: 21120121
AbstractFunctional brain imaging has been used in humans to noninvasively investigate the neural mechanisms underlying the generation of sleep stages. On the one hand, REM sleep has been associated with the activation of the pons, thalamus, limbic areas, and temporo-occipital cortices, and the deactivation of prefrontal areas, in line with theories of REM sleep generation and dreaming properties. On the other hand, during non-REM (NREM) sleep, decreases in brain activity have been consistently found in the brainstem, thalamus, and in several cortical areas including the medial prefrontal cortex (MPFC), in agreement with a homeostatic need for brain energy recovery. Benefiting from a better temporal resolution, more recent studies have characterized the brain activations related to phasic events within specific sleep stages. In particular, they have demonstrated that NREM sleep oscillations (spindles and slow waves) are indeed associated with increases in brain activity in specific subcortical and cortical areas involved in the generation or modulation of these waves. These data highlight that, even during NREM sleep, brain activity is increased, yet regionally specific and transient. Besides refining the understanding of sleep mechanisms, functional brain imaging has also advanced the description of the functional properties of sleep. For instance, it has been shown that the sleeping brain is still able to process external information and even detect the pertinence of its content. The relationship between sleep and memory has also been refined using neuroimaging, demonstrating post-learning reactivation during sleep, as well as the reorganization of memory representation on the systems level, sometimes with long-lasting effects on subsequent memory performance. Further imaging studies should focus on clarifying the role of specific sleep patterns for the processing of external stimuli, as well as the consolidation of freshly encoded information during sleep.
van Geijlswijk, Ingeborg M.; Korzilius, Hubert P. L. M.; Smits, Marcel G.
doi: 10.1093/sleep/33.12.1605pmid: 21120122
AbstractStudy Objectives:To perform a meta-analysis of the efficacy and safety of exogenous melatonin in advancing sleep-wake rhythm in patients with delayed sleep phase disorder.Design:Meta analysis of papers indexed for PubMed, Embase, and the abstracts of sleep and chronobiologic societies (1990–2009).Patients:Individuals with delayed sleep phase disorder.Interventions:Administration of melatonin.Measurements and Results:A meta-analysis of data of randomized controlled trials involving individuals with delayed sleep phase disorder that were published in English, compared melatonin with placebo, and reported 1 or more of the following: endogenous melatonin onset, clock hour of sleep onset, wake-up time, sleep-onset latency, and total sleep time. The 5 trials including 91 adults and 4 trials including 226 children showed that melatonin treatment advanced mean endogenous melatonin onset by 1.18 hours (95% confidence interval [CI]: 0.89–1.48 h) and clock hour of sleep onset by 0.67 hours (95% CI: 0.45–0.89 h). Melatonin decreased sleep-onset latency by 23.27 minutes (95% CI: 4.83 -41.72 min). The wake-up time and total sleep time did not change significantly.Conclusions:Melatonin is effective in advancing sleep-wake rhythm and endogenous melatonin rhythm in delayed sleep phase disorder.
Seelig, Amber D.; Jacobson, Isabel G.; Smith, Besa; Hooper, Tomoko I.; Boyko, Edward J.; Gackstetter, Gary D.; Gehrman, Philip; Macera, Carol A.; Smith, Tyler C.; Millennium Cohort Study Team
doi: 10.1093/sleep/33.12.1615pmid: 21120123
Abstract Study Objectives: To determine the associations between deployment in support of the wars in Iraq and Afghanistan and sleep quantity and quality. Design: Longitudinal cohort study Setting: The Millennium Cohort Study survey is administered via a secure website or US mail. Participants: Data were from 41,225 Millennium Cohort members who completed baseline (2001-2003) and follow-up (2004-2006) surveys. Participants were placed into 1 of 3 exposure groups based on their deployment status at follow-up: nondeployed, survey completed during deployment, or survey completed postdeployment. Interventions: N/A Measurements and Results: Study outcomes were self-reported sleep duration and trouble sleeping, defined as having trouble falling asleep or staying asleep. Adjusted mean sleep duration was significantly shorter among those in the deployed and postdeployment groups compared with those who did not deploy. Additionally, male gender and greater stress were significantly associated with shorter sleep duration. Personnel who completed their survey during deployment or postdeployment were significantly more likely to have trouble sleeping than those who had not deployed. Lower self-reported general health, female gender, and reporting of mental health symptoms at baseline were also significantly associated with increased odds of trouble sleeping. Conclusions: Deployment significantly influenced sleep quality and quantity in this population though effect size was mediated with statistical modeling that included mental health symptoms. Personnel reporting combat exposures or mental health symptoms had increased odds of trouble sleeping. These findings merit further research to increase understanding of temporal relationships between sleep and mental health outcomes occurring during and after deployment. Sleep, deployment, Millennium Cohort, mental health, veterans ACCORDING TO A 2008 REPORT FROM THE NATIONAL SLEEP FOUNDATION, AMERICANS ARE WORKING MORE AND SLEEPING LESS, WITH THE AVERAGE work day lasting 9 hours 28 minutes and time in bed only 6 hours 55 minutes.1 The US military is at particularly high risk for sleep disturbances due to hazardous working conditions, inconsistent work hours, harsh environments, routine exposure to loud noises, and crowded sleeping spaces.2,3 Exposures to these adverse working conditions are often intensified during deployments, including the current increased operational tempo, with lengthy and frequent deployments, as well as demanding training exercises. Deployment-related factors may lead to sleep complaints, including circadian de-synchronosis, total or partial sleep deprivation, lengthy sleep latency, and wakening after sleep onset that may, in turn, exacerbate mental and physical health symptoms following deployment.2,3 The quantity and quality of sleep affect many aspects of physical and mental health.4–12 Military personnel deployed in support of Operation Iraqi Freedom and Operation Enduring Freedom may be at increased risk for chronic sleep loss, as well as many other adverse physical and mental conditions, compared with nondeployed military personnel.13–15 Sleep deprivation has been studied extensively and is associated with many physical and psychological effects, including increased risk-taking behavior,7,11 decreased threat detection,10 impaired decision making,7,11,12 performance degradation,4,8,9 mood disturbances,8 and tunnel vision.6 Short sleep duration has also been associated with obesity, weight gain, and heart disease.5,16,17 A recent cross-sectional study of 156 deployed US Air Force Airmen found that 40% of respondents suffered from at least 1 sleep disturbance, and 75% of respondents reported diminished sleep quality while deployed when compared to sleep quality at home.3 The purpose of this study was to determine any association between deployment in support of the operations in Iraq and Afghanistan and sleep quantity and quality in a large military population. We hypothesized that military personnel who had deployed would have more trouble sleeping and sleep less than those who have not deployed. The Millennium Cohort Study18 includes all Service branches of the US military, activeduty, Reserve, and National Guard personnel. A substantial proportion (22%) of cohort members were deployed in support of the operations in Iraq and Afghanistan between baseline and follow-up surveys. This population provided valuable information on sleep patterns, as well as behavioral, occupational, and demographic characteristics among participants who completed their survey during and after deployment. Methods Study Population and Data Sources Data for these analyses were from the first enrollment panel (Panel 1) of the Millennium Cohort Study,18 launched in 2001, prior to the current operations in Iraq and Afghanistan. Subsequent enrollment (Panels 2 and 3) has resulted in a total study population of over 150,000 cohort members. Panel 1 is composed of a population-based sample of 77,047 people, including members from all Service branches, as well as Reserve and National Guard members. Several groups were oversampled, including those previously deployed to Southwest Asia, Bosnia, or Kosovo from 1998 to 2000, Reserve/Guard personnel, and female Service members, to ensure adequate statistical power to draw meaningful inferences in these important subgroups of the population. Millennium Cohort participants are surveyed every 3 years. The questionnaire gathers a wide range of data regarding physical and mental health, deployment and deployment-related exposures, occupation, women's health, demographics, and behavioral health including sleep, smoking, alcohol, physical activity, and use of complementary and alternative therapies.18,19 In mid-2001, over 214,000 personnel on military rosters were contacted and invited to participate in the first panel of the Millennium Cohort; 77,047 (36%) were enrolled from 2001 to 2003. Of these individuals, 55,021 (71%) completed a follow-up questionnaire from 2004 to 2006. Individuals who deployed before the baseline assessment or who took their baseline survey while deployed were excluded from these analyses (n = 2222). Additionally, participants missing any information on demographic, military, health, or behavioral characteristics at baseline or follow-up were excluded (n = 11,574), allowing for a final study population of 41,225. Deployment dates were provided by the Defense Manpower Data Center. Participants were placed into 1 of 3 deployment categories based on submission of their follow-up survey in relation to their deployment dates. Participants placed in the “nondeployed” category had not deployed at the time of their follow-up survey submission. Participants placed in the “post-deployment” category returned from deployment at least 2 weeks prior to submitting their follow-up survey. By allowing a 2 week lag time we could account for the reporting period for the sleep duration question, which asks for the number of hours of sleep in an average 24-h period over the past month. Therefore, if a participant returned from deployment at least 2 weeks prior to completing the questionnaire, the most recent portion and at least half of the reporting period would have occurred following the deployment. The “deployed” group consisted of participants who submitted their follow-up survey during deployment or individuals who returned from a deployment less than 2 weeks before submitting their follow-up survey. Baseline military and demographic information included sex, birth year, education, race/ethnicity, marital status, service branch and component, military pay grade, and occupation (see Table 1 for subgroup categories). For regression analyses, birth year was categorized into 4 groups: 1960 and before, 1960–1969, 1970–1979, 1980 and later. Self-reported behavioral characteristics were assessed from the Millennium Cohort questionnaire. History of potential alcohol dependence was evaluated using the CAGE (Cutting down, Annoyance by criticism, Guilty feeling, and Eye-openers)20 responses, where at least 1 positive response represented potential problems with alcohol. Current smokers were identified as individuals who reported smoking at least 100 cigarettes in their lifetime and had not tried, or were unsuccessful at, quitting. Current smokers were compared with past and nonsmokers. Body mass index (weight [kg]/height [m2]) was categorized according to the Centers for Disease Control and Prevention standardized cut points to classify individuals as underweight (< 18.5), healthy weight (18.5–24.9), overweight (25.0–29.9), and obese (> 30.0) and analyzed using these categories for regression analyses. Life stressors, including divorce, bankruptcy, sexual assault or harassment, violence, death of a loved one, or illness or injury were assessed using scoring mechanisms adapted from the Holmes and Rahe Social Readjustment Rating Scale.21 In addition, the questionnaire includes a history of provider-diagnosed sleep apnea. Table 1 Demographic, behavioral, and military characteristics by deployment status of 41,225 Millennium Cohort members . No Deployment Before Follow-up Survey* . Follow-up Survey During Deployment* . Follow-up Survey Postdeployment* . . n = 30,190 . n = 1771 . n = 9264 . Baseline Characteristics . n (%) . n (%) . n (%) . Sex: Male 21,432(71.0) 1496 (84.5) 7640 (82.5) Age (mean) 35.7 32.2 33.1 Race/ethnicity White, non-Hispanic 21,780(72.1) 1206(68.1) 6570 (70.9) Black, non-Hispanic 3526(11.7) 192(10.8) 966(10.4) Other 4884(16.8) 373(21.1) 1728(18.7) Marital status Married 20,465(67.8) 1150(64.9) 6066 (65.5) Never married 7525 (24.9) 538 (30.4) 2614 (28.2) Divorced, widowed, separated 2200 (7.3) 83 (4.7) 584 (6.3) Service component: Active duty 15,946(52.8) 976 (55.1) 5714(61.7) Military pay grade: Enlisted 21,639(71.7) 1299(73.3) 6770 (73.1) Self-reported general health Fair/poor 2249 (7.5) 106(6.0) 511 (5.5) Good 9186(30.4) 508 (28.7) 2698 (29.1) Very good/excellent 18,755(62.1) 1157(65.3) 6055 (65.4) Life stressors† Low/mild 25,234 (83.6) 1539(86.9) 8134(87.8) Moderate 4022(13.3) 203(11.5) 977(10.6) Major 934(3.1) 29(1.6) 153(1.6) Posttraumatic stress disorder 1170(3.9) 64 (3.6) 265 (2.9) Depression 870 (2.9) 50 (2.8) 183(2.0) Anxiety 554(1.8) 28(1.6) 136(1.5) Panic 356(1.2) 15(0.8) 62 (0.7) Sleep apnea 840 (2.8) 32(1.8) 200 (2.2) Body mass index (mean) 26.1 26.1 26.0 Current smoker 4395(14.6) 330(18.6) 1549(16.7) Problem drinker‡ 5589(18.5) 368 (20.8) 1770(19.1) Follow-up Characteristics Combat§ NA 1010(57.0) 4690 (50.6) Posttraumatic stress disorder 1273(4.2) 96 (5.4) 448 (4.8) Depression 939 (3.1) 63 (3.6) 253 (2.7) Anxiety 675 (2.2) 43 (2.4) 201 (2.2) Panic 505(1.7) 13(0.7) 155(1.7) Children¶ 1466(16.7) 41 (14.9) 192(11.8) . No Deployment Before Follow-up Survey* . Follow-up Survey During Deployment* . Follow-up Survey Postdeployment* . . n = 30,190 . n = 1771 . n = 9264 . Baseline Characteristics . n (%) . n (%) . n (%) . Sex: Male 21,432(71.0) 1496 (84.5) 7640 (82.5) Age (mean) 35.7 32.2 33.1 Race/ethnicity White, non-Hispanic 21,780(72.1) 1206(68.1) 6570 (70.9) Black, non-Hispanic 3526(11.7) 192(10.8) 966(10.4) Other 4884(16.8) 373(21.1) 1728(18.7) Marital status Married 20,465(67.8) 1150(64.9) 6066 (65.5) Never married 7525 (24.9) 538 (30.4) 2614 (28.2) Divorced, widowed, separated 2200 (7.3) 83 (4.7) 584 (6.3) Service component: Active duty 15,946(52.8) 976 (55.1) 5714(61.7) Military pay grade: Enlisted 21,639(71.7) 1299(73.3) 6770 (73.1) Self-reported general health Fair/poor 2249 (7.5) 106(6.0) 511 (5.5) Good 9186(30.4) 508 (28.7) 2698 (29.1) Very good/excellent 18,755(62.1) 1157(65.3) 6055 (65.4) Life stressors† Low/mild 25,234 (83.6) 1539(86.9) 8134(87.8) Moderate 4022(13.3) 203(11.5) 977(10.6) Major 934(3.1) 29(1.6) 153(1.6) Posttraumatic stress disorder 1170(3.9) 64 (3.6) 265 (2.9) Depression 870 (2.9) 50 (2.8) 183(2.0) Anxiety 554(1.8) 28(1.6) 136(1.5) Panic 356(1.2) 15(0.8) 62 (0.7) Sleep apnea 840 (2.8) 32(1.8) 200 (2.2) Body mass index (mean) 26.1 26.1 26.0 Current smoker 4395(14.6) 330(18.6) 1549(16.7) Problem drinker‡ 5589(18.5) 368 (20.8) 1770(19.1) Follow-up Characteristics Combat§ NA 1010(57.0) 4690 (50.6) Posttraumatic stress disorder 1273(4.2) 96 (5.4) 448 (4.8) Depression 939 (3.1) 63 (3.6) 253 (2.7) Anxiety 675 (2.2) 43 (2.4) 201 (2.2) Panic 505(1.7) 13(0.7) 155(1.7) Children¶ 1466(16.7) 41 (14.9) 192(11.8) * At submission of follow-up survey, those in the nondeployed group had not yet deployed, those in the postdeployment group had completed at least 1 deployment, and those in the deployed group were currently deployed. † History of life stress, including items such as divorce, assault, or having a family member die, was assessed by applying scoring mechanisms from the Holmes and Rahe Social Readjustment Rating Scale. ‡ Problem drinking is defined as at least 1 positive response to the CAGE questions (Cutting down, Annoyance by criticism, Guilty feeling, and Eye-openers). § At follow-up, had personally experienced combat or a combat-like situation, such as witnessing a person's death due to war, disaster, or tragic event. ¶ Frequencies of women reporting having children in the last 3 years at baseline and follow-up. Open in new tab Table 1 Demographic, behavioral, and military characteristics by deployment status of 41,225 Millennium Cohort members . No Deployment Before Follow-up Survey* . Follow-up Survey During Deployment* . Follow-up Survey Postdeployment* . . n = 30,190 . n = 1771 . n = 9264 . Baseline Characteristics . n (%) . n (%) . n (%) . Sex: Male 21,432(71.0) 1496 (84.5) 7640 (82.5) Age (mean) 35.7 32.2 33.1 Race/ethnicity White, non-Hispanic 21,780(72.1) 1206(68.1) 6570 (70.9) Black, non-Hispanic 3526(11.7) 192(10.8) 966(10.4) Other 4884(16.8) 373(21.1) 1728(18.7) Marital status Married 20,465(67.8) 1150(64.9) 6066 (65.5) Never married 7525 (24.9) 538 (30.4) 2614 (28.2) Divorced, widowed, separated 2200 (7.3) 83 (4.7) 584 (6.3) Service component: Active duty 15,946(52.8) 976 (55.1) 5714(61.7) Military pay grade: Enlisted 21,639(71.7) 1299(73.3) 6770 (73.1) Self-reported general health Fair/poor 2249 (7.5) 106(6.0) 511 (5.5) Good 9186(30.4) 508 (28.7) 2698 (29.1) Very good/excellent 18,755(62.1) 1157(65.3) 6055 (65.4) Life stressors† Low/mild 25,234 (83.6) 1539(86.9) 8134(87.8) Moderate 4022(13.3) 203(11.5) 977(10.6) Major 934(3.1) 29(1.6) 153(1.6) Posttraumatic stress disorder 1170(3.9) 64 (3.6) 265 (2.9) Depression 870 (2.9) 50 (2.8) 183(2.0) Anxiety 554(1.8) 28(1.6) 136(1.5) Panic 356(1.2) 15(0.8) 62 (0.7) Sleep apnea 840 (2.8) 32(1.8) 200 (2.2) Body mass index (mean) 26.1 26.1 26.0 Current smoker 4395(14.6) 330(18.6) 1549(16.7) Problem drinker‡ 5589(18.5) 368 (20.8) 1770(19.1) Follow-up Characteristics Combat§ NA 1010(57.0) 4690 (50.6) Posttraumatic stress disorder 1273(4.2) 96 (5.4) 448 (4.8) Depression 939 (3.1) 63 (3.6) 253 (2.7) Anxiety 675 (2.2) 43 (2.4) 201 (2.2) Panic 505(1.7) 13(0.7) 155(1.7) Children¶ 1466(16.7) 41 (14.9) 192(11.8) . No Deployment Before Follow-up Survey* . Follow-up Survey During Deployment* . Follow-up Survey Postdeployment* . . n = 30,190 . n = 1771 . n = 9264 . Baseline Characteristics . n (%) . n (%) . n (%) . Sex: Male 21,432(71.0) 1496 (84.5) 7640 (82.5) Age (mean) 35.7 32.2 33.1 Race/ethnicity White, non-Hispanic 21,780(72.1) 1206(68.1) 6570 (70.9) Black, non-Hispanic 3526(11.7) 192(10.8) 966(10.4) Other 4884(16.8) 373(21.1) 1728(18.7) Marital status Married 20,465(67.8) 1150(64.9) 6066 (65.5) Never married 7525 (24.9) 538 (30.4) 2614 (28.2) Divorced, widowed, separated 2200 (7.3) 83 (4.7) 584 (6.3) Service component: Active duty 15,946(52.8) 976 (55.1) 5714(61.7) Military pay grade: Enlisted 21,639(71.7) 1299(73.3) 6770 (73.1) Self-reported general health Fair/poor 2249 (7.5) 106(6.0) 511 (5.5) Good 9186(30.4) 508 (28.7) 2698 (29.1) Very good/excellent 18,755(62.1) 1157(65.3) 6055 (65.4) Life stressors† Low/mild 25,234 (83.6) 1539(86.9) 8134(87.8) Moderate 4022(13.3) 203(11.5) 977(10.6) Major 934(3.1) 29(1.6) 153(1.6) Posttraumatic stress disorder 1170(3.9) 64 (3.6) 265 (2.9) Depression 870 (2.9) 50 (2.8) 183(2.0) Anxiety 554(1.8) 28(1.6) 136(1.5) Panic 356(1.2) 15(0.8) 62 (0.7) Sleep apnea 840 (2.8) 32(1.8) 200 (2.2) Body mass index (mean) 26.1 26.1 26.0 Current smoker 4395(14.6) 330(18.6) 1549(16.7) Problem drinker‡ 5589(18.5) 368 (20.8) 1770(19.1) Follow-up Characteristics Combat§ NA 1010(57.0) 4690 (50.6) Posttraumatic stress disorder 1273(4.2) 96 (5.4) 448 (4.8) Depression 939 (3.1) 63 (3.6) 253 (2.7) Anxiety 675 (2.2) 43 (2.4) 201 (2.2) Panic 505(1.7) 13(0.7) 155(1.7) Children¶ 1466(16.7) 41 (14.9) 192(11.8) * At submission of follow-up survey, those in the nondeployed group had not yet deployed, those in the postdeployment group had completed at least 1 deployment, and those in the deployed group were currently deployed. † History of life stress, including items such as divorce, assault, or having a family member die, was assessed by applying scoring mechanisms from the Holmes and Rahe Social Readjustment Rating Scale. ‡ Problem drinking is defined as at least 1 positive response to the CAGE questions (Cutting down, Annoyance by criticism, Guilty feeling, and Eye-openers). § At follow-up, had personally experienced combat or a combat-like situation, such as witnessing a person's death due to war, disaster, or tragic event. ¶ Frequencies of women reporting having children in the last 3 years at baseline and follow-up. Open in new tab Standardized instruments were used to assess mental health disorders at baseline and at follow-up. Posttraumatic stress disorder (PTSD) symptoms were quantified using the PTSD Checklist-Civilian Version (PCL-C). The PCL-C is a 17-item questionnaire used to assess the severity of avoidance, hyperarousal, and intrusion symptoms. A participant was defined as having PTSD symptoms if at least 3 avoidance symptoms, 2 hyperarousal symptoms, and 1 intrusion symptom are endorsed at “moderate” or higher levels.22 Depression, other anxiety, and panic disorder symptoms were assessed according to Patient Health Questionnaire (PHQ) scoring algorithms.23 Additionally, combat-related exposures were assessed at follow-up. Combat exposure was defined by at least 1 positive response to questions that asked whether participants had personally (1) witnessed a death due to war, disaster, or tragic event; (2) witnessed instances of physical abuse, (3) been exposed to dead or decomposing bodies, (4) been exposed to maimed soldiers or civilians, or (5) been exposed to prisoners of war or refugees. Mothers of young children or pregnant women were identified within the Cohort. Mothers of young children were defined as women who reported giving birth within the last 3 years at baseline or follow-up. Pregnant women were those who reported not having menstrual periods at follow-up due to pregnancy or recent childbirth. Outcomes Sleep duration The questionnaire contains a single question that asks “Over the past month, how many hours of sleep did you get in an average 24-hour period?” Participants were able to write in their sleep duration, rounded to the nearest hour. Sleep duration was assessed as a continuous variable. Sleep disorders Trouble sleeping during a 1-month time frame was assessed at follow-up using questions from both the PHQ for anxiety and the PCL-C. The PHQ asks, “Over the last 4 weeks, how often have you experienced trouble falling asleep or staying asleep?” Possible responses included “not at all,” “several days,” or “more than half the days.” The PCL-C asks, “In the past month, have you had trouble falling asleep or staying asleep?” The respondent is able to mark “not at all,” “a little bit,” “moderately,” “quite a bit,” or “extremely.” A single dichotomous variable was created combining the 2 questions. Participants with trouble sleeping were defined as those who responded “moderately,” or above on the PCL-C sleep item or “several days” or longer on the PHQ anxiety sleep item. Statistical Analyses Chi-square tests of association were used to assess unadjusted relationships between trouble sleeping and all covariates. Baseline and follow-up unadjusted mean sleep durations were calculated across the 3 deployment groups. In the multivariable analyses, interactions were tested between deployment and service, sex, and symptoms at follow-up of PTSD, depression, anxiety, and panic, where a = 0.05. Collinearity was evaluated among all independent variables. The main analyses for this study examined 2 outcomes assessed at follow-up, sleep duration and trouble sleeping, using multivariable modeling techniques that adjusted for all demographic, military, health, and behavioral variables measured at the baseline assessment. Sleep duration, a continuous outcome, was examined using analysis of covariance (ANCOVA) while adjusting for baseline sleep duration in addition to the other covariates. Pairwise comparisons of adjusted mean sleep duration between each category of the covariates were performed using the Tukey-Kramer test statistic.24 Trouble sleeping, a dichotomous outcome (yes vs no), was examined using multivariable logistic regression. Further analyses were performed to examine whether inclusion of mental health symptoms and combat exposures assessed at follow-up affected the associations between deployment and the outcomes of sleep duration and trouble sleeping. The study had approval from the San Diego State University and the Naval Health Research Center Institutional Review Boards, and informed consent was obtained from all subjects. Data management and statistical analyses were performed using SAS statistical software, version 9.2 (SAS Institute, Inc., Cary, North Carolina). Results The demographic characteristics of the study participants at baseline and follow-up are shown in Table 1. Of the 41,225 participants included in these analyses, 30,190 had not deployed at the time they submitted their follow-up survey, 9264 had completed at least 1 deployment before submitting their follow-up survey, and 1771 completed their follow-up survey during deployment. While the demographic distribution across the 3 deployment groups was similar overall, some differences were noted. The nondeployed group was older and had a higher proportion of women when compared to the deployed and postdeployment groups. In addition, the deployed group was comprised of almost 80% Army, while the nondeployed and postdeployment groups were about 50% Army (see Table S1, available online only at www.journalsleep.org). Unadjusted mean sleep duration was comparable across the three deployment groups for most covariates (data not shown), with average sleep duration approximately 6.5 h and very little to no difference between baseline and follow-up measures. Those with the shortest unadjusted sleep duration had current or past deployment experience and symptoms of PTSD, depression, anxiety, or panic at the follow-up assessment. Similarly, proportions of unadjusted self-reported trouble sleeping were comparable across deployment groups with between 20% and 30% of the population, for most baseline characteristics, reporting trouble sleeping (data not shown). Based on chi-square tests of association, in general, a higher proportion of individuals reporting symptoms of PTSD, depression, anxiety, or panic also reported trouble sleeping, and these proportions were slightly higher among individuals with these symptoms at follow-up than at baseline. Based on ANCOVA regression modeling, adjusted average sleep duration varied by deployment category (Table 2, Model A). Those in the deployed and postdeployment groups reported significantly shorter sleep duration than the nondeployed group. However, adjusted mean sleep duration for the deployed and postdeployment groups were not significantly different from each other. Average sleep duration for the nondeployed, post-deployment, and deployed groups were 6.56, 6.47, and 6.46 h, respectively, after adjusting for covariates measured at baseline. Pairwise comparisons showed that those reporting significantly shorter sleep duration were more likely to be male, born in 1960 or later, black non-Hispanic, serving in the Army or Marine Corps, active-duty members, electronic equipment repair specialists, in good or fair/poor general health, overweight, current smokers, or had experienced moderate or major life stressors (Table S2, available online only at www.journalsleep. org). After adding the potential mediators of follow-up mental health, and combat variables to the model (Table 2, Model B), adjusted average sleep duration was 6.31 to 6.33 h across the 3 deployment groups. Additionally, those reporting symptoms of PTSD or anxiety and those reporting combat exposures had the shortest adjusted sleep duration. Baseline and follow-up mental health variables were not collinear with each other (variance inflation factor < 4). Additionally, sex and the follow-up mental health variables did not modify the relationship between sleep duration and deployment (P > 0.05). Table 2 Adjusted mean sleep duration (h) at follow-up (July 2004-January 2006) of Millennium Cohort participants . Mean sleep duration at follow-up n = 38,435 . Characteristics . Hours . P-value . Model A* Deployment Status† < 0.01 Non Deployed 6.56a Post deployment 6.47b Deployed 6.46b Model B‡ Deployment status† 0.84 Non deployed 6.33a Post deployment 6.33a Deployed 6.31a Combat§¶ < 0.01 No 6.40a Yes 6.25b Posttraumatic stress disorder¶ < 0.01 No 6.54a Yes 6.11b Depression¶ 0.07 No 6.37a Yes 6.29a Anxiety¶ < 0.01 No 6.44a Yes 6.21b Panic¶ 0.21 No 6.36a Yes 6.29a . Mean sleep duration at follow-up n = 38,435 . Characteristics . Hours . P-value . Model A* Deployment Status† < 0.01 Non Deployed 6.56a Post deployment 6.47b Deployed 6.46b Model B‡ Deployment status† 0.84 Non deployed 6.33a Post deployment 6.33a Deployed 6.31a Combat§¶ < 0.01 No 6.40a Yes 6.25b Posttraumatic stress disorder¶ < 0.01 No 6.54a Yes 6.11b Depression¶ 0.07 No 6.37a Yes 6.29a Anxiety¶ < 0.01 No 6.44a Yes 6.21b Panic¶ 0.21 No 6.36a Yes 6.29a * Adjusted for sex, birth year, race/ethnicity, education, marital status, service branch, service component, pay grade, occupation, general health, life stressors, baseline sleep duration, sleep apnea, BMI, smoking status, problem drinking, and baseline symptoms of PTSD, depression, anxiety, and panic. † At submission of follow-up survey, those in the nondeployed group had not yet deployed, those in the post deployment group had completed at least one deployment, and those in the deployed group were currently deployed. ‡ Adjusted for all variables displayed in Model A and combat, follow-up symptoms of PTSD, depression, anxiety, and panic. § At follow-up, had personally experienced combat or combat-like situation such as witnessing a person's death due to war, disaster or tragic event. ¶ Assessed at follow-up. Open in new tab Table 2 Adjusted mean sleep duration (h) at follow-up (July 2004-January 2006) of Millennium Cohort participants . Mean sleep duration at follow-up n = 38,435 . Characteristics . Hours . P-value . Model A* Deployment Status† < 0.01 Non Deployed 6.56a Post deployment 6.47b Deployed 6.46b Model B‡ Deployment status† 0.84 Non deployed 6.33a Post deployment 6.33a Deployed 6.31a Combat§¶ < 0.01 No 6.40a Yes 6.25b Posttraumatic stress disorder¶ < 0.01 No 6.54a Yes 6.11b Depression¶ 0.07 No 6.37a Yes 6.29a Anxiety¶ < 0.01 No 6.44a Yes 6.21b Panic¶ 0.21 No 6.36a Yes 6.29a . Mean sleep duration at follow-up n = 38,435 . Characteristics . Hours . P-value . Model A* Deployment Status† < 0.01 Non Deployed 6.56a Post deployment 6.47b Deployed 6.46b Model B‡ Deployment status† 0.84 Non deployed 6.33a Post deployment 6.33a Deployed 6.31a Combat§¶ < 0.01 No 6.40a Yes 6.25b Posttraumatic stress disorder¶ < 0.01 No 6.54a Yes 6.11b Depression¶ 0.07 No 6.37a Yes 6.29a Anxiety¶ < 0.01 No 6.44a Yes 6.21b Panic¶ 0.21 No 6.36a Yes 6.29a * Adjusted for sex, birth year, race/ethnicity, education, marital status, service branch, service component, pay grade, occupation, general health, life stressors, baseline sleep duration, sleep apnea, BMI, smoking status, problem drinking, and baseline symptoms of PTSD, depression, anxiety, and panic. † At submission of follow-up survey, those in the nondeployed group had not yet deployed, those in the post deployment group had completed at least one deployment, and those in the deployed group were currently deployed. ‡ Adjusted for all variables displayed in Model A and combat, follow-up symptoms of PTSD, depression, anxiety, and panic. § At follow-up, had personally experienced combat or combat-like situation such as witnessing a person's death due to war, disaster or tragic event. ¶ Assessed at follow-up. Open in new tab Results from multivariable logistic regression modeling the trouble sleeping outcome (Table 3) showed that the adjusted odds for reporting trouble sleeping were significantly increased for personnel in the deployed or postdeployment groups when compared to the nondeployed group. Additionally, smoking (odds ratio [OR] 1.16, 95% confidence interval [CI] 1.01, 1.24) and problem drinking (OR 1.50, 95% CI 1.41, 1.59) were independent predictors of trouble sleeping. Those with the highest adjusted odds of reporting trouble sleeping were individuals who reported baseline mental health symptoms and those who reported fair/poor general health (Table S3, available online only at www.journalsleep.org). Members of the Reserve/Guard, officers and race/ethnicities other than white, non-Hispanic, had significantly reduced odds of reporting trouble sleeping. Table 3 Adjusted* odds of follow-up trouble sleeping among Millennium Cohort participants . n =38,435 . . Deployment Status† . AOR . 95% CI . Non Deployed 1.00 Post deployment 1.21 (1.14,1.29) Deployed 1.28 (1.14,1.43) . n =38,435 . . Deployment Status† . AOR . 95% CI . Non Deployed 1.00 Post deployment 1.21 (1.14,1.29) Deployed 1.28 (1.14,1.43) CI, confidence interval; AOR, adjusted odds ratio. * Adjusted for sex, birth year, race/ethnicity, education, marital status, service branch, service component, pay grade, occupation, general health, life stressors, baseline sleep duration, sleep apnea, BMI, smoking status, problem drinking, and baseline symptoms of PTSD, depression, anxiety, and panic. † At submission of follow-up survey, those in the nondeployed group had not yet deployed, those in the post deployment group had completed at least one deployment, and those in the deployed group were currently deployed. Open in new tab Table 3 Adjusted* odds of follow-up trouble sleeping among Millennium Cohort participants . n =38,435 . . Deployment Status† . AOR . 95% CI . Non Deployed 1.00 Post deployment 1.21 (1.14,1.29) Deployed 1.28 (1.14,1.43) . n =38,435 . . Deployment Status† . AOR . 95% CI . Non Deployed 1.00 Post deployment 1.21 (1.14,1.29) Deployed 1.28 (1.14,1.43) CI, confidence interval; AOR, adjusted odds ratio. * Adjusted for sex, birth year, race/ethnicity, education, marital status, service branch, service component, pay grade, occupation, general health, life stressors, baseline sleep duration, sleep apnea, BMI, smoking status, problem drinking, and baseline symptoms of PTSD, depression, anxiety, and panic. † At submission of follow-up survey, those in the nondeployed group had not yet deployed, those in the post deployment group had completed at least one deployment, and those in the deployed group were currently deployed. Open in new tab Follow-up anxiety symptoms modified the relationship between deployment status and trouble sleeping (P = 0.01), so the logistic regression model adjusting for follow-up mental health and combat exposures was stratified by presence or absence of anxiety or panic symptoms. Participants reporting anxiety or panic symptoms at follow-up were combined because all deployers with panic symptoms at follow-up also reported trouble sleeping. Among participants who did not report symptoms of anxiety or panic at follow-up, the highest adjusted odds of trouble sleeping were reported by those concurrently reporting symptoms of other mental health disorders (Table 4). Also, those in the post-deployment group had significantly reduced adjusted odds of reporting trouble sleeping compared to those not deployed. Among participants who did report symptoms of anxiety or panic at follow-up, the highest adjusted odds of trouble sleeping were reported by those with follow-up mental health symptoms, but to a much lesser degree than those without anxiety or panic. Table 4 Adjusted* odds of follow-up trouble sleeping among Millennium Cohort participants . No Symptoms of Anxisty or Panic n = 37,249 . Symptoms of Anxisty or Panic n=1,186 . Follow-Up Characteristics . AOR . 95% CI . AOR . 95% CI . Deployment status† Non deployed 1.00 1.00 Post deployment 0.91 (0.84, 0.99) 0.89 (0.43,1.81) Deployed 0.97 (0.84,1.11) 1.28 (0.33,4.93) Combat‡ No 1.00 1.00 Yes 1.61 (1.45,1.78) 1.44 (0.63, 3.32) Posttraumatic stress disorder No 1.00 1.00 Yes 12.91 (10.54,15.81) 3.88 (2.24, 6.75) Depression No 1.00 1.00 Yes 12.42 (9.29,16.60) 2.84 (1.60,5.07) . No Symptoms of Anxisty or Panic n = 37,249 . Symptoms of Anxisty or Panic n=1,186 . Follow-Up Characteristics . AOR . 95% CI . AOR . 95% CI . Deployment status† Non deployed 1.00 1.00 Post deployment 0.91 (0.84, 0.99) 0.89 (0.43,1.81) Deployed 0.97 (0.84,1.11) 1.28 (0.33,4.93) Combat‡ No 1.00 1.00 Yes 1.61 (1.45,1.78) 1.44 (0.63, 3.32) Posttraumatic stress disorder No 1.00 1.00 Yes 12.91 (10.54,15.81) 3.88 (2.24, 6.75) Depression No 1.00 1.00 Yes 12.42 (9.29,16.60) 2.84 (1.60,5.07) CI, confidence interval; AOR, adjusted odds ratio. * Adjusted for all other variables in table, plus sex, birth year, race/ethnicity, education, marital status, service branch, service component, pay grade, occupation, general health, life stressors, baseline sleep duration, sleep apnea, BMI, smoking status, problem drinking, and baseline symptoms of PTSD, depression, anxiety, and panic. † At submission of follow-up survey, those in the nondeployed group had not yet deployed, those in the post deployment group had completed at least one deployment, and those in the deployed group were currently deployed. ‡ At follow-up, had personally experienced combat or combat-like situation such as witnessing a person's death due to war, disaster or tragic event. Open in new tab Table 4 Adjusted* odds of follow-up trouble sleeping among Millennium Cohort participants . No Symptoms of Anxisty or Panic n = 37,249 . Symptoms of Anxisty or Panic n=1,186 . Follow-Up Characteristics . AOR . 95% CI . AOR . 95% CI . Deployment status† Non deployed 1.00 1.00 Post deployment 0.91 (0.84, 0.99) 0.89 (0.43,1.81) Deployed 0.97 (0.84,1.11) 1.28 (0.33,4.93) Combat‡ No 1.00 1.00 Yes 1.61 (1.45,1.78) 1.44 (0.63, 3.32) Posttraumatic stress disorder No 1.00 1.00 Yes 12.91 (10.54,15.81) 3.88 (2.24, 6.75) Depression No 1.00 1.00 Yes 12.42 (9.29,16.60) 2.84 (1.60,5.07) . No Symptoms of Anxisty or Panic n = 37,249 . Symptoms of Anxisty or Panic n=1,186 . Follow-Up Characteristics . AOR . 95% CI . AOR . 95% CI . Deployment status† Non deployed 1.00 1.00 Post deployment 0.91 (0.84, 0.99) 0.89 (0.43,1.81) Deployed 0.97 (0.84,1.11) 1.28 (0.33,4.93) Combat‡ No 1.00 1.00 Yes 1.61 (1.45,1.78) 1.44 (0.63, 3.32) Posttraumatic stress disorder No 1.00 1.00 Yes 12.91 (10.54,15.81) 3.88 (2.24, 6.75) Depression No 1.00 1.00 Yes 12.42 (9.29,16.60) 2.84 (1.60,5.07) CI, confidence interval; AOR, adjusted odds ratio. * Adjusted for all other variables in table, plus sex, birth year, race/ethnicity, education, marital status, service branch, service component, pay grade, occupation, general health, life stressors, baseline sleep duration, sleep apnea, BMI, smoking status, problem drinking, and baseline symptoms of PTSD, depression, anxiety, and panic. † At submission of follow-up survey, those in the nondeployed group had not yet deployed, those in the post deployment group had completed at least one deployment, and those in the deployed group were currently deployed. ‡ At follow-up, had personally experienced combat or combat-like situation such as witnessing a person's death due to war, disaster or tragic event. Open in new tab A separate sub-analysis of mothers of young children and pregnant women was conducted. In this population, adjusted sleep duration varied significantly based on deployment group (data not shown), with nondeployed compared to postdeployment mothers and pregnant women reporting significantly longer sleep (5.84 vs. 5.58 h, P < 0.01) and marginally significantly longer sleep comparing nondeployed to those deployed (5.84 vs. 5.45 h, P = 0.06). Adjusted mean sleep duration did not differ significantly between deployment and postdeployment. Results for mothers of young children and pregnant women (Table 5) were similar to the larger population with respect to the relationship between trouble sleeping and mental health disorders. Those reporting follow-up symptoms of PTSD, depression, anxiety, and panic had higher odds of trouble sleeping than those with no mental health symptoms; however, deployment status was not significantly associated with trouble sleeping in these women. Table 5 Adjusted odds of trouble sleeping among mothers of young children and pregnant women, the Millennium Cohort study (July 2004-January 2006) . n= 2,790 . . Characteristics . AOR . 95% CI . Model A* Deployment status† Non Deployed 1.00 Post deployment 1.29 (1.00,1.66) Deployed 1.43 (0.86, 2.38) Model B‡ Deployment status† Non Deployed 1.00 Post deployment 0.97 (0.68,1.38) Deployed 1.04 (0.57,1.89) Posttraumatic stress disorder§ No 1.00 Yes 7.14 (3.98,12.79) Depression§ No 1.00 Yes 7.12 (3.40,14.89) Anxiety§ No 1.00 Yes 10.71 (4.36, 26.32) Panic§ No 1.00 Yes 3.61 (1.68,7.78) . n= 2,790 . . Characteristics . AOR . 95% CI . Model A* Deployment status† Non Deployed 1.00 Post deployment 1.29 (1.00,1.66) Deployed 1.43 (0.86, 2.38) Model B‡ Deployment status† Non Deployed 1.00 Post deployment 0.97 (0.68,1.38) Deployed 1.04 (0.57,1.89) Posttraumatic stress disorder§ No 1.00 Yes 7.14 (3.98,12.79) Depression§ No 1.00 Yes 7.12 (3.40,14.89) Anxiety§ No 1.00 Yes 10.71 (4.36, 26.32) Panic§ No 1.00 Yes 3.61 (1.68,7.78) CI, confidence interval; AOR, adjusted odds ratio. * Adjusted for sex, birth year, race/ethnicity, education, marital status, service branch, service component, pay grade, occupation, general health, life stressors, baseline sleep duration, sleep apnea, BMI, smoking status, problem drinking, and baseline symptoms of PTSD, depression, anxiety, and panic. † At submission of follow-up survey, those in the nondeployed group had not yet deployed, those in the post deployment group had completed at least one deployment, and those in the deployed group were currently deployed. ‡ Adjusted for all variables displayed in Model A and combat, follow-up symptoms of PTSD, depression, anxiety, and panic. § Assessed at follow-up. Open in new tab Table 5 Adjusted odds of trouble sleeping among mothers of young children and pregnant women, the Millennium Cohort study (July 2004-January 2006) . n= 2,790 . . Characteristics . AOR . 95% CI . Model A* Deployment status† Non Deployed 1.00 Post deployment 1.29 (1.00,1.66) Deployed 1.43 (0.86, 2.38) Model B‡ Deployment status† Non Deployed 1.00 Post deployment 0.97 (0.68,1.38) Deployed 1.04 (0.57,1.89) Posttraumatic stress disorder§ No 1.00 Yes 7.14 (3.98,12.79) Depression§ No 1.00 Yes 7.12 (3.40,14.89) Anxiety§ No 1.00 Yes 10.71 (4.36, 26.32) Panic§ No 1.00 Yes 3.61 (1.68,7.78) . n= 2,790 . . Characteristics . AOR . 95% CI . Model A* Deployment status† Non Deployed 1.00 Post deployment 1.29 (1.00,1.66) Deployed 1.43 (0.86, 2.38) Model B‡ Deployment status† Non Deployed 1.00 Post deployment 0.97 (0.68,1.38) Deployed 1.04 (0.57,1.89) Posttraumatic stress disorder§ No 1.00 Yes 7.14 (3.98,12.79) Depression§ No 1.00 Yes 7.12 (3.40,14.89) Anxiety§ No 1.00 Yes 10.71 (4.36, 26.32) Panic§ No 1.00 Yes 3.61 (1.68,7.78) CI, confidence interval; AOR, adjusted odds ratio. * Adjusted for sex, birth year, race/ethnicity, education, marital status, service branch, service component, pay grade, occupation, general health, life stressors, baseline sleep duration, sleep apnea, BMI, smoking status, problem drinking, and baseline symptoms of PTSD, depression, anxiety, and panic. † At submission of follow-up survey, those in the nondeployed group had not yet deployed, those in the post deployment group had completed at least one deployment, and those in the deployed group were currently deployed. ‡ Adjusted for all variables displayed in Model A and combat, follow-up symptoms of PTSD, depression, anxiety, and panic. § Assessed at follow-up. Open in new tab Discussion To our knowledge, this is the first study to prospectively examine the sleep patterns of military service members, in relation to deployment, from all branches and components of the service. Millennium Cohort data allowed for the unique opportunity to examine the sleep of military service men and women at 2 points in time across different deployment experiences. This was an exploratory investigation to characterize sleep patterns among Cohort participants and to determine whether any subgroups of the population, particularly while deployed, are at increased risk for sleep problems. Our findings suggest that the current deployments do affect sleep as those who were deployed or had returned from a deployment had significantly shorter adjusted sleep duration and increased adjusted odds of reporting trouble sleeping compared with those who had not deployed. Deployment status, however, did not significantly affect sleep duration in the models that adjusted for follow-up mental health conditions and combat exposures. Additionally, in stratified analyses, deployment status resulted in significantly reduced odds of reporting trouble sleeping among those in the postdeployment group with no symptoms of anxiety or panic at follow-up. These findings suggest that the relationships between deployment and sleep duration and trouble sleeping are mediated by the effects of combat exposures and mental health symptoms. Adjusted average sleep time was fairly short, with almost every subgroup of the study population reporting approximately 6.5 hours. Research suggests that moderate sleep restriction (limiting sleep to 5 to 7 h per night) may have lasting effects on performance that cannot be quickly recovered.9 In contrast, those with extreme sleep deprivation (less than 3 hours per night for 7 days), are able to return to near normal performance after 3 nights of recovery sleep.9 Thus, as long as there is time to recuperate, even extreme sleep deprivation on an intermittent basis may not be detrimental to combat troops. Additionally, participants who reported combat exposures had an adjusted average sleep duration of 6.25 hours, which is lower than both the deployed and postdeployment groups. Combat deployers also had significantly increased odds of reporting trouble sleeping among those deployed and those returning from deployment. This is not surprising, as combat and deployment environments can be physically and mentally demanding. The statistically significant difference in adjusted sleep duration between those with combat experience and those without was 9 minutes and may not be clinically relevant. However, those who reported combat were 52% to 74% more likely to also report trouble sleeping when compared to those with no combat experience. Those reporting PTSD symptoms at follow-up were sleeping 26 minutes less per day than those not reporting symptoms, which may be clinically relevant, but many subgroups of the population, despite being significantly different, had average sleep durations that differed by less than 15 minutes. Sleep disturbances commonly co-occur with mental health conditions.25,27 Whether sleep disturbance is a precursor or consequence of certain mental health conditions is unclear. Recent findings, however, suggest that those with insomnia are at increased risk of developing depression and that insomnia is comorbid with, rather than secondary to, depression.26 Similarly, sleep disturbance has been shown to be a core feature of PTSD, rather than a consequence of this disorder.27 Individuals reporting diminished quality of life have also been shown to report more sleep disturbances.28 The high odds of trouble sleeping among those with mental health disorders and lower self-rated general health is supported by similar findings in a large military cohort. Among the US general population, those sleeping less than 7 hours or more than 9 hours had increased odds of depression when compared to those sleeping 7 hours.29 Similarly, those sleeping more or less than 7 hours had increased odds of anxiety when compared to those sleeping 7 hours.29 The reported increases in mental health morbidities among deployers, especially those experiencing combat, has raised long-term health concerns for warfighters.13–15 Approaches to enhancing or increasing the quality and duration of sleep during deployment deserves further investigation as a possible means to potentially reduce the occurrence of comorbid mental health disorders. Trouble sleeping was found to occur at follow-up in 25.0% of those who had not deployed, 30.5% of those who completed their follow-up survey during deployment, and 27.1% of those who completed their follow-up survey postdeployment. Many of the characteristics that were associated with longer sleep durations (Table S2, available online only at www.journalsleep. org) were also associated with higher odds of trouble sleeping (Table S3, available online only at www.journalsleep.org). For example, women reported significantly longer sleep duration than men (6.56 hrs vs. 6.44 h), but women also had higher odds (OR 1.56, 95% CI 1.45, 1.67) of trouble sleeping. This discordance between sleep duration and trouble sleeping may suggest that those who have the most trouble sleeping are spending more time in bed trying to fall asleep or are taking medication to help them sleep. The large number of mothers with young children and pregnant women (n = 2790) in our study allowed an analysis of sleep measures in this unique military subpopulation. Adjusted mean sleep duration in this population (5.84 hours for nondeployed, 5.58 for those postdeployment, and 5.45 for those deployed) was almost 1 hour shorter than women in general (6.56 h). Pregnant women with mental health symptoms at follow-up were also significantly more likely to report trouble sleeping (Table 5) than pregnant women who did not indicate symptoms of mental health disorders. These results were similar, although smaller in magnitude, to those seen in the regression analyses for the entire study population. Studies looking at the civilian population30–34 have also noted that new mothers and pregnant women report more sleep problems and shorter sleep durations than other women, with mean sleep durations in these studies ranging from 6.5–7.5 hours.31–33 The current study reports a similar unadjusted sleep duration (6.7 h) but much lower duration after adjusting for other variables in the model (5.45–5.84 h). Varying study methods may account for some of these discrepancies, but it is also probable that the normal stressors of impending/new motherhood may be increased in military women who have the possibility of future deployments and separation from their families. Sleep problems may be a symptom of mental health disorders and other comorbidities in this subgroup and warrants more focused research on parental stress in the context of military deployment. This study has limitations. Self-reported data are subject to recall bias, may lack precision, and, thus, may not be a valid measure of actual sleep duration35,36 Research has shown that self-reported duration of sleep overestimates actual sleep time by approximately 1 hour when compared with sleep measured by wrist actigraphy.36 Also, sleep duration was measured in whole number increments, similar to other cohort studies,29,37,38 and might result in a loss of precision and possibly affect the ability to detect differences between groups. In this study, it was not possible to determine if short sleep durations were caused by insomnia versus the effect of a busy schedule that does not allow for longer sleep times. In addition, it was not possible to adjust for several influential variables that could affect sleep patterns, such as prescribed sleep medications, other medications with sleep related side effects, and stimulants (e.g., caffeine), since these data were not available. Although of interest, it was also not possible to investigate sleep patterns among specific occupational groups that may have a greater propensity for disruption of normal sleep patterns, such as submarine crew members while underway. Despite these limitations, there were several strengths. The Millennium Cohort represents all Service branches and components of the US military, including active duty and Reserve/ Guard personnel. The large sample size allows for robust comparisons and the ability to detect small differences in subgroups of our study population. Thorough evaluations of possible biases suggest the Cohort is representative of military personnel in terms of demographic and mental health characteristics and that participants report health and exposure data reliably.18,39–41 Finally, the Cohort questionnaires measure sleep in several different ways, permitting assessment of the outcome from several perspectives. In conclusion, deployment status was significantly associated with shorter self-reported average sleep duration and increased trouble sleeping. Additionally, exposure to combat during deployment was an independent predictor of both shorter sleep duration and trouble sleeping. Commanders, medical providers, and military personnel should be aware of the increased risk for sleep disturbance among those who display symptoms of PTSD, depression, anxiety or panic, and among those with combat exposure. Our findings also suggest that follow-up symptoms of mental health and combat exposures mediate the relationship between deployment and sleep. A more in-depth look at possible short- and long-term health outcomes among those reporting the shortest sleep durations and among mothers and pregnant women is recommended. If poor sleep contributes to the occurrence, persistence, or severity of mental health disorders or poor job performance, then the promotion of healthier sleep patterns, including recovery time following extreme sleep deprivation, among military service members may be beneficial in this population. Disclaimer The views expressed in this work are those of the authors, and do not reflect the official policy or position of the Department of the Navy, Department of the Army, Department of the Air Force, Department of Defense, or the US Government. The funding organization (Military Operational Medicine Research Program) had no role in the design and conduct of the study; collection, analysis, or preparation of data; or preparation, review, or approval of this manuscript. VA Puget Sound Health Care System provided support for Dr. Boyko's participation in this research. Acknowledgments In addition to the authors, the Millennium Cohort Study Team includes Melissa Bagnell, Lacy Farnell, Gia Gumbs, Nisara Granado, Jaime Horton, Kelly Jones, Molly Kelton, Cynthia LeardMann, Travis Leleu, Gordon Lynch, Jamie McGrew, Amanda Pietrucha, Teresa Powell, Donald Sandweiss, Beverly Sheppard, Katherine Snell, Steven Spiegel, Kari Welch, Martin White, James Whitmer, and Charlene Wong, from the Department of Deployment Health Research Naval Health Research Center, San Diego, CA; Paul Amoroso, from the Madigan Army Medical Center, Tacoma, WA; Gregory Gray, from the College of Public Health and Health Professions, University of Florida. Gainesville, FL; James Riddle; Margaret Ryan from the Naval Hospital Camp Pendleton, Camp Pendleton, CA; and Timothy Wells from the US Air Force Research Laboratory, Wright-Patterson Air Force Base, OH. The authors thank the Millennium Cohort Study participants, without whom these analyses would not be possible. We thank Scott Seggerman from the Management Information Division, US Defense Manpower Data Center, Monterey, CA; Michelle Stoia from the Naval Health Research Center; and all the professionals from the US Army Medical Research and Materiel Command, especially those from the Military Operational Medicine Research Program, Fort Detrick, MD. We appreciate the support of the Henry M. Jackson Foundation for the Advancement of Military Medicine, Rockville, MD. References 1. Sleep in America Poll. Available at: http://www.sleepfoundation.org/ article/sleep-america-polls/2008-sleep-performance-and-the-workplace . Accessed January , 2010 . 2. Ferrer CF , Jr., Bisson RU, French J Orcadian rhythm desynchronosis in military deployments: a review of current strategies . Aviat Space Environ Med 1995 ; 66 : 571 – 8 . Google Scholar PubMed OpenURL Placeholder Text WorldCat 3. Peterson AL , Goodie JL, Satterfield WA, Brim WL Sleep disturbance during military deployment . 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Miller, Nita Lewis; Shattuck, Lawrence G.; Mateangas, Panagiotis
doi: 10.1093/sleep/33.12.1623pmid: 21120124
AbstractStudy Objectives:The study provided an opportunity to observe sleep patterns in a college-age population attending the United States Military Academy.Design:This 4-year longitudinal study investigated sleep patterns of cadets. A stratified sample of 80 cadets had sleep patterns monitored using actigraphy for 8 months: one month in both fall and spring academic semesters over a 4-year period.Setting:Data were collected at the United States Military Academy, West Point, NY.Participants:Participants were members of the class of 2007 (n~1300) ranging in age from 17 to 22 when entering USMA.Measurements and Results:A sample of the class (n = 80) wore wrist activity monitors and completed activity logs for one month in fall and spring academic semesters for the 4-year period. On average over the 4 years, cadets slept < 5.5 h on school nights. Cadets napped extensively, perhaps in an attempt to compensate for chronic sleep debt. Cadets slept more during fall than spring semesters. Male and female cadet sleep patterns varied dramatically, with males consistently receiving less sleep than females (~21 m for nighttime sleep and ~23 m for daily sleep).Conclusions:Cadet sleep at USMA is related to academic year, semester, season, sex, school day or weekend, and day of the week. These students suffer from chronic sleep debt. Restrictions imposed by the military academy limit the generalizability of the findings to other college age populations.
Miller, Nita, Lewis;Shattuck, Lawrence, G.;Mateangas,, Panagiotis
doi: 10.1093/sleep.33.12.1623pmid: N/A
Abstract Study Objectives: The study provided an opportunity to observe sleep patterns in a college-age population attending the United States Military Academy. Design: This 4-year longitudinal study investigated sleep patterns of cadets. A stratified sample of 80 cadets had sleep patterns monitored using actigraphy for 8 months: one month in both fall and spring academic semesters over a 4-year period. Setting: Data were collected at the United States Military Academy, West Point, NY. Participants: Participants were members of the class of 2007 (n~1300) ranging in age from 17 to 22 when entering USMA. Measurements and Results: A sample of the class (n = 80) wore wrist activity monitors and completed activity logs for one month in fall and spring academic semesters for the 4-year period. On average over the 4 years, cadets slept < 5.5 h on school nights. Cadets napped extensively, perhaps in an attempt to compensate for chronic sleep debt. Cadets slept more during fall than spring semesters. Male and female cadet sleep patterns varied dramatically, with males consistently receiving less sleep than females (~21 m for nighttime sleep and ~23 m for daily sleep). Conclusions: Cadet sleep at USMA is related to academic year, semester, season, sex, school day or weekend, and day of the week. These students suffer from chronic sleep debt. Restrictions imposed by the military academy limit the generalizability of the findings to other college age populations. Sleep deprivation, actigraphy, adolescent sleep patterns, college-age students, military education RESEARCHERS HAVE REPORTED THE DISTINCT CHANGES THAT OCCUR IN SLEEP PATTERNS WHEN HUMANS ENTER ADOLESCENCE.1,–4 THESE CHANGES are marked by both a delay in and an extension of the major sleep period. When allowed to conform to individual sleep preference, individuals in this age group experience later bedtimes and awaken much later, a pattern that corresponds to the naturally occurring melatonin levels of this age group.5,6 While this change in sleep pattern coincides with pubertal onset in the teen years, Roenneberg et al. reported that this tendency to sleep late may continue throughout the lifespan7 Their model of the timing of self-selected sleep identified 3 important determinants: genetic disposition, sleep debt accumulated on workdays, and light exposure. The study also identified significant differences between the amount of sleep for males and females (females had 17 m more sleep on weekdays), although sleep amount in the 2 groups was not significantly different on free days. Individual sleep requirements also vary considerably8,9 and have been attributed to differences in human physiology.10,11 Even though adults need on average approximately 7.5 to 8 h of daily sleep, both ends of this distribution exist with short sleepers (< 6.5 h of sleep) and long sleepers (> 8.5 h of sleep).10,–13 Numerous studies indicate that sleep is often a problem for college-age students, with mounting evidence that this age group commonly suffers from disordered sleep patterns that often result in chronic sleep debt.14,–18 This trend may be worsening, perhaps due in part to the 24–7 nature of life in the twenty-first century,19,20 which is marked by advances in technology that surround the individual with connectedness.21,22 With the omnipresence of the internet, laptop computers, and caffeine dispensaries, modern society differs dramatically from that of just a few decades ago. Today, there is little down time when individuals are forced to “tune out” and disconnect from technology. This trend is especially alarming in light of a growing body of literature highlighting the association between chronic sleep debt and insomnia and a variety of physical and mental health issues.23,–25 Additional evidence points to the role of sleep in maintaining the body's ability to ward off infections through the immune system.26 Along with the negative health consequences of sleep deprivation, its effect on individual cognitive performance has been documented in multiple studies.27,–29 Memory consolidation, long-term recall, and retrieval, particularly of novel material, is affected by sleep restriction30,–32 Mishaps and safety violations are closely associated with inadequate sleep.33 Sleep deprivation is not uncommon in military life, especially during exercises, demanding training periods, and combat.34,35 For this reason, Army regulations address sleep related issues as part of combat/operational stress and battle fatigue (a military term for combat stress symptoms and reactions).36 Historically, many members of the military view sleep as an indulgence; sleep deprivation in the interest of duty is even revered.37 In military training and eduction settings, the schedule of activities is both rigorous and strictly enforced. Combined with extremely limited opportunities for sleep, the typical schedule often results in cumulative sleep debt and involuntary forced circadian desynchrony.18 Grades and other performance measures suffer when adolescents and young adults experience restricted sleep schedules. This finding was confirmed in a study of test scores of US Navy Recruits at Great Lakes, Illinois.38 In another study of military members, Killgore et al. found significant correlations between sleep and test scores in military students attending the Noncommissioned Officer and Warrant Officer Candidate Schools at Fort Rucker, AL.39 These studies point to a strong relationship between sleep and academic performance and are aligned with findings of controlled laboratory studies. We do not completely understand the natural history of sleep in an individual. Few, if any, longitudinal studies observe sleep patterns over the course of an individual's life span. This study seeks to fill a portion of this gap by studying sleep patterns over a 4-year period in late adolescence and young adulthood. The conditions at USMA provide a natural laboratory to examine restricted sleep patterns in adolescents and young adults. The present study has 2 goals: (1) determine the key factors associated with Cadet sleep patterns at USMA; (2) evaluate whether and to what extent Cadets attending USMA, who are military and college-age students, are chronically sleep deprived. Typical USMA Cadet Schedule Over the course of a week, the schedules of cadets attending USMA fall into 2 distinct categories: school days (Monday through Friday), and weekends (Saturday and Sunday). On school days, USMA cadets follow a fairly rigid daily schedule. The Academy's senior leadership modifies the daily schedule from time to time based on feedback from the faculty, staff, and cadets. During this study, cadets arose no later than 06:30 (reveille) in order to attend a mandatory morning formation at 06:55. A mandatory “midnight lights out” policy was put in place at the beginning of the second year of the study. However, the researchers were aware of both authorized and unauthorized deviations from these policies. For example, cadets were authorized to leave their rooms no earlier than 05:15 to participate in individual or team athletic training prior to the morning formation. Several cadets reported staying up past the midnight “lights out” in order to complete their homework or their military duties. Therefore, even though the schedule afforded 6.5 h of protected sleep time, in practice, many cadets received much less sleep, either because they woke up prior to 06:30, went to sleep past midnight, or both. Table 1 shows a typical USMA cadet school day schedule. Table 1 Typical school day schedule at USMA 06:30 Reveille 06:55–7:30 Breakfast Formation (mandatory) 07:35–11:45 Class (55 minutes per class) or study time 12:05–12:40 Lunch 12:45–13:40 Commandant/Dean Time 13:50–15:50 Class or study 16:10:17:45 Intramural, club or intercollegiate athletics; parades; extracurricular activities; or free time 18:30–19:15 Supper (optional except Thursdays) 19:15–19:30 Cadet Duties 19:30–20:30 Study conditions/Extracurricular activities 20:30–23:30 Study time 23:30 Taps 24:00 Lights Out 06:30 Reveille 06:55–7:30 Breakfast Formation (mandatory) 07:35–11:45 Class (55 minutes per class) or study time 12:05–12:40 Lunch 12:45–13:40 Commandant/Dean Time 13:50–15:50 Class or study 16:10:17:45 Intramural, club or intercollegiate athletics; parades; extracurricular activities; or free time 18:30–19:15 Supper (optional except Thursdays) 19:15–19:30 Cadet Duties 19:30–20:30 Study conditions/Extracurricular activities 20:30–23:30 Study time 23:30 Taps 24:00 Lights Out Open in new tab Table 1 Typical school day schedule at USMA 06:30 Reveille 06:55–7:30 Breakfast Formation (mandatory) 07:35–11:45 Class (55 minutes per class) or study time 12:05–12:40 Lunch 12:45–13:40 Commandant/Dean Time 13:50–15:50 Class or study 16:10:17:45 Intramural, club or intercollegiate athletics; parades; extracurricular activities; or free time 18:30–19:15 Supper (optional except Thursdays) 19:15–19:30 Cadet Duties 19:30–20:30 Study conditions/Extracurricular activities 20:30–23:30 Study time 23:30 Taps 24:00 Lights Out 06:30 Reveille 06:55–7:30 Breakfast Formation (mandatory) 07:35–11:45 Class (55 minutes per class) or study time 12:05–12:40 Lunch 12:45–13:40 Commandant/Dean Time 13:50–15:50 Class or study 16:10:17:45 Intramural, club or intercollegiate athletics; parades; extracurricular activities; or free time 18:30–19:15 Supper (optional except Thursdays) 19:15–19:30 Cadet Duties 19:30–20:30 Study conditions/Extracurricular activities 20:30–23:30 Study time 23:30 Taps 24:00 Lights Out Open in new tab The mandatory midnight lights out policy was instituted at the beginning of fall 2004 and continued through spring 2007. Saturday and Sunday mornings provided the only opportunities for cadets to sleep late. Weekends also represent the major opportunity for cadets to engage in social activities. All cadets participate in athletics, either on intramural or intercollegiate teams. Methods Participants A stratified sample of 80 cadets of the USMA Class of 2007 (n~1300) was randomly selected to wear wrist activity monitors (WAMs) and complete activity logs. This sample was selected on the basis of sex, unit (company to which the cadet was assigned), and athletic status (participation on either an intramural or an intercollegiate athletic team). The Corps of Cadets is comprised of approximately 85% males and 15% females. In order to have a sufficient number of females in the study for statistical purposes, females were over-sampled. Therefore, the 80 cadets in the sample at the beginning of each data collection period included 56 males (70%) and 24 females (30%). At the beginning of the study, all members of the Class of 2007 ranged in age from 17 to 22; the age of the sample of participants ranged from 18 to 23 (mean = 18.8 y, SD = 0.89 y, median = 19 y). When participants were lost from the study due to attrition, either from withdrawing from USMA or withdrawing from the sleep study, replacements were drawn from a pool of volunteers matched by sex, unit, and athletic status. Although menstrual cycle is known is to be associated with lower reported sleep quality and increased sleep disturbances,40”43 this factor was not recorded during the study. Participants were treated in accordance with ethical standards established by the American Psychological Association. The research methods used in this study were approved by the USMA Human Subjects Use Committee. All participants gave written informed consent prior to participating in the study. Equipment Estimates of cadet sleep were obtained using wrist activity monitors (WAMs) (Actigraph Model AW-64; Mini Mitter Respironics, Inc.) Actigraphy has been used extensively as a method for the objective assessment of sleep,44,45 although some constraints do exist on its acceptability.46,47 Actigraphic recordings followed the recommendations of Standards of Practice Committee of the American Academy of Sleep Medicine (2002). Actigraphic epoch length was 60 sec. Participants were instructed to wear the WAMs on the non-dominant hand at all times of the day and night during the study period. Cadets filled out a paper and pencil activity log to indicate their activities during the study. This log divided each day into 15-m increments and was tailored to the specific activities of the military lifestyle at USMA. Analysis of the actigraphic recordings was conducted using Actiware Version 5.01.0007 software with the following parameters: Epoch Length = 1 minute, Wake Threshold Selection = Medium, Sleep Interval Detection Algorithm = Immobile Minutes, Immobile Minutes for Sleep Onset = 10, Immobile Minutes for Sleep End =10. The software facilitated the calculation of sleep episode duration. Statistical analysis was conducted with Microsoft Excel and JMP Release 7.0 from the SAS Institute. Procedure A stratified random sample of the students in the Class of 2007 (approximately 80 participants) was selected to wear WAMs concurrently for approximately 30 days during 4 fall academic semesters (2003 to 2006), and again during 4 spring academic semesters (2004 to 2007). In order to have equivalent fall and spring data collection, these periods were always situated between mid-October and early December, and from the beginning of March to early May. Each of the 8 data-collection periods included about 4 contiguous weeks of normal academic activities with no holiday or examination periods. Cadets were asked to fill out a daily activity log when they wore the wrist activity monitors. On these logs, participants indicated their work and rest activities, especially those periods when they slept or napped. As much as possible, the same 80 cadets were studied through each of the eight 30-day data collection periods. Cadets lost to the study, either through attrition from the Academy or by choosing to drop out of the study, were replaced by cadets in the same year group. They were also matched by sex and athletic status. Sleep estimates (sleep episode duration, and bedtime/wakeup time) were derived from the actigraphic recordings in conjunction with event markers (when used), and were verified by the self-reported activity logs. After determining the bed and wakeup times, the Actiware software was used to calculate sleep duration. Sleep estimates based only on self-report (i.e., sleep estimates from activity logs without actigraphy data) were excluded from the analysis to avoid systematic errors.48 However, sleep estimates when only actigraphy was available were included. Because cadets have a rigid and tightly controlled daily schedule, assessing nightly sleep episodes was straightforward. The assessment of naps was not as simple, however. The cadet daily schedule includes class attendance and study time during which activity is decreased. In the absence of sleep logs, these periods of low activity could erroneously be scored as naps. During the academic year (AY) 2004 (i.e., fall 2003 and spring 2004), cadets did not fill out the activity logs consistently; thus, sleep logs were not used for analysis of sleep during AY 2004 and nap analysis was not conducted for AY 2004. The study protocol was approved in advance by both the Naval Postgraduate School and USMA Institutional Review Boards. Each participant provided written informed consent before enrolling in the study. Analytical Approach The analytical approach was designed to assess the amount of sleep received by Cadets at USMA and to determine if this quantity was related to a number of independent variables: academic year (2003–2007), semester (first through eighth), season (fall or spring), day category (school day or weekend), week day (Sunday through Saturday), and sex (male or female). The main dependent variables examined were nighttime sleep (that is, the major nighdy sleep episode) and daily sleep (24-h total). Actigraphic recordings were used to determine bedtime, wake-up time, and sleep episode duration. These data were entered into a Microsoft Excel spreadsheet. Statistical analysis was conducted with Excel and JMP Release 7.0. When needed, data normality was assessed with the Shapiro-Wilk W test. Analysis of variance, effect size calculation, and nonparametric tests were used when appropriate to assess the study responses. Statistical significance was indicated for 2-tailed P values < 0.05. RESULTS Table 2 shows the number of cadets whose sleep was used for analysis by semester and year. The table also indicates the numbers and percentages of males and females and the attrition rate for each semester. There were 2 major causes of data loss: equipment failure and cadet attrition. In a few cases each semester, actigraphy data were lost due to mechanical failures of the WAMs. In addition, each semester, a small percentage of cadets chose to withdraw from the study (either to leave USMA or for other reasons). The highest cadet attrition rate occurred in the fall semester of 2005. This time point coincided with a major decision point in a cadet's education; at that point, cadets had to leave or make a final commitment to remain at USMA. If cadets decided to remain at the academy, they incurred an obligation of 5 years of military service upon graduation. The attrition rate in Table 2 refers to the aggregated percentage of cadets who withdrew from the study for that specific semester, whatever the reason. To fill in for those cadets lost to attrition the previous semester, we recruited additional members from the Class of 2007, matching by sex and athletic status, to ensure that there were 80 study participants enrolled at all times. Table 2 Participating cadets with actigraphy data Semester Participants Males Females Attrition rate Fall 2003 (AY 2004-1) 73 51 (70%) 22 (30%) - Spring 2004 (AY 2004-2) 75 52 (69%) 23(31%) 2.5% Fall 2004 (AY 2005-1) 74 53 (72%) 21 (28%) 5.0% Spring 2005 (AY 2005-2) 69 50 (73%) 19(28%) 1.3% Fall 2005 (AY 2006-1) 72 51 (71%) 21 (29%) 8.8% Spring 2006 (AY 2006-2) 55 35 (64%) 20 (36%) 0.0% Fall 2006 (AY 2007-1) 69 49(71%) 20 (29%) 2.5% Spring 2007 (AY 2007-2) 56 40(71%) 16(29%) 3.8% Semester Participants Males Females Attrition rate Fall 2003 (AY 2004-1) 73 51 (70%) 22 (30%) - Spring 2004 (AY 2004-2) 75 52 (69%) 23(31%) 2.5% Fall 2004 (AY 2005-1) 74 53 (72%) 21 (28%) 5.0% Spring 2005 (AY 2005-2) 69 50 (73%) 19(28%) 1.3% Fall 2005 (AY 2006-1) 72 51 (71%) 21 (29%) 8.8% Spring 2006 (AY 2006-2) 55 35 (64%) 20 (36%) 0.0% Fall 2006 (AY 2007-1) 69 49(71%) 20 (29%) 2.5% Spring 2007 (AY 2007-2) 56 40(71%) 16(29%) 3.8% Open in new tab Table 2 Participating cadets with actigraphy data Semester Participants Males Females Attrition rate Fall 2003 (AY 2004-1) 73 51 (70%) 22 (30%) - Spring 2004 (AY 2004-2) 75 52 (69%) 23(31%) 2.5% Fall 2004 (AY 2005-1) 74 53 (72%) 21 (28%) 5.0% Spring 2005 (AY 2005-2) 69 50 (73%) 19(28%) 1.3% Fall 2005 (AY 2006-1) 72 51 (71%) 21 (29%) 8.8% Spring 2006 (AY 2006-2) 55 35 (64%) 20 (36%) 0.0% Fall 2006 (AY 2007-1) 69 49(71%) 20 (29%) 2.5% Spring 2007 (AY 2007-2) 56 40(71%) 16(29%) 3.8% Semester Participants Males Females Attrition rate Fall 2003 (AY 2004-1) 73 51 (70%) 22 (30%) - Spring 2004 (AY 2004-2) 75 52 (69%) 23(31%) 2.5% Fall 2004 (AY 2005-1) 74 53 (72%) 21 (28%) 5.0% Spring 2005 (AY 2005-2) 69 50 (73%) 19(28%) 1.3% Fall 2005 (AY 2006-1) 72 51 (71%) 21 (29%) 8.8% Spring 2006 (AY 2006-2) 55 35 (64%) 20 (36%) 0.0% Fall 2006 (AY 2007-1) 69 49(71%) 20 (29%) 2.5% Spring 2007 (AY 2007-2) 56 40(71%) 16(29%) 3.8% Open in new tab Over the entire 4-year data collection period, 13,645 days of activity and rest were examined. This number included 17,285 individual sleep episodes, comprising both nighttime sleep and daytime naps. Each of the 8 data collection periods was approximately 30 contiguous days in length. In order to calculate daily sleep (that is, the amount of sleep obtained in a 24-h period), naps taken during the day had to be combined with the night's sleep either before or after the nap. That is, a nap taken during the day on Friday could have been added to the previous Thursday night's sleep episode or to the sleep episode on Friday night. Daily sleep (that is, the amount of sleep obtained in a 24-h period) was calculated by combining any nap taken during the day with the prior night's sleep. For AY 2005, 2006, and 2007, the average daily (24 h) sleep was 5 h 46 m (SD = 1 hr 28 m, median = 5 h 33 m). Napping data were not recorded for AY 2004; therefore, daily sleep could not be estimated. Over all 4 years of this study, average bedtime, wake time, and actual nighttime sleep are shown in Table 3. Table 3 Average bedtime, wakeup time and actual nighttime sleep per day category (school or weekend night) Parameter School night Weekend night Combined Bedtime 00:25 SD = 1 h 06 m 01:11 SD = 1 h42 m 00:35 SD = 1 h18 m Wakeup time 06:18 SD = 51 m 08:35 SD = 2h12 m 06:43 SD = 1 h 40 m Actual nighttime sleep 5 h 03 m SD = 1 h 02 m 6 h 29 m SD = 1 h 51 m 5 h24 m SD = 1 h 25 m Parameter School night Weekend night Combined Bedtime 00:25 SD = 1 h 06 m 01:11 SD = 1 h42 m 00:35 SD = 1 h18 m Wakeup time 06:18 SD = 51 m 08:35 SD = 2h12 m 06:43 SD = 1 h 40 m Actual nighttime sleep 5 h 03 m SD = 1 h 02 m 6 h 29 m SD = 1 h 51 m 5 h24 m SD = 1 h 25 m Open in new tab Table 3 Average bedtime, wakeup time and actual nighttime sleep per day category (school or weekend night) Parameter School night Weekend night Combined Bedtime 00:25 SD = 1 h 06 m 01:11 SD = 1 h42 m 00:35 SD = 1 h18 m Wakeup time 06:18 SD = 51 m 08:35 SD = 2h12 m 06:43 SD = 1 h 40 m Actual nighttime sleep 5 h 03 m SD = 1 h 02 m 6 h 29 m SD = 1 h 51 m 5 h24 m SD = 1 h 25 m Parameter School night Weekend night Combined Bedtime 00:25 SD = 1 h 06 m 01:11 SD = 1 h42 m 00:35 SD = 1 h18 m Wakeup time 06:18 SD = 51 m 08:35 SD = 2h12 m 06:43 SD = 1 h 40 m Actual nighttime sleep 5 h 03 m SD = 1 h 02 m 6 h 29 m SD = 1 h 51 m 5 h24 m SD = 1 h 25 m Open in new tab Figure 1 shows the distribution of nighttime sleep across all participants for the entire 4-year period. Figure 1 Open in new tabDownload slide Distribution of nighttime sleep duration (for all participants over all four years, AY 2004-2007). Figure 1 Open in new tabDownload slide Distribution of nighttime sleep duration (for all participants over all four years, AY 2004-2007). Figure 2 shows the 24-h daily sleep (nighttime sleep plus daytime naps) for the final 3 years at USMA. Daily sleep for the first year at USMA (AY 2004) is not included because cadets did not fill out activity logs for that year. Figure 2 Open in new tabDownload slide Distribution of daily sleep duration (all participants for AY 2005, 2006, and 2007). Figure 2 Open in new tabDownload slide Distribution of daily sleep duration (all participants for AY 2005, 2006, and 2007). Analysis of variance and effect size analysis showed that sleep time (either nighttime or daily sleep) was associated with academic year, semester, season (fall or spring), sex, weekday/ weekend category (that is, school day or weekend), and day of the week. Table 4 shows the significance level of these findings. (See text and subsequent tables and figures for direction of these parameter effects.) Table 4 Significant predictors of nighttime and daily sleep duration (base average sleep amount per cadet) Nighttime Sleep All AY Daily Sleep AY 2005, 2006, 2007 Parameter Test statistic/ P-value Effect size Cohen'ss d or f2 Test statistic/ P-value Effect size Cohen'ss d or f2 Academic F3,310 = 9.85 f2 = 0.08 F2,230 =179 f2 = 0.16 year P< 0.0001 P< 0.0001 Semester F7,533 = 7.46 f2 = 0.10 F5,387 = 11.74 P = 0.15 P< 0.0001 P< 0.0001 Season 1-sided t= 1.749 d = 0.26 F1,176 = 6.07 d = 0.37 P = 0.0409 P = 0.0147 Sex F1,95= 10.21 P = 0.0019 d = 0.79 F1,95 = 1136 P = 0.0011 d = 0.86 P = 0.0019 P = 0.0019 Day F1,192 = 263.9 d = 2.33 F1,183 = 207.7 d = 2.12 category P< 0.0001 P< 0.0001 Week day F6,670=128.6 f2 = 1.15 F6,634= 93.4 f2 = 0.88 P< 0.0001 P< 0.0001 Nighttime Sleep All AY Daily Sleep AY 2005, 2006, 2007 Parameter Test statistic/ P-value Effect size Cohen'ss d or f2 Test statistic/ P-value Effect size Cohen'ss d or f2 Academic F3,310 = 9.85 f2 = 0.08 F2,230 =179 f2 = 0.16 year P< 0.0001 P< 0.0001 Semester F7,533 = 7.46 f2 = 0.10 F5,387 = 11.74 P = 0.15 P< 0.0001 P< 0.0001 Season 1-sided t= 1.749 d = 0.26 F1,176 = 6.07 d = 0.37 P = 0.0409 P = 0.0147 Sex F1,95= 10.21 P = 0.0019 d = 0.79 F1,95 = 1136 P = 0.0011 d = 0.86 P = 0.0019 P = 0.0019 Day F1,192 = 263.9 d = 2.33 F1,183 = 207.7 d = 2.12 category P< 0.0001 P< 0.0001 Week day F6,670=128.6 f2 = 1.15 F6,634= 93.4 f2 = 0.88 P< 0.0001 P< 0.0001 Open in new tab Table 4 Significant predictors of nighttime and daily sleep duration (base average sleep amount per cadet) Nighttime Sleep All AY Daily Sleep AY 2005, 2006, 2007 Parameter Test statistic/ P-value Effect size Cohen'ss d or f2 Test statistic/ P-value Effect size Cohen'ss d or f2 Academic F3,310 = 9.85 f2 = 0.08 F2,230 =179 f2 = 0.16 year P< 0.0001 P< 0.0001 Semester F7,533 = 7.46 f2 = 0.10 F5,387 = 11.74 P = 0.15 P< 0.0001 P< 0.0001 Season 1-sided t= 1.749 d = 0.26 F1,176 = 6.07 d = 0.37 P = 0.0409 P = 0.0147 Sex F1,95= 10.21 P = 0.0019 d = 0.79 F1,95 = 1136 P = 0.0011 d = 0.86 P = 0.0019 P = 0.0019 Day F1,192 = 263.9 d = 2.33 F1,183 = 207.7 d = 2.12 category P< 0.0001 P< 0.0001 Week day F6,670=128.6 f2 = 1.15 F6,634= 93.4 f2 = 0.88 P< 0.0001 P< 0.0001 Nighttime Sleep All AY Daily Sleep AY 2005, 2006, 2007 Parameter Test statistic/ P-value Effect size Cohen'ss d or f2 Test statistic/ P-value Effect size Cohen'ss d or f2 Academic F3,310 = 9.85 f2 = 0.08 F2,230 =179 f2 = 0.16 year P< 0.0001 P< 0.0001 Semester F7,533 = 7.46 f2 = 0.10 F5,387 = 11.74 P = 0.15 P< 0.0001 P< 0.0001 Season 1-sided t= 1.749 d = 0.26 F1,176 = 6.07 d = 0.37 P = 0.0409 P = 0.0147 Sex F1,95= 10.21 P = 0.0019 d = 0.79 F1,95 = 1136 P = 0.0011 d = 0.86 P = 0.0019 P = 0.0019 Day F1,192 = 263.9 d = 2.33 F1,183 = 207.7 d = 2.12 category P< 0.0001 P< 0.0001 Week day F6,670=128.6 f2 = 1.15 F6,634= 93.4 f2 = 0.88 P< 0.0001 P< 0.0001 Open in new tab Many of the cadets napped; quite possibly, this napping was a strategy to compensate for the chronic sleep deprivation they experienced. Over one-quarter (26.2%) of all cadet-days (n = 2541) included at least one nap episode. The overall effect of napping on daily sleep of all participants (the average increase from nighttime to daily sleep) was 4.4% or 17.3 m (SD = 9.3% or SD = 39.2 m). For only those cadet-days which included nap episodes, the effect of napping on daily sleep increased by 16.8% or 66.1 m (SD = 11.1%, median = 14.1% or SD = 51.4 m, median = 50.0 m). The average daily sleep (per cadet by semester) increased annually by 12 and 19 m across the last 3 years at USMA (F2,230 = 17.9911, P < 0.0001). Cadets obtained the least daily sleep (AY 2004 excluded) during their second year at the USMA (5.56 h), while receiving the most daily sleep during the fourth year (6.07 h). As seen in Figure 3, this increase in daily sleep across years was significant for both male and female cadets (P < 0.0001) for each year of the study. Figure 3 Open in new tabDownload slide Daily sleep by sex (F,M) and academic year (AY) for AY 2005, 2006 and 2007. Figure 3 Open in new tabDownload slide Daily sleep by sex (F,M) and academic year (AY) for AY 2005, 2006 and 2007. Figure 4 illustrates the change in nighttime sleep by academic year and sex. Nighttime sleep increased consistently over the first 3 years, but there was a significant decrease in nighttime sleep during the fourth year (F3,310 = 9.8522, P < 0.0001), corresponding to the increased demands and responsibilities faced by cadets in their final year at USMA. Figure 4 shows that this annual pattern was consistent for both male and female cadets. Figure 4 Open in new tabDownload slide Nighttime sleep by sex (F,M) and academic year (AY). Figure 4 Open in new tabDownload slide Nighttime sleep by sex (F,M) and academic year (AY). Figure 5 shows the average daily (24-h) and nighttime sleep by academic year. Cadets employed napping strategies during the day quite possibly to supplement their nighttime sleep during the last 3 years. They received the least nighttime sleep during their first year at the Academy, then received significantly more in the 2 subsequent years, with nighttime sleep amount dropping again in their final year. This drop in nighttime sleep probably is caused by additional duties and responsibilities during the final year and is compensated, to some extent, by an increase in daytime napping. Figure 5 Open in new tabDownload slide Average nighttime and daily sleep by academic year (AY). The dotted line shows the average sleep during the night, while the solid line is the average level of daily (24 h) sleep. Figure 5 Open in new tabDownload slide Average nighttime and daily sleep by academic year (AY). The dotted line shows the average sleep during the night, while the solid line is the average level of daily (24 h) sleep. Seasonal Trends The next step in the analysis was to investigate nighttime and daily sleep patterns by semester. Figure 6 shows an obvious zigzag pattern between sleep in the spring and fall semesters. This pattern is evident for both daily (solid line) and nighttime (dashed line) sleep. With the single exception of the first semester, cadets slept significantly less in the spring than in fall semester. Again, daily sleep for AY 2004 is excluded from the analysis since cadets did not report their napping in the first year of the study. Figure 6 Open in new tabDownload slide Nighttime and daily sleep by semester. The dashed line shows the average sleep during the night, while the solid line is the average level of daily (24 h) sleep. Figure 6 Open in new tabDownload slide Nighttime and daily sleep by semester. The dashed line shows the average sleep during the night, while the solid line is the average level of daily (24 h) sleep. As shown in Table 5, the seasonal difference in sleep quantity also resulted in differences in bedtime and wakeup time. There were significant differences for all categories except for bedtime on school nights. These results are based on AY 2005,2006, and 2007 only. After AY 2004, the administrative leadership at USMA instituted a mandatory lights out policy that promoted earlier bedtimes. It should be noted that the early morning mandatory breakfast formation requires cadets to wake up earlier than they would otherwise choose and quite possibly earlier than their civilian counterparts. Table 5 Seasonal effect on bedtimes and wake-up times for school nights and weekend nights for Academic Years 2005,2006, and 2007 Time Fall Spring Test statistic P-value Effect size (Cohen’ s d) School nights Bed time 00:23 00:21 F1,7508=1.26 0.2625 0.03 Wake-up 06:16 06:13 F1,7508=6.21 0.0127 0.05 Weekend nights Bed time 01:17 00:53 F1,2242=30.81 < 0.0001 0.24 Wake-up 08:56 08:16 F1,2242 53.36 < 0.0001 0.31 Time Fall Spring Test statistic P-value Effect size (Cohen’ s d) School nights Bed time 00:23 00:21 F1,7508=1.26 0.2625 0.03 Wake-up 06:16 06:13 F1,7508=6.21 0.0127 0.05 Weekend nights Bed time 01:17 00:53 F1,2242=30.81 < 0.0001 0.24 Wake-up 08:56 08:16 F1,2242 53.36 < 0.0001 0.31 Open in new tab Table 5 Seasonal effect on bedtimes and wake-up times for school nights and weekend nights for Academic Years 2005,2006, and 2007 Time Fall Spring Test statistic P-value Effect size (Cohen’ s d) School nights Bed time 00:23 00:21 F1,7508=1.26 0.2625 0.03 Wake-up 06:16 06:13 F1,7508=6.21 0.0127 0.05 Weekend nights Bed time 01:17 00:53 F1,2242=30.81 < 0.0001 0.24 Wake-up 08:56 08:16 F1,2242 53.36 < 0.0001 0.31 Time Fall Spring Test statistic P-value Effect size (Cohen’ s d) School nights Bed time 00:23 00:21 F1,7508=1.26 0.2625 0.03 Wake-up 06:16 06:13 F1,7508=6.21 0.0127 0.05 Weekend nights Bed time 01:17 00:53 F1,2242=30.81 < 0.0001 0.24 Wake-up 08:56 08:16 F1,2242 53.36 < 0.0001 0.31 Open in new tab As can be seen in Table 6, this pattern of less sleep in the spring semester was seen for both males and females (analysis based on average sleep per cadet by semester for AY 2005 – 2007). Consistent for both sexes, the decrease in sleep amount from fall to spring semesters ranged from 12 to 15 m for nighttime sleep (females: fall = 5.84 h, spring = 5.59 h; males: fall = 5.45 h, spring =5.25 h), and from 14 to 18 m for daily sleep (females: fall = 6.17 h, spring = 5.87 h; males: fall = 5.75 h, spring = 5.52 h). In this study population, male cadets got consistently less sleep than their female counterparts. This difference was approximately 21 m for nighttime sleep and 23 m for daily sleep amount. The seasonal effect by sex is shown in Figure 7. Figure 7 Open in new tabDownload slide Difference in sleep amount for females (F) and males (M). Nighttime and daily sleep are based on AY 2005,2006, and 2007. Figure 7 Open in new tabDownload slide Difference in sleep amount for females (F) and males (M). Nighttime and daily sleep are based on AY 2005,2006, and 2007. Analysis of variance of cadets’ sleep patterns showed that there was a significant difference in the amount of sleep on school nights compared to weekend nights. For both nighttime and daily sleep, cadets received significantly less sleep on school nights compared to weekend nights. When averaging over all semesters, this difference was approximately 1.4 h, as seen in Figure 8. That is, cadets received 27% more sleep on weekend nights than on school nights. Figure 8 Open in new tabDownload slide Difference between nighttime and daily sleep for school and weekend nights. Nighttime sleep is based on AY 2004-2007; Daily sleep is based on AY 2005, 2006, and 2007. Figure 8 Open in new tabDownload slide Difference between nighttime and daily sleep for school and weekend nights. Nighttime sleep is based on AY 2004-2007; Daily sleep is based on AY 2005, 2006, and 2007. When analyzing sleep on a day-to-day basis, there are clearly 2 distinct categories: schooldays (Monday to Friday) and weekends. As already shown, cadets received significantly more sleep on the 2 weekend nights. Sleep on school nights is fairly homogeneous due to the inflexibility of the USMA schedule. Saturday and Sunday sleep patterns are significantly different from each other, however (nighttime sleep for all AY: F1,3237 = 162.7313, P < 0.0001, Cohen's d = 0.45; daily sleep for AY 2005, 2006, and 2007: F1,2228 = 94.7078, P < 0.0001, Cohen's d = 0.41). In general, cadets received more daily sleep on Sunday (i.e., sleep on Saturday nights plus Sunday naps) than on Saturday (i.e., sleep on Friday night plus Saturday naps) Δ = 0.81 h; daily sleep for AY 2005, 2006, and 2007: Δ = 0.74 h). The sex differences (all P < 0.001) are illustrated in Figure 9. Figure 9 Open in new tabDownload slide Sex differences in daily sleep by season and day of the week (AY 2005,2006, and 2007). Figure 9 Open in new tabDownload slide Sex differences in daily sleep by season and day of the week (AY 2005,2006, and 2007). Sex Differences We examined the amount of sleep received by individual cadets using average daily sleep amounts (i.e., sleep received in a 24-h period). Average daily sleep per participant during AY 2005, 2006, and 2007 ranged from 4.39 h to 7.35 h. Sex played a significant role in determining sleep amount (F1,91 = 11.7235, P = 0.0009, Cohen's d = 0.84), with male cadets receiving an average of 5.68 h daily sleep, whereas female cadets received 6.04 h. Furthermore, males, as compared to females, had more variability in daily sleep as shown in Table 7. Table 6 The effect of season on nighttime and daily sleep by sex for Academic Years 2005, 2006, and 2007 Sex Time Test statistic P-value Effect size (Cohen’s d) Males Nighttime Sleep F1,284 = 9.16 0.0027 0.36 Daily sleep F1,284 = 3.88 0.0017 0.38 Females Nighttime Sleep F1,105 = 7.41 0.0076 0.53 Daily sleep F1,105 = 7.97 0.0057 0.54 Sex Time Test statistic P-value Effect size (Cohen’s d) Males Nighttime Sleep F1,284 = 9.16 0.0027 0.36 Daily sleep F1,284 = 3.88 0.0017 0.38 Females Nighttime Sleep F1,105 = 7.41 0.0076 0.53 Daily sleep F1,105 = 7.97 0.0057 0.54 Open in new tab Table 6 The effect of season on nighttime and daily sleep by sex for Academic Years 2005, 2006, and 2007 Sex Time Test statistic P-value Effect size (Cohen’s d) Males Nighttime Sleep F1,284 = 9.16 0.0027 0.36 Daily sleep F1,284 = 3.88 0.0017 0.38 Females Nighttime Sleep F1,105 = 7.41 0.0076 0.53 Daily sleep F1,105 = 7.97 0.0057 0.54 Sex Time Test statistic P-value Effect size (Cohen’s d) Males Nighttime Sleep F1,284 = 9.16 0.0027 0.36 Daily sleep F1,284 = 3.88 0.0017 0.38 Females Nighttime Sleep F1,105 = 7.41 0.0076 0.53 Daily sleep F1,105 = 7.97 0.0057 0.54 Open in new tab Table 7 Average daily sleep variability by sex (in hours for AY 2005,2006, and 2007) Sex Min 10% quantile 25% quantile Median 75% quantile 90% quantile Max Females 5.37 5.49 5.74 6.11 6.34 6.53 6.91 Male 4.39 5.20 5.38 5.61 6.01 6.19 7.35 Sex Min 10% quantile 25% quantile Median 75% quantile 90% quantile Max Females 5.37 5.49 5.74 6.11 6.34 6.53 6.91 Male 4.39 5.20 5.38 5.61 6.01 6.19 7.35 Open in new tab Table 7 Average daily sleep variability by sex (in hours for AY 2005,2006, and 2007) Sex Min 10% quantile 25% quantile Median 75% quantile 90% quantile Max Females 5.37 5.49 5.74 6.11 6.34 6.53 6.91 Male 4.39 5.20 5.38 5.61 6.01 6.19 7.35 Sex Min 10% quantile 25% quantile Median 75% quantile 90% quantile Max Females 5.37 5.49 5.74 6.11 6.34 6.53 6.91 Male 4.39 5.20 5.38 5.61 6.01 6.19 7.35 Open in new tab The distribution of sleep for males and females is illustrated in Figures 10 and 11. Figure 10 Open in new tabDownload slide Average daily sleep for male participants (AY 2005, 2006, and 2007). Figure 10 Open in new tabDownload slide Average daily sleep for male participants (AY 2005, 2006, and 2007). Figure 11 Open in new tabDownload slide Average daily sleep for female participants (AY 2005, 2006, and 2007). Figure 11 Open in new tabDownload slide Average daily sleep for female participants (AY 2005, 2006, and 2007). As already noted, cadets slept significantly more on weekends. The average increase of daily sleep per participant was 1.31 h (SD = 1.07 h), or 25.0% (SD = 21.8%). The increase in daily sleep on weekends was related to daily sleep on school nights. That is, those cadets who slept less during the week probably attempted to compensate for their accumulated sleep debt by sleeping more on weekends. This effect was more pronounced for male cadets. These results are shown in Table 8 (one outlier deleted). Table 8 Difference in daily sleep between school nights and weekends by sex Sex Difference in Daily Sleep Test statistic/P-value Correlation r Males Abs F1,260= 37.6, P< 0.0001 -0.36 % F1,260= 69.5, P< 0.0001 -0.46 Females Abs F1.98= 3.56, P = 0.0623 -0.19 % F1.98= 10.2, P = 0.0019 -0.31 Sex Difference in Daily Sleep Test statistic/P-value Correlation r Males Abs F1,260= 37.6, P< 0.0001 -0.36 % F1,260= 69.5, P< 0.0001 -0.46 Females Abs F1.98= 3.56, P = 0.0623 -0.19 % F1.98= 10.2, P = 0.0019 -0.31 Open in new tab Table 8 Difference in daily sleep between school nights and weekends by sex Sex Difference in Daily Sleep Test statistic/P-value Correlation r Males Abs F1,260= 37.6, P< 0.0001 -0.36 % F1,260= 69.5, P< 0.0001 -0.46 Females Abs F1.98= 3.56, P = 0.0623 -0.19 % F1.98= 10.2, P = 0.0019 -0.31 Sex Difference in Daily Sleep Test statistic/P-value Correlation r Males Abs F1,260= 37.6, P< 0.0001 -0.36 % F1,260= 69.5, P< 0.0001 -0.46 Females Abs F1.98= 3.56, P = 0.0623 -0.19 % F1.98= 10.2, P = 0.0019 -0.31 Open in new tab Discussion This study assessed cadet sleep patterns at USMA, and evaluated whether, and to what extent, these military, college-age students are chronically sleep deprived. Our results show that sleep of cadets at USMA is associated with academic year, semester, season, sex, day category (school day or weekend), and day of the week. The major conclusion from this longitudinal study is that cadets experience significant and chronic sleep deprivation during their four years of study at USMA. Given that the physiologically recommended requirement for sleep in adolescents and young adults is 8.5 to 9.25 h,49 the cadet sleep debt is considerable, averaging over 3 hours per day for each day they attend USMA. Upon graduation, cadets have accumulated a sleep debt of thousands of hours per individual cadet. Although there are large individual differences in vulnerability to sleep deprivation,50 there is no question that this entire population is sleep deprived. Even on weekends, when cadets sleep more than on schoolnights, these students consistently receive much less sleep than is recommended for their (or any) age group. On average, cadets receive 5 h 3 m of sleep at night and only 5 h 27 m (median = 5 h 23 m) for daily (24 h) sleep on schooldays, significantly less than the 7 h 4 m of average night sleep reported in the Wolfson and Carskadon (1998) survey of sleep in 19 year olds 25 or other related studies.15,19,51 Most of the differences in sleep reported in this study are less than 30 minutes in duration; however, in this constrained environment for these chronically sleep-deprived cadets, 30 minutes represents a substantial discrepancy. A striking finding is that participants in the USMA study receive on average only 1 h 24 m of “catch-up” sleep on weekends, whereas other studies with college students report increased sleep during weekends51 or an additional 2 hours on weekends.19,25 The cadets at USMA are not only more sleep deprived than their civilian peers, but they also obtain less catch-up sleep than is needed during weekends to compensate for their accumulated sleep debt. The potentially negative effects of this sleep restriction include risks to both academic performance and health.27,28,52,53 The increase in daily sleep on weekends compared to school nights was more pronounced in those cadets who slept less during the week. This finding indicated that the greater the sleep debt accumulated during week days, the more pronounced the cadets’ need to compensate for the lost sleep on weekends. A further concern resulting from this study is the future behavior of these military cadets. As part of their collegiate experience, they have been forced to practice poor sleep hygiene which could quite possibly result in lifelong habits that could follow them into their military careers. This possibility is especially worrisome in light of the potential for degraded decision making and impaired judgment by these cadets once they leave the academy and begin active duty military service. Harrison and Horne reviewed the impact of sleep deprivation on various cognitive activities, including decision making.54 While many studies focus on the effects of sleep deprivation on simple and monotonous task performance, realistic tasks such as high-level decision making are commonly experienced by managers and military leaders who work extended hours during times of crises. In their review, they suggest that sleep deprivation may not affect complex rule-based logical task performance—perhaps due to the heightened interest and compensatory efforts by participants. However, they conclude that sleep deprivation does affect decisions that involve creative solutions, dynamic replan-ning, managing competing demands, and complex communication—all critical macro-cognitive activities inherent in military environments. Harrison and Horne discuss the critical role of the prefrontal cortex (PFC) in tasks of this nature and suggest that sleep deprivation “presents particular difficulties for sleepdeprived decision makers who require these latter skills during emergency situations.”54 Recent studies have also linked sleep deprivation and changes in the activity of the PFC, strengthening this argument. Our analysis shows two distinct patterns in USMA cadet sleep: an annual pattern with cadets getting progressively more sleep with each year, and a seasonal pattern with less sleep in spring than in fall semesters. During the last year at USMA, cadets received more daily sleep than during their previous years. However, in this final year, they got less sleep at night but took more naps during the day, probably attempting to compensate, resulting in an overall increase in daily sleep. The study shows that the closer cadets get to graduation, the more sleep they obtain. However, in general, discretionary time also increased with seniority, raising the question about whether the increase in sleep is situational or developmental. We are not able to distinguish between these two potential causes. Nevertheless, the rigid daily schedule at USMA, combined with the significant effect of cadet seniority in daily activities, provide evidence that this increase in sleep can reasonably be situational. As has been reported in this paper, cadets received more nighttime sleep in fall 2004 than fall 2003. There are at least three factors that contributed to this increase. First, there was a mandatory lights out policy for the Corps of Cadets that was implemented in fall 2004. After data from the first year of the longitudinal study were analyzed, the authors presented the results to the Academy's senior leadership (spring 2004). Subsequent discussions considered various courses of action to increase the amount of sleep cadets received. The Superintendent decided to reinstate a mandatory lights out policy at the beginning of the fall 2004 semester. While this decision was less than ideal in the middle of a longitudinal study, the authors had no control over this change in schedule. Second, freshman cadets often have duties they must carry prior to breakfast. These duties require them to awaken early. When they become sophomore cadets they are no longer required to perform these duties. Third, the general trend at the Academy is that, as cadets progress from freshmen to seniors, they have more discretionary time, as well as more privileges and responsibilities. Cadets may use at least some of their increased discretionary time to get more sleep. In line with other findings, we found seasonal differences in the sleep patterns of cadets. Other researchers have reported seasonal differences in sleep with individuals getting significantly more sleep in fall and winter than in spring and summer55,56 This same pattern is evident in the USMA cadets with one notable exception. In their first semester (fall of 2003) cadets got less sleep than in the spring semester. We attribute this anomalous finding to the fact that cadets had just arrived at USMA and were experiencing the stress that often accompanies indoctrination into military education and training regimens. Our study also found significant sex differences in sleep patterns. Males sleep less than females in every condition, whether fall or spring, day of the week, weekend, or school night. One possible explanation for this finding may be social differences between males and females57 or differences in the amount of sleep needed based on the sleep/wake circadian cycle.58 We did not control for menstrual cycle, since this was not the focus of the current study. However, since we collected approximately one month of data for each of the eight semesters of the study, it is certain that menstrual cycles are represented in the variability of the data. Any menstrual cycle effect would be expected to decrease female cadets’ sleep, therefore reducing the difference in sleep found in females as compared to male cadets. Still another possible explanation for the sex differences we observed may be differences in circadian chronotype. Supporting this idea, a recent study by Adan and Natale conducted in a large university population that showed significant differences in circadian typology between males and females (P < 0.0001), with the males presenting a more pronounced evening preference.59 The generalizability of these findings to the rest of the population is limited for a number of reasons. The specific attributes of the military academic environment with its highly structured schedule are in clear contradiction to the latitude found in the daily schedule of typical college life. There is much less variability in the cadet sleep patterns than those of a typical college student because of the rigidity of the cadet daily schedule. In addition, the corps of cadets represents a highly selected population and may not be representative of the general population or of college-age students elsewhere. The participants who chose to remain in the study (and to remain at USMA) represent a self-selected population, in that they may be better able to tolerate sleep deprivation than the general college student population. Finally, the attrition rates (i.e., attrition from the Academy or from the study) are explained by the fact that this is a longitudinal study spanning eight one-month data collection periods over four years. Even though the replacement cadets were demographically matched with those choosing to withdraw, the drop-out rate is always an issue of concern in longitudinal studies. These findings are part of a larger study conducted at USMA, and are focused on the sleep patterns of the cadets. Subsequent articles will address napping patterns, patterns of attrition, and the effect of chronotype and personality traits on academic performance in USMA. This study is the first step towards a validated quantification of sleep patterns in military educational institutions. In light of the enormous challenges facing these military cadets upon graduation, the poor sleep hygiene that they have practiced during their four years in school fosters extremely bad habits that may carry over to the workplace. Our findings clearly demonstrate the significant sleep deprivation experienced by cadets, with all the negative consequences that this result implies. Acknowledgements We would like to acknowledge the support provided by the senior leadership of the United States Military Academy, West Point, NY. Without their permission and unflagging encouragement, this study would never have been completed. We received funding for the study from the Program Executive Office - Soldier (PEO-Soldier) of the U.S. Army. Dr. Mary Carskadon and her colleagues in the Providence Sleep Research Interest Group (PSRIG) offered invaluable insights in the design and implementation of the study. Enthusiastic graduate students from the Naval Postgraduate School assisted in various analyses. Lastly, we acknowledge the selfless service of the Cadets of the Class of 2007 from the United States Military Academy, many of whom are currently serving their country both here and abroad. Location of Work: Naval Postgraduate School, Monterey, CA and United States Military Academy, West Point, NY References 1. Carskadon MA , Vieira C , Acebo C . 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Troxel, Wendy, M.;Buysse, Daniel, J.;Matthews, Karen, A.;Kip, Kevin, E.;Strollo, Patrick, J.;Hall,, Martica;Drumheller,, Oliver;Reis, Steven, E.
doi: 10.1093/sleep.33.12.1633pmid: N/A
Abstract Background: Sleep complaints are highly prevalent and associated with cardiovascular disease (CVD) morbidity and mortality. This is the first prospective study to report the association between commonly reported sleep symptoms and the development of the metabolic syndrome, a key CVD risk factor. Methods: Participants were from the community-based Heart Strategies Concentrating on Risk Evaluation study. The sample was comprised of 812 participants (36% African American; 67% female) who were free of metabolic syndrome at baseline, had completed a baseline sleep questionnaire, and had metabolic syndrome evaluated 3 years after baseline. Apnea-hypopnea index (AHI) was measured cross-sectionally using a portable monitor in a subset of 290 participants. Logistic regression examined the risk of developing metabolic syndrome and its components according to individual sleep symptoms and insomnia syndrome. Results: Specific symptoms of insomnia (difficulty falling asleep [DFA] and “unrefreshing” sleep), but not a syndromal definition of insomnia, were significant predictors of the development of metabolic syndrome. Loud snoring more than doubled the risk of developing the metabolic syndrome and also predicted specific metabolic abnormalities (hyperglycemia and low high-density lipoprotein cholesterol). With further adjustment for AHI or the number of metabolic abnormalities at baseline, loud snoring remained a significant predictor of metabolic syndrome, whereas DFA and unrefreshing sleep were reduced to marginal significance. Conclusion: Difficulty falling asleep, unrefreshing sleep, and, particularly, loud snoring, predicted the development of metabolic syndrome in community adults. Evaluating sleep symptoms can help identify individuals at risk for developing metabolic syndrome. Metabolic syndrome, sleep-disordered breathing, insomnia, cardiovascular risk THE CLUSTERING OF CARDIOVASCULAR RISK FACTORS KNOWN AS THE METABOLIC SYNDROME IS STRONGLY AND PROSPECTIVELY LINKED WITH incident cardiovascular events, diabetes, and mortality.1 Given that prevalence rates of the metabolic syndrome are estimated at 20% in the adult population,2 identifying modifiable risk factors associated with the development of the metabolic syndrome is of critical public health importance. Several prospective studies have documented an independent relationship between sleep disturbances, including sleep disordered breathing (SDB) and sleep duration, and increased risk of developing individual components of the metabolic syndrome, including obesity, hypertension, glucose intolerance, and diabetes.3,–8 Additionally, a handful of cross-sectional studies have shown that a broader range of self-reported sleep disturbances, including snoring, sleep duration, difficulty initiating and maintaining sleep, and poor sleep quality, as well as polysomnographically assessed sleep architecture are associated with prevalent metabolic syndrome.9,–13 However, to our knowledge, no study to date has prospectively examined the relationship between sleep disturbances that commonly present in clinical practice and the development of the metabolic syndrome. Given evidence that there may be added prognostic value of the metabolic syndrome, over and above its individual components, and the inability of cross-sectional studies to support the proposed direction of causality between sleep disturbances and metabolic dysregulation, prospective evidence is clearly needed to examine whether sleep disturbances predict the development of the metabolic syndrome. Most epidemiologic studies of sleep and CVD risk have examined sleep symptoms rather than sleep disorders identified by diagnostic criteria, such as insomnia or obstructive sleep apnea syndrome (OSAS), which are the two most common adult sleep disorders.14 Understanding the relative impact of sleep symptoms versus sleep disorders may have important public health implications given that symptoms are much more prevalent than disorders,14 and the putative mechanisms linking sleep with cardiometabolic consequences may differ for symptoms and disorders. Moreover, there is considerable overlap in SDB and insomnia symptoms. Thus, to better understand the pathophysiology underlying links between sleep symptoms and cardiometabolic risk, it is critical to examine the independent effects of respective insomnia and SDB symptoms as well as more formally defined disorders. The present study investigated the degree to which insomnia or SDB predicted the development of the metabolic syndrome and its component factors (hyperglycemia, central adiposity, hypertension, hypertriglyceridemia, and low high density lipoprotein cholesterol) over a 3-year period in a community sample. We used two different case definitions for both insomnia and SDB. For insomnia, we examined individual insomnia symptoms as well as insomnia syndrome, which included a sleep complaint along with reported daytime impairment. For SDB, we examined whether snoring, the most common symptom of OSA, predicted the development of the metabolic syndrome, independent of apnea-hypopnea index, a physiological indicator of OSA. Given racial and gender differences in the prevalence of sleep disorders and the metabolic syndrome, for significant effects, we explored whether relationships between sleep symptoms and the metabolic syndrome differed among men and women and among blacks and whites. Methods Study Overview and Population Participants were recruited from Heart SCORE, an ongoing, community-based prospective study (N = 2000) designed to examine the differential effects of race and gender on cardiovascular risk. Subject eligibility criteria for Heart SCORE included age 45 to 74 years, residence in the greater Pittsburgh metropolitan area, ability to undergo baseline and annual follow-up visits, and absence of known comorbidity expected to limit life expectancy to less than 5 years. For the present study, we further excluded a small number of participants (n = 67) who self-identified as race other than black or white (due to our interest in examining racial differences in the effects), those who did not have the metabolic syndrome evaluated at baseline (n = 86), those who were classified as having the metabolic syndrome or diabetes at baseline (n = 598), or those who were missing sleep or covariate data at baseline or metabolic syndrome at follow-up (n = 437). Thus, the final sample consisted of 812 participants who met study eligibility criteria and had key study variables assessed. Follow-up analyses which additionally adjusted for apnea-hypopnea index (AHI) were conducted in a subset of 294 participants who had volunteered to participate in the home apnea screening protocol at their annual visit, were not currently being treated for OSA, did not self-report a diagnosis of OSA, and had other key study variables available. In addition, n = 4 individuals were excluded due to having AHIs > 50 (> 3 SDs from the mean). As compared to those who had AHI data available, those who did not have AHI evaluated were older and less likely to self-report frequent loud snoring during sleep (P values < 0.05). All subjects provided written informed consent approved by the Institutional Review Board of the University of Pittsburgh. Data Collection Metabolic syndrome At the baseline and subsequent annual visits, a 12-h fasting blood draw was collected and anthropometric measures were taken. At baseline, assays were performed using standard techniques in the clinical laboratory of the University of Pittsburgh Medical Center except for plasma lipids and lipoprotein sub-fractions which were quantified by a commercial laboratory using a vertical auto profile (VAP, Atherotech, Birmingham, AL). During annual follow-up visits, measurements were taken of fasting glucose and lipids (HDL, total cholesterol, and triglycerides, with calculation of LDL) using standard clinical laboratory techniques (Cholesetch LDX System). Waist circumference was measured using a measuring tape at the narrowest point between the iliac crest and the lowest rib. The primary outcome was the presence or absence of the metabolic syndrome at the 3rd year follow-up visit, as quantified by the National Cholesterol Education Program's (NCEP) Adult Treatment Panel III report (ATP III) guidelines.15 Specifically, the syndrome is defined by presence of ≥ 3 of the following risk factors: fasting glucose ≥ 110 mg/dL, triglycerides ≥ 150 mg/dL, HDL-C < 40 mg/dL (for males) or < 50 mg/dL (for females), waist circumference > 102 cm (for males) or > 88 cm (for females), systolic blood pressure (SBP) ≥ 130 mm Hg, or diastolic blood pressure ≥ 85 mm Hg. If participants had blood pressure or glucose values within the normal range but were taking antihypertensive or glucose-lowering medications, they were classified as meeting criteria for blood pressure or glucose abnormalities, respectively. Sleep symptoms/insomnia syndrome Sleep disturbances related to insomnia and SDB were assessed via the Insomnia Symptom Questionnaire (ISQ) and the Multivariable Apnea Prediction Questionnaire (MAP).16 The ISQ is a self-report instrument designed to screen for symptoms experienced in the past month associated with the diagnostic criteria for primary insomnia. The ISQ has recently been validated17 using classical test theory and item response theory (IRT). In addition, to assessing specific sleep symptoms, the ISQ includes additional criteria related to daytime impairments consistent with diagnostic criteria for insomnia (e.g., “Have your sleep difficulties affected your work? Social life?”). The presence of insomnia syndrome was coded as 1 for individuals endorsing at least one insomnia-related sleep complaint (difficulty falling asleep, difficulty staying asleep, frequent awakenings, feeling sleep is not sound, or feeling sleep is un-refreshing), with frequency criteria ≥ 3 times per week and the endorsement of at least one symptom of daytime impairment, rated as moderate to extremely severe. The SDB symptoms (e.g., loud snoring, choking, or gasping during sleep) were derived from the MAP, which has previously been validated and shown good psychometric properties.16 Items on the ISQ and MAP were re-coded such that individuals who endorsed the symptom ≥ 3 times per week were coded as 1 and others coded as 0. Covariates Detailed demographic and medical histories were collected at the baseline visit. In addition, lifestyle characteristics including smoking history (current or former smoker versus never a smoker), and alcohol consumption (> 4 drinks per week versus ≤ 4 drinks per week) were measured by study-specific questionnaires. Physical activity was assessed by the Lipids Research Clinics Questionnaire18 and analyzed as a categorical variable (sedentary versus non-sedentary lifestyle). Depressive symptoms were assessed using the Center for Epidemiological Studies Depression Scale19 (coded as a binary outcome using the clinical cutoff ≥ 16 to indicate the presence of clinically significant depressive symptoms). Nested within the cohort study was an intervention study that randomly assigned subjects to receive in the initial year a usual care (“advice only”) regimen or a behavioral modification intervention to reduce CVD risk. Study randomization assignment (usual care or behavioral modification within 1 year), was also included as a covariate in all models; however, analyses demonstrated that randomization was unrelated to either sleep symptoms or metabolic syndrome incidence. Apnea-hypopnea index (AHI) AHI was evaluated in a subset of 294 participants using a previously validated portable monitor that measures airflow and snoring via a nasal pressure signal20 (ApneaLink, ResMed Corp). An apnea was defined as a decrease in airflow of ≥ 80% from baseline for ≥ 10 sec. A hypopnea was defined as a decrease in airflow for ≥ 30% but < 80% from baseline for ≥ 10 sec. AHI was evaluated for participants who had ≥ 4 h of data available from the portable monitor and was analyzed as a continuous covariate, with 4 extreme outliers (> 3 standard deviations from the mean) removed for a final N of 290. Portable monitoring was added to the HeartSCORE protocol following the baseline assessment, and was collected throughout the first 3 years of the study. Thus, AHI was included in secondary analyses as a covariate to examine the independent contribution of self-reported SDB and insomnia symptoms (assessed at baseline) and development of the metabolic syndrome. Given the inability to establish temporal precedence of AHI prior to the metabolic syndrome assessment, AHI was considered only as a cross-sectional covariate and not as a prospective risk factor. Statistical Methods Differences in baseline demographic, psychosocial, lifestyle characteristics, and sleep measures were compared between those who developed the metabolic syndrome over the follow-up period versus those who did not develop the metabolic syndrome by use of χ2 tests for categorical variables and student t-tests for continuous variables. Multivariate logistic regression was used to examine the relationship between individual sleep symptoms or insomnia syndrome and risk of developing the metabolic syndrome, with adjustment for covariates including age, sex, race (white/ black), marital status (married or partnered/ unmarried or unpartnered), study randomization assignment, smoking history, physical activity, alcohol consumption, and depressive symptoms. To reduce the number of statistical comparisons, relationships among sleep symptoms and individual components of the metabolic syndrome were restricted to the sleep symptoms which predicted the metabolic syndrome. For each metabolic syndrome component, we excluded participants who met criteria for that component at baseline. We also evaluated whether observed relationships between sleep symptoms and metabolic syndrome risk differed among blacks and whites or among men and women, by entering the race and gender interaction terms in separate logistic regression models. To determine the relative contribution of SDB or insomnia symptoms to the prediction of metabolic syndrome, follow-up logistic regression models simultaneously entered insomnia or SDB-related symptoms that were related to the metabolic syndrome and also examined whether relationships persisted after further adjustment for AHI (in the subset with AHI available). Finally, for significant effects of sleep symptoms on the development of the metabolic syndrome, we examined the results of models that statistically adjusted for the number of baseline metabolic abnormalities. Results Table 1 presents the frequency of metabolic abnormalities at baseline and at 3-year follow-up. The frequency of meeting criteria for individual metabolic syndrome components increased from baseline to follow-up for all components. Over the 3-year follow-up period, 14% of the sample (n = 115) developed the metabolic syndrome. As shown in Table 2, African Americans and participants endorsing a sedentary lifestyle were more likely to develop the metabolic syndrome over the 3 years. Of the sleep symptoms, difficulty falling asleep (DFA), unrefreshing sleep, and loud snoring significantly predicted the development of the metabolic syndrome at follow-up in unadjusted models (Table 3). Insomnia syndrome was not related to incident metabolic syndrome in unadjusted models. AHI was significantly associated with development of the metabolic syndrome. Table 1 Frequency of metabolic abnormalities at baseline and 3-year follow-up (N = 812) Metabclie Syndrome Abrormalities Baseline 3-year follow-up %(n) %(n) BP abnormal 52 (420) 60 (489) HDL abnormal 9(69) 18(145) Triglycerides abnormal 10(78) 15(118) Waist circumference abnormal 36 (295) 40 (325) Glucose abnormal 3(28) 9(71) Count of Metabolic Abnormalities %(n) %(n) None 27(217) 21 (170) 1 metabolic abnormality 37 (300) 35 (283) 2 metabolic abnormalities 36 (295) 30 (224) 3 or more metabolic abnormalities - 14(115) Metabclie Syndrome Abrormalities Baseline 3-year follow-up %(n) %(n) BP abnormal 52 (420) 60 (489) HDL abnormal 9(69) 18(145) Triglycerides abnormal 10(78) 15(118) Waist circumference abnormal 36 (295) 40 (325) Glucose abnormal 3(28) 9(71) Count of Metabolic Abnormalities %(n) %(n) None 27(217) 21 (170) 1 metabolic abnormality 37 (300) 35 (283) 2 metabolic abnormalities 36 (295) 30 (224) 3 or more metabolic abnormalities - 14(115) Open in new tab Table 1 Frequency of metabolic abnormalities at baseline and 3-year follow-up (N = 812) Metabclie Syndrome Abrormalities Baseline 3-year follow-up %(n) %(n) BP abnormal 52 (420) 60 (489) HDL abnormal 9(69) 18(145) Triglycerides abnormal 10(78) 15(118) Waist circumference abnormal 36 (295) 40 (325) Glucose abnormal 3(28) 9(71) Count of Metabolic Abnormalities %(n) %(n) None 27(217) 21 (170) 1 metabolic abnormality 37 (300) 35 (283) 2 metabolic abnormalities 36 (295) 30 (224) 3 or more metabolic abnormalities - 14(115) Metabclie Syndrome Abrormalities Baseline 3-year follow-up %(n) %(n) BP abnormal 52 (420) 60 (489) HDL abnormal 9(69) 18(145) Triglycerides abnormal 10(78) 15(118) Waist circumference abnormal 36 (295) 40 (325) Glucose abnormal 3(28) 9(71) Count of Metabolic Abnormalities %(n) %(n) None 27(217) 21 (170) 1 metabolic abnormality 37 (300) 35 (283) 2 metabolic abnormalities 36 (295) 30 (224) 3 or more metabolic abnormalities - 14(115) Open in new tab Table 2 Sample characteristics according to the presence or absence of the metabolic syndrome at 3-year follow-up (N = 812) Metabolic Syndrome Absent (86%) % (n) Present (14%) %(n) P-value Gender 0.92 Male 86 (227) 14 (38) Female 86 (470) 14 (77) Race 0.06 African American 83 (244) 17(51) Caucasian 88 (453) 12(64) Married/ Partnered 0.92 Yes 86 (454) 14 (76) No 86 (241) 14 (39) Randomized to intervention 0.65 Yes 85 (82) 16(15) No 86(615) 14(100) Current/ past smoker 86 (635) 14(103) 0.60 Consumed ≥ 4 alcoholic drinks/week 87(101) 13(15) 0.77 Sedentary lifestyle 80 (214) 20 (55) 0.00 Depression (CESD ≥ 16) 84 (82) 16(16) 0.54 Mean (SD) Mean (SD) Age 59 (7.51) 59 (6.84) 0.76 Metabolic Syndrome Absent (86%) % (n) Present (14%) %(n) P-value Gender 0.92 Male 86 (227) 14 (38) Female 86 (470) 14 (77) Race 0.06 African American 83 (244) 17(51) Caucasian 88 (453) 12(64) Married/ Partnered 0.92 Yes 86 (454) 14 (76) No 86 (241) 14 (39) Randomized to intervention 0.65 Yes 85 (82) 16(15) No 86(615) 14(100) Current/ past smoker 86 (635) 14(103) 0.60 Consumed ≥ 4 alcoholic drinks/week 87(101) 13(15) 0.77 Sedentary lifestyle 80 (214) 20 (55) 0.00 Depression (CESD ≥ 16) 84 (82) 16(16) 0.54 Mean (SD) Mean (SD) Age 59 (7.51) 59 (6.84) 0.76 Sample restricted to those who were free of the metabolic syndrome at baseline. Open in new tab Table 2 Sample characteristics according to the presence or absence of the metabolic syndrome at 3-year follow-up (N = 812) Metabolic Syndrome Absent (86%) % (n) Present (14%) %(n) P-value Gender 0.92 Male 86 (227) 14 (38) Female 86 (470) 14 (77) Race 0.06 African American 83 (244) 17(51) Caucasian 88 (453) 12(64) Married/ Partnered 0.92 Yes 86 (454) 14 (76) No 86 (241) 14 (39) Randomized to intervention 0.65 Yes 85 (82) 16(15) No 86(615) 14(100) Current/ past smoker 86 (635) 14(103) 0.60 Consumed ≥ 4 alcoholic drinks/week 87(101) 13(15) 0.77 Sedentary lifestyle 80 (214) 20 (55) 0.00 Depression (CESD ≥ 16) 84 (82) 16(16) 0.54 Mean (SD) Mean (SD) Age 59 (7.51) 59 (6.84) 0.76 Metabolic Syndrome Absent (86%) % (n) Present (14%) %(n) P-value Gender 0.92 Male 86 (227) 14 (38) Female 86 (470) 14 (77) Race 0.06 African American 83 (244) 17(51) Caucasian 88 (453) 12(64) Married/ Partnered 0.92 Yes 86 (454) 14 (76) No 86 (241) 14 (39) Randomized to intervention 0.65 Yes 85 (82) 16(15) No 86(615) 14(100) Current/ past smoker 86 (635) 14(103) 0.60 Consumed ≥ 4 alcoholic drinks/week 87(101) 13(15) 0.77 Sedentary lifestyle 80 (214) 20 (55) 0.00 Depression (CESD ≥ 16) 84 (82) 16(16) 0.54 Mean (SD) Mean (SD) Age 59 (7.51) 59 (6.84) 0.76 Sample restricted to those who were free of the metabolic syndrome at baseline. Open in new tab Table 3 Sleep characteristics according to the presence or absence of the metabolic syndrome at 3-year follow-up (N = 812) Metabolic Syndrome Absent (86%) %(n) Present (14%) %(n) P-value Difficulty falling asleep 0.01 Yes 78 (87) 22 (25) No 87(610) 13(90) Difficulty staying sleep 0.82 Yes 85(186) 15(32) No 86(510) 14 (83) Frequent awaking from sleep 0.36 Yes 84(271) 16(50) No 87 (425) 13(65) Sleep is not sound 0.09 Yes 82 (148) 18(33) No 87 (549) 13(82) Sleep is unrefreshing 0.02 Yes 80(128) 20 (32) No 87 (569) 13(83) Snoring/gasping during sleep 0.23 Yes 82 (85) 18(19) No 86(612) 14 (96) Loud snoring 0.001 Yes 75 (73) 26 (25) No 88 (620) 12(88) Stop breathing/choking during sleep 0.12 Yes 74(17) 26(6) No 86 (678) 14(109) Insomnia syndrome 0.17 Yes 81 (63) 19(15) No 86 (634) 14(100) Mean (SD) Mean (SD) AHI (n = 290) 9.0 (8.55) 12.67(9.06) 0.01 Metabolic Syndrome Absent (86%) %(n) Present (14%) %(n) P-value Difficulty falling asleep 0.01 Yes 78 (87) 22 (25) No 87(610) 13(90) Difficulty staying sleep 0.82 Yes 85(186) 15(32) No 86(510) 14 (83) Frequent awaking from sleep 0.36 Yes 84(271) 16(50) No 87 (425) 13(65) Sleep is not sound 0.09 Yes 82 (148) 18(33) No 87 (549) 13(82) Sleep is unrefreshing 0.02 Yes 80(128) 20 (32) No 87 (569) 13(83) Snoring/gasping during sleep 0.23 Yes 82 (85) 18(19) No 86(612) 14 (96) Loud snoring 0.001 Yes 75 (73) 26 (25) No 88 (620) 12(88) Stop breathing/choking during sleep 0.12 Yes 74(17) 26(6) No 86 (678) 14(109) Insomnia syndrome 0.17 Yes 81 (63) 19(15) No 86 (634) 14(100) Mean (SD) Mean (SD) AHI (n = 290) 9.0 (8.55) 12.67(9.06) 0.01 Sample restricted to those who were free of the metabolic syndrome at baseline. Unless otherwise noted, values represent n (%) in each metabolic syndrome category for those who endorsed the sleep symptom “frequently or always” or who met the defined criteria for insomnia syndrome. AHI refers to apnea hypopnea index. Open in new tab Table 3 Sleep characteristics according to the presence or absence of the metabolic syndrome at 3-year follow-up (N = 812) Metabolic Syndrome Absent (86%) %(n) Present (14%) %(n) P-value Difficulty falling asleep 0.01 Yes 78 (87) 22 (25) No 87(610) 13(90) Difficulty staying sleep 0.82 Yes 85(186) 15(32) No 86(510) 14 (83) Frequent awaking from sleep 0.36 Yes 84(271) 16(50) No 87 (425) 13(65) Sleep is not sound 0.09 Yes 82 (148) 18(33) No 87 (549) 13(82) Sleep is unrefreshing 0.02 Yes 80(128) 20 (32) No 87 (569) 13(83) Snoring/gasping during sleep 0.23 Yes 82 (85) 18(19) No 86(612) 14 (96) Loud snoring 0.001 Yes 75 (73) 26 (25) No 88 (620) 12(88) Stop breathing/choking during sleep 0.12 Yes 74(17) 26(6) No 86 (678) 14(109) Insomnia syndrome 0.17 Yes 81 (63) 19(15) No 86 (634) 14(100) Mean (SD) Mean (SD) AHI (n = 290) 9.0 (8.55) 12.67(9.06) 0.01 Metabolic Syndrome Absent (86%) %(n) Present (14%) %(n) P-value Difficulty falling asleep 0.01 Yes 78 (87) 22 (25) No 87(610) 13(90) Difficulty staying sleep 0.82 Yes 85(186) 15(32) No 86(510) 14 (83) Frequent awaking from sleep 0.36 Yes 84(271) 16(50) No 87 (425) 13(65) Sleep is not sound 0.09 Yes 82 (148) 18(33) No 87 (549) 13(82) Sleep is unrefreshing 0.02 Yes 80(128) 20 (32) No 87 (569) 13(83) Snoring/gasping during sleep 0.23 Yes 82 (85) 18(19) No 86(612) 14 (96) Loud snoring 0.001 Yes 75 (73) 26 (25) No 88 (620) 12(88) Stop breathing/choking during sleep 0.12 Yes 74(17) 26(6) No 86 (678) 14(109) Insomnia syndrome 0.17 Yes 81 (63) 19(15) No 86 (634) 14(100) Mean (SD) Mean (SD) AHI (n = 290) 9.0 (8.55) 12.67(9.06) 0.01 Sample restricted to those who were free of the metabolic syndrome at baseline. Unless otherwise noted, values represent n (%) in each metabolic syndrome category for those who endorsed the sleep symptom “frequently or always” or who met the defined criteria for insomnia syndrome. AHI refers to apnea hypopnea index. Open in new tab In the multivariate logistic regression models (Figure 1) which entered each symptom or insomnia syndrome in separate models, DFA, unrefreshing sleep, and loud snoring significantly predicted metabolic syndrome incidence (P < 0.05). There were no significant race or gender interactions for either DFA or loud snoring on metabolic syndrome incidence (analyses not shown). The remaining sleep symptoms and the syndromal definition of insomnia were unrelated to the development of the metabolic syndrome; however, all estimates were in the direction of being associated with higher risk of developing the metabolic syndrome. Analyses of the individual metabolic syndrome components revealed that loud snoring was a significant predictor of hyperglycemia and low HDL (Table 4); however, DFA and unrefreshing sleep did not predict any of the individual metabolic abnormalities. Figure 1 Open in new tabDownload slide Odds of developing the metabolic syndrome according to sleep symptoms with covariate adjustment. Filled diamonds depict adjusted odds ratios of developing the metabolic syndrome; filled circles depict lower and upper 95% confidence limits. Odds ratios are adjusted for age, sex, race (white/black), marital status (married/unmarried), study randomization, smoking status (ever/never), alcohol consumption (0-3 drinks per week/4 or more drinks per week), sedentary lifestyle (yes/no), and presence of clinically significant depressive symptoms (yes/no). Figure 1 Open in new tabDownload slide Odds of developing the metabolic syndrome according to sleep symptoms with covariate adjustment. Filled diamonds depict adjusted odds ratios of developing the metabolic syndrome; filled circles depict lower and upper 95% confidence limits. Odds ratios are adjusted for age, sex, race (white/black), marital status (married/unmarried), study randomization, smoking status (ever/never), alcohol consumption (0-3 drinks per week/4 or more drinks per week), sedentary lifestyle (yes/no), and presence of clinically significant depressive symptoms (yes/no). Table 4 Relationship between sleep symptoms and individual components of the metabolic syndrome Meta bolic Syndrome Components Central adiposity criterion OR (95% CI) Glucose criterion OR (95% CI) Blood pressure criterion OR (95% CI) Triglycerides criterion OR (95% CI) HDL criterion OR (95% CI) N^ = 517 N^ = 782 N^ = 391 N^ = 514 N^ = 742 Difficulty falling asleep 1.00(0.49–2.04) 1.00(0.45–2.22) 1.25(0.64–2.43) 1.25(0.52–3.01) 1.11(0.59–2.09) Unrefreshing sleep 1.01(0.53–1.93) 1.66(0.88–3.10) 1.39(0.78–2.48) 0.77(0.41–1.44) 1.29(0.76–2.17) Loud snoring 0.80(0.32–1.98) 2.15(1.09–4.24) 0.64(0.30–1.39) 2.11 (0.85–5.26) 1.92(1.06–3.48) Meta bolic Syndrome Components Central adiposity criterion OR (95% CI) Glucose criterion OR (95% CI) Blood pressure criterion OR (95% CI) Triglycerides criterion OR (95% CI) HDL criterion OR (95% CI) N^ = 517 N^ = 782 N^ = 391 N^ = 514 N^ = 742 Difficulty falling asleep 1.00(0.49–2.04) 1.00(0.45–2.22) 1.25(0.64–2.43) 1.25(0.52–3.01) 1.11(0.59–2.09) Unrefreshing sleep 1.01(0.53–1.93) 1.66(0.88–3.10) 1.39(0.78–2.48) 0.77(0.41–1.44) 1.29(0.76–2.17) Loud snoring 0.80(0.32–1.98) 2.15(1.09–4.24) 0.64(0.30–1.39) 2.11 (0.85–5.26) 1.92(1.06–3.48) ^For each metabolic syndrome component, analyses are restricted to those who did not meet criteria for that abnormality at baseline. Covariates include: age, sex, race (white/black), marital status (married/unmarried), study randomization, smoking status (ever/never), alcohol consumption (0-3 drinks per week/4 or more drinks per week), sedentary lifestyle (yes/no), and presence of clinically significant depressive symptoms (yes/no). Open in new tab Table 4 Relationship between sleep symptoms and individual components of the metabolic syndrome Meta bolic Syndrome Components Central adiposity criterion OR (95% CI) Glucose criterion OR (95% CI) Blood pressure criterion OR (95% CI) Triglycerides criterion OR (95% CI) HDL criterion OR (95% CI) N^ = 517 N^ = 782 N^ = 391 N^ = 514 N^ = 742 Difficulty falling asleep 1.00(0.49–2.04) 1.00(0.45–2.22) 1.25(0.64–2.43) 1.25(0.52–3.01) 1.11(0.59–2.09) Unrefreshing sleep 1.01(0.53–1.93) 1.66(0.88–3.10) 1.39(0.78–2.48) 0.77(0.41–1.44) 1.29(0.76–2.17) Loud snoring 0.80(0.32–1.98) 2.15(1.09–4.24) 0.64(0.30–1.39) 2.11 (0.85–5.26) 1.92(1.06–3.48) Meta bolic Syndrome Components Central adiposity criterion OR (95% CI) Glucose criterion OR (95% CI) Blood pressure criterion OR (95% CI) Triglycerides criterion OR (95% CI) HDL criterion OR (95% CI) N^ = 517 N^ = 782 N^ = 391 N^ = 514 N^ = 742 Difficulty falling asleep 1.00(0.49–2.04) 1.00(0.45–2.22) 1.25(0.64–2.43) 1.25(0.52–3.01) 1.11(0.59–2.09) Unrefreshing sleep 1.01(0.53–1.93) 1.66(0.88–3.10) 1.39(0.78–2.48) 0.77(0.41–1.44) 1.29(0.76–2.17) Loud snoring 0.80(0.32–1.98) 2.15(1.09–4.24) 0.64(0.30–1.39) 2.11 (0.85–5.26) 1.92(1.06–3.48) ^For each metabolic syndrome component, analyses are restricted to those who did not meet criteria for that abnormality at baseline. Covariates include: age, sex, race (white/black), marital status (married/unmarried), study randomization, smoking status (ever/never), alcohol consumption (0-3 drinks per week/4 or more drinks per week), sedentary lifestyle (yes/no), and presence of clinically significant depressive symptoms (yes/no). Open in new tab To examine the independent contributions of insomnia versus SDB-related symptoms, the next set of logistic models simultaneously entered DFA or unrefreshing sleep with loud snoring. As shown in Figure 2, Model 1, both DFA and loud snoring remained significant independent predictors of the metabolic syndrome (OR = 1.78; CI: 1.05, 3.02 and OR = 2.23; CI: 1.30, 3.82). Unrefreshing sleep was reduced to marginal significance (Model 2; OR = 1.56; CI: 0.96, 2.53) with additional adjustment for loud snoring. Figure 2 Open in new tabDownload slide Effect of insomnia symptoms and loud snoring on metabolic syndrome incidence, with and without adjustment for AHI. Filled diamonds depict adjusted odds ratios of developing the metabolic syndrome; filled circles depict lower and upper 95% confidence limits. Odds ratios are adjusted for age, sex, race (white/black), marital status (married/unmarried), smoking status (ever/never), alcohol consumption (0-3 drinks per week/4 or more drinks per week), sedentary lifestyle (yes/no), and presence of clinically significant depressive symptoms (yes/no). AHI refers to Apnea Hypopnea Index (continuous, with n = 4 outliers removed). N for Models 1 and 2 = 805 due to missing data. N for Model 3 = 290 (subsample with AHI). Figure 2 Open in new tabDownload slide Effect of insomnia symptoms and loud snoring on metabolic syndrome incidence, with and without adjustment for AHI. Filled diamonds depict adjusted odds ratios of developing the metabolic syndrome; filled circles depict lower and upper 95% confidence limits. Odds ratios are adjusted for age, sex, race (white/black), marital status (married/unmarried), smoking status (ever/never), alcohol consumption (0-3 drinks per week/4 or more drinks per week), sedentary lifestyle (yes/no), and presence of clinically significant depressive symptoms (yes/no). AHI refers to Apnea Hypopnea Index (continuous, with n = 4 outliers removed). N for Models 1 and 2 = 805 due to missing data. N for Model 3 = 290 (subsample with AHI). To examine the independent effects of DFA and loud snoring on the prediction of metabolic syndrome incidence, over and above the influence of AHI, a logistic regression model entered DFA, loud snoring, and AHI simultaneously, in addition to demographic, psychosocial, and lifestyle risk factors (Figure 2, Model 2). Once again, loud snoring remained an independent predictor of the metabolic syndrome incidence (OR = 3.01; CI: 1.39, 6.55), whereas DFA was reduced to marginal statistical significance (OR = 1.91; CI: 0.80, 4.58). AHI was also significantly associated with the metabolic syndrome in the subsample who had apnea link data (OR per 5 units =1.23; 1.02, 1.47). Finally, to examine whether DFA, unrefreshing sleep, or loud snoring predicted the development of the metabolic syndrome over and above baseline metabolic abnormalities, we conducted logistic regression models that statistically adjusted for the number of metabolic abnormalities present at baseline (0, 1, or 2). With adjustment for baseline metabolic abnormalities, the effects of DFA and unrefreshing sleep became marginally significant (OR = 1.64, CI: 0.95, 2.82 and OR = 1.54; CI: 0.94, 2.52, respectively), whereas the effect of loud snoring remained statistically significant (OR = 1.78, CI: 1.02,3.12). Discussion Previous research has shown that self-reported sleep duration and sleep-disordered breathing are prospectively linked with the development of individual components of the metabolic syndrome, including diabetes or glucose intolerance, hypertension, and obesity.3,–8 To our knowledge, this is the first prospective study to evaluate the broader range of sleep symptoms commonly presented in clinical practice in relation to the development of the metabolic syndrome over a 3-year follow-up period. The endorsement of frequent loud snoring more than doubled the risk of developing the metabolic syndrome, and the endorsement of difficulty falling asleep increased the risk by 80%, even after adjusting for demographic, psychosocial, and lifestyle characteristics. These effects were similar among men and women and among whites and blacks in our community sample of middle-aged adults. Finally, our findings showed that in a population selected to be free of the metabolic syndrome at baseline, loud snoring, but not DFA or unrefreshing sleep, predicted the development of the metabolic syndrome even after accounting for the number of metabolic abnormalities present at baseline. This finding raises the possibility that loud snoring may in fact be a causal risk factor for cardiometabolic dysregulation. Consistent with evidence suggesting that loud snoring may be implicated in the pathophysiology of CVD independent of its relationship with AHI,12,21 we found that self-reported loud snoring was associated with a 3 -fold risk of developing the metabolic syndrome even after adjusting for DFA and AHI in our sub sample with AHI evaluated. Moreover, our findings linking loud snoring with the development of hyperglycemia and HDL abnormalities, but not with the blood pressure criterion of the metabolic syndrome, suggests the possibility of alternative pathways linking loud snoring with metabolic dysregulation, other than that due to sympathetic activation resulting from hypoxia. In particular, Hedner et al.22 hypothesized that snoring-related vibrations may directly lead to atherogenesis via damage to the endothelial wall and subsequent triggering of the inflammatory cascade. Consistent with this hypothesis, in humans, objectively measured snoring in the laboratory was associated with a more than 10-fold increased risk of having carotid atherosclerosis, even after accounting for AHI and nocturnal hypoxemia.21 Snoring-related sleep fragmentation may also have a direct adverse impact on cardiometabolic profiles, by leading to weight gain, perhaps consequent to Cortisol up-regulation23 and physical inactivity.24 Alternatively, given limitations of the AHI assessment in the current study (smaller sample size, use of portable monitoring), loud snoring may have provided a more reliable measure of chronic exposure of SDB than one night of airflow monitoring. Previous findings that have related sleep disturbances, such as inadequate sleep or difficulty initiating or maintaining sleep, to cardiometabolic risk factors or diabetes, are often interpreted as evidence of the physical health consequences of insomnia. These interpretations of sleep symptoms rather than the associated syndrome, per se, belie the reality that the evidence base linking insomnia with cardiovascular morbidity and mortality is far from conclusive.25 Indeed, our data suggest that DFA and unrefreshing sleep, which may be isolated sleep disturbances, or symptoms related to any one of several sleep disorders, such as insomnia, obstructive sleep apnea, or restless legs syndrome, are risk factors for the development of the metabolic syndrome; however, the syn-dromal definition of insomnia was unrelated to the metabolic syndrome. The symptom of DFA but not the symptom of unrefreshing sleep was an independent predictor of the metabolic syndrome even after accounting for the influence of loud snoring. This finding may reflect greater shared variance between the experience of unrefreshing sleep and loud snoring as compared to DFA and loud snoring. Indeed, there was a significant overlap in reporting the symptoms of unrefreshing sleep and loud snoring (x2 = 10.24, P < 0.01), but there was not a significant overlap between reporting DFA and loud snoring (x2 = 1.86, P > 0.10). Thus, sleep initiation problems may reflect a more “pure” measure of the core dysregulation of insomnia, whereas unrefreshing sleep may represent a more general symptom of sleep disturbance that is common to both insomnia and SDB. Previous evidence has linked difficulty falling asleep with diabetes incidence7 and with mortality.28 Difficulty falling asleep is less contaminated with the expected age-related increase in wakefulness during the night.29 Prolonged sleep latency may reflect a state of emotional and physiological hyperarousal, which has been linked with heightened sympathetic activation and hypercortisolemia23—both of which have been implicated in the pathophysiology of insulin resistance and metabolic syndrome. Evidence suggests that experimentally induced sleep curtailment at the beginning of the night is associated with activation of a transcription factor (NF)-kB that serves a critical role in cellular inflammatory signaling.30 Given purported links between inflammatory processes and metabolic perturbations,31 inflammatory upregulation may serve as a pathway linking difficulty falling asleep with the development of the metabolic syndrome. While difficulty falling asleep and unrefreshing sleep were predictors of the metabolic syndrome, neither of these symptoms predicted any of the individual factors comprising the metabolic syndrome. These findings are consistent with the notion that the metabolic syndrome indeed represents a syndrome, perhaps with a common etiology and with synergistic prognostic value, rather than the mere sum of its component factors. Notably, the analyses focusing on individual metabolic components may have lacked statistical power to detect significant differences, particularly for the central adiposity and blood pressure criterion, due to the fact that the sample was restricted to those individuals not meeting that specific criterion at baseline. Among those classified as having insomnia syndrome using more stringent diagnostic criteria, which includes impact on daytime functioning as well as any sleep complaint, 35% reported having DFA and 40% reported having unrefreshing sleep. Despite this overlap, insomnia syndrome was not related to metabolic syndrome incidence. These findings suggest that specific symptoms, DFA, and unrefreshing sleep, may be stronger predictors of cardiometabolic outcomes than the syndrome of insomnia which includes a combination of at least one sleep complaint (including, but not limited to DFA or unrefreshing sleep) as well as a symptom of daytime impairment. This lack of a statistically significant relationship between insomnia syndrome and the metabolic syndrome, may have important treatment implications, as worries about the health consequences of insomnia are a key perpetuating factor in the disorder.26 The experience of daytime consequences may be germane to the decision to treat insomnia and may be relevant to the psychiatric morbidity associated with insomnia, but may not be an adequate indicator of disease severity in terms of predicting cardiovascular risk. On the other hand, recent evidence from Vgontzas and colleagues27 suggests that the presence of insomnia in conjunction with a physiologic indicator of disease severity (polysomnographically determined short sleep duration) strongly and significantly predicts hypertension risk, suggesting that this phenotype of insomnia may represent a higher threshold of risk, relevant in the prediction of cardiovascular outcomes. The current findings are strengthened by the large, representative sample of community-dwelling white and black men and women with thorough laboratory assessments of metabolic risk factors both at baseline and at 3-year follow-up. We also examined a wide range of sleep disturbances commonly presenting in clinical practice and assessed the relative contribution of SDB and insomnia symptoms using different case definitions. In addition, analyses controlled for a host of covariates (including AHI) that may have confounded the results. Limitations A limitation of the study is that we were unable to examine the relative contribution of AHI versus self-reported sleep symptoms in predicting incident metabolic syndrome, due to the cross-sectional nature of the AHI assessment and that AHI was assessed in a relatively small subsample of the study cohort. Moreover, AHI was determined via nasal airflow alone. Thus, we did not have a direct measure of intermittent hypoxemia. In addition, we did not have objective measures of sleep disturbances; however, self-reports of snoring and sleep latency are correlated with objective measurements.32 Finally, because the study did not include a measure of sleep duration we cannot determine the degree to which current findings may be influenced by short or long sleep durations—factors which have been reliably linked with cardiometabolic risk in previous investigations. To better understand the putative mechanisms linking sleep disturbances with cardiovascular risk, future prospective studies should examine the combination of subjective sleep complaints in conjunction with physiological indicators of poor sleep in relation to cardiovascular morbidity and mortality. Summary This study provides the first prospective evidence to support a directional link between commonly reported sleep symptoms, including difficulty falling asleep and loud snoring and development of the metabolic syndrome. These relationships persisted after accounting for the effects of AHI and other relevant covariates, and suggest that loud snoring, in particular, is independently implicated in cardiometabolic risk, rather than merely being a marker for OSA. Insomnia, defined using more stringent diagnostic criteria, was not a risk factor for the development of the metabolic syndrome. Given that in the general population, sleep complaints are considerably more prevalent than either insomnia or obstructive sleep apnea syndromes, these findings have far-reaching implications for public health, particularly given epidemic levels of obesity and its associated cardiometabolic consequences, which are associated with sleep disturbances. These findings reflect the utility of assessing for common sleep complaints in routine clinical practice as these individuals may be at elevated risk for the development of the metabolic syndrome. Acknowledgements Funding for this research was provided by the Commonwealth of Pennsylvania Department of Health Contract ME-02-384), and the National Institutes of Health HL076852, HL076858, and CTSA/ N-CTRC #RR024153. Support for the first author was provided by the National Heart Lung Blood Institute (NHLBI) K23HL093220. Apnealink funding was providing by the nonprofit ResMed Foundation. The Department and the National Institutes of Health specifically disclaims responsibility for any analyses, interpretations, or conclusions. Dr. Steven E. Reis has had full access to the data, and takes responsibility for the integrity of the data and accuracy of the analysis. References 1. Lakka HM , Laaksonen DE , Lakka TA , et al. The metabolic syndrome and total cardiovascular disease mortality in middle-aged men . JAMA 2002 ; 288 : 2709 – 16 . Google Scholar Crossref Search ADS PubMed WorldCat 2. Ford ES , Giles WH , Dietz WH . 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