Steps toward developing a comprehensive fatigue monitoring and mitigation solution: perspectives from a cohort of United States Naval Surface Force officers.

Study Objectives: This study analyzed fatigue and its management in US Naval Surface Force warships, focusing on understanding current practices and barriers, and examining the influence of organizational and individual factors on managing chronic fatigue. Furthermore, this study explored the impact of organizational and individual factors on fatigue management. Methods: As part of a larger study, 154 naval officers (mean ±â€…standard deviation; 31.5 ±â€…7.0 years; 8.8 ±â€…6.8 years of service; 125 male, and 29 female) completed a fatigue survey. The survey addressed (1) self-reported fatigue, (2) fatigue observed in others, (3) fatigue monitoring strategies, (4) fatigue mitigation strategies, and (5) barriers to fatigue mitigation. Logistic and ordinal regressions were performed to examine the effect of individual (i.e. sleep quality and years in military service) and organizational (i.e. ship-class) factors on fatigue outcomes. Results: Fatigue was frequently experienced and observed by 23% and 54% of officers, respectively. Of note, officers often monitored fatigue reactively (i.e. 65% observed others nodding off and 55% observed behavioral impairments). Still, officers did not frequently implement fatigue mitigation strategies, citing few operationally feasible mitigation strategies (62.3%), being too busy (61.7%), and not having clear thresholds for action (48.7%). Fatigue management varies across organizational factors, which must be considered when further developing fatigue management strategies. Conclusions: Fatigue remains a critical concern aboard surface force ships and it may be better addressed through development of objective sleep and fatigue monitoring tools that could inform leadership decision-making.

Advancing a U.S. navy shipboard infrastructure for sleep monitoring with wearable technology.

Development of fatigue management solutions is critical to U.S. Navy populations. This study explored the operational feasibility and acceptability of commercial wearable devices (Oura Ring and ReadiBand) in a warship environment with 845 Sailors across five ship cohorts during at-sea operations ranging from 10 to 31 days. Participants were required to wear both devices and check-in daily with research staff. Both devices functioned as designed in the environment and reliably collected sleep-wake data. Over 10,000 person-days at-sea, overall prevalence of Oura and ReadiBand use was 69% and 71%, respectively. Individual use rates were 71 ± 38% of days underway for Oura and 59 ± 34% for ReadiBand. Analysis of individual factors showed increasing device use and less device interference with age, and more men than women found the devices comfortable. This study provides initial support that commercial wearables can contribute to infrastructures for operational fatigue management in naval environments.

Dawn of a New Dawn: Advances in Sleep Health to Optimize Performance.

Optimal sleep health is a critical component to high-level performance. In populations such as the military, public service (eg, firefighters), and health care, achieving optimal sleep health is difficult and subsequently deficiencies in sleep health may lead to performance decrements. However, advances in sleep monitoring technologies and mitigation strategies for poor sleep health show promise for further ecological scientific investigation within these populations. The current review briefly outlines the relationship between sleep health and performance as well as current advances in behavioral and technological approaches to improving sleep health for performance.

Sleep deficiency, operational fatigue and the interplay of compromising factors: Analysis to aid in fatigue management.

The United States Navy is a high-reliability organization that must maintain optimum performance under challenging conditions. One key challenge for sailors is obtaining sufficient sleep, which can lead to fatigue and other outcomes that compromise operational readiness. Identifying sleep issues and their causes is critical for military leaders to care for their personnel, and to make informed, risk-based operational decisions. Though previous studies in shipboard environments have implicated factors responsible for insufficient sleep (e.g. poor sleep environment and work demands), there has been less research into characterizing the complex interplay among such factors in relation to sleep and work-related fatigue outcomes. This study seeks to address this gap. Data were drawn from the Afloat Safety Climate Assessment Survey of 7617 sailors from 73 ships. The survey included demographic characteristics and measures of crew endurance (e.g. sleep, occupational impairment due to fatigue). Descriptive analyses characterized the presence and severity of sleep issues across subpopulations and operational settings (e.g. the type of ship); structural equation modelling techniques characterized and quantified the statistical associations among factors. The results indicate that sleep deficits are widespread, holding across subpopulations and operational settings. Though sleep deficits varied across subpopulations, no group obtained an average of more than 7 hr of sleep per night. Fatigue-induced occupational functional impairment was directly related to sleep deficiency, and sleep environment and job-related factors were contributors to sleep deficiency. Moreover, job-related factors emerged as potentially more consequential. Lastly, factors may exist aboard a ship that could help promote better sleep.

Sleep health mediates the relationship between physical activity and depression symptoms.

PURPOSE: This study aimed to investigate cross-sectional associations between physical activity, sleep health, and depression symptoms using mediation models. METHODS: Participants (N = 1576, MAge = 39.3 years, 40% female) were recruited online from Amazon's Mechanical Turk crowd-sourcing service. Physical activity was measured using a single-item self-report measure and depression symptoms were reported using the Hospital Anxiety and Depression Scale. Sleep health was measured using the 6-question RUSATED Sleep Health survey V2.0. RESULTS: Good sleep health (direct effect: ß = - .273, t = - 13.87, p < .0001) and high levels of physical activity (direct effect: ß = - .092, t = - 4.73, p < .0001) were both individually associated with fewer depression symptoms. Sleep health significantly mediated 19% of the association between physical activity and depression symptoms (indirect effect: ß = - .022, 95% CI [- .036 to - .008]), while physical activity significantly mediated 3% of the relationship between sleep health and depression symptoms (indirect effect: ß = - .008, 95% CI [- .014 to - .003]). CONCLUSION: Physical activity and sleep health act as predictors and mediators of depression symptoms.

The association between physical activity and a composite measure of sleep health.

PURPOSE: Physical activity has been associated with several individual dimensions of sleep. However, the association between physical activity and sleep health, a construct that emphasizes the multidimensional nature of sleep, has not been explored. This analysis examined the relationship between physical activity and a composite measure of sleep health. METHODS: A total of 114 adults (66% female, 60.3 ± 9.2 years) were included in the analyses. Participants reported daily light-intensity physical activity (LPA) and moderate- and vigorous-intensity physical activity (MVPA) via diary, while wearing a pedometer (Omron HJ-720ITC) to measure daily steps. Sleep health was measured using the RU_SATED questionnaire, which addresses regularity of sleep patterns, satisfaction with sleep, daytime alertness, and sleep timing, efficiency, and duration. Multiple linear regression, binary logistic regression, and analysis of covariance (ANCOVA) were utilized for analyses. RESULTS: Mean sleep health score was 9.6 ± 2.4 (0 [poor]-12 [good]). Participants reported 62.9 ± 66.0 and 51.2 ± 51.2 min/day of LPA and MVPA, respectively, and took 5585.5 ± 2806.7 steps/day. Greater MVPA was associated with better sleep health (ß = 0.27, P = 0.005) and sleep health scores differed between those reporting < 30 min/day and ≥ 60 min/day of MVPA (P = 0.004). Greater MVPA was associated with higher odds of having good sleep satisfaction (OR = 1.58 [1.14-2.20], P < 0.01), timing (OR = 2.07 [1.24-3.46], P < 0.01), and duration (OR = 1.48 [1.02-2.18], P = 0.04). Pedometer-based physical activity and LPA were not related to sleep health or its individual dimensions. CONCLUSIONS: In middle- to older-aged adults, higher-intensity activity, but not lower-intensity or volume of activity, was associated with greater sleep health. These data suggest that physical activity intensity may be important for sleep health.

Field-based Measurement of Sleep: Agreement between Six Commercial Activity Monitors and a Validated Accelerometer.

OBJECTIVE: To examine agreement between multiple commercial activity monitors (CAMs) and a validated actigraph to measure sleep. METHODS: Thirty adults without sleep disorders wore an Actiwatch Spectrum (AW) and alternated wearing 6 CAMs for one 24-h period each (Fitbit Alta, Jawbone Up3, Misfit Shine 2, Polar A360, Samsung Gear Fit2, Xiaomi Mi Band 2). Total sleep time (TST) and wake after sleep onset (WASO) were compared between edited AW and unedited CAM outputs. Comparisons between AW and CAM data were made via paired t-tests, mean absolute percent error (MAPE) calculations, and intra-class correlations (ICC). Intra-model reliability was performed in 10 participants who wore a pair of each AW and CAM model. RESULTS: Fitbit, Jawbone, Misfit, and Xiaomi overestimated TST relative to AW (53.7-80.4 min, P ≤ .001). WASO was underestimated by Fitbit, Misfit, Samsung and Xiaomi devices (15.0-27.9 min; P ≤ .004) and overestimated by Polar (27.7 min, P ≤ .001). MAPEs ranged from 5.1% (Samsung) to 25.4% (Misfit) for TST and from 36.6% (Fitbit) to 165.1% (Polar) for WASO. TST ICCs ranged from .00 (Polar) to .92 (Samsung), while WASO ICCs ranged from .38 (Misfit) to .69 (Samsung). Differences were similar between poor sleepers (Pittsburgh Sleep Quality Index global score >5; n = 10) and good sleepers. Intra-model reliability analyses revealed minimal between-pair differences and high ICCs. CONCLUSIONS: Agreement between CAMs and AW varied by device, with greater agreement observed for TST than WASO. While reliable, variability in agreement across CAMs with traditional actigraphy may complicate the interpretation of CAM data obtained for clinical or research purposes.