https://orcid.org/0000-0002-9829-4347
Abstract
This field study (a) assessed sleep quality of sailors on the U.S. Navy (USN) ships while underway, (b) investigated whether the Pittsburgh Sleep Quality Index (PSQI) scores were affected by occupational factors and sleep attributes, and (c) assessed whether the PSQI could predict impaired psychomotor vigilance performance.
Longitudinal field assessment of fit-for-duty USN sailors performing their underway duties (N = 944, 79.0% males, median age 26 years). Participants completed questionnaires, wore actigraphs, completed logs, and performed the wrist-worn 3-min Psychomotor Vigilance Task (PVT).
Sailors slept on average 6.60 ± 1.01 h/day with 86.9% splitting their sleep into more than one episode/day. The median PSQI Global score was 8 (interquartile range [IQR] = 5); 80.4% of the population were classified as “poor sleepers” with PSQI scores >5. PSQI scores were affected by sailor occupational group, rank, daily sleep duration, and number of sleep episodes/day. Sleep quality showed a U-shape association with daily sleep duration due to the confounding effect of split sleep. Sailors with PSQI scores >9 had 21.1% slower reaction times (p < 0.001) and 32.8%–61.5% more lapses combined with false starts (all p < 0.001) than sailors with PSQI scores ≤9. Compared to males and officers, females and enlisted personnel had 86% and 23% higher risk, respectively, of having PSQI scores >9. Sailors in the PSQI > 9 group had more pronounced split sleep.
Working on Navy ships is associated with elevated PSQI scores, a high incidence of poor sleep, and degraded psychomotor vigilance performance. The widely used PSQI score>5 criterion should be further validated in active-duty service member populations.
The Pittsburgh Sleep Quality Index (PSQI) is a widely used tool to assess sleep quality. PSQI has not been validated, however, for military populations. Based on a sample of fit-for-duty USN sailors performing their duties on ships, our results showed that sleep quality was associated with individual sailor occupational group/work schedule, rank, daily sleep duration, and number of sleep episodes/day. All occupational groups had a large percentage of poor sleepers (PSQI score > 5), whereas a higher criterion (PSQI score > 9) was associated with impaired psychomotor vigilance performance. These findings expand our knowledge regarding sleep quality at sea and the usefulness of PSQI in the military, but also stress the need for further validation of the PSQI > 5 criterion on military populations.
Introduction
As part of a multiyear effort, researchers at the Naval Postgraduate School have undertaken a series of studies to validate a number of widely used screening tools in the military operational environment. The first study focused on the utility of the Epworth Sleepiness Scale (ESS) [1] to determine if ESS scores were predictive of actigraphically determined sleep and psychomotor vigilance performance of active duty crewmembers working their normal duties on a U.S. Navy (USN) ship [2]. The next study assessed whether the ESS used with the Fatigue Severity Scale could differentiate between subjective sleepiness and subjective fatigue [3].
The current study focuses on the Pittsburgh Sleep Quality Index (PSQI), a self-rated screening questionnaire which assesses sleep quality and disturbances [4]. The clinimetric and clinical properties of the PSQI, using a cutoff criterion score of more than 5, suggest its utility both in psychiatric clinical practice and research activities to distinguish between good and poor sleepers [4]. Being the most commonly used general sleep measure [5, 6], the PSQI has been used and validated in various populations; in primary insomnia patients [7], in healthy control subjects [4], in various sleep and psychiatric disorders [4, 8], in samples with health conditions [9], in patients with chronic pain [10, 11], and in elderly populations [12, 13]. However [14], the validity of PSQI in active duty military personnel and veterans is not well investigated. Our review showed only two studies that focused explicitly on the psychometric properties of PSQI in military populations, mainly in military service veterans and active duty service members with sleep problems [5, 15, 16].
Along these lines, Troxel and colleagues noted that few studies in military populations have used the full, validated instrument [6]. Our review failed to identify any studies focusing explicitly on the operational factors affecting PSQI scores in active duty members working in the operational maritime and naval environment. In agreement with Troxel et al. [6] that the PSQI needs further validation in military populations, our field study has three goals. First, assess subjective sleep quality in a large sample of active duty service members on USN ships while performing their underway duties. Second, investigate the association between PSQI scores and work schedules used on USN ships. Third, determine whether PSQI scores can differentiate amongst levels of psychomotor vigilance performance. This study is part of a multiyear effort designed to systematically and empirically assess a wide range of watchstanding schedules which are used in the USN, measure the work and rest patterns of sailors in a variety of shipboard operational environments, and provide insight and guidance for future naval operations.
Methods
Participants
Sailors assigned to seven surface combatants of the USN (one Nimitz-class aircraft carrier, one Ticonderoga-class cruiser, and five Arleigh Burke-class destroyers) were recruited and enrolled in studies of work and rest patterns. Data were collected in six periods (December 2012, May 2013, June and November 2014, June 2017, December 2017–January 2018). Two ships were conducting underway training exercises and five ships were operationally deployed. All sailors onboard during the study periods were eligible to participate. Study procedures were approved by the Institutional Review Board of the Naval Postgraduate School. Participants provided written informed consent.
Based on their dominant work schedule, sailors were classified into one of four occupational groups. “Watchstanders” included sailors who “stood watch,” a period of time during which a sailor is assigned specific, detailed responsibilities on a recurring basis [17]. The daily schedule of the watchstanders is knitted around their watch during which time they cannot leave their post unless relieved of duty. The watchstander group included sailors working various watchstanding schedules, with ~70% on fixed schedules standing watch at the same time every day (e.g. 3 h-on/9 h-off, 4 h-on/8 h-off, 6 h-on/18 h-off) and ~30% on rotating schedules standing watch at different times every day (e.g. 5 h-on/10 h-off, 5 h-on/15 h-off).
“Non-watchstanders” were divided into three sub-groups: “maintenance shiftworkers” included sailors performing maintenance on fixed 12-h shifts, with the day shift commencing early in the morning hours and the night shift commencing in early evening hours. Compared to the shifts of watchstanders, a maintenance shift is more flexible, that is, it includes more self-paced tasks and sailors can take brief rest periods if needed. “Galley workers” included sailors involved in food preparation who work between early morning and late evening in the ship’s kitchen or galley. In general, galley workers slept at night. Lastly, the “Dayworkers” group included sailors who worked during the morning to early evening hours and slept at night. Regardless of their occupational group, while underway all sailors are responsible for carrying out various duties during their time off watch/shift, for example, attending meetings, training, drills, or other work and operational commitments.
Equipment and instruments
Actigraphy
Sleep was assessed by wrist-worn actigraphy and activity logs following existing recommendations [18, 19]. Specifically, we used information from activity logs to manually determine start and end times of rest/sleep intervals using the actigraphy data as the primary source for the sleep analysis. Within each rest/sleep interval, the actigraphically assessed sleep was automatically calculated. Rest/sleep episodes were distinctly different from sleep periods and were readily identified.
The Motionlogger Watch (Ambulatory Monitoring, Inc. [AMI], Ardsley, NY) and the Spectrum (Philips-Respironics [PR], Bend, OR) actiwatch were used. Data for both devices were collected in 1-min epochs. AMI data (collected in the Zero-Crossing Mode) were scored using Action W version 2.7.2155 software using the Cole–Kripke algorithm with rescoring rules. The criterion for sleep and wake episodes was 5 min; the sleep latency criterion was no more than 1 min awake in a 20-min period (all default values for this software). PR data were scored using Actiware software version 6.0.0 (Phillips Respironics, Bend, OR) using the medium sensitivity threshold (40 counts per epoch), with 10 immobile minutes as the criterion for sleep onset and sleep end (all default values for this software). Previous research has shown that AMI data analyzed with Cole–Kripke and PR data analyzed with medium sensitivity parameters assess total sleep time for an approximately 8-h night sleep episode with 3 min precision [20].
Psychomotor vigilance performance
Performance data were collected using a 3-min version of the original 10-min Psychomotor Vigilance Task—PVT [21] which was embedded in the AMI actigraphs [22, 23]. The PVT is a simple reaction time test where participants are required to press a button in response to a visual stimulus. The nominal interstimulus interval ranged from 2 to 10 s. The actigraphic display was lit (red backlight on) for one second and the letters “PUSH” were used as the visual stimuli; the response time was then displayed in milliseconds.
Questionnaires
The pre-study questionnaire included demographic information (age, gender, rate/rank, department, use of caffeinated beverages (e.g. tea, coffee, soft drinks, and energy drinks), use of nicotine products, having an exercise routine, taking medications—prescribed or over-the-counter). The end-of-study questionnaire asked the participants to indicate whether they stood watch during the underway and to complete the Pittsburg Sleep Quality Index—PSQI [4] to assess sleep quality. From the 24 PSQI items, 19 are self-rated and 5 items are rated by the bedpartner or the roommate. The self-rated questions yield seven component scores (sleep quality, sleep latency, duration, sleep efficiency, sleep disturbances, sleep medication use, and daytime dysfunction) rated from 0 (better) to 3 (worse). The total score, ranging from 0 (better) to 21 (worse), is the summation of the component scores. Individuals with a PSQI total score ≤5 are characterized as good sleepers, whereas scores >5 are associated with poor sleep quality. The PSQI has a sensitivity of 89.6% and a specificity of 86.5% (κ = 0.75, p < 0.001) in non-military populations, and an internal consistency α = 0.83 [4]. Even though the original version of PSQI referred to sleep quality during the previous month, the ecological validity of the tool, that is, subjects’ accuracy in recalling sleep quality, has been demonstrated for various reporting periods from 3 days to 1 month [24]. The period of recall for the PSQI we used was 2 weeks.
Study design and procedures
The information presented herein is a subset of measures from multiple field assessments on USN ships. Data were collected using a prospective naturalistic design with an underway data collection period of 7–18 days. Initially, sailors completed the pre-study questionnaire which included demographic questions. Then over the data collection period, sailors were asked to wear an actiwatch, complete their activity log once per day, and perform the 3-min PVT. Watchstanders and shiftworkers were asked to perform the PVT before and after each watch/shift; non-shiftworkers were asked to perform the PVT once after awakening and once before bedtime. The protocol of PVT data collection for the watchstanders/shiftworkers ensured that their cognitive performance was assessed during the operationally relevant watch/shift periods. At the end of the data collection, sailors completed the post-study questionnaire. At the beginning of each data collection, sailors had been assigned to the same daily schedule for at least 3 days.
Analytical approach
Initially, 944 sailors were enrolled (Figure 1). Sailors using medications (sleeping aids, anti-inflammatory, and anti-depressant drugs—n = 57) or with missing data (n = 15) were excluded. Therefore, the analysis was based on 872 sailors (655 with actigraphy data and 267 with PVT data).
Participation flow chart.
PVT performance was assessed by mean reaction time (RT), mean response speed (1/RT), fastest 10% RT (i.e. 10th percentile of RT), slowest 10% of 1/RT (i.e. 10th percentile of 1/RT), percentage of 335 and 500 ms lapses, percentage of false starts, and percentage of lapses combined with false starts. No imputation was applied to PVT data. Participants were included in the PVT data analysis only when they performed the PVT on at least 50% of the days in the data collection period; the pattern of missing tests did not show a systematic bias. Based on these criteria, PVT adherence was ~75% and was not associated with age, gender, or rank (all p > 0.05). Sleep analysis was based on sleep episode duration, awake period duration, daily sleep duration, and on the number of sleep episodes per day. The metric “average number of sleep episodes/day” is calculated as the ratio of the number of sleep episodes during the data collection period divided by the number of data collection days. Initially, we calculated the average number of sleep episodes/day for each participant. Next, we calculated the grand average number of sleep episodes/day for those sailors who napped during the data collection period. Sleep episodes recorded in sleep logs were used to impute missing actigraphic data and accounted for 1.7% of all sleep episodes. Imputation was applied to sleep data only when: (a) there was a gap in actigraphy data within which the activity log showed a sleep interval and (b) the pattern of actigraphy data, assisted by the activity logs, was such to assure confidence in the interpolation of the sleep interval. Sleep and PVT metrics were aggregated to get an average score for each individual over the entire study period. Therefore, both sleep and PVT metrics provided an overall estimate of sailor alertness and performance during the data collection period.
Statistical analysis was conducted using JMP statistical software (JMP Pro 15, SAS Institute, Cary, NC). First, all variables underwent descriptive statistical analysis to identify anomalous entries and to determine demographic characteristics. Next, we compared the occupational groups in terms of demographic characteristics and PSQI scores. General linear model analysis was used to assess the predictor factors of the PSQI Global scores. Potential predictor factors included sailor occupational group, rank group, daily sleep duration, number of sleep episodes per day, and the interaction term between daily sleep duration and the number of sleep episodes per day. Lastly, partition analysis was used to explore the association between PVT response speed and PSQI Global score.
Data normality was assessed with the Shapiro–Wilk W test. Correlations were assessed with Spearman’s rho. Fisher’s Exact test was used for pairwise comparisons. Tukey–Kramer Honest Significant Difference (HSD) test and Dunn method for joint ranking were used for multiple comparisons. An alpha level of 0.05 was used to determine statistical significance. Post hoc statistical significance was assessed using the Benjamini–Hochberg False Discovery Rate (BH-FDR) controlling procedure [25] with q = 0.20. Summaries of continuous data are reported as mean (M) ± standard deviation (SD) or median (MD)—interquartile range (IQR) as appropriate.
Results
Participants had a median age of 25 (IQR = 7) years, and were predominantly males (692, 79.4%) and enlisted personnel (731, 83.8%). In terms of demographic characteristics, the study sample did not differ substantively from the population of active duty service members in the USN [26]. Approximately 9% (n = 82) of the sailors were using prescription or over-the-counter medications, that is, allergy drugs (24, 2.77%), high blood pressure drugs (12, 1.38%), acid reflux drugs (11, 1.27%), anti-emetic drugs (8, 0.92%), migraine/headaches drugs (3, 0.35%), anti-viral drugs (2, 0.23%), and other (22, 2.54%).
As assessed by actigraphy, the average duration of sleep episodes was 4.63 ± 1.40 h, whereas the average duration of awake periods was 11.6 ± 3.32 h. Participants slept an average of 6.60 ± 1.01 h daily (ranging from 1.83 to 9.52 h), with 569 (86.9%) sailors splitting their sleep into 1.5 episodes per day (median value with IQR = 0.58). The median PSQI Global score was 8 (IQR = 5), ranging from 1 to 18. PSQI scores indicated that 80.4% of the participants were “poor sleepers” (PSQI score > 5).
From the 872 sailors, 666 were watchstanders and 206 non-watchstanders (i.e. 39 maintenance shiftworkers, 32 galley workers, and 135 dayworkers). Occupational groups did not differ in terms of gender (Fisher’s Exact test, p = 0.174), but watchstanders were on average ~2.6 years younger than dayworkers (Dunn method for joint ranking p = 0.012).
Compared to the other occupational groups, watchstanders had the shortest sleep episodes (4.32 ± 1.23 h; Dunn method for joint ranking, all p < 0.01) and the shortest awake periods (11.0 ± 3.10 h; all p < 0.01) but their average daily sleep duration (6.51 ± 1.03 h) differed only from dayworkers (p < 0.001). Galley workers and watchstanders had the highest (worst) PSQI Global scores (Table 1). Compared to dayworkers, watchstanders had worse PSQI scores in terms of the Global score, sleep latency, sleep duration, habitual sleep efficiency, sleep quality, and daytime dysfunction. The same pattern was evident also in galley workers. Specifically, compared to dayworkers, galley workers had worse PSQI scores in terms of the Global score, sleep duration, and sleep quality. These results are shown in Table 1. Using the cutoff criterion of PSQI Global score >5, 84 (62.2%) dayworkers were identified as “poor sleepers” as compared to 30 (76.9%) maintenance shiftworkers (Fisher’s Exact test, p = 0.125 compared to dayworkers), 27 (84.4%) galley workers (p = 0.021), and 560 (84.1%) watchstanders (p < 0.001).
Sleep attributes and PSQI scores
| PSQI | Entire sample (N = 872) | Dayworkers (n = 135) | Maintenance shiftworkers (n = 39) | Galley workers (n = 32) | Watchstanders (n = 666) |
|---|---|---|---|---|---|
| Sleep attributes | |||||
| Sleep episode duration (h), M ± SD|| | 4.63 ± 1.40 | 5.75 ± 1.43 | 6.03 ± 1.23 | 5.37 ± 1.51 | 4.33 ± 1.23*3,†2,††3 |
| Awake period duration (h), M ± SD|| | 11.6 ± 3.32 | 13.2 ± 3.20 | 12.9 ± 3.38 | 15.9 ± 3.26¶1 | 11.0 ± 3.10*3,†2,††3 |
| Daily sleep duration (h), M ± SD|| | 6.60 ± 1.01 | 7.06 ± 0.86 | 6.74 ± 0.97 | 6.54 ± 0.83 | 6.51 ± 1.03*3 |
| Sailors with split sleep, # (%)# | 569 (86.9%) | 67 (69.8%) | 16 (61.5%) | 9 (42.9%)¶1 | 477 (93.2%)*3,†3,††3 |
| Sleep episodes per day (#), MD (IQR)||,¶¶ | 1.5 (0.58) | 1.29 (0.43) | 1.44 (0.48) | 1.22 (0.39) | 1.55 (0.61)*3,††1 |
| PSQI, MD (IQR) | |||||
| Global score | 8 (5) | 7 (5) | 8 (4) | 9 (6.5) ¶2 | 9 (5)*1 |
| Sleep latency | 1 (1) | 1 (2) | 1 (1) | 1.5 (2) | 2 (1)*2 |
| Sleep duration | 2 (1) | 1 (2) | 1 (1) | 2 (0.75) ¶3,§1 | 2 (0)*1,†2 |
| Habitual sleep efficiency | 0 (1) | 0 (1) | 0 (2) | 0.5 (1) | 1 (2)*1 |
| Sleep disturbances | 1 (1) | 1 (1) | 1 (1) | 1 (1) | 1 (1) |
| Subjective sleep quality | 1 (1) | 1 (1) | 1 (1) | 2 (1) ¶3 | 1 (1)*2,††1 |
| Use of sleeping medication | 0 (0) | 0 (0) | 0 (0) | 0 (1) | 0 (0) |
| Daytime dysfunction | 1 (1) | 1 (0) | 1 (0) | 1 (1) | 1 (1)*3 |
| PSQI | Entire sample (N = 872) | Dayworkers (n = 135) | Maintenance shiftworkers (n = 39) | Galley workers (n = 32) | Watchstanders (n = 666) |
|---|---|---|---|---|---|
| Sleep attributes | |||||
| Sleep episode duration (h), M ± SD|| | 4.63 ± 1.40 | 5.75 ± 1.43 | 6.03 ± 1.23 | 5.37 ± 1.51 | 4.33 ± 1.23*3,†2,††3 |
| Awake period duration (h), M ± SD|| | 11.6 ± 3.32 | 13.2 ± 3.20 | 12.9 ± 3.38 | 15.9 ± 3.26¶1 | 11.0 ± 3.10*3,†2,††3 |
| Daily sleep duration (h), M ± SD|| | 6.60 ± 1.01 | 7.06 ± 0.86 | 6.74 ± 0.97 | 6.54 ± 0.83 | 6.51 ± 1.03*3 |
| Sailors with split sleep, # (%)# | 569 (86.9%) | 67 (69.8%) | 16 (61.5%) | 9 (42.9%)¶1 | 477 (93.2%)*3,†3,††3 |
| Sleep episodes per day (#), MD (IQR)||,¶¶ | 1.5 (0.58) | 1.29 (0.43) | 1.44 (0.48) | 1.22 (0.39) | 1.55 (0.61)*3,††1 |
| PSQI, MD (IQR) | |||||
| Global score | 8 (5) | 7 (5) | 8 (4) | 9 (6.5) ¶2 | 9 (5)*1 |
| Sleep latency | 1 (1) | 1 (2) | 1 (1) | 1.5 (2) | 2 (1)*2 |
| Sleep duration | 2 (1) | 1 (2) | 1 (1) | 2 (0.75) ¶3,§1 | 2 (0)*1,†2 |
| Habitual sleep efficiency | 0 (1) | 0 (1) | 0 (2) | 0.5 (1) | 1 (2)*1 |
| Sleep disturbances | 1 (1) | 1 (1) | 1 (1) | 1 (1) | 1 (1) |
| Subjective sleep quality | 1 (1) | 1 (1) | 1 (1) | 2 (1) ¶3 | 1 (1)*2,††1 |
| Use of sleeping medication | 0 (0) | 0 (0) | 0 (0) | 0 (1) | 0 (0) |
| Daytime dysfunction | 1 (1) | 1 (0) | 1 (0) | 1 (1) | 1 (1)*3 |
Statistical significance for differences: “1”: p < 0.05; “2”: p < 0.01; “3”: p < 0.001.
*Difference between “Watchstanders” and “Dayworkers” groups.
†Difference between “Watchstanders” and “Maintenance shiftworkers” groups.
††Difference between “Watchstanders” and “Galley workers” groups.
¶Difference between “Galley workers” and “Dayworkers” groups.
§Difference between “Galley workers” and “Maintenance shiftworkers”.
||Multiple comparisons with non-parametric Dunn method for joint ranking.
#Pairwise comparisons with Fisher’s Exact Test. Post hoc analysis for statistical significance with the BH-FDR controlling procedure.
¶¶For sailors with split sleep.
Sleep attributes and PSQI scores
| PSQI | Entire sample (N = 872) | Dayworkers (n = 135) | Maintenance shiftworkers (n = 39) | Galley workers (n = 32) | Watchstanders (n = 666) |
|---|---|---|---|---|---|
| Sleep attributes | |||||
| Sleep episode duration (h), M ± SD|| | 4.63 ± 1.40 | 5.75 ± 1.43 | 6.03 ± 1.23 | 5.37 ± 1.51 | 4.33 ± 1.23*3,†2,††3 |
| Awake period duration (h), M ± SD|| | 11.6 ± 3.32 | 13.2 ± 3.20 | 12.9 ± 3.38 | 15.9 ± 3.26¶1 | 11.0 ± 3.10*3,†2,††3 |
| Daily sleep duration (h), M ± SD|| | 6.60 ± 1.01 | 7.06 ± 0.86 | 6.74 ± 0.97 | 6.54 ± 0.83 | 6.51 ± 1.03*3 |
| Sailors with split sleep, # (%)# | 569 (86.9%) | 67 (69.8%) | 16 (61.5%) | 9 (42.9%)¶1 | 477 (93.2%)*3,†3,††3 |
| Sleep episodes per day (#), MD (IQR)||,¶¶ | 1.5 (0.58) | 1.29 (0.43) | 1.44 (0.48) | 1.22 (0.39) | 1.55 (0.61)*3,††1 |
| PSQI, MD (IQR) | |||||
| Global score | 8 (5) | 7 (5) | 8 (4) | 9 (6.5) ¶2 | 9 (5)*1 |
| Sleep latency | 1 (1) | 1 (2) | 1 (1) | 1.5 (2) | 2 (1)*2 |
| Sleep duration | 2 (1) | 1 (2) | 1 (1) | 2 (0.75) ¶3,§1 | 2 (0)*1,†2 |
| Habitual sleep efficiency | 0 (1) | 0 (1) | 0 (2) | 0.5 (1) | 1 (2)*1 |
| Sleep disturbances | 1 (1) | 1 (1) | 1 (1) | 1 (1) | 1 (1) |
| Subjective sleep quality | 1 (1) | 1 (1) | 1 (1) | 2 (1) ¶3 | 1 (1)*2,††1 |
| Use of sleeping medication | 0 (0) | 0 (0) | 0 (0) | 0 (1) | 0 (0) |
| Daytime dysfunction | 1 (1) | 1 (0) | 1 (0) | 1 (1) | 1 (1)*3 |
| PSQI | Entire sample (N = 872) | Dayworkers (n = 135) | Maintenance shiftworkers (n = 39) | Galley workers (n = 32) | Watchstanders (n = 666) |
|---|---|---|---|---|---|
| Sleep attributes | |||||
| Sleep episode duration (h), M ± SD|| | 4.63 ± 1.40 | 5.75 ± 1.43 | 6.03 ± 1.23 | 5.37 ± 1.51 | 4.33 ± 1.23*3,†2,††3 |
| Awake period duration (h), M ± SD|| | 11.6 ± 3.32 | 13.2 ± 3.20 | 12.9 ± 3.38 | 15.9 ± 3.26¶1 | 11.0 ± 3.10*3,†2,††3 |
| Daily sleep duration (h), M ± SD|| | 6.60 ± 1.01 | 7.06 ± 0.86 | 6.74 ± 0.97 | 6.54 ± 0.83 | 6.51 ± 1.03*3 |
| Sailors with split sleep, # (%)# | 569 (86.9%) | 67 (69.8%) | 16 (61.5%) | 9 (42.9%)¶1 | 477 (93.2%)*3,†3,††3 |
| Sleep episodes per day (#), MD (IQR)||,¶¶ | 1.5 (0.58) | 1.29 (0.43) | 1.44 (0.48) | 1.22 (0.39) | 1.55 (0.61)*3,††1 |
| PSQI, MD (IQR) | |||||
| Global score | 8 (5) | 7 (5) | 8 (4) | 9 (6.5) ¶2 | 9 (5)*1 |
| Sleep latency | 1 (1) | 1 (2) | 1 (1) | 1.5 (2) | 2 (1)*2 |
| Sleep duration | 2 (1) | 1 (2) | 1 (1) | 2 (0.75) ¶3,§1 | 2 (0)*1,†2 |
| Habitual sleep efficiency | 0 (1) | 0 (1) | 0 (2) | 0.5 (1) | 1 (2)*1 |
| Sleep disturbances | 1 (1) | 1 (1) | 1 (1) | 1 (1) | 1 (1) |
| Subjective sleep quality | 1 (1) | 1 (1) | 1 (1) | 2 (1) ¶3 | 1 (1)*2,††1 |
| Use of sleeping medication | 0 (0) | 0 (0) | 0 (0) | 0 (1) | 0 (0) |
| Daytime dysfunction | 1 (1) | 1 (0) | 1 (0) | 1 (1) | 1 (1)*3 |
Statistical significance for differences: “1”: p < 0.05; “2”: p < 0.01; “3”: p < 0.001.
*Difference between “Watchstanders” and “Dayworkers” groups.
†Difference between “Watchstanders” and “Maintenance shiftworkers” groups.
††Difference between “Watchstanders” and “Galley workers” groups.
¶Difference between “Galley workers” and “Dayworkers” groups.
§Difference between “Galley workers” and “Maintenance shiftworkers”.
||Multiple comparisons with non-parametric Dunn method for joint ranking.
#Pairwise comparisons with Fisher’s Exact Test. Post hoc analysis for statistical significance with the BH-FDR controlling procedure.
¶¶For sailors with split sleep.
Predictors of PSQI Global scores
Next, we assessed the predictors of the PSQI Global scores (Table 2). Potential predictors included sailor work schedule group, rank group, daily sleep duration, number of sleep episodes per day, and the interaction term between daily sleep duration and number of sleep episodes per day. Daily sleep duration and the number of sleep episodes per day were not correlated (Spearman’s rho = 0.046, p = 0.245). The overall model was statistically significant, F(14, 640) = 8.06, p < 0.001. Adjusted for ship and gender, PSQI Global scores were associated with work schedule group (p = 0.004) and rank group (p < 0.001). Watchstanders and galley workers had higher (worse) PSQI Global scores than dayworkers (Dunnett’s test; p = 0.009 and p = 0.044 respectively), whereas officers had lower (better) PSQI Global scores compared to enlisted personnel. In terms of sleep attributes, daily sleep duration (p = 0.013), number of sleep episodes per day (p = 0.048), and the interaction between daily sleep duration and the number of sleep episodes per day (p = 0.011) were statistically significant predictors. The association between sleep attributes and PSQI Global scores becomes more evident in Figure 2. The upper diagram shows that PSQI scores have a U-shape association with daily sleep duration. Longer daily sleep duration may be associated with low (better) and high (worse) PSQI scores. The reason is the number of sleep episodes that contribute to the accumulation of the daily sleep duration (lower diagram). Longer daily sleep duration, which in the naval environment can be achieved with napping, is associated with worse sleep quality as assessed by PSQI Global score. In contrast, longer daily sleep duration accrued with fewer sleep episodes (i.e. sleep is consolidated in longer sleep episodes) is associated with better sleep quality (i.e. lower PSQI Global scores.)
Factors for PSQI Global scores
| Factor | Coefficient | 95% confidence interval | P-value |
|---|---|---|---|
| Gender (female) | – | – | 0.605 |
| Ship | – | – | 0.153 |
| Sleep episodes per day* | −14.5 | −28.8 to −0.128 | 0.048 |
| Daily sleep duration* | −9.033 | −16.1 to −1.92 | 0.013 |
| Daily sleep duration × Sleep episodes per day | 7.26 | 1.66 to 12.9 | 0.011 |
| Work schedule group | – | – | 0.004 |
| Control vs. Watchstanders | −0.681 | −1.33 to −0.032 | 0.040 |
| Galley Workers vs. Watchstanders | 1.11 | 0.070 to 2.14 | 0.036 |
| Maintenance Shiftworkers vs. Watchstanders | −0.862 | −1.81 to −0.085 | 0.075 |
| Enlisted personnel | 0.790 | 0.462 to 1.12 | <0.001 |
| Factor | Coefficient | 95% confidence interval | P-value |
|---|---|---|---|
| Gender (female) | – | – | 0.605 |
| Ship | – | – | 0.153 |
| Sleep episodes per day* | −14.5 | −28.8 to −0.128 | 0.048 |
| Daily sleep duration* | −9.033 | −16.1 to −1.92 | 0.013 |
| Daily sleep duration × Sleep episodes per day | 7.26 | 1.66 to 12.9 | 0.011 |
| Work schedule group | – | – | 0.004 |
| Control vs. Watchstanders | −0.681 | −1.33 to −0.032 | 0.040 |
| Galley Workers vs. Watchstanders | 1.11 | 0.070 to 2.14 | 0.036 |
| Maintenance Shiftworkers vs. Watchstanders | −0.862 | −1.81 to −0.085 | 0.075 |
| Enlisted personnel | 0.790 | 0.462 to 1.12 | <0.001 |
Note: Analysis conducted on the 655 sailors with questionnaire and actigraphy data.
*Square-root transformed values.
Factors for PSQI Global scores
| Factor | Coefficient | 95% confidence interval | P-value |
|---|---|---|---|
| Gender (female) | – | – | 0.605 |
| Ship | – | – | 0.153 |
| Sleep episodes per day* | −14.5 | −28.8 to −0.128 | 0.048 |
| Daily sleep duration* | −9.033 | −16.1 to −1.92 | 0.013 |
| Daily sleep duration × Sleep episodes per day | 7.26 | 1.66 to 12.9 | 0.011 |
| Work schedule group | – | – | 0.004 |
| Control vs. Watchstanders | −0.681 | −1.33 to −0.032 | 0.040 |
| Galley Workers vs. Watchstanders | 1.11 | 0.070 to 2.14 | 0.036 |
| Maintenance Shiftworkers vs. Watchstanders | −0.862 | −1.81 to −0.085 | 0.075 |
| Enlisted personnel | 0.790 | 0.462 to 1.12 | <0.001 |
| Factor | Coefficient | 95% confidence interval | P-value |
|---|---|---|---|
| Gender (female) | – | – | 0.605 |
| Ship | – | – | 0.153 |
| Sleep episodes per day* | −14.5 | −28.8 to −0.128 | 0.048 |
| Daily sleep duration* | −9.033 | −16.1 to −1.92 | 0.013 |
| Daily sleep duration × Sleep episodes per day | 7.26 | 1.66 to 12.9 | 0.011 |
| Work schedule group | – | – | 0.004 |
| Control vs. Watchstanders | −0.681 | −1.33 to −0.032 | 0.040 |
| Galley Workers vs. Watchstanders | 1.11 | 0.070 to 2.14 | 0.036 |
| Maintenance Shiftworkers vs. Watchstanders | −0.862 | −1.81 to −0.085 | 0.075 |
| Enlisted personnel | 0.790 | 0.462 to 1.12 | <0.001 |
Note: Analysis conducted on the 655 sailors with questionnaire and actigraphy data.
*Square-root transformed values.
Daily sleep duration and number of sleep episodes per day by PSQI Global score. Cubic splines were applied to generate the smooth lines (upper diagram: lambda = 1.90; lower diagram: lambda = 32.9).
PVT performance and PSQI Global Scores
Partition analysis results suggested that a PSQI Global score of 9 could be used as a cutoff criterion for grouping sailors in terms of their PVT response speed (LogWorth = 8.72). Figure 3 shows PVT response speed versus PSQI Global score. Based on the partition results, sailors with PVT data were classified into three groups. As shown in Table 3, sailors with PSQI Global scores of 5 or less (the conventional criterion for good sleepers) did not differ from sailors with a score between 5 and 9. Compared to sailors with PSQI Global scores of 9 or less, however, sailors with a PSQI Global score of more than 9 had reaction times that were 21.1% slower (p < 0.001), 32.8% more lapses of 355 ms combined with false starts (p < 0.001), and 61.5% more lapses of 500 ms combined with false starts (p < 0.001). Compared to males and officers, females and enlisted personnel had 86% and 23% higher risk, respectively, of having a PSQI Global score of more than 9. PSQI groups also differed in the number of sleep episodes per day with sailors in the PSQI > 9 group having more pronounced split sleep.
PVT response speed versus PSQI Global score. A cubic spline was applied to generate the smooth line (lambda = 19.3).
Comparison between PSQI groups
| Variable | PSQI ≤ 5 (n = 37) | 5 < PSQI ≤ 9 (n = 129) | PSQI > 9 (n = 101) |
|---|---|---|---|
| Age in years, MD (IQR)¶ | 24 (8) | 26 (8) | 25 (6) |
| Sex (females), # (%)§ | 8 (21.6%) | 22 (17.1%) | 34 (33.7%)†2 |
| Enlisted personnel, # (%)§ | 26 (70.3%) | 99 (76.7%) | 93 (92.1%)*2,†2 |
| Watchstanders, # (%)§ | 34 (91.9%) | 111 (86.1%) | 93 (92.1%) |
| Daily sleep duration (h), MD (IQR)¶ | 6.68 (1.64) | 6.54 (1.35) | 6.63 (1.77) |
| Sleep episodes per day (#), MD (IQR)¶ | 1.25 (0.53) | 1.42 (0.60) | 1.70 (0.62)*3,†2 |
| PVT metrics | |||
| Mean RT (ms), MD (IQR)¶ | 293 (87.4) | 303 (94.5) | 369 (133)*2,†3 |
| Mean 1/RT, M ± SD†† | 4.00 ± 0.67 | 3.90 ± 0.70 | 3.38 ± 0.77*3,†3 |
| Fastest 10% RT (ms), MD (IQR)¶ | 191 (42.0) | 198 (48.7) | 226 (57.0)*3,†3 |
| Slowest 10% 1/RT, M ± SD†† | 2.40 ± 0.62 | 2.42 ± 0.64 | 1.99 ± 0.63*2,†3 |
| False Starts (FS) (%), MD (IQR)¶ | 1.00 (2.05) | 1.31 (1.68) | 1.16 (1.34)*2,†3 |
| Lapses 500 ms (%), MD (IQR)¶ | 6.04 (8.13) | 6.01 (67.33) | 10.4 (10.9)*2,†3 |
| Lapses 355 ms (%), MD (IQR)¶ | 12.3 (12.0) | 14.1 (15.4) | 26.6 (24.9)*3,†3 |
| Lapses 500 ms + FS (%), MD (IQR)¶ | 7.60 (8.85) | 7.43 (7.11) | 12.1 (10.6)*2,†3 |
| Lapses 355 ms + FS (%), MD (IQR) ¶ | 13.8 (11.2) | 15.8 (16.4) | 28.2 (25.6)*3,†3 |
| Variable | PSQI ≤ 5 (n = 37) | 5 < PSQI ≤ 9 (n = 129) | PSQI > 9 (n = 101) |
|---|---|---|---|
| Age in years, MD (IQR)¶ | 24 (8) | 26 (8) | 25 (6) |
| Sex (females), # (%)§ | 8 (21.6%) | 22 (17.1%) | 34 (33.7%)†2 |
| Enlisted personnel, # (%)§ | 26 (70.3%) | 99 (76.7%) | 93 (92.1%)*2,†2 |
| Watchstanders, # (%)§ | 34 (91.9%) | 111 (86.1%) | 93 (92.1%) |
| Daily sleep duration (h), MD (IQR)¶ | 6.68 (1.64) | 6.54 (1.35) | 6.63 (1.77) |
| Sleep episodes per day (#), MD (IQR)¶ | 1.25 (0.53) | 1.42 (0.60) | 1.70 (0.62)*3,†2 |
| PVT metrics | |||
| Mean RT (ms), MD (IQR)¶ | 293 (87.4) | 303 (94.5) | 369 (133)*2,†3 |
| Mean 1/RT, M ± SD†† | 4.00 ± 0.67 | 3.90 ± 0.70 | 3.38 ± 0.77*3,†3 |
| Fastest 10% RT (ms), MD (IQR)¶ | 191 (42.0) | 198 (48.7) | 226 (57.0)*3,†3 |
| Slowest 10% 1/RT, M ± SD†† | 2.40 ± 0.62 | 2.42 ± 0.64 | 1.99 ± 0.63*2,†3 |
| False Starts (FS) (%), MD (IQR)¶ | 1.00 (2.05) | 1.31 (1.68) | 1.16 (1.34)*2,†3 |
| Lapses 500 ms (%), MD (IQR)¶ | 6.04 (8.13) | 6.01 (67.33) | 10.4 (10.9)*2,†3 |
| Lapses 355 ms (%), MD (IQR)¶ | 12.3 (12.0) | 14.1 (15.4) | 26.6 (24.9)*3,†3 |
| Lapses 500 ms + FS (%), MD (IQR)¶ | 7.60 (8.85) | 7.43 (7.11) | 12.1 (10.6)*2,†3 |
| Lapses 355 ms + FS (%), MD (IQR) ¶ | 13.8 (11.2) | 15.8 (16.4) | 28.2 (25.6)*3,†3 |
Analysis conducted on the 267 sailors with questionnaire, actigraphy, and PVT data.
Statistical significance for differences: “1”: p < 0.05; “2”: p < 0.01; “3”: p < 0.001.
*Difference between “PSQI > 9” and “PSQI ≤ 5” groups.
†Difference between “PSQI > 9” and “5 < PSQI ≤ 9” groups.
††Multiple comparisons with Tukey–Kramer Honest Significant Difference (HSD) test.
¶Multiple comparisons with Dunn method for joint ranking.
§Pairwise comparisons with Fisher’s Exact Test. Post hoc analysis for statistical significance with the BH-FDR controlling procedure.
Comparison between PSQI groups
| Variable | PSQI ≤ 5 (n = 37) | 5 < PSQI ≤ 9 (n = 129) | PSQI > 9 (n = 101) |
|---|---|---|---|
| Age in years, MD (IQR)¶ | 24 (8) | 26 (8) | 25 (6) |
| Sex (females), # (%)§ | 8 (21.6%) | 22 (17.1%) | 34 (33.7%)†2 |
| Enlisted personnel, # (%)§ | 26 (70.3%) | 99 (76.7%) | 93 (92.1%)*2,†2 |
| Watchstanders, # (%)§ | 34 (91.9%) | 111 (86.1%) | 93 (92.1%) |
| Daily sleep duration (h), MD (IQR)¶ | 6.68 (1.64) | 6.54 (1.35) | 6.63 (1.77) |
| Sleep episodes per day (#), MD (IQR)¶ | 1.25 (0.53) | 1.42 (0.60) | 1.70 (0.62)*3,†2 |
| PVT metrics | |||
| Mean RT (ms), MD (IQR)¶ | 293 (87.4) | 303 (94.5) | 369 (133)*2,†3 |
| Mean 1/RT, M ± SD†† | 4.00 ± 0.67 | 3.90 ± 0.70 | 3.38 ± 0.77*3,†3 |
| Fastest 10% RT (ms), MD (IQR)¶ | 191 (42.0) | 198 (48.7) | 226 (57.0)*3,†3 |
| Slowest 10% 1/RT, M ± SD†† | 2.40 ± 0.62 | 2.42 ± 0.64 | 1.99 ± 0.63*2,†3 |
| False Starts (FS) (%), MD (IQR)¶ | 1.00 (2.05) | 1.31 (1.68) | 1.16 (1.34)*2,†3 |
| Lapses 500 ms (%), MD (IQR)¶ | 6.04 (8.13) | 6.01 (67.33) | 10.4 (10.9)*2,†3 |
| Lapses 355 ms (%), MD (IQR)¶ | 12.3 (12.0) | 14.1 (15.4) | 26.6 (24.9)*3,†3 |
| Lapses 500 ms + FS (%), MD (IQR)¶ | 7.60 (8.85) | 7.43 (7.11) | 12.1 (10.6)*2,†3 |
| Lapses 355 ms + FS (%), MD (IQR) ¶ | 13.8 (11.2) | 15.8 (16.4) | 28.2 (25.6)*3,†3 |
| Variable | PSQI ≤ 5 (n = 37) | 5 < PSQI ≤ 9 (n = 129) | PSQI > 9 (n = 101) |
|---|---|---|---|
| Age in years, MD (IQR)¶ | 24 (8) | 26 (8) | 25 (6) |
| Sex (females), # (%)§ | 8 (21.6%) | 22 (17.1%) | 34 (33.7%)†2 |
| Enlisted personnel, # (%)§ | 26 (70.3%) | 99 (76.7%) | 93 (92.1%)*2,†2 |
| Watchstanders, # (%)§ | 34 (91.9%) | 111 (86.1%) | 93 (92.1%) |
| Daily sleep duration (h), MD (IQR)¶ | 6.68 (1.64) | 6.54 (1.35) | 6.63 (1.77) |
| Sleep episodes per day (#), MD (IQR)¶ | 1.25 (0.53) | 1.42 (0.60) | 1.70 (0.62)*3,†2 |
| PVT metrics | |||
| Mean RT (ms), MD (IQR)¶ | 293 (87.4) | 303 (94.5) | 369 (133)*2,†3 |
| Mean 1/RT, M ± SD†† | 4.00 ± 0.67 | 3.90 ± 0.70 | 3.38 ± 0.77*3,†3 |
| Fastest 10% RT (ms), MD (IQR)¶ | 191 (42.0) | 198 (48.7) | 226 (57.0)*3,†3 |
| Slowest 10% 1/RT, M ± SD†† | 2.40 ± 0.62 | 2.42 ± 0.64 | 1.99 ± 0.63*2,†3 |
| False Starts (FS) (%), MD (IQR)¶ | 1.00 (2.05) | 1.31 (1.68) | 1.16 (1.34)*2,†3 |
| Lapses 500 ms (%), MD (IQR)¶ | 6.04 (8.13) | 6.01 (67.33) | 10.4 (10.9)*2,†3 |
| Lapses 355 ms (%), MD (IQR)¶ | 12.3 (12.0) | 14.1 (15.4) | 26.6 (24.9)*3,†3 |
| Lapses 500 ms + FS (%), MD (IQR)¶ | 7.60 (8.85) | 7.43 (7.11) | 12.1 (10.6)*2,†3 |
| Lapses 355 ms + FS (%), MD (IQR) ¶ | 13.8 (11.2) | 15.8 (16.4) | 28.2 (25.6)*3,†3 |
Analysis conducted on the 267 sailors with questionnaire, actigraphy, and PVT data.
Statistical significance for differences: “1”: p < 0.05; “2”: p < 0.01; “3”: p < 0.001.
*Difference between “PSQI > 9” and “PSQI ≤ 5” groups.
†Difference between “PSQI > 9” and “5 < PSQI ≤ 9” groups.
††Multiple comparisons with Tukey–Kramer Honest Significant Difference (HSD) test.
¶Multiple comparisons with Dunn method for joint ranking.
§Pairwise comparisons with Fisher’s Exact Test. Post hoc analysis for statistical significance with the BH-FDR controlling procedure.
Discussion
The PSQI Global scores of both watchstanders and dayworkers in our sample of the USN sailors (median 9 and 7, respectively) were on average 40% higher than their military and civilian peers in other occupational environments [27–32]. That is, sleep quality of sailors οn the USN ships while underway is worse than other military and civilian occupations. The consistent differences between the USN sailors and workers in other settings, both in shift- and day-workers, are indicative of the effect of the occupational stressors which are idiosyncratic to the naval operational environment [33, 34].
PSQI scores were associated with sailors’ work schedules and rank. Watchstanders and galley workers had worse sleep quality compared to dayworkers, whereas enlisted personnel had worse sleep quality compared to officers. We postulate that the latter difference can be attributed to two reasons. First, enlisted personnel live in more crowded sleeping quarters compared to officers, a factor associated with less satisfied service members and increased noise in the compartment [35, 36]. Second, the prevalence of undiagnosed sleep apnea may be higher in enlisted personnel given that obesity is higher in enlisted personnel compared to officers [37, 38].
In terms of sleep attributes, our results shed light upon the complex relation between daily sleep duration, napping (i.e. splitting daily sleep in more than one sleep episodes per day), and sleep quality in the naval environment where sleep opportunities are limited. In such environments, napping can be a useful (and perhaps the only) method to accrue sleep, but subjective sleep quality is negatively affected if sleep is split into multiple episodes. Hence, the PSQI Global score showed a U-shaped association with daily sleep duration. That is, multiple sleep episodes (i.e. increases in duration of daily sleep that are accrued in multiple sleep episodes) are associated with worse sleep quality, whereas fewer sleep episodes are associated with better sleep quality. The U-shape association between daily sleep duration and outcomes of interest is evident in studies assessing mortality [39, 40] and in a recent study on resilience in military populations [41]. Seelig and colleagues attributed their findings to underlying disorders that are not accounted for in their study. Our findings, however, show that even splitting sleep in more than one sleep episode per day (a common phenomenon in the military operational environment) may confound the effect of sleep duration.
Notably, our finding that daily sleep duration was associated with PSQI Global scores does not agree with two other studies [4, 14]. We postulate two possible explanations. First, some studies assessed only the linear association between daily sleep duration and PSQI scores without considering the confounding effect of split sleep. Second, PSQI provides an estimate of average sleep over a period of time, and, therefore, it is not sensitive to daily variability as assessed by polysomnographic studies [4].
Another interesting finding was the high prevalence of “poor sleepers” in our sample (~80%) which is comparable (89%) to a sample of post-deployed ADSMs and veterans of Operation Enduring Freedom and Operation Iraqi Freedom (OIF) [42], but higher than the prevalence (48.6%) in 1,957 servicemembers across all branches reported recently [6]. The identification of poor sleepers, however, was based on the widely used criterion of a PSQI Global score greater than 5 which has not been validated on military populations [6]. Given the elevated average PSQI scores found in military personnel, the use of elevated cutoff PSQI scores is likely better suited to differentiate military personnel with sleep disorders [15].
Also, PSQI Global scores were a predictor of degraded psychomotor performance. Compared to crewmembers with PSQI scores of 9 or less, individuals with PSQI scores greater than 9 experienced slower reaction times by ~20% and greater numbers of lapses combined with false starts by approximately 33% or more. Using the PSQI cutoff score of 9, approximately 35% of our entire sample was at risk of degraded psychomotor vigilance performance. Of note, the prevalence of PSQI scores greater than 9 in our sample is doubled compared to the corresponding 18% found in a survey across all branches of the military [6].
Our findings raise a number of issues. The first issue pertains to the utility of napping for the USN sailors while underway in light of our finding that split sleep has a detrimental effect on sleep quality. Even though consolidated sleep is preferable to splitting sleep into more than one sleep episode, our studies have shown that long rest opportunities are rare at sea, especially for watchstanders [34, 43]. Napping may be the only viable method to accrue sleep in an occupational environment saturated with various duties and events not controlled by the sailor (work and operational events which cannot be planned ahead). Along these lines, the USN Comprehensive Fatigue and Endurance Management Policy (CFEMP) recommends that under ordinary conditions, underway sailors should receive either one uninterrupted 7-h period of sleep or an uninterrupted 5-h period with a 2-h nap [44]. Second, our results on sleep quality emphasize the importance of appropriate sleeping conditions in berthing compartments. Sleep-related habitability factors like environmental conditions and bedding can have detrimental effects on sailor well-being [45]. We should also consider the long-term implications of our findings. Sailors in the USN live and work in underway conditions for long periods of time, even for weeks and months at a time. A typical deployment lasts 6 months or more, and sailors frequently experience multiple deployments during their career. Chronic exposure to insufficient sleep has been associated with sleep and circadian disorders, obesity, diabetes, and other health issues [43, 46, 47]. These effects may continue even after service members retire [48].
The current study has a number of limitations which may inform future efforts. First, all our sailors were deemed to be fit for duty, but we are not aware of their actual health status. We asked, however, about what medications they were receiving, both prescription and the over-the-counter medication. Second, all ranks on the ship were not equally represented in our study sample. This diversity may have introduced a social desirability bias in the responses between different groups, for example, officers versus enlisted personnel. Also, our overall study sample was fairly large but the occupational groups differed in size. The decision to compare the occupational groups regardless of their size was based on the importance of this comparison and the fact that our study samples were, in general, representative of the actual size of the occupational groups in the ships we studied. Future efforts, however, should include larger samples of maintenance shiftworkers and galley workers. Lastly, sailor work schedules when underway are also associated with the number of sailors available for each duty/work activity, training/experience, rank, ship organizational structure and mission, organizational unit (department, division) the sailor belongs in, work hours per day, whether working on a fixed or rotating schedule, etc. Future research should further investigate these important occupational factors. Furthermore, our study has some key strengths. All data were collected in the field while sailors were conducting their underway duties on a number of ships. The demographics of the participants are representative of the USN populations of sailors in terms of age, gender, and officer/enlisted personnel ratio.
Taken together, our results show the challenges sailors face when working on ships. Chronic sleep deprivation, split sleep, and deteriorated sleep quality are characteristics endemic to the naval operational environment [34, 43]. Even though widely used, further research is needed to assess sleep quality in military operational settings and the association between PSQI scores and occupational attributes of the military environment.
Funding
The various studies in this paper were supported by Office of the Chief of Naval Operations (OPNAV), 21st Century Sailor Office (N17), Office of Naval Research (ONR) Naval Research Program, and the Naval Medical Research Center’s, Naval Advanced Medical Development Program.
Author Contributions
Conception by PM. Study design, data collection, and analysis by NLS and PM. Both authors interpreted data, edited the manuscript, and approved the final draft.
Disclosure Statement
Financial disclosure: PM and NLS have no financial relationships relevant to this article to disclose.
Non-financial disclosure: PM and NLS declare no conflict of interest.
References
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