Predicting the timing and duration of sleep in an operational setting using social factors.

In recent years, there has been increasing interest in the use of bio-mathematical models to predict alertness, performance, and/or fatigue in operational settings. Current models use only biological factors to make their estimations, which can be limited in operational settings where social and geo-physical factors also dictate when sleep occurs. The interaction between social and biological factors that help determine the timing and duration of sleep during layover periods have been investigated in order to create and initially validate a mathematical model that may better predict sleep in the field. Participants were 32 male transmeridian airline pilots (17 captains, 10 first officers, and 5 second officers) flying the Sydney-Bangkok-London-Singapore-Sydney (SYD-LHR) pattern. Participants continued their regular schedule while wearing activity monitors and completing sleep and work diaries. The theoretical sleep timing model underpinning this analysis consists of separate formulations for short (<32 h) and long (>32 h) break periods. Longer break periods are split into three distinct phases-recovery (break start until first local night), personal (first local night until last local night), and preparation phases (last local night until break end)-in order to exploit potential differences specific to each. Furthermore, an iterative procedure combining prediction and retrodiction (i.e., using future duty timing information to predict current sleep timing) was developed to optimize predictive ability. Analysis found an interaction between the social and circadian sleep pressures that changed over the break period. Correlation analysis indicated a strong relationship between the actual sleep and new model's predictions (r = 0.7-0.9), a significant improvement when compared to existing models (r = 0.1-0.4). Social and circadian pressures play important roles in regulating sleep for international flight crews. An initial model has been developed in order to regulate sleep in these crews. The initial results have shown promise when applied to small sets of data; however, more rigorous validation must be carried out.