Assessment of Sleep Disruption and Sleep Quality in Naval Special ...

MILITARY M EDICINE, 180, 7:803, 2015

Assessment of Sleep Disruption and Sleep Quality in Naval Special Warfare Operators LT Erica Harris, MSC USN*; Marcus K. Taylor, P hD f; Sean P.A. Drummond, PhD±§U; Gerald E. Larson, P hD f; CDR Eric G. Potterat, MSC USNJ]

ABSTRACT Little is known about sleep in elite military populations who are exposed to higher operational demands, unpredictable training, deployment, and mission cycles. Twenty-nine Naval Special Warfare (NSW) Operators wore an actiwatch for an 8-day/7-night period for objective sleep assessment and completed a nightly sleep log. A total of 170 nights of actigraphically recorded sleep were collected. When comparing objectively versus subjectively recorded sleep parameter data, statistically significant differences were found. Compared with sleep log data, actigraphy data indi cate NSW Operators took longer to fall asleep (an average of 25.82 minutes), spent more time awake after sleep onset (an average of 39.55 minutes), and demonstrated poorer sleep efficiency (83.88%) (ps < 0.05). Self-reported sleep quality during the study period was 6.47 (maximum score = 10). No relationships existed between the objectively derived sleep indices and the self-reported measure of sleep quality (rs = -0.29 to 0.09, all ps > 0.05). Strong inter-relationships existed among the subjectively derived sleep indices (e.g., between self-reported sleep quality and sleep efficiency; r = 0.61, p < 0.001). To our knowledge, this is the first study to objectively and subjectively quantify sleep among NSW Operators. These findings suggest sleep maintenance and sleep efficiency are impaired when compared to normative population data.

INTRODUCTION Sleep is a physiological need that varies among humans. Although there is no consensus regarding the amount of sleep the average individual needs to maintain health and performance, epidemiological studies suggest average sleep need is in the 7.5 to 8.0 hours range.1'2 Within the armed services, though, obtaining the recommended amount of sleep is difficult, and an ethos exists that sleeping is a sign of weakness.3 Yet, in a culture where optimizing performance is of utmost importance, it is worth noting that myriad stud ies have demonstrated that even minor decrements in sleep *Health and Behavioral Sciences Department, Naval Health Research Center, 140 Sylvester Road. San Diego, CA 92106. fWarfighter Performance, Naval Health Research Center, 140 Sylvester Road, San Diego, CA 92106. ^Veterans Affairs Healthcare System, 3350 La Jolla Village Drive, San Diego, CA 92161. §Department of Psychiatry, University of California San Diego, 9500 Gilman Drive, San Diego, CA 92093. || VA Center of Excellence for Stress and Mental Health, 3350 La Jolla Village Drive, San Diego, CA 92161. ^Naval Special Warfare Command. 2000 Trident Way, Building 603M. San Diego, CA 92155. CDR Potterat and LT Harris are military service members. This work was prepared as part of their official duties. Title 17 U.S.C. § 105 provides that “Copyright protection under this title is not available for any work of the United States Government.” Title 17 U.S.C. § 101 defines a U.S. Government work as a work prepared by a military service member or employee of the U.S. Government as part of that person’s official duties. Approved for public release; distribution is unlimited. This research has been conducted in compliance with all applicable federal regulations governing the protection of human subjects in research (Naval Health Research Center.2012.0006). 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 Defense, or the U.S. Government. doi: 10.7205/MILMED-D-14-00436

MILITARY MEDICINE, Vol. 180, July 2015

can negatively affect performance.4-6 Prior research on chronic sleep fragmentation and sleep deprivation points to decreased behavioral alertness and cognitive performance and increased mood changes.3'6'7 A host of physiologic changes can occur as a result of short-term sleep restriction that might further contribute to declining health.3 These physiologic changes include lower glucose tolerance,9 increased blood pressure, activation of the sympathetic nervous system, reduced leptin levels, increased inflammatory markers, more heart-related events, and early mortality.8' 10 Moreover, sleep disruptions are a symptom and significant component of post-traumatic stress disorder,11-14 predict the onset of depression,1'1 are associated with increased suicidal ideation,16 and increase the risk of accidents.17 How sleep disruption progresses in service members is currently unknown, but such problems do exist.18 Prior stud ies have consistently demonstrated deficits in sleep quantity and quality among military personnel. Sleep is reported to be extremely poor in deployed settings with 15% of military personnel averaging as little as 4.5 hours of sleep per night.19 Assessment of sleep during combat deployments was recently addressed by Taylor et al.20 They found that 67% of service members deployed in support of Operation Enduring Freedom slept, on average, less than 6 hours per night. Interestingly, these effects seem to persist upon redeployment. Several stud ies have found that among Vietnam combat veterans, overall sleep is poor postdeployment.u ' 14 21 Data from the Millen nium Cohort Study22 revealed that previously deployed per sonnel have significantly fewer hours of sleep and more sleep complaints. A recent cross-sectional study conducted by Mysliwiec et al2"' detailed sleep problems experienced by veterans of Operation Enduring Freedom/Operation Iraqi Freedom (OEF/OIF). Among a sample of 725 military per sonnel, over 85% of whom had previously deployed, almost

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NSW Operator Sleep Assessment

42% of the sample reported sleeping less than 5 hours per night. The most frequent sleep diagnoses included the following: 27.2% prevalence of mild obstructive sleep apnea (OSA), 24.7% insomnia, 24% moderate-to-severe obstruc tive sleep apnea, and 5.1% paradoxical insomnia.23 Addi tional sleep disruptions among returning combat veterans from OEF/OIF included numerous nighttime awakenings, high levels of hypoxia, excessive daytime sleepiness, and low levels of rapid eye movement sleep.24 Although sleep disruptions have been identified, prevention of sleep prob lems linked to deployment/redeployment cycles remains an elusive goal.18 The aim of this study was to answer the following research question: Do Naval Special Warfare (NSW) Opera tors, a unique population within the U.S. Navy, exhibit aber rant sleep characteristics on objective (via actigraphy) and/or subjective (via self-report) sleep measures? NSW Operators represent a group of elite military personnel, are trained to handle an array of challenging operational situations, and are expected to perform at a consistently optimal level. Because of a prolonged and exhaustive screening process, team mem bers may be resilient in the face of extreme situational demands, but they are not immune to sleep disruptions. Anecdotal evidence suggests that 75% of NSW Operators reporting for general medical issues within 6 months after deployment endorsed sleep disruptions (personal communi cation, NSW medical staff, April 23, 2012). Furthermore, during clinical appointments, nearly 40% report difficulty falling asleep or staying asleep during this same time frame (personal communication, NSW medical staff, April 23, 2012). These subjective self-reports of sleep disruption are worri some considering the need for unfailing performance and readiness in NSW Operators; however, studies in other populations have revealed a mismatch between subjective report and objective sleep measures.25-28 To our knowledge, since we are unaware of any previous work objectively or subjectively examining sleep disruptions in this population, a more rigorous characterization of sleep is necessary to determine the true extent of the problem and to ultimately develop evidence-based sleep improvement interventions. With this in mind, there were two main objectives of this study. First, we wanted to document the characteristics of sleep disruption among NSW Operators. Second, we sought to understand the relationship between objectively derived sleep indices and self-reported sleep indices and sleep quality.

METHODS P a rtic ip a n ts

Twenty-nine active duty male NSW Operators (21 enlisted personnel, 7 officers, and 1 chief warrant officer) participated during a low operational tempo training period. Potential par ticipants met inclusion criteria if they were active duty NSW Operators assigned to Naval Special Warfare Group ONE (Coronado, California).

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P ro c e d u re s

This study was approved by the Naval Health Research Center Institutional Review Board. Participants attended an information session in which they were briefed on study pro cedures and signed an informed consent document if they agreed to enroll. They were asked to complete a basic demo graphic questionnaire including information such as age, eth nicity, military occupational specialty, and use of prescription, nutritional supplements, or over-the-counter dmgs, sleep aids (e.g., Ambien), energy drinks, caffeine, and other stimulants. Self-Reported Sleep Measure

Participants were instructed to keep a sleep log to record sleep activities during the 8-day/7-night study period. There were a total of 11 questions to answer. Sample sleep log questions included the following: How long did it take you to fall asleep last night (in minutes)? Rate the overall quality of your sleep last night (1 = poor to 10 = excellent). Sleep parameter data were calculated from the sleep log. Wrist Actigraphy

Actiwatches provide an activity-derived measure of sleep based on movement in the naturalistic environment and are a more economical approach than utilizing standard poly somnography.29'30 This study used the Motionlogger Actiwatch (Ambulatory Monitoring, Ardsley, New York), which uses an accelerometer to measure movement. All watches in this study were configured to collect data in 1-minute epochs. Data were downloaded using Motionlogger WatchWare soft ware version 1.60.0.1 and were visually inspected by 2 trained scorers. Action-W 2.0 user’s guide version 2.6.9905 (Ambu latory Monitoring) was used to analyze all data. Zero cross ing mode was the sampling mode (Cole-Kripke) used to assess movement. Sleep parameters were scored using the manufacturer’s software and included duration (time in bed [TIB]), total sleep time (TST), sleep efficiency, sleep latency, and wake after sleep onset (WASO; see Fig. 1). All participants received detailed instructions about how to use the actiwatch, and to enhance subject compliance, they were asked to wear it according to personal preference (domi nant vs. nondominant wrist). Participants wore the actiwatch 24 hours/day. They were instructed to wear the Actiwatch for a period of 8 days/7 nights and to remove it only if they would be swimming in salt water, engaged in an activity that might damage the watch, or if the watch interfered with their professional duties. If they took the watch off at any time, they were instructed to document this in their sleep logs and to provide an explanation. Participants were instructed to tap the event marker any time they were going to sleep, including naps, and again upon awakening. S ta tis tic a l A n a lys is

All statistical analyses were conducted using SPSS, version 19.0 (IBM Inc., Armonk, New York). All participants had


NSW Operator Sleep Assessment NSW Operator Sleep Assessment 1 A + B + C + D = Total sleep time (TST)

Wake

rS 0

Sleep

Wake

Sleep

Wake

Sleep

rS

rS 1

rS 2

rS

B

A

c

Sleep

Wake

rS

rS

rS

3

D

4

Wake

Sleep latency

FIGURE 1. Sleep parameters. All sleep parameters are calculated as time in minutes. TIB, time in bed, refers to the “down period” = lights out to lights on; TST, total sleep time, defined as the total amount of time spent sleeping; also referred to as the sleep onset to sleep offset interval (“o-o” interval); Sleep efficiency is the amount of time spent sleeping while in bed and is calculated as TST/TIB x 100%; Sleep latency is the amount of time it took the subject on average to fall asleep; WASO, wake after sleep onset, defined as the amount of time spent awake after lights-out and sleep onset occurs.

3 or more nights of actigraphy data. Counts and percentages are reported for categorical variables and means and stan dard deviations summarize continuous variables. Indepen dent variables were as follows: total amount of minutes spent in bed (TIB, also defined as the onset-to-offset period); total amount of minutes spent sleeping (TST); sleep latency, amount of time (in minutes) it took on average to fall asleep; total amount of minutes spent awake after lights out and sleep onset (WASO); and the amount of time spent sleeping while in bed (sleep efficiency). The main dependent variable included self-reported sleep quality. Paired samples t tests were used to examine differences for selected objective versus subjective sleep parameters. Pearson product-moment corre lations assessed relationships among objectively and subjec tively derived sleep parameters and sleep quality. Statistical significance was set at p < 0.05.

RESULTS Table I shows participants’ demographic and descriptive data. All participants rated themselves to be in good overall physical and mental health. Age ranged from 20 to 45 years (.M = 32.14). The average participant had 10 years of mili tary experience, ranging from 3 to 22 years, and over half of the sample (51.4%) had a 4-year college degree. Most

MILITARY M EDICINE, Vol. 180, July 2015

TABLE I.

Variable

Demographic and Descriptive Characteristics of NSW Operators M (SD)

Age, Years 32.14 (7.22) Military Service, Years 10.35 (6.22) Education High School Diploma/GED 2-Year College 4-Year College Graduate School Rank Enlisted Chief Warrant Officer Officer Smoke or Use Tobacco No Yes Current Use of Caffeine, Energy Drinks, or Stimulants No Yes Sleep Apnea Diagnosis No Yes Sleep Aids or Medications None Ambien

% («) n/a n/a 24.1 17.2 51.7 6.9

(7) (5) (15) (2)

72.4 (21) 3.4(1) 24.1 (7) 82.8 (24) 17.2 (5) 17.2 (5) 82.8 (24) 100.0 (29) 0.0 (0) 89.7 (26) 10.3 (3)

n/a, not applicable; NSW. Naval Special Warfare; SD. standard deviation.

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participants indicated they were neither smokers nor tobacco users (82.8%). Of the sample, 82.8% endorsed consuming caffeine, energy drinks, soda, tea, or alternative stimulants on a daily basis. Though not shown in Table I, a total of 13/ 29 participants (44.8%) endorsed taking some sort of nutri tional supplement. Two participants (6.9%) endorsed using over-the-counter ergogenic substances (e.g., creatine mono hydrate, dehydroepiandrosterone) within the past 3 months, 2 (6.9%) reported using fish oil, 6 (20.7%) reported vitamin/ multivitamin use, and 3 (10.3%) endorsed nutritional supple ment use but did not specify what type. The majority did not use any sleep aids or medications (89.7%). Only 3 partici pants (10.3%) endorsed using Ambien to facilitate sleep (Table I). No participants reported a diagnosis of sleep apnea. Nightly averages of objective (actigraphy) and subjective (sleep log) sleep parameter data are shown in Table 11. NSW Operators contributed a total of 170 nights (M = 5.93, Mdn - 7.00) of actigraphically recorded sleep. There was no difference for TIB between the objective and subjective measures of sleep, p = 0.07. TST from actigraphy data (M = 383.10 minutes, 6 hours and 23 minutes) was significantly lower than self-reported TST (M = 411.74 minutes, 6 hours and 51 minutes), p < 0.002.The range of TST in this sample for actigraphy data was from 255.33 minutes (4 hours and 15 minutes) to 482.29 minutes (8 hours and 2 minutes). The range of TST for the sleep log data was from 265.00 minutes (4 hours and 25 minutes) to 620.25 minutes (10 hours and 20 minutes). Sleep efficiency was significantly lower for actigraphy data (83.88%) than for sleep log data (90.07%), p < 0.001. Self-reported sleep latency for the sleep logs was significantly lower than sleep latency for actigraphy data, p < 0.01. WASO calculated via self-report (M - 28.04) was lower than WASO for actigraphy (M = 39.55), p = 0.05. Pearson product-moment correlation coefficients (r) are presented in Table III for the objectively and subjectively derived sleep parameters and a global self-assessment of sleep quality. Self-reported sleep quality averaged 6.47 on a

TABLE II.

Nightly Averages of Objective and Subjective Sleep Parameters for NSW Operators

Sleep Parameter TIB (Minutes) TST (Minutes) Sleep Efficiency (%) Sleep Latency (Minutes) WASO (Minutes)

Objective M (SD) 457.70 383.10 83.88 25.82 39.55

(56.29) (54.21) (8.37) (15.85) (28.26)

Subjective M (SD) 456.38 411.74 90.07 17.23 28.04

(56.20) (68.85) (8.37) (9.02) (35.04)

p Value 0.07 0.002 0.001

0.01 0.05

NSW, Naval Special Warfare; SD, standard deviation; TIB, time in bed (also referred to as the “down period” = lights out to lights on); TST, total sleep time (defined as the total amount of time spent sleeping; also referred to as the sleep onset to sleep offset interval [“o-o” interval]); Sleep efficiency is the amount of time spent sleeping while in bed and is calculated as TST/ TIB x 100%; Sleep latency is the amount of time it took the subject on aver age to fall asleep; WASO. wake after sleep onset (defined as the amount of time spent awake after lights-out and sleep onset occurs).

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TABLE III. Correlations Between Objectively and Subjectively Derived Sleep Parameters and Sleep Quality" for NSW Operators Objective Subjective Sleep Quality p Value Sleep Quality p Value

Sleep Parameter TIB (Minutes) TST (Minutes) Sleep Efficiency (%) Sleep Latency (Minutes) WASO (Minutes)

0.00 0.08 0.09 -0.29

0.98 0.67 0.63 0.11

-0.02 0.30 0.61 -0.14

0.91 0.12 0.001 0.47

-0.05

0.79

-0.60

0.001

“Sleep quality was assessed by self-report on a scale of 1 (poor) to 10 (excel lent). NSW. Naval Special Warfare; TIB, time in bed (also referred to as the “down period” = lights out to lights on); TST, total sleep time (defined as the total amount of time spent sleeping; also referred to as the sleep onset to sleep offset interval [“o-o” interval]); Sleep efficiency is the amount of time spent sleeping while in bed and is calculated as TST/T1B X 100%; Sleep latency is the amount of time it took the subject on average to fall asleep; WASO, wake after sleep onset (defined as the amount of time spent awake after lights-out and sleep onset occurs).

10-point scale. There were no statistically significant rela tionships between the objective sleep parameters (using actigraphy) and self-reported sleep quality. For the sleep parameters calculated from the sleep log, greater sleep effi ciency correlated with higher ratings of sleep quality (r = 0.61, p < 0.001), and WASO was inversely correlated with ratings of sleep quality (r = -0.60, p < 0.001). DISCUSSION To our knowledge, this is the first study to objectively and subjectively quantify sleep among NSW Operators. Overall, study data suggest impaired sleep quality, sleep maintenance (WASO), and clinically impaired levels of sleep efficiency, indicating potential sleep disruptions for this population. Deficits in sleep quantity and quality were evident in both actigraphy and self-reported sleep data. Also, there is incon gruence in objective and subjective sleep indices, specifi cally, participants self-reported significantly shorter time to fall asleep (sleep latency) and spending less time awake after falling asleep (WASO) compared with objectively derived scores. Furthermore, actigraphically derived sleep efficiency was below the clinically accepted level of 85%.31 TST for both actigraphically and self-reported measures are less than recommended nightly durations of 7.5 to 8.0 hours.1'" The actigraphically derived TST is close to normative TST data for those aged 30 to 39 years at 375.80 minutes (6 hours and 16 minutes).32 And, the selfreported TST duration, which is greater than the objective TST, is consistent with the literature regarding differences between these TST assessment methods.28 It is not merely the total number of hours slept but also the quality of that sleep that is of concern. We found sleep efficiency calculated via actigraphy quantified below the 85% clinically acceptable level.31 Low sleep efficiency may result from either an increase in sleep latency, waking during sleep, or both. Although normative data show approximately



94% sleep efficiency for 30 to 39-year-olds,32'33 participants in this study demonstrated a much lower sleep efficiency level for both objective (83.33%) and subjective measures (90.7%). In comparison, a sample of deployed U.S. Air Force service members who self-reported TIB and TST demonstrated sleep efficiency levels below the 85% thresh old.19 This information is extremely useful in the context of discussing sleep with the health care provider. Even if the health care provider asks the patient the generic question “How are you sleeping?”, current results suggest there is value in asking about specific sleep parameters. Such objec tive data can provide valuable clinical information regarding quality of a patient’s sleep. The combination of detailed sub jective evaluations coupled with objective measures, and the fact that poor quality sleep is known to be nonrestorative,34 supports the Taylor et al 2013 SEAL Sleep Working Group (SSWG) technical report recommendation to focus more atten tion on sleep in the NSW Operator. These results demonstrate objectively derived sleep latency and WASO are significantly higher than these same parameters self-reported in the sleep logs and also are higher than normative data.32'33 Specifically, actigraphically derived sleep latency among NSW Operators was an average of 25.82 minutes. Though on par with normative data from the National Sleep Foundation Sleep in America Poll36 (23 minutes), Ohayon et al32 reported norms for this index at 10.5 minutes. Furthermore, WASO times for our sample collected by actigraphy (approximately 40 minutes) indicate our sample falls within the clinical range for this parameter when compared with normative data.32 These findings sug gest sleep maintenance is a problem and that sleep problems may be even more severe than realized. The findings from this study reflect what technically should be “recovery” sleep for participants since the study occurred during a low operational training period. NSW Operators cycle in and out of high-intensity training periods, so if these current findings are reflective of their best sleep, we estimate their sleep during high operational tempos is poorer than what is reported here. With this in mind, we rec ommend that thorough and accurate sleep histories be rou tinely collected by medical personnel34 and periodically reassessed as also mentioned in the SSWG technical report. Supplementary information such as each individual’s total sleep requirement, current presence of sleep fragmentation or other disruptions, and habitual timing of sleep (circadian rhythm phase) should also be acquired as mentioned in the SSWG report.34 The authors of the SSWG report also put forth the recom mendation that a force-wide sleep surveillance program be established to monitor sleep disruption among NSW Opera tors to determine where sleep disruption occurs in the train ing cycle. Additional strategies by the SSWG to improve sleep include sleep education programming, environmental regulation to improve sleep quality, hiring a full-time sleep professional, and establishing an NSW sleep clinic to provide


sleep kits, sleeping tips, and biofeedback strategies. Further, despite rigorous training schedules, adequate recovery periods should also be implemented.36 Maintenance of proper diet, exercise, “strategic napping,” and establishing good sleep hygiene are other ways to enhance sleep.36 The limitations of this study include reduced specificity for actigraphy measures (e.g., detecting someone is lying in bed awake but not sleeping) and the cross-sectional design. Because of the latter, we are limited in our generalizations beyond our sample. The opportunity to participate in this study was open to all military personnel within the specific groups, but a selection bias may exist. That is, some may have participated in the study because they believed they had poor sleep.37 Furthermore, the impact of the various types of nutritional supplements used by 13 of the partici pants and their potential effects on sleep quality is unknown but worth mentioning. To our knowledge, this is the first study of its kind among NSW Operators. Understanding the relationship between objectively derived sleep indices and self-reported sleep indices and sleep quality in this population is critical for employing appropriate and effective treatments and enabling the maintenance and improvement of the readiness of these elite military personnel. Future research should address the combat experiences of participants since sleep disruptions are likely to be more prevalent in those with a traumatic brain injury.38'39 Assessing the relationship among head injury, sleep, and alertness in combat zones40 is also an additional line of investigation. And finally, a program investigating the physiology of sleep,41 underlying the sleep efficiency scores reported here and how they relate to perfor mance and resiliency in this population, could help provide focus on specific areas to achieve the highest level of perfor mance among NSW Operators. Such a study is currently under way in our laboratory. ACKNOWLEDGMENTS The authors would like to thank Dr. Gena Glickman and Dr. Elizabeth Harrison for comments on earlier drafts of the manuscript. This project was supported by the Office of Naval Research, Code 30 (Human Performance, Training, and Evaluation) under Work Unit No. N1024.

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