Beyond the three-process model of alertness:: Estimating phase, time on shift, and successive night effects

This paper starts by summarizing the development and refinement of the additive three-process model of alertness first published by Folkard and Akerstedt in 1987. It reviews some of the successes that have been achieved by the model in not only predicting variations in subjective alertness on abnormal sleep-wake schedules but also in accounting for objective measures of sleep latency and duration. Nevertheless, predictions derived from the model concerning alertness on different shifts, and over successive night shifts, are difficult to reconcile with published data on accident risk. In light of this, we have examined two large sets of alertness ratings with a view to further refining the model and identifying additional factors that may influence alertness at any given point in time. Our results indicate that, at least for the range of sleep durations and wake-up times commonly found on rotating shift systems, we may assume the phase of the endogenous circadian component of alertness (process C) to be "set" by the time of waking. Such an assumption considerably enhanced the predictive power of the model and yielded remarkably similar phase estimates to those obtained by maximizing the post-hoc fit of the model. We then examined the manner in which obtained ratings differed from predicted values over a complete 8-day cycle of two, 12-h shift systems. This revealed a pronounced "first night compensation effect" that resulted in shift workers rating themselves as progressively more alert than would be predicted over the course of the first night shift. However, this appeared to be achieved only at the cost of lowered ratings on the second night shift. Finally, we were able to identify a "time on shift" effect whereby, with the exception of the first night shift, alertness ratings decreased over the course of each shift before showing a modest "end effect." We conclude that the identification of these additional components offers the possibility that in the future we may be able to predict trends in accident risk on abnormal sleep-wake schedules.