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.

Layover sleep prediction for cockpit crews during transmeridian flight patterns.

INTRODUCTION: Current models of fatigue and alertness use a combination of biological (circadian) and homeostatic factors to predict sleep and wake. Such models do not include social factors in their calculations. The aim of our analysis was to compare the relative contributions of social and biological factors in models designed to predict the total sleep time (TST) during layover periods between transmeridian flights. METHOD: The study actigraphically collected sleep information from 86 cockpit crew (mean age 46.7 yr, SD 4.3 yr) during round-trip patterns from Australia to Los Angeles (n=15), Europe (n=42), New York (n=10), and Hong Kong (n=19). Linear regression models were constructed to predict TST using data from airline schedules. This schedule information included layover length, flight duration, the number of night hours at the destination (social hours), the number of night hours in Australian Eastern Standard time (biological hours), and time zone displacement. These models were then validated using independent data. RESULTS: Analysis indicated that the schedule data was highly correlated. Linear regression analyses indicated that social night hours account for more variance than biological night hours (r = 0.8 vs. 0.7). Additionally, the layover length achieved a correlation coefficient of 0.9. These results were strengthened when the model parameters were applied to the cross-validation dataset. DISCUSSION: Social night hours significantly influence sleep during international layovers and may be a better predictor than biological night hours. More research must be carried out to determine the validity of these findings in a larger, randomly collected flight sample.

The impact of extended leave on sleep and alertness in the Australian rail industry.

In the past, scientific studies have investigated the effects of shift timing and duration on sleep and alertness in the rail industry. To our knowledge no research has been conducted to determine the effects of extended break lengths (>48 h) on these factors. Hence, this study analyses the work and rest schedules of 304 Australian rail employees (mean age 41.3 yr, standard deviation 7.4 yr) to determine the effect of prior break lengths (12-169 h) on sleep and subjective alertness at work after periods of leave. Extended break periods (>48 h) were found to increase the length of the sleep prior to returning to work and reduce the total wake time to the end of the first shift, but did not influence levels of subjective alertness immediately prior to the commencement of the first shift. Research into the influence of longer break periods (>169 h) is needed in order to make definitive conclusions regarding sensible return to work policies after extended leave within the Australian rail industry.