## Fatigue Modeling in Support of Flight and Duty Time Rule-making

1. Introduction

During the development of proposed new rules govern ing flight and duty time limitations for aircrew, t he ARC and the FAA utilized fatigue modeling as one tool to as sist with the evaluation of the relative safety ris k of alternative rules. Modeling can compare scenarios within a com mon set of fatigue assumptions and provide a way to evaluate the relative risk of alternative scheduling rules. Fatigue risk is a continuous process; fatigue risk is said to increase as performance declines and the risk of attention l apses or cognitive errors increase. Rule-making, b y its nature, defines discrete limits based on relative levels of risk between alternatives. Fatigue modeling can a ssist with that process by providing relative comparisons of altern atives to guide limitations that will ultimately be expected to improve safety.

2. Fatigue Science and Modeling Methodology
The science of sleep and fatigue has evolved signif icantly. This evolution has led to an understanding of factors that influence fatigue, the related effects on crewmembe r performance effectiveness, and the necessary miti gation techniques. There are several scientific models of human performance versus fatigue available to the i ndustry, and any may be used to assist in the planning and imple menting of flight operations. The FAA has selected the Sleep, Activity, Fatigue, and Task Effectiveness (SAFTE) f atigue model and associated Fatigue Avoidance Sched uling Tool (FAST) software (SAFTE/FAST) as its tools to a ssist in evaluating flight and duty time limits. Th is model currently is used by the Department of Transportati on and the Department of Defense to assess fatigue risk factors. The SAFTE/FAST model has demonstrated the ability t o accurately identify fatigue risk factors when the model is properly used. This model has been demonstrated to incorporate the elements of fatigue and performance effectiveness with the highest accuracy and resolut ion of available programs and is easily applied to specific city pair operations. This modeling, combined with real-world experience, data collection, fatigue management st udies, and scientific review form the basis for FAA decision-m aking

3. Background on the SAFTETM Model and FASTTM Modeling S oftware
During the past ten years, the US Air Force, the US Army, and the Federal Railroad Administration (FRA ) have sponsored the development of a model of human fatig ue and circadian variation, as well as a scheduling tool based upon the model that will be used to minimize aircre w fatigue. The software was developed by companies involved in an Air Force project and is called the Fatigue Avoi dance Scheduling Tool (FASTTM). Fatigue predictions are derived from the Sleep, Activity, Fatigue, and Task Effectiveness (SAFTETM) model invented by Dr. Steve n Hursh of the Institutes for Behavior Resources, Inc. The model has undergone considerable testing and valida tion against available human performance data. Predictions are h ighly correlated with speed on a psychomotor vigila nce test, lapse frequency, and overall performance on a batte ry of cognitive tests. Importantly, the model has b een shown to be valid for conditions of chronic sleep restrictio n as well as acute sleep deprivation. The SAFTETM mo del has received a broad scientific review by a group spons ored by the US Department of Defense and Department of Transportation. In an independent evaluation of six existing fatigue models, the most recent version o f SAFTETM was found to be most accurate for predicting effect s of restricted sleep on both cognitive performance and subjectiveevaluation of fatigue (Von Dongen, Aviation, Space, and Environmental Medicine , March, 2004, vol. 75, no. 3,

section II). Recent studies conducted by the Feder al Railroad Administration have validated that the model can predict accident likelihood and cost (severity) bas ed on evaluation of the sleep and performance effec ts of work schedules (DOT/FRA/ORD-08/04 and DOT/FRA/ORD-11/13) . The FAST tool has also been shown to make good estimates of the amount of sleep likely to be achie ved by the average person under irregular work sche dules (FRA brief report RR11-02).
The SAFTETM model predicts cognitive effectiveness, which ranges from 100 to zero, with 100 representin g the typical well rested person’s best daily performance . The model assumes that the average person can be awake for 16 hrs and sleep for eight hours each day during their circadian night without suffering a cumulative sle ep debt. Decreases from 100 can be interpreted as percent re ductions in vigilance speed relative to this hypoth etical well rested person. For an average person with a bedtim e of 2300 and a wakeup time of 0700, a score of 90 is approximately the level of effectiveness immediatel y prior to going to bed at the end of the day. If that person remains awake for 18.5 hrs, then effectiveness will decline to about 77. Effectiveness will reach a t ypical circadian low of about 70 after being awake 22 hrs or at abou t 0500 in the morning. These benchmarks represent the joint effect of time awake and circadian oscillation in p erformance.
For comparison purposes, it is low effectiveness in the model that represents fatigue risk. For any g iven schedule, effectiveness varies and can be plotted as a distri bution of values. The majority of time is usually well above 80, often above 90. It is time in the lower tail of th at effectiveness distribution that represents risk. One way to calculate the size of that exposure to risk is to set a bench mark effectiveness value and calculate the percent of duty time below that benchmark. For the studies reported her e, the benchmark for this calculation of exposure t ime was 77. An effectiveness level of 77 was chosen as a benchm ark because it is approximately equivalent to the e ffectiveness of someone with a blood alcohol concentration of 0. 05. However, this value should not be interpreted a s a demarcation between safe and unsafe; but rather, it is a convenient way to assess risk exposure with t he assumption that, all else being constant, a schedule with less duty time below 77 represents less fatigue risk re lative to a schedule with more duty time below 77. This approach is app ropriate for comparison of individual schedules, as reported below, using the model as a representative of the a verage person. Purpose
The purpose of this document is to review the fatig ue modeling studies that were conducted in support of flight and duty time rule-making. The following sections of this report review the basic modeling assumption s and findings relative to consecutive nights of duty and the effects of split duty.

4. Consecutive Nights without and with Breaks and Naps

4.1. Modeling Approach 4.1.1. Duty period started with a report time of 2200. To tal duty time was 11 hrs, including any break time. 4.1.1.1. Examined five consecutive nights in a week. 4.1.1.2. Considered a series of cases in which a mid-duty br eak was specified and assumed all sleep during the break was of good value: 83.5% restorati ve, considering the quality of the environment. 4.1.1.3. All breaks started at 0200 and after the break, the duty period resumed to a total of 11 hrs. 4.1.1.4. Considered 1.5, 2, 2.5, 3 and 4 hr mid-duty breaks.4.1.1.5. Time in bed was assumed to be 30 min less than the break: 15 min to get to sleep and 15 min to recover from inertia. 4.1.1.6. Daytime sleep was assumed to be 6.5 hrs, consistent with reports of sleep in night duty shift workers (Thorne, et al. 2008, Budnick, et al. 1994, Knauth, et al. 1980; Wilkinson, R. T. 1992). 4.1.1.7. No waypoints were considered, assumed entrainment t o local time and normal phase, and examined one week of operations. 4.1.2. Each of the combinations of mid-duty (night) sleep and daytime sleep was evaluated with the SAFTE/FAST model. 4.1.3. Overall patterns of performance ranging from 100 (n ormal best) to below 70 were plotted and compared. 4.1.4. As a basis of comparison, time below a criterion of 77 was calculated, excluding the time during the breaks. 4.1.5. A summary of the break conditions considered is sho wn in Table 1.

Table 1: Summary of break conditions considered.


## Break Duration Break Start Break end Time in Bed Duration 4 hr 0200 0600 3:30 3 hr 0200 0500 2:30 2.5 hr 0200 0430 2:00 2 hr 0200 0400 1:15 1.5 hr 0200 0330 1:00

4.1.6. For comparison, considered a base case without breaks or mid-duty nap opportunities. 4.1.6.1. FTP was eleven hours starting at 2200, no split dut y. 4.1.6.2. Daytime time in bed (TIB) was 6.5 hrs. 4.1.6.3. Prior to first night duty, evening nap of 2 hrs at 1700. 4.2. Summary of results

4.2.1. Results for the base case is shown in the Figure 1 and summarized in subsequ ent charts and tables as the no break case. 5. Time below criterion (77) occurred on each day and increased across the first three days the week. 6. Minimum effectiveness also consistently decreased a cross the week from 73 on the first day to 62 on the fifth day.Figure 1: Baseline cases for consecutive nights an d no breaks.

6.1.1. Results for 6.5 hrs Daytime Time in Bed (TIB) with and without breaks are summarized in Table 2 and Charts 2 and 3 (good break sleep), assuming 6.5 hrs of daytime TIB for sleep.

7. Relative to no sleep (with or without a break), bre aks between 2 and 4 hrs with sleep are superior to a 11 hr night duty period without sleep for the entire week. 8. The 3 and 4 hr break cases remain above 77 througho ut a five day sequence. 9. The 2.5 hr break remains almost entirely above 77 f or three day and is superior to no break for the entire week. 10. The 2 hr breaks remain above 77 for one day and is superior to no break for the entire week. 11. The 1.5 hr break does not avoid some time below 77 and is superior to no break for the first two days.

Table 2: Percent Duty Time Below 77 for each day of five day sequence, good break sleep, 6.5 hr daytim e TIB. The green shaded conditions all have less time below criterio n than the base case without a break. 

# Duration of Break w/ Good Sleep No Sleep 4 hrs 3 hrs 2.5 hrs 2 hr 1.5 hr No Break
1 0.0 0.0 0.0 0.4 12.5 41.7
2 0.0 0.0 0.0 7.4 38.4 50.2 Days 3 0.0 0.0 0.8 21.1 58.8 52.0
4 0.0 0.0 3.9 30.9 66.7 52.0
5 0.0 0.0 6.1 37.2 68.2 50.90 10 20 30 40 50 60 70 80

1 2 3 4 5 P e r c e n t O n - d u t y T i m e ( e x c l u d i n g b r e a k ) B e l o w C r i t e r i o n ( 7 7 )

Days of the Week



## Consecutive Nights of Duty with Breaks

No break 1.5 hr 2 hr 2.5 hr 3 hr
4 Break Duration

Figure 2: Percent Duty Time Below 77 for each day of five day sequence, good break sleep, 6.5 hr dayt ime TIB



## 4 hr Break 3 hr Break 2 hr Break 1.5 hr BreakFigure 3: Effectiveness across five days of duty w ith good break sleep and 6.5 hr daytime TIB. The d uration of the mid-duty break is noted in each panel.

12. Split Duty Analysis with 14 hr FDP

12.1. Modeling Approach

12.1.1. The modeling study was designed to assess for a fli ght duty period (FDP) of 14 hrs that started at 2200, the potential benefits of inserting a break w ith a sleep opportunity. The objective was to evaluate if the benefits would be sufficient to jus tify a longer FDP and to determine the minimum break duration that would justify an FDP extension. The break durations considered were as follows:

• 1 hr

• 1.5 hr

• 2 hr

• 2.5 hr

• 3 hr

• 3.5 hr

• 4 hr 12.1.2. The following additional assumptions guided the mod eling analysis:

• Breaks were scheduled to be included in the hours b etween 2200 and 0500.

• Assumed time in bed (TIB) with good sleep that star ted 15 min after start of break and ended 15 min prior to end of break.

• Considered cases with a total FDP up to 14 hrs (inc luding break) with total sleep held constant at 8 hrs per day.

• Considered cases with a total FDP up to 14 hrs (inc luding break) with daytime TIB of 6.5 hr at home, independent of the TIB during th e break. 12.2. Results: 12.2.1. The results were evaluated in terms of duty time be low an effectiveness of 77, a benchmark used for comparison purposes. Assuming a constant 6.5 hr daytime TIB, all breaks equal to or longer than 3 hrs and up to 4 hrs ensured no duty t ime below 77 across the entire week, see Figure 4.Figure 4 Split duty – constant 6.5 hr daytime TIB a nd 3, 3.5, and 4 hr breaks - minimal time below 77.

12.2.2. A similar result was found when we assumed a consta nt TOTAL TIB (break and daytime sleep) of 8 hrs, breaks equal to or longer than 3 hrs have mi nimal and no duty time below 77 across the entire week. Breaks of 2 hrs or less have consider able duty time below 77 and a break of 2.5 hrs has duty time below 77 during the last three duty p eriods, Figure 5.

Figure 5: Split Duty – 8 hrs total TIB, 3, 3.5, an d 4 hr Breaks – Minimal Time Below 77.

12.2.3. Breaks of 1 to 2.5 hrs are less effective in avoidi ng duty time below 77, Figure 6. For breaks of 1 to 2 hrs, time below 77 is experienced across the e ntire week; for breaks of 2.5 hrs, time below 77 begins to occur on day 3. Results are summarized i n Table 3.7m 25m 38m BCL 1 hr Break 1.5 hr Break

2 hr Break 2.5 hr Break

Figure 6: Split Duty - 10 hr FTP plus Break, as in dicated.

Table 3: Percent duty time below effectiveness 77 a ssuming 6.5 hrs daytime TIB. Percent Time Below Criterion (77) – 6.5 hrs Daytime TIB
Day Duration of Break (hrs)

1 1.5 2 2.5 3 3.5 4

1 35.5 2.0 0.3 0 0.0 0.0 0.0

2 63.2 37.0 7.0 0 0.0 0.0 0.0

3 64.7 57.5 21.7 1.2 0.0 0.0 0.0

4 64.3 67.8 31.7 4.2 0.0 0.0 0.0

5 62.8 69.0 37.8 6.3 0.0 0.0 0.01.1.1. The results are summarized in Table 7 and Figure 7 in which there was a constant 6.5 hrs of daytime TIB. For breaks of duration 3 hrs to 4 hrs , there was no time during the week of five duty periods with effectiveness projected to be below ef fectiveness of 77.

0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0

1 2 3 4 5 P e r c e n t D u t y T i m e B e l o w C r i t e r i o n ( 7 7 )

Days 1 Hr Break 1.5 Hr Break 2 Hr Break 2.5 Hr Break 3 Hr Break 3.5 Hr Break 4 Hr Break

Figure 7: Percent duty time below effectiveness of 77 across five duty days with breaks from between 1 and 4 hrs in duration.

1.1.2. The results lead to the following conclusions:

• The analysis was conducted with total duty time inc luding the break extending up to 14 hrs. Since breaks greater than 3 hrs prevent all time be low 77 and breaks end after the low point of the WOCL, extending duty up to 14 hrs does not incr ease risk provided the rest during breaks is good and additional daytime sleep is assumed to be constant with at least 6.5 hrs sleep. 1.1.3. Increasing breaks beyond 4 hrs and FTP beyond 14 hr s creates complications because it is difficult to get the entire break within the 2200 to 0500 win dow AND have it follow a single flight segment. Such schedules (if permitted) could also induce a g radual phase shift because the duty period plus the required 10 hr rest sums to greater than 24 hrs

. 

### References:

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Thorne H, Hampton S, Morgan L, Skene DJ, Arendt J. (2008). Differences in sleep, light, and circadian phase in offshore 18:00-06:00 h and 19:00-07:00 hr shift workers. Chronobiology International, 25 (2&3), 225-235.

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