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This paper summarizes work to validate and develop further the homeostatic and circadian component of a quantitative (computerized) three-process model for predicting alertness/sleepiness in daily living. The model uses sleep data as input and contains circadian and homeostatic components (amount of prior wake and amount of prior sleep), which are summed to yield predicted alertness on a scale between 1 and 16. The present validation was carried out using regression analysis, with sleepiness-related electroencephalographic parameters (alpha power density) from field and laboratory studies as criteria. The results showed that variations in alpha-power density in truck drivers, train drivers and laboratory subjects could be predicted with considerable accuracy (r2 > 0.70) from the model, as could subjective alertness. Levels < or = 7 on the 16-point scale were defined as critically low alertness. The paper also describes a simplified, graphic, paper version of the computation model, visualized as a two-dimensional "alertness nomogram". It is suggested that the studied components of the model may serve as tools for evaluating work/rest schedules in terms of sleep-related safety risks.
Åkerstedt et al. (Sun,) studied this question.