The use of almost any actigraph-based sleep/wake assessment strategy is better for deriving the input data for fatigue-prediction models than reliance upon self-reported sleep histories (which are typically highly inaccurate). And the use of an objective and validated fatigue-prediction model overcomes the difficulties associated with self-assessments of fatigue (which also have been proven highly unreliable). However, it is essential that actigraphy classification accurately discriminates periods of in-bed awake from periods of in-bed sleep since the level of the sleep reservoir is one of the two primary influences on fatigue-risk predictions. Coupling the Fatigue Science actigraphy approach with an accurate fatigue-risk assessment model such as the Fatigue Science SAFTE, provides an operationally-useful fatigue- measurement strategy that will facilitate fatigue-risk management systems in transportation, industrial, and other contexts. (Author/publisher)
Abstract