Comparison of mathematical model predictions to experimental data of fatigue and performance

Aviat Space Environ Med. 2004 Mar;75(3 Suppl):A15-36.


As part of the "Fatigue and Performance Modeling Workshop," six modeling teams made predictions for temporal profiles of fatigue and performance in five different scenarios. One scenario was based on a laboratory study of fatigue and performance during 88 h of extended wakefulness with or without nap opportunities. Another scenario was based on a field study of alertness in freight locomotive engineers. Two scenarios were based on laboratory studies with various conditions of chronic sleep restriction and recovery. There was a theoretical scenario for future ultra-long-range flight operations as well. Experimental data were available for all scenarios except the latter. The model predictions were compared with the experimental data; after linear scaling using mixed-effects regression, mean square errors were computed to quantify goodness-of-fit. The six models were also compared among each other on the basis of these mean square errors. The present paper provides detailed information about the results of these comparisons. The models were capable of predicting the data for some scenarios fairly well. However, predicting the data for the two scenarios involving chronic sleep restriction was more problematic. Differences among the predictions from the six models were relatively small, suggesting that these models have a broad common basis. More experimental research is needed to yield new insights for the further development of fatigue and performance models.

Publication types

  • Comparative Study
  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.
  • Research Support, U.S. Gov't, P.H.S.

MeSH terms

  • Adult
  • Aircraft
  • Chronic Disease
  • Data Interpretation, Statistical
  • Fatigue / physiopathology*
  • Female
  • Humans
  • Male
  • Middle Aged
  • Models, Biological*
  • Sleep / physiology*
  • Sleep Deprivation / complications*
  • Task Performance and Analysis*