Exhibits Vol 2 - Independent Pilots Association

FATIGUE MODEL VALIDATION
71
Fatigue Score
90
80
70
60
50
40
30
20
10
GRT Response Time
1 3 5 7 9 11 13 15 17 19 21 23 25 27
Hours of Wakefulness
40
30
20
10
0
-10
-20
Mean Relative
Performance
Fatigue Score
90
80
70
60
50
40
30
20
10
GRT Error Rate
1 4 7 10 13 16 19 22 25
Hours of Wakefulness
6
4
2
0
-2
-4
-6
-8
Mean Relative
Performance
Fatigue Score
90
80
70
60
50
40
30
20
10
VIG Error Rate
1 3 5 7 9 11 13 15 17 19 21 23 25 27
Hours of Wakefulness
10
8
6
4
2
0
-2
-4
-6
-8
-10
Mean Relative
Performance
Fatigue Score
90
80
70
60
50
40
30
20
10
TRK Score
30
20
10
0
-10
-20
-30
1 3 5 7 9 11 13 15 17 19 21 23 25 27
Hours of Wakefulness
Mean Relative
Performance
Fatigue Score
90
80
70
60
50
40
30
20
10
VIG Response Time
25
20
15
10
5
0
-5
1 3 5 7 9 11 13 15 17 19 21 23 25 27
Hours of Wakefulness
Mean Relative
Performance
Figure 3. Predicted fatigue score (left y-axis; open circles) and mean relative performance (right y-axis; filled squares) for each of the indicated
performance measures against increasing during wakefulness.
subsequently produced negative correlation coefficients (R), as
predicted fatigue scores increased as relative performance
values decreased, and vice-versa.
Regression Analyses
The regression equations for performance measures were
determined separately, with either fatigue score or BAC as the
dependent measure. Table 1 displays the regression equations
for each of the six measures with fatigue score as the
dependent measure. Table 2 displays the regression equations
for each of the measures with BAC as the dependent measure.
The best-fit polynomial regression equations were then
simultaneously solved for both fatigue score and BAC. It was
found that solving polynomial equations in some cases
produced a complex number (i.e., square root of a negative
number) with no straightforward mathematical solution.
Below, we report numerical solutions for each statistically
Table 1
significant performance measure where these were available
from solved polynomial regressions. Table 3 displays the BAC
equivalent values (rounded to 2 decimal places) of
performance decline based on fatigue scores from 10 to 100.
Time-series Analysis
Time-series analyses revealed that the circadian rhythms in
performance measures that were predicted by the work-related
fatigue model and those observed in the collected data differed
by between one and four hours. In all six measures, the workrelated
fatigue model predicted that the performance minimum
occurred earlier than what was measured in the laboratory.
Table 5 displays the lag maximum time for each of the six
measures (difference between the predicted and ‘maximum fit’
time relationships). The lag maximum time indicates the
number of hours that the fatigue model’s predictions precede
the actual performance minimums.
Table 2
Measure Regression Equation R 2 p
Y 1
GRT Mean Response 0-0.12 x FAT
+ 0.01 x FAT
2
0.68