Abstract Introduction Accumulating evidence suggests that trends in salivary cortisol after awakening may be reliable biological predictors of morbidity and mortality. In a sample of elite military men, our lab recently established summary parameters of morning cortisol as well as their stability across 2 d of repeated sampling. Materials and Methods In this follow-on study, we evaluated summary parameters and their relationships to theoretically relevant demographic (i.e., age, body mass index) and biobehavioral correlates (i.e., blood pressure [BP], sleep parameters, fatigue, and perceived stress). Fifty-eight male active duty U.S. Navy SEALs self-collected salivary samples on 2 consecutive, midweek workdays upon waking (WAKE), WAKE+30 min, WAKE+60 min, 4 p.m., and 9 p.m. in a nondeployed, free-living setting. Resting BP was measured manually, and sleep–wake periods were objectively derived using actigraphy. Daily fatigue and perceived stress were measured by self-report. Summary parameters of morning cortisol magnitude (i.e., peak value [Peak], area under the curve in terms of ground [AUCG], and average of morning samples [AVE]) were assessed with respect to each demographic and biobehavioral item via correlational analyses. A subgroup of 29 participants was selected for compliance with salivary sampling in the morning across 2 d. Results Perceived stress was positively associated with Peak (r = 0.437, p < 0.05), AUCG (r = 0.500, p < 0.01), and AVE (r = 0.506, p < 0.01). Total sleep time was also positively associated with Peak (r = 0.378, p < 0.05). There were borderline associations between some summary parameters and diastolic BP, percent sleep, and wake after sleep onset. Age, systolic BP, body mass index, time in bed, sleep efficiency, and fatigue did not associate with morning cortisol. Conclusions Preliminary evidence of morning cortisol summary parameters as biobehavioral indicators was established, and these parameters appeared to associate with stress and sleep in elite military men. INTRODUCTION The glucocorticoid cortisol serves as a stress response biomarker as well as a reliable and valid index of stress-induced reactions. In addition to increasing the availability of energy substrates to the body, one of cortisol’s main actions is as an anti-inflammatory, and endogenously secreted cortisol has a multifaceted role. Unlike other stress hormones (e.g., catecholamines) cortisol secretion is more gradual. From wake to bed time, salivary cortisol’s curve is diurnal, with a marked peak approximately 30–45 min after waking, followed by a precipitous decline for the remainder of the day.1 This prominent morning pinnacle is known as the cortisol awakening response (CAR) and is the typical pattern in healthy persons. The CAR is a standalone response to awakening and is separate from the diurnal rhythm. Functionally, this pattern of release is consistent with the body’s physiological response to waking and the need to mobilize fuel sources.2 However, in a dysfunctional state, this archetype may become flatter or steeper in decline.3,4 Postawakening cortisol concentration (PACC) includes CAR and the period up to about 1 h postawakening. An expert consensus recently identified noteworthy CAR covariates which were used in the present study’s evaluation of PACC. These covariates included age, body mass index (BMI), and sleep quality.5 While hypothalamic–pituitary–adrenal axis dysfunction is thought to be a component of the aging process, changes in cortisol secretion are likely not associated with aging itself.6 However, there is a significant effect of age on cortisol response to a pharmacological or psychological challenge,7 thus implying that age and stress-induced cortisol release are linked. With respect to BMI, hypersecretion of cortisol over long periods of time is linked to increases in central adiposity and alterations in PACC, and it is also associated with unfavorable metabolic profiles.8 Finally, Lasikiewicz et al3 observed that postwaking cortisol was reduced in those who reported poorer sleep quality. In this study, additional covariates of particular interest in a military population were blood pressure (BP), daily fatigue, and perceived stress. Modifications in salivary cortisol have been seen in hypertensive patients.9 While there are limited data on the association between daily fatigue and PACC, a lower cortisol area under the curve (AUC) after awakening is observed in those with chronic fatigue syndrome.10 And, although no association has been observed between PACC and perceived stress in nonmilitary populations,3 a large body of research supports the theory that chronic stress exposure alters PACC. Walvekar et al11 found a strong positive association between morning serum cortisol and perceived stress. It was also recently reported that perturbances in the PACC pattern such as higher awakening values, but lower postwaking values (at 30 and 50 min), may be linked with situations that are considered very stressful.4 In this follow-on evaluation from our previous study of elite military men,12 selected summary parameters of PACC were correlated with demographic (i.e., age and BMI) as well as biobehavioral characteristics (i.e., BP, objective sleep parameters, perceived stress,13 and fatigue). We expected that age, BMI, and BP would be negatively correlated with the summary parameters characterizing PACC magnitude (i.e., peak cortisol concentration between the three wake-dependent values [Peak], AUC of the wake-dependent values in terms of ground [AUCG], and average of the three wake-dependent values [AVE]). Alternatively, it was hypothesized that objective measures of sleep quality would positively correlate with PACC. We have previously reported excellent stability in the summary parameters of cortisol magnitude for this population.12 These factors could be predictive of health outcomes and potentially used as early indicators of chronic operational stress in a warfighter long before they present with traditional signs of disease or disorder. Furthermore, examination of wake- or evening-based indices of specific demographic or biobehavioral correlates could be powerful tools in predicting morbidity and mortality3,8,9 across the military career or lifespan. METHODS Subjects Subjects were male active duty military members of the elite Sea, Air, and Land (SEAL) community, assigned to Naval Special Warfare Group ONE, in San Diego, CA, USA. No subjects were deployed; rather, all were in a routine training status at their home station. Those who expressed an interest in participating attended an in-person meeting to review the study details and to provide written informed consent. This study protocol was approved by the Naval Health Research Center Institutional Review Board (Protocol NHRC.2012.0006). Inclusion, Exclusion, and Compliance Criteria Imposed exclusion criteria were smoking (smokeless tobacco use was permitted with strict compliance criteria) and current diagnosis of type 1 or type 2 diabetes with prescribed medication. Compliance instructions prior to each salivary sampling and assessment included refraining from alcohol consumption within 12 h, limiting major meals and smokeless tobacco use within 1 h, omitting caffeine or dairy product consumption within 30 min, and avoiding acidic or high-sugar foods/liquids or salivary stimulants (e.g., gum, candy) within 10 min. Salivary Sampling Protocol Complete details of the salivary sampling protocol are detailed elsewhere.12 Briefly, 1 salivary sample was collected 5 times per day, for 2 d, a total of 10 samples. Participants self-collected their samples using oral swabs (Salimetrics, Inc., Carlsbad, CA, USA) in a free-living environment. Standardized instructions for self-administration of samples were provided both in person and in written format. Participants were instructed to collect their samples immediately upon awakening, 30 min after awakening, 60 min after awakening, and at 4:00 p.m. (midafternoon) and 9:00 p.m. (evening). After the waking sample, subjects were directed to rinse their mouth with water 10 min before sampling and to place their oral swabs in the freezer after collection. All participants were encouraged to maintain their typical daily routines. Upon completion of the 2-d salivary sampling, a member of the research staff arranged to pick up the samples and store them at −80°C until processing. Actigraphy The Motionlogger Actigraph (Ambulatory Monitoring, Ardsley, NY, USA) was used to provide an objective indicator of the time of awakening, which was compared against self-reported morning sampling times as a proxy of compliance. Sleep and wake classifications were determined using the validated Cole–Kripke algorithm, and sleep summary metrics were calculated. Percent sleep, time in bed (TIB), total sleep time (TST), wake after sleep onset (WASO), and sleep efficiency were considered independent components of sleep quality. Percent sleep was calculated as a percentage of TST divided by the time between sleep onset and the final awakening. WASO was measured as the time spent awake after initially falling asleep until the final awakening. Sleep efficiency was calculated as a percentage of TST divided by TIB. The details of this protocol are further outlined elsewhere.14 Determination of Salivary Cortisol Salivary cortisol was assayed in duplicate using a commercially available enzyme immunoassay specifically designed for use with saliva without modifications to the manufacturer’s recommended protocol (Item #1–3002; Salimetrics). The test uses 25 μL of saliva per determination, has a lower limit of sensitivity (0.003 μg/dL), a standard curve range (from 0.012 μg/dL to 3.0 μg/dL), an average intra-assay coefficient of variation (CoV; 3.5%), and an average inter-assay CoV (5.1%). Method accuracy determined by spike recovery averaged 100.8%, and linearity determined by serial dilution averaged 91.7%. Values used in statistical analyses were the average of duplicate assays for each sample. Data Analysis Data were analyzed using SPSS Statistics, version 23.0 (IBM, Armonk, NY, USA). Compliance with sampling time was previously established in this sample.12 A participant was classified as compliant if the self-reported sampling time deviated ≤15 min from the target time, relative to actigraphy-derived wake time, for all three morning time points on both sampling days (a total of six time points). Operational definitions of the summary parameters of magnitude and their stability across 2 d have been detailed elsewhere.12 Distribution characteristics for all continuous variables were examined to determine if assumptions of normality were met, following conservative predefined limits (e.g., skewness between −1 and 1, kurtosis between −3 and 3)12 Variables exceeding any of these limits were transformed prior to performing the relevant statistical test. All data transformations reduced skewness and kurtosis to acceptable levels, with the exception of sleep efficiency (skewness = −1.4). For each model assessing biobehavioral correlates, theoretically relevant variables (i.e., age, BMI, education, ethnicity, and number of deployments as a SEAL) were evaluated as potential covariates following standardized selection criteria.12 None of the identified demographic or biobehavioral correlates met criteria as covariates. The linear associations of selected cortisol summary parameters were evaluated with respect to each demographic and biobehavioral correlate via Pearson product-moment correlation models (0.1–0.3 = low, 0.3–0.5 = moderate, 0.5–1.0 = high15). For exploratory purposes, nuanced patterns were constructed via quartile splits followed by one-way analysis of variance and post hoc subgroup comparisons. All hypothesis tests were two-sided and the probability of committing a type I error was set at 0.05. Due to the exploratory nature of cortisol quartile examination, the p-value was not adjusted for multiple comparisons. RESULTS Subject Characteristics Fifty-eight SEALs participated as part of a larger study evaluating sleep disruption.14 A subgroup of 29 subjects was compliant for morning sampling across 2 d. Mean ± SE age was 34.5 ± 1.4 yr and year of military service for this sample was 13.2 ± 1.4. Seven (24.1%) were officers, 3 (10.3%) were chief warrant officers, and 19 (65.5%) were enlisted. Most participants were Caucasian (79.3%), two (6.9%) endorsed smokeless tobacco use, nine (31.0%) reported any type of nutritional supplement use, and the majority (72.4%) reported use of caffeine and/or energy drinks. Twenty-three had previously deployed for special operations missions. BMI, systolic BP (SBP), and diastolic BP (DBP) were 27.8 ± 0.4 kg/m2, 115.7 ± 1.7 mmHg, and 80.1 ± 1.5 mmHg, respectively. Of these 29 subjects, 6 (25.0%) and 8 (33.3%) were in the elevated range for SBP (>120 mmHg) and DBP (>80 mmHg),16 respectively. The 10-item Perceived Stress Scale (PSS; Cronbach’s α = 0.08413) mean total score was 10.6 ± 1.0. Sleep summary metrics included average percent sleep (93.0 ± 0.9%; range: 82.9–98.4), TST (421.4 ± 9.8 min; range: 269.3–500.7), TIB (481.3 ± 8.8 min; range: 352.7–534.6), WASO (31.7 ± 3.8 min; range: 6.7–84.7), and sleep efficiency (87.6 ± 1.4%; range: 61.8–94.6). Daily fatigue in the past 4 wk was a notable average of 6.0 ± 0.4 (1–10 scale, with 10 as extremely fatigued). Morning Cortisol Summary Parameters and Demographic/Biobehavioral Correlates All results are summarized in the Table I. Perceived stress was positively associated with Peak (r = 0.437, p < 0.05), AUCG (r = 0.500, p < 0.01), and AVE (r = 0.506, p < 0.01) cortisol levels. Twenty-five percent of the variance in PACC AUCG was explained by PSS scores (Fig. 1). Exploratory quartile comparisons for PACC AUCG with respect to perceived stress revealed a curvilinear pattern (Fig. 2) such that quartile 1 (AUCG ≤ 23.79 mg/dL) was associated with the lowest stress scores and quartile 4 (AUCG > 38.55 mg/dL) with the highest scores [F (3, 23) = 3.251, p = 0.04 for overall analysis of variance]. TABLE I. Associations Between Demographic/Biobehavioral Correlates and Morning Cortisol Summary Parameter (Cortisol Measures of Magnitude) Demographic or Biobehavioral Correlate Peak AUCG AVE Age −0.006 −0.330 −0.292 BMI 0.014 −0.048 0.049 SBP −0.288 −0.164 −0.194 DBP −0.383* −0.194 −0.208 Perceived stress 0.437** 0.500*** 0.506*** Daily fatigue 0.120 0.098 0.139 Percent sleep 0.039 −0.368* −0.368* TST 0.378** −0.002 0.022 TIB 0.288 −0.006 0.006 WASO 0.019 0.352* 0.355* Sleep efficiency 0.224 −0.002 0.018 Daily fatigue 0.120 0.098 0.139 Summary Parameter (Cortisol Measures of Magnitude) Demographic or Biobehavioral Correlate Peak AUCG AVE Age −0.006 −0.330 −0.292 BMI 0.014 −0.048 0.049 SBP −0.288 −0.164 −0.194 DBP −0.383* −0.194 −0.208 Perceived stress 0.437** 0.500*** 0.506*** Daily fatigue 0.120 0.098 0.139 Percent sleep 0.039 −0.368* −0.368* TST 0.378** −0.002 0.022 TIB 0.288 −0.006 0.006 WASO 0.019 0.352* 0.355* Sleep efficiency 0.224 −0.002 0.018 Daily fatigue 0.120 0.098 0.139 Peak, peak concentration at any of the three wake-dependent measurements; AUCG, total area under the curve of all three morning measurements, accounting for the distance between each measurement as well as the distance of each measure from the ground (i.e., zero); AVE, average of the three wake-dependent measurements. Associations are given as Pearson’s r values. *p = 0.06–0.07; **p < 0.05; ***p < 0.01. TABLE I. Associations Between Demographic/Biobehavioral Correlates and Morning Cortisol Summary Parameter (Cortisol Measures of Magnitude) Demographic or Biobehavioral Correlate Peak AUCG AVE Age −0.006 −0.330 −0.292 BMI 0.014 −0.048 0.049 SBP −0.288 −0.164 −0.194 DBP −0.383* −0.194 −0.208 Perceived stress 0.437** 0.500*** 0.506*** Daily fatigue 0.120 0.098 0.139 Percent sleep 0.039 −0.368* −0.368* TST 0.378** −0.002 0.022 TIB 0.288 −0.006 0.006 WASO 0.019 0.352* 0.355* Sleep efficiency 0.224 −0.002 0.018 Daily fatigue 0.120 0.098 0.139 Summary Parameter (Cortisol Measures of Magnitude) Demographic or Biobehavioral Correlate Peak AUCG AVE Age −0.006 −0.330 −0.292 BMI 0.014 −0.048 0.049 SBP −0.288 −0.164 −0.194 DBP −0.383* −0.194 −0.208 Perceived stress 0.437** 0.500*** 0.506*** Daily fatigue 0.120 0.098 0.139 Percent sleep 0.039 −0.368* −0.368* TST 0.378** −0.002 0.022 TIB 0.288 −0.006 0.006 WASO 0.019 0.352* 0.355* Sleep efficiency 0.224 −0.002 0.018 Daily fatigue 0.120 0.098 0.139 Peak, peak concentration at any of the three wake-dependent measurements; AUCG, total area under the curve of all three morning measurements, accounting for the distance between each measurement as well as the distance of each measure from the ground (i.e., zero); AVE, average of the three wake-dependent measurements. Associations are given as Pearson’s r values. *p = 0.06–0.07; **p < 0.05; ***p < 0.01. FIGURE 1. View largeDownload slide Association between perceived stress and morning cortisol AUCG. Twenty-five percent of the variance (R2 linear = 0.250) in morning cortisol was explained by PSS scores. FIGURE 1. View largeDownload slide Association between perceived stress and morning cortisol AUCG. Twenty-five percent of the variance (R2 linear = 0.250) in morning cortisol was explained by PSS scores. FIGURE 2. View largeDownload slide Perceived stress by morning cortisol AUCG quartiles (p = 0.04). Exploratory quartile comparisons for morning cortisol AUCG with respect to perceived stress revealed a curvilinear pattern such that quartile 1 (AUCG ≤ 23.79 mg/dL) was associated with the lowest PSS scores and quartile 4 (AUCG > 38.55 mg/dL) with the highest scores [F(3,23) = 3.251, p = 0.04 for overall analysis of variance]. FIGURE 2. View largeDownload slide Perceived stress by morning cortisol AUCG quartiles (p = 0.04). Exploratory quartile comparisons for morning cortisol AUCG with respect to perceived stress revealed a curvilinear pattern such that quartile 1 (AUCG ≤ 23.79 mg/dL) was associated with the lowest PSS scores and quartile 4 (AUCG > 38.55 mg/dL) with the highest scores [F(3,23) = 3.251, p = 0.04 for overall analysis of variance]. TST was only associated with Peak (r = 0.378, p < 0.05) cortisol levels. There were borderline associations (p = 0.06–0.07) between (1) Peak and diastolic BP, (2) AUCG and percent sleep, (3) AVE and percent sleep, (4) AUCG and WASO, and (5) AVE and WASO. No associations were observed between PACC and any other demographic or biobehavioral correlates. Finally, there was no relationship between perceived stress and TST (r = 0.228, p = 0.244). DISCUSSION This the first report on the relationships between the morning cortisol profiles of elite military men and their demographic and biobehavioral characteristics. Preliminary evidence of morning cortisol summary parameters as biobehavioral health indicators was established in this specialized military population. Specifically, we identified that PACC is positively associated with perceived stress and TST. Intuitively, the cortisol rhythm is blunted or flattened under adverse situations and more pronounced in healthier conditions. The literature is mixed about the relationship between morning cortisol and perceived stress. Older studies using the PSS have shown that higher scores were linked with increased waking cortisol, but more recent research has found no such connection. Higher cortisol immediately after awakening was observed in police officers who self-reported medium to high stress for major occupational stressors versus those who reported low stress for the same stressors.4 Interestingly, while the AUCG did not differ between these groups, those in the low rating group had a greater mean Peak, which is indicative of a “healthier” morning cortisol response in some cases. Results of the present study, on the other hand, imply that higher perceived stress increases both cortisol Peak and total hormonal output, and thus, are not aligned with Violanti et al.’s findings.4 Perhaps, this is due to methodological differences in perceived stress measurement since our observations are in agreement with Walvekar et al,11 whose study described a robust association between serum cortisol and PSS scores in a group of law enforcement officers. Even though our sample of elite military men were in garrison at the time of study and had an average PSS score (10.6) that was slightly lower than the norm for men (12.1),17 there was a strong correlation between PACC and perceived stress. In our exploratory analyses, AUCG quartiles had a curvilinear pattern in relationship to perceived stress (Fig. 2), but were not statistically compelling. Overall, these varied findings underscore the need for more research among unique populations (e.g., law enforcement and military) experiencing different types of stress (e.g., traumatic vs. nontraumatic) in various contexts (e.g., in garrison vs. deployed, training vs. operations). Subsequently, determination of optimal/non-optimal PACC profiles for different populations and environments can be made. Associations between PACC and sleep measures were mixed. TST and WASO positively associated with PACC, percent sleep was inversely correlated, and there was no association with TIB. While many studies have evaluated the connection between CAR and self-reported sleep quantity and quality, there are few reports on the specific association between PACC and objectively derived indicators of sleep. Lasikiewicz et al3 found lower AUCG values in those with lower reported sleep quality, and Hansen et al18 observed low morning cortisol concentrations in those with greater reported sleep problems. But both of those studies used self-reported sleep measures, were in nonmilitary groups, and evaluated slightly older populations. One unique study used actigraphy,19 but there was no connection between actual sleep duration and waking cortisol. In that study, only the average waking cortisol value was evaluated, which may explain why the authors did not observe any relationship between salivary cortisol and sleep. Altogether, these diverse results reinforce that the true interrelationships between hypothalamic–pituitary–adrenal axis function, cortisol secretion, and sleep quantity and quality still need to be clearly defined. Furthermore, more conclusive outcomes are likely only achievable through the use of objective sleep measures and rigorous assessment of CAR or PACC. This study’s strengths include general adherence to the expert consensus guidelines for CAR, and morning cortisol research.5 Although this investigation did not measure CAR per se, the guidelines for morning cortisol assessment were followed: control for sampling accuracy, maximize participant compliance, control for covariates, procure three postawakening samples at ideal time points, and acknowledge the distinction between CAR and PACC. Although this investigation preceded Stalder et al,5 limitations should be noted. A longer sampling period is recommended for the evaluation of CAR with trait-like factors like age, which was an independent variable in our study. Also, since subjects self-administered salivary samples without direct oversight from the research team, only the first sampling time (WAKE) was objectively verified using actigraphy. We are presently devising an objective tracking system spanning all sampling time points. While the consensus guidelines also recommended a strict sampling accuracy margin of ±5 min, our compliance criterion was ±15 min. The current literature is mixed on this topic and many researchers still consider a margin of ±15 min to be acceptable. Additionally, a shorter time frame may be unrealistic for most free-living subjects. Finally, as this study had only elite military participants, these findings may not be generalizable to a broader population. Overall, these limitations are balanced by the strong reliability of these measures of magnitude,12 inclusion of only compliant subjects, use of standardized cortisol assessment procedures, and the ecological validity of data collected in a free-living environment. While there may be more intricacies with the measurement and clinical interpretation of morning cortisol, it is a critical component of the hormonal profile. As mentioned earlier, acute increases in cortisol are necessary for physiological/psychological responses and adaptations. However, assessment of morning cortisol levels over time can be used to ensure health and readiness of the U.S. Armed Forces, especially for those who are at greater risk of chronic stress exposure. After all, the routine biosurveillance of other hormones (e.g., testosterone, dehydroepiandrosterone) is now a common practice, particularly in professional athletics. Regular evaluation of morning cortisol in service members could also support the practice of preventive and personalized medicine. Future directions could include the evaluation of morning cortisol concentrations in training scenarios or deployed settings. Funding This study was supported by a grant from the Office of Naval Research, Code 30 (Human Performance, Training, and Evaluation), under work unit N1204. Acknowledgements Appreciation is extended to Genieleah Padilla for her data collection contributions and to Michelle LeWark for her editorial expertise. REFERENCES 1 Kudielka BM, Buchta J, Uhde A, Wüst S: Circadian cortisol profiles and psychological self-reports in shift workers with and without recent change in the shift rotation system. Biol Psychol 2007; 74: 92– 103. Google Scholar CrossRef Search ADS PubMed 2 Wilhelm I, Born J, Kudielka B.M, Schultz W, Wüst S: Is the cortisol awakening rise a response to awakening? Psychoneuroendocrinology 2007; 32( 4): 358– 66. Google Scholar CrossRef Search ADS PubMed 3 Lasikiewicz N, Hendrickx H, Talbot D, Dye L: Exploration of basal diurnal salivary cortisol profiles in middle-aged adults: associations with sleep quality and metabolic parameters. Psychoneuroendocrinology 2008; 33( 2): 143– 51. Google Scholar CrossRef Search ADS PubMed 4 Violanti JM, Fekedulegn D, Andrew ME, et al. : The impact of perceived intensity and frequency of police work occupational stressors on the cortisol awakening response (CAR): findings from the BCOPS study. Psychoneuroendocrinology 2017; 75: 124– 31. 5 Stalder T, Kirschbaum C, Kudielka BM, et al. : Assessment of the cortisol awakening response: expert consensus guidelines. Psychoneuroendocrinology 2016; 63: 414– 32. 6 Lupien SJ, Fiocco A, Wan N, et al. : Stress hormones and human memory function across the lifespan. Psychoneuroendocrinology 2005; 30( 3): 225– 42. 7 Otte C, Hart S, Neylan TC, et al. : A meta-analysis of cortisol response to challenge in human aging: importance of gender. Psychoneuroendocrinology 2005; 30( 1): 80– 91. 8 Kuehl LK, Hinkelmann K, Muhtz C, et al. : Hair cortisol and cortisol awakening response are associated with criteria of the metabolic syndrome in opposite directions. Psychoneuroendocrinology 2015; 51: 365– 70. 9 Wirtz PH, von Känel R, Emini L, et al. : Evidence for altered hypothalamus-pituitary-adrenal axis functioning in systemic hypertension: blunted cortisol response to awakening and lower negative feedback sensitivity. Psychoneuroendocrinology 2007; 32( 5): 430– 36. 10 Roberts AD, Wessely S, Chalder T, Papadopoulos A, Cleare AJ: Salivary cortisol response to awakening in chronic fatigue syndrome. Br J Psychiatry 2004; 184: 136– 41. 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Hillsdale, NJ, Lawrence Erlbaum Associates, 1988. 16 Mancia G, De Backer G, Dominiczak A, et al. : 2007 Guidelines for the management of arterial hypertension. Eur Heart J 2007; 28( 12): 1462– 536. 17 Cohen S, Williamson G: Perceived stress in a probability sample of the United States. In: The Social Psychology of Health: Claremont Symposium on Applied Social Psychology . Edited by Spacapan S, Oskamp S Newbury Park, CA, Sage, 1988. 18 Hansen AM, Thomsen JF, Kaergaard A, et al. : Salivary cortisol and sleep problems among civil servants. Psychoneuroendocrinology 2012; 37( 7): 1086– 95. 19 Zhang J, Ma RC, Kong AP, et al. : Relationship of sleep quantity and quality with 24-hour urinary catecholamines and salivary awakening cortisol in healthy middle-aged adults. Sleep 2011; 34( 2): 225– 33. Author notes The views expressed in this article are those of the authors and do not necessarily reflect the official policy or position of the Department of the Navy, Department of the Army, Department of the Air Force, Department of Veterans Affairs, Department of Defense, or the U.S. Government. © Association of Military Surgeons of the United States 2018. All rights reserved. For permissions, please e-mail: email@example.com. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices)
Military Medicine – Oxford University Press
Published: Apr 6, 2018
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