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The propensity for air mobility missions to exhaust aircrews is strongly dependent on operational tempo. Most flying is performed during periods of low to moderate operational tempo, but a major flight safety risk can emerge when operational tempo becomes very high. This risk can be managed by software tools that contain fatigue and sleep behavior modeling, but optimization/validation of the model using the specific target population is required to ensure that the modeled predictions are accurate. The goal of the study was to validate the sleep behavior model settings for a fatigue modeling tool that is used within the RCAF, the Fatigue Avoidance Scheduling Tool, taking into account the organizational requirements for pre- and postflight routines, especially within the Air Mobility force. Materials and methods Four Royal Canadian Air Force Air Mobility Squadrons from Canadian Forces Base Trenton took part in this trial over a 3-month period (May 3 to August 21, 2016). All 22 missions of the trial included long-range transmeridian flights. All members of the participating aircrew wore wrist actigraphs to measure their sleep. We compared cognitive effectiveness modeling scenarios (preharmonization) based on the SAFTE-FAST sleep behavior model with its default settings against cognitive effectiveness modeling scenarios based on actigraphically-measured sleep. The measured sleep was then harmonized against the predicted sleep to optimize accuracy of the sleep behavior algorithm. During the harmonization process, the “Autosleep” prediction settings were optimized to match the actigraphically-measured sleep timings. Results Prior to the harmonization effort, the sleep behavior algorithm overpredicted the sleep obtained by CAF Aircrews. The most significant adjustment to the sleep behavior model was the increase in commute time to account for briefing, flight planning, debriefing, and postflight activities. Following harmonization, the sleep behavior model provided nearly perfect estimates of overall fatigue risk against missions modeled with actigraphically-measured sleep. For both measured and predicted sleep, most of the time in flight was in a low-fatigue, high-cognitive effectiveness state (90%–95% cognitive effectiveness). Conclusions Current Fatigue Risk Management Systems require accurate fatigue and sleep behavior modeling, which can only be achieved by studying specific target populations to determine their culture of work/rest routines, and optimizing sleep behavior model settings accordingly.

The Importance of Validating Sleep Behavior Models for Fatigue Management Software in Military Aviation