Equal to or better than: The application of statistical non-inferiority to fatigue risk management

How effective are Fatigue Risk Management Systems (FRMS)? A review

OBJECTIVE

Fatigue Risk Management Systems (FRMS) are a data-driven set of management practices for identifying and managing fatigue-related safety risks. This approach also considers sleep and work time, and is based on ongoing risk assessment and monitoring. This narrative review addresses the effectiveness of FRMS, as well as barriers and enablers in the implementation of FRMS. Furthermore, this review draws on the literature to provide evidence-based policy guidance regarding FRMS implementation.

METHODS

Seven databases were drawn on to identify relevant peer-reviewed literature. Relevant grey literature was also reviewed based on the authors' experience in the area. In total, 2129 records were screened based on the search strategy, with 231 included in the final review.

RESULTS

Few studies provide an evidence-base for the effectiveness of FRMS as a whole. However, FRMS components (e.g., bio-mathematical models, self-report measures, performance monitoring) have improved key safety and fatigue metrics. This suggests FRMS as a whole are likely to have positive safety outcomes. Key enablers of successful implementation of FRMS include organisational and worker commitment, workplace culture, and training.

CONCLUSIONS

While FRMS are likely to be effective, in organisations where safety cultures are insufficiently mature and resources are less available, these systems may be challenging to implement successfully. We propose regulatory bodies consider a hybrid model of FRMS, where organisations could choose to align with tight hours of work (compliance) controls. Alternatively, where organisational flexibility is desired, a risk-based approach to fatigue management could be implemented.

Flight Crew Alertness and Sleep Relative to Timing of In-Flight Rest Periods in Long-Haul Flights

BACKGROUND: In-flight breaks are used during augmented long-haul flight operations, allowing pilots a sleep opportunity. The U.S. Federal Aviation Administration duty and rest regulations restrict the pilot flying the landing to using the third rest break. It is unclear how effective these restrictions are on pilots ability to obtain sleep. We hypothesized there would be no difference in self-reported sleep, alertness, and fatigue between pilots taking the second vs. third rest breaks.METHODS: Pilots flying augmented operations in two U.S.-based commercial airlines were eligible for the study. Volunteers completed a survey at top-of-descent (TOD), including self-reported in-flight sleep duration, and Samn-Perelli fatigue and Karolinska Sleepiness Scale ratings. We compared the second to third rest break using noninferiority analysis. The influence of time of day (home-base time; HBT) was evaluated in 4-h blocks using repeated measures ANOVA.RESULTS: From 787 flights 500 pilots provided complete data. The second rest break was noninferior to the third break for self-reported sleep duration (1.5 0.7 h vs. 1.4 0.7 h), fatigue (2.0 1.0 vs. 2.9 1.3), and sleepiness (2.6 1.4 vs. 3.8 1.8) at TOD for landing pilots. Measures of sleep duration, fatigue, and sleepiness were influenced by HBT circadian time of day.DISCUSSION: We conclude that self-reported in-flight sleep, fatigue, and sleepiness from landing pilots taking the second in-flight rest break are equivalent to or better than pilots taking the third break. Our findings support providing pilots with choice in taking the second or third in-flight rest break during augmented operations.Gregory KB, Soriano-Smith RN, Lamp ACM, Hilditch CJ, Rempe MJ, Flynn-Evans EE, Belenky GL. Flight crew alertness and sleep relative to timing of in-flight rest periods in long-haul flights. Aerosp Med Hum Perform. 2021; 92(2):8391.

Delta: The Value That Matters in Fatigue Risk Management

INTRODUCTION: Noninferiority or equivalence testing are often used when comparing a novel pharmaceutical, operation, or procedure to the current standard designated as safe. Noninferiority and equivalence testing require estimates of a metric called delta: the margin of meaningful difference. Inappropriate delta margins can lead to invalid conclusions, thereby creating uncertainty about a studys scientific credibility. We recommend that a working group be convened with the following goals: 1) to evaluate delta values currently in use in aviation; 2) to determine if it is possible to develop a systematic, evidence-based, and replicable process to derive delta values based on statistical properties from population data, rather than a mixture of evidence- and opinion-based processes; and 3) based on the findings of the second goal, update the current delta values in use in aviation. This working group should include, at a minimum, government agencies and other key stakeholders using these values within operational settings.Lamp ACM, Rempe MJ, Belenky GL. Delta: the value that matters in fatigue risk management. Aerosp Med Hum Perform. 2021; 92(2):127128.

Generalizability of a biomathematical model of fatigue's sleep predictions

Introduction: Biomathematical models of fatigue (BMMF) predict fatigue during a work-rest schedule on the basis of sleep-wake histories. In the absence of actual sleep-wake histories, sleep-wake histories are predicted directly from work-rest schedules. The predicted sleep-wake histories are then used to predict fatigue. It remains to be determined whether workers organize their sleep similarly across operations and thus whether sleep predictions generalize.Methods: Officers (n = 173) enrolled in the Buffalo Cardio-Metabolic Occupational Police Stress study were studied. Officers' sleep-wake behaviors were measured using wrist-actigraphy and predicted using a BMMF (FAID Quantum) parameterized in aviation and rail. Sleepiness (i.e. Karolinska Sleepiness Scale (KSS) ratings) was predicted using actual and predicted sleep-wake data. Data were analyzed using sensitivity analyses.Results: During officers' 16.0 ± 1.9 days of study participation, they worked 8.6 ± 3.1 shifts and primarily worked day shifts and afternoon shifts. Across shifts, 7.0 h ± 1.9 h of actual sleep were obtained in the prior 24 h and associated peak KSS ratings were 5.7 ± 1.3. Across shifts, 7.2 h ± 1.1 h of sleep were predicted in the prior 24 h and associated peak KSS ratings were 5.5 ± 1.2. The minute-by-minute predicted and actual sleep-wake data demonstrated high sensitivity (80.4%). However, sleep was observed at all hours-of-the-day, but sleep was rarely predicted during the daytime hours.Discussion: The sleep-wake behaviors predicted by a BMMF parameterized in aviation and rail demonstrated high sensitivity with police officers' actual sleep-wake behaviors. Additional night shift data are needed to conclude whether BMMF sleep predictions generalize across operations.

Sleep in the United States Military

The military lifestyle often includes continuous operations whether in training or deployed environments. These stressful environments present unique challenges for service members attempting to achieve consolidated, restorative sleep. The significant mental and physical derangements caused by degraded metabolic, cardiovascular, skeletomuscular, and cognitive health often result from insufficient sleep and/or circadian misalignment. Insufficient sleep and resulting fatigue compromises personal safety, mission success, and even national security. In the long-term, chronic insufficient sleep and circadian rhythm disorders have been associated with other sleep disorders (e.g., insomnia, obstructive sleep apnea, and parasomnias). Other physiologic and psychologic diagnoses such as post-traumatic stress disorder, cardiovascular disease, and dementia have also been associated with chronic, insufficient sleep. Increased co-morbidity and mortality are compounded by traumatic brain injury resulting from blunt trauma, blast exposure, and highly physically demanding tasks under load. We present the current state of science in human and animal models specific to service members during- and post-military career. We focus on mission requirements of night shift work, sustained operations, and rapid re-entrainment to time zones. We then propose targeted pharmacological and non-pharmacological countermeasures to optimize performance that are mission- and symptom-specific. We recognize a critical gap in research involving service members, but provide tailored interventions for military health care providers based on the large body of research in health care and public service workers.