What you can measure you can manage
Gaining insight into worker sleep/wake patterns is essential when developing appropriate controls for fatigue risk management.
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Fatigue Science combines US Army-developed biomathematical fatigue modelling with laboratory-tested sleep data capture in order to accurately predict human fatigue.
SAFTE Fatigue Model
The SAFTE Fatigue Model (Sleep, Activity, Fatigue, and Task Effectiveness) is the world's leading biomathematical fatigeu model. Developed by the US Army research lab and validated by the US Department of Transportation, it analyses sleep data in order to accurately predict fatigue.
Readiband wearable device
The Readiband captures high-resolution sleep data, validated against the clinical gold standard of polysomnography with 92% accuracy. Sleep data is transmitted to the cloud automatically for SAFTE Fatigue Model analysis as soon as workers arrive for duty.
The SAFTE Fatigue Model analyzes a robust array of sleep factors
We know intuitively that a lack of sleep causes fatigue, but knowing one’s sleep quantity is only one of the many factors necessary in order to create an accurate fatigue prediction.
Accordingly, the SAFTE Fatigue Model accounts for a robust array of relevant sleep factors, including acute sleep interruptions, cumulative sleep debt, and the consistency of sleep onset and wake times, and circadian disruptions that influence a change in cognitive function.
The SAFTE Fatigue Model provides an objective measure of fatigue impairment
The SAFTE Alertness Score is a validated metric for assessing the impact of human fatigue at any given moment. At a score of 100, no loss in performance is expected on account of sleep loss. A score of 70 or below indicates a state known as “fatigue impairment.”
Fatigue’s effects are comparable to alcohol impairment
Extensive biomathematical research, including work relied on by the US Dept. of Transportation, has found that SAFTE Alertness Scores reliably indicate human impairment in terms of reaction time and the comparable effects of alcohol.
The SAFTE Fatigue Model predicts how fatigue will evolve over time
When provided information around an individual’s prior sleep history, the SAFTE Fatigue Model can accurately predict changes in cognitive effectiveness throughout the course of the day or night. It even takes in to consideration the influence of factors such as the internal body-clock and and seasonal light exposure based on one’s geographic location.
To predict fatigue, the SAFTE Fatigue Model requires accurate, high-resolution sleep data.
Readiband Wearable Device
In order to provide valid fatigue predictions, the SAFTE Fatigue Model requires accurate measures of sleep duration, quality and timing — precise data not available from off-the-shelf consumer fitness trackers.
Unlike consumer fitness trackers, our Readiband wearable device provides high-resolution sleep data that has been validated at 92% comparability against in-laboratory (PSG-based) sleep measurement.