Fatigue Risk Management Preferences for Consumer Sleep Technologies and Data Sharing in Aviation.

INTRODUCTION: Employees from any type of aviation services industry were asked to give their opinions about the usefulness of consumer sleep technologies (CSTs) during operations and their willingness to share data from CSTs with their organizations for fatigue risk management purposes under a variety of circumstances.METHODS: Respondents provided information about position in aviation and use of CST devices. Respondents ranked sleep issues and feedback metrics by perceived level of importance to operational performance. Respondents rated their likelihood to share data with their organization under a series of hypothetical situations.RESULTS: Between January-July 2023, 149 (N = 149) aviation professionals responded. Pilots comprised 72% (N = 108) of respondents; 84% (N = 125) of all respondents worked short- or medium-haul operations. "Nighttime operations" and "inconsistent sleep routines" ranked as the most important issues affecting sleep. "Sleep quality history" and "projected alertness levels" ranked as most important feedback metrics for personal management of fatigue. Respondents were split between CST users (N = 64) and nonusers (N = 68). CST users did not indicate a strong preference for a specific device brand. The most-reported reason for not using a CST was due to not owning one or no perceived need. Respondents indicated greater likelihood of data sharing under conditions where the device was provided to them by their organization.DISCUSSION: These results suggest that aviation professionals are more concerned about schedule-related disturbances to sleep than they are about endogenous sleep problems. Organizations may be able to increase compliance to data collection for fatigue risk management by providing employees with company-owned CSTs of any brand.Devine JK, Choynowski J, Hursh SR. Fatigue risk management preferences for consumer sleep technologies and data sharing in aviation. Aerosp Med Hum Perform. 2024; 95(5):265-272.

Consumer preferences for sleep-tracking wearables: The role of scientific evaluation and endorsement.

OBJECTIVES: Accuracy and relevance to health outcomes are important to researchers and clinicians who use consumer sleep technologies, but economic demand motivates consumer sleep technology design. This report quantifies the value of scientific relevance to the general consumer in a dollar amount to convey the importance of device accuracy in terms that consumer sleep technology manufacturers can appreciate. METHODS: Survey data were collected from 368 participants on Amazon mTurk. Participants ranked sleep metrics, evaluation methods, and scientific endorsement by perceived level of importance. Participants indicated their likelihood of purchasing a hypothetical consumer sleep technology that had either (1) not been evaluated or endorsed; (2) had been evaluated but not endorsed, and; (3) had been evaluated and endorsed by a sleep science authority. Demand curves determined the relative value of each consumer sleep technology. RESULTS: Devices that were evaluated and endorsed had the most value, followed by those only evaluated, and then those with no evaluation. The unit price at which there was 50% probability of purchase increased by $30 or $48 for evaluation or endorsement, respectively, relative to a nonvalidated device. Respondents indicated the most valuable sleep metric was sleep duration, the most important evaluation method was against laboratory/hospital standards for sleep, and that the highest value of endorsement came from a medical institution. CONCLUSIONS: Consumer demand is greatest for a device that has been evaluated by an independent laboratory and is endorsed by a medical institution. Consumer sleep technology manufacturers may be able to increase sales by partnering with sleep science authorities to produce a scientifically superior device.

Predictive Biomathematical Modeling Compared to Objective Sleep During COVID-19 Humanitarian Flights.

BACKGROUND: Biomathematical modeling software like the Sleep, Activity, Fatigue, and Task Effectiveness (SAFTE) model and Fatigue Avoidance Scheduling Tool (FAST) help carriers predict fatigue risk for planned rosters. The ability of a biomathematical model to accurately estimate fatigue risk during unprecedented operations, such as COVID-19 humanitarian ultra-long-range flights, is unknown. Azul Cargo Express organized and conducted five separate humanitarian missions to China between May and July 2020. Prior to conducting the missions, a sleep-prediction algorithm (AutoSleep) within SAFTE-FAST was used to predict in-flight sleep duration and pilot effectiveness. Here we compare AutoSleep predictions against pilots' sleep diary and a sleep-tracking actigraphy device (Zulu watch, Institutes for Behavior Resources) from Azul's COVID-19 humanitarian missions.METHODS: Pilots wore Zulu watches throughout the mission period and reported sleep duration for their in-flight rest periods using a sleep diary. Agreement between AutoSleep, diary, and Zulu watch measures was compared using intraclass correlation coefficients (ICC). Goodness-of-fit between predicted effectiveness distribution between scenarios was evaluated using the R² statistic.RESULTS: A total of 20 (N = 20) pilots flying across 5 humanitarian missions provided sleep diary and actigraphy data. ICC and R² values were >0.90, indicating excellent agreement between sleep measures and predicted effectiveness distribution, respectively.DISCUSSION: Biomathematical predictions of in-flight sleep during unprecedented humanitarian missions were in agreement with actual sleep patterns during flights. These findings indicate that biomathematical models may retain accuracy even under extreme circumstances. Pilots may overestimate the amount of sleep that they receive during extreme flight-duty periods, which could constitute a fatigue risk.Devine JK, Garcia CR, Simoes AS, Guelere MR, de Godoy B, Silva DS, Pacheco PC, Choynowski J, Hursh SR. Predictive biomathematical modeling compared to objective sleep during COVID-19 humanitarian flights. Aerosp Med Hum Perform. 2022; 93(1):4-12.

Pilot Sleep Behavior across Time during Ultra-Long-Range Flights.

Fatigue risk to the pilot has been a deterrent for conducting direct flights longer than 12 h under normal conditions, but such flights were a necessity during the COVID-19 pandemic. Twenty (N = 20) pilots flying across five humanitarian missions between Brazil and China wore a sleep-tracking device (the Zulu watch), which has been validated for the estimation of sleep timing (sleep onset and offset), duration, efficiency, and sleep score (wake, interrupted, light, or deep Sleep) throughout the mission period. Pilots also reported sleep timing, duration, and subjective quality of their in-flight rest periods using a sleep diary. To our knowledge, this is the first report of commercial pilot sleep behavior during ultra-long-range operations under COVID-19 pandemic conditions. Moreover, these analyses provide an estimate of sleep score during in-flight sleep, which has not been reported previously in the literature.

Sleep and high-risk behavior in military service members: a mega-analysis of four diverse U.S. Army units.

Experimental sleep restriction and deprivation lead to risky decision-making. Further, in naturalistic settings, short sleep duration and poor sleep quality have been linked to real-world high-risk behaviors (HRB), such as reckless driving or substance use. Military populations, in general, tend to sleep less and have poorer sleep quality than nonmilitary populations due to a number of occupational, cultural, and psychosocial factors (e.g. continuous operations, stress, and trauma). Consequently, it is possible that insufficient sleep in this population is linked to HRB. To investigate this question, we combined data from four diverse United States Army samples and conducted a mega-analysis by aggregating raw, individual-level data (n = 2,296, age 24.7 ± 5.3). A negative binomial regression and a logistic regression were used to determine whether subjective sleep quality (Pittsburgh Sleep Quality Index [PSQI], Insomnia Severity Index [ISI], and duration [h]) predicted instances of military-specific HRB and the commission of any HRB (yes/no), respectively. Poor sleep quality slightly elevated the risk for committing HRBs (PSQI Exp(B): 1.12 and ISI Exp(B): 1.07), and longer duration reduced the risk for HRBs to a greater extent (Exp(B): 0.78), even when controlling for a number of relevant demographic factors. Longer sleep duration also predicted a decreased risk for commission of any HRB behaviors (Exp(B): 0.71). These findings demonstrate that sleep quality and duration (the latter factor, in particular) could be targets for reducing excessive HRB in military populations. These findings could therefore lead to unit-wide or military-wide policy changes regarding sleep and HRB.

Repeated Concussions Impair Behavioral and Neurophysiological Changes in the Motor Learning System.

Background. Concussions affect nearly 3 million people a year and are the leading cause of traumatic brain injury-related emergency department visits among youth. Evidence shows neuromotor regions are sensitive to concussive events and that motor symptoms may be the earliest clinical manifestations of neurodegenerative traumatic brain injuries. However, little is known about the effects repeated concussions play on motor learning. Namely, how does concussion acuity (time since injury) affect different behavioral and neurophysiological components of motor learning? Methods. Using a 3-pronged approach, we assessed (1) behavioral measures of motor learning, (2) neurophysiological measures underlying retention of motor learning known as occlusion, and (3) quantitative survey data capturing affective symptoms of each participant. Collegiate student athletes were stratified across 3 groups depending on their concussion history: (1) NonCon, no history of concussion; (2) Chronic, chronic-state of concussion (>1 year postinjury), or (3) Acute, acute state of concussion (<2 weeks postinjury). Results. We found that athletes in both the acute and chronic state of injury following a concussion had impaired retention and aberrant occlusion in motor skill learning as compared with athletes with no history of concussion. Moreover, higher numbers of previous concussions (regardless of the time since injury) correlated with more severe behavioral and neurophysiological motor impairments by specifically hindering neurophysiological mechanisms of learning to affect behavior. Conclusions. These results indicate the presence of motor learning impairment that is evident within 2 weeks postinjury. Our findings have significant implications for the prognosis of concussion and emphasize the need for prevention, but can also direct more relevant rehabilitation treatment targets.

Practice parameters for the use of actigraphy in the military operational context: the Walter Reed Army Institute of Research Operational Research Kit-Actigraphy (WORK-A).

BACKGROUND: The Walter Reed Army Institute of Research (WRAIR) Operational Research Kit-Actigraphy (WORK-A) is a set of unique practice parameters and actigraphy-derived measures for the analysis of operational military sleep patterns. The WORK-A draws on best practices from the literature and comprises 15 additional descriptive variables. Here, we demonstrate the WORK-A with a sample of United States Army Reserve Officers' Training Corps (ROTC) cadets (n = 286) during a month-long capstone pre-commissioning training exercise. METHODS: The sleep of ROTC cadets (n = 286) was measured by Philips Actiwatch devices during the 31-day training exercise. The preliminary effectiveness of the WORK-A was tested by comparing differences in sleep measures collected by Actiwatches as calculated by Philips Actiware software against WORK-A-determined sleep measures and self-report sleep collected from a subset of ROTC cadets (n = 140). RESULTS: Actiware sleep summary statistics were significantly different from WORK-A measures and self-report sleep (all P ≤ 0.001). Bedtimes and waketimes as determined by WORK-A major sleep intervals showed the best agreement with self-report bedtime (22:21 ± 1:30 vs. 22:13 ± 0:40, P = 0.21) and waketime (04:30 ± 2:17 vs. 04:31 ± 0:47, P = 0.68). Though still significantly different, the discrepancy was smaller between the WORK-A measure of time in bed (TIB) for major sleep intervals (352 ± 29 min) and self-report nightly sleep duration (337 ± 57 min, P = 0.006) than that between the WORK-A major TIB and Actiware TIB (177 ± 42, P ≤ 0.001). CONCLUSIONS: Default actigraphy methods are not the most accurate methods for characterizing soldier sleep, but reliable methods for characterizing operational sleep patterns is a necessary first step in developing strategies to improve soldier readiness. The WORK-A addresses this knowledge gap by providing practice parameters and a robust variety of measures with which to profile sleep behavior in service members.

Sleep disturbances and predictors of nondeployability among active-duty Army soldiers: an odds ratio analysis of medical healthcare data from fiscal year 2018.

BACKGROUND: The impact of sleep disorders on active-duty soldiers' medical readiness is not currently quantified. Patient data generated at military treatment facilities can be accessed to create research reports and thus can be used to estimate the prevalence of sleep disturbances and the role of sleep on overall health in service members. The current study aimed to quantify sleep-related health issues and their impact on health and nondeployability through the analysis of U.S. military healthcare records from fiscal year 2018 (FY2018). METHODS: Medical diagnosis information and deployability profiles (e-Profiles) were queried for all active-duty U.S. Army patients with a concurrent sleep disorder diagnosis receiving medical care within FY2018. Nondeployability was predicted from medical reasons for having an e-Profile (categorized as sleep, behavioral health, musculoskeletal, cardiometabolic, injury, or accident) using binomial logistic regression. Sleep e-Profiles were investigated as a moderator between other e-Profile categories and nondeployability. RESULTS: Out of 582,031 soldiers, 48.4% (n = 281,738) had a sleep-related diagnosis in their healthcare records, 9.7% (n = 56,247) of soldiers had e-Profiles, and 1.9% (n = 10,885) had a sleep e-Profile. Soldiers with sleep e-Profiles were more likely to have had a motor vehicle accident (pOR (prevalence odds ratio) =4.7, 95% CI 2.63-8.39, P ≤ 0.001) or work/duty-related injury (pOR = 1.6, 95% CI 1.32-1.94, P ≤ 0.001). The likelihood of nondeployability was greater in soldiers with a sleep e-Profile and a musculoskeletal e-Profile (pOR = 4.25, 95% CI 3.75-4.81, P ≤ 0.001) or work/duty-related injury (pOR = 2.62, 95% CI 1.63-4.21, P ≤ 0.001). CONCLUSION: Nearly half of soldiers had a sleep disorder or sleep-related medical diagnosis in 2018, but their sleep problems are largely not profiled as limitations to medical readiness. Musculoskeletal issues and physical injury predict nondeployability, and nondeployability is more likely to occur in soldiers who have sleep e-Profiles in addition to these issues. Addressing sleep problems may prevent accidents and injuries that could render a soldier nondeployable.

Objective changes in activity levels following sleep extension as measured by wrist actigraphy.

OBJECTIVE/BACKGROUND: It is widely established that insufficient sleep can lead to adverse health outcomes. Paradoxically, epidemiologic research suggests that individuals who report habitual nightly sleep greater than 9 h also are at risk for adverse health outcomes. Further, studies have shown that long sleepers have decreased activity levels, which may partially explain the relationship between long sleep duration and mortality. The influence of sleep extension (longer time in bed) on levels of daily activity has not yet been established. The current study examined whether a week of sleep extension altered activity levels within the subsequent daily waking active and sleep period in order to determine whether increased time in bed indeed is related to decreased activity levels. METHODS: A total of 26 healthy volunteers wore wrist accelerometer devices (Actiwatch 2.0, Philips) in order to objectively measure sleep and activity for six days during their normal schedules and for six days during a sleep extension (10 h time in bed) intervention. RESULTS: There were no significant or clinically-relevant differences in 24-h activity or activity during the active or sleep period between baseline and sleep extension conditions. There were no main or interaction effects of day and condition when daily activity counts were compared between baseline and sleep extension conditions for the 24 h period (Day: F(5, 21) = 1.92, p = 0.12; Condition: F(1,25) = 2.93, p = 0.09; Day by Condition: F(5,21) = 0.32, p = 0.83), Active Waking Period (Day: F(5,25) = 1.53, p = 0.18; Condition: F(1,25) = 0.26, p = 0.61; Day by Condition: F(5,21) = 0.55, p = 0.74) or Nightly Sleep (Day: F(5,21) = 0.86, p = 0.51; Condition: F(1,25) = 1.78, p = 0.19; Day by Condition: F(5,21) = 0.79, p = 0.56) periods. In contrast, there was a main effect of condition when examining sleep duration by day between conditions (Day: F(5,21) = 1.60, p = 0.16; Condition: F(1,25) = 167.31, p < 0.001; Day by Condition: F(5,21) = 2.31, p = 0.07), such that sleep duration was longer during the sleep extension condition. DISCUSSION: Sleep duration increased during six days of a sleep extension protocol but activity levels remained similar to their baseline (normal) sleep schedule. The current findings suggest that extending time in bed alone does not alter waking activity counts in young healthy adults. The link between extended sleep and adverse health outcomes may be attributable to other phenotypic factors, or other biological correlates of extended sleep and poor health.