THE PREVALENCE of shiftwork has substantially

A Model to Predict Work-Related Fatigue Based on Hours of Work Gregory D. Roach, Adam Fletcher, and Drew Dawson ROACH GD, FLETCHER A, DAWSON D. A model to predict workrelated fatigue based on hours of work. Aviat Space Environ Med 2004; 75(3, Suppl.):A61 9. Several research groups have developed models for estimating the work-related fatigue associated with shiftworkers duty schedules. In June 2002, invited members of seven of these groups attended the Fatigue and Performance Modeling Workshop in Seattle, WA. At the workshop, each group described the background and conceptual basis of their model, and an independent party compared the models predictions with performance and sleepiness data from five laboratory- and workplace-based scenarios. One of these models, the Fatigue Audit InterDyne (FAID), can be used to quantify the work-related fatigue associated with any duty schedule using hours of work (i.e., start/end times of work periods) as the sole input. The objectives of the current paper were to: 1) describe the background and conceptual basis of FAID; 2) present FAID-based predictions for four of the scenarios; and 3) discuss the advantages of, and possible improvements to, FAID. The analyses conducted to compare the predictive power of each model are described in detail by Van Dongen elsewhere in this issue. Keywords: fatigue, modeling, shiftwork, performance, sleepiness. THE PREVALENCE of shiftwork has substantially increased in most industrialized economies in the last three decades (19), largely due to changes in customer demands and community expectations, combined with the arrival of global competition. Consequently, employees in many industries are now required to work extended shifts and/or to work shifts that are outside the standard 9-to-5, Monday-to-Friday work week. The sleep loss and body clock disruption associated with these work demands may lead to increased levels of work-related fatigue, which manifests as reduced alertness, impaired neurobehavioral performance, increased sleepiness, and/or greater risk of injury and accident (e.g., 1,2,4). While the deleterious effects of shiftwork have been well established, most organizations have struggled to manage work-related fatigue in a systematic manner. Indeed, organizations and regulators in many industries have attempted to manage fatigue through prescriptive hours-of-service rules. These rules typically specify maximum shift lengths and minimum break durations, but they take little or no account of the physiological determinants of fatigue (e.g., influences of the circadian system). To a certain extent, a rules-based approach has been forced by a lack of simple, reliable, valid, cost-effective tools for managing fatigue. Recently, however, several research groups have developed fatigue models designed to quantify the impact of shiftwork schedules on employees levels of sleepiness, alertness, and/or performance. Most early fatigue models shared a common feature: they required actual or estimated sleep times as one of several inputs. This requirement was reasonable for researchers estimating the effects of fatigue in laboratory-based studies, but it posed difficulties for organizations wishing to estimate the effects of fatigue in workplace settings. In such situations, a reliance on actual sleep times as a model input is problematic because it is difficult, costly, and time-consuming to collect objective measures of sleep/wake for every possible duty schedule, and even then, the fatigue levels associated with a given schedule can only be estimated after it has been introduced. Furthermore, a reliance on estimated sleep times as a model input is problematic because, unless the process can be automated, it requires whoever creates the fatigue model s input file to make a subjective judgment. Taking a fundamentally different approach, the University of South Australia s Center for Applied Behavioral Science has developed and validated a fatigue model, the Fatigue Audit InterDyne (FAID), which requires hours of work (i.e., start/end times of work periods) as the sole input (6 11). A major advantage of FAID is that it does not require actual or estimated sleep/wake records as an input. Rather, it assigns a recovery value to time away from work based on the amount of sleep that is likely to be obtained in nonwork periods, depending on their length and the time of day that they occur. CONCEPTUAL BASIS OF FAID Simple input requirements were considered a necessary feature of FAID because it was primarily developed to be used by organizations in workplace settings, rather than by researchers in laboratory-based studies. In FAID, a duty schedule is viewed as a time-varying function whereby an individual is considered to exist in either one of two states: work or non-work. Consequently, any duty schedule can be expressed as a From the Centre for Sleep Research, University of South Australia. Address reprint requests to: Drew Dawson, Ph.D., Centre for Sleep Research, University of South Australia, Level 5 Basil Hetzel Institute, The Queen Elizabeth Hospital, Woodville SA 5011, Australia; drew.dawson@unisa.edu.au. Gregory Roach is a Post-doctoral Research Fellow, Centre for Sleep Research, University of South Australia. Reprint & Copyright by Aerospace Medical Association, Alexandria, VA. A61

Fig. 1. Conceptual basis of FAID. (a) Any duty schedule can be expressed as a square wave function oscillating between work and non-work. (b) The work-related fatigue associated with a duty schedule represents the balance between fatigue caused by work periods and recovery obtained in non-work periods. square wave function continuously oscillating between work and non-work (Fig. 1). Furthermore, each individual can be considered as a black box into which a specific duty schedule signal is input, and from which a continuously varying fatigue/recovery signal is output. A mathematical description of the transfer function in FAID that links the input and output functions of this signal processing system is provided in Dawson and Fletcher (6). FAID estimates are based on the notion that the work-related fatigue associated with a duty schedule represents the balance between two competing forces: those that produce fatigue during work periods; and those that reverse the effects of fatigue, i.e., produce recovery, during non-work periods (Fig. 1). The fatigue value of work periods and recovery value of non-work periods depend on their length, circadian timing, and recency (as described below). Length and Circadian Timing of Work and Non-Work Periods Generally, the fatigue value of a work period varies as a function of its length (13,18) and circadian timing (12). Essentially, longer work periods are more fatiguing than shorter work periods, and work periods that occur during subjective nighttime are more fatiguing than work periods that occur during subjective daytime. Thus, the transfer function in FAID that links work period inputs to fatigue outputs has a linear component (length of work period) and a sinusoidal component (circadian timing of work period). On the basis of previous research, it is assumed that the circadian component of fatigue closely matches the daily rhythm of core body temperature (5,14,24). Consequently, it is represented in FAID by an approximately sinusoidal function, with a period of 24 h, a maximum fatigue value at 05:00, and a minimum fatigue value at 17:00. It is further assumed that the recovery value of a non-work period is proportional to the amount of sleep that is obtained in that period, and that this varies as a function of the non-work period s length (22) and circadian timing (3,16,21). Indeed, workplace studies show that more sleep is obtained in longer non-work periods than in shorter non-work periods, and that a greater amount of sleep is obtained in non-work periods that occur during subjective nighttime than in nonwork periods that occur during subjective daytime (15,17,23). Consequently, the transfer function in FAID that links non-work period inputs to recovery outputs also has a linear component (length of non-work period) and a sinusoidal component (circadian timing of non-work period). The circadian component of recovery closely matches the daily rhythm of sleep propensity such that, similar to fatigue, it is represented in FAID by an approximately sinusoidal function, with a period of 24 h, a maximum recovery value at 05:00, and a minimum recovery value at 17:00. In FAID, the total fatigue value of a work period is determined by summing the values of the fatigue function across the work period. Similarly, the total recovery value of a non-work period is determined by summing the values of the recovery function across the non-work period. The overall fatigue score is an algebraic function of these fatigue and recovery values, calculated on the basis of the recent history (i.e., previous 7 d) of work and non-work periods. Using this approach, it is possible to estimate the work-related fatigue associated with any duty schedule using hours of work as the sole input. Recency of Work and Non-Work Periods The timing and length of work and non-work periods are not the only determinants of work-related fatigue in FAID. Importantly, the fatigue/recovery values of work/non-work periods are weighted such that more recent work/non-work periods make a greater relative contribution to the overall fatigue score than less recent work/non-work periods. In FAID, this function has been arbitrarily defined to decay linearly from a peak weighting value of 168/168 for the current hour, to a minimum weighting value of 1/168 for the hour that occurred 7 d (168 h) previously. Thus, the relative contribution of work and non-work periods reduces linearly from one to zero over the period of a week, such that work and non-work periods that occurred more than 168 h in the past make no contribution to the overall fatigue score. Saturation Individuals cannot store recovery to offset against potential future fatigue; they can only recover from fatigue that has already been accumulated. Essentially, this means that excess sleep cannot be banked to draw on in the future. Consequently, FAID contains a saturation function that limits the total value of recov- A62

ery that can be accumulated at any time, such that the overall fatigue score cannot be less than zero. Secondary Transfer Functions It is important to note that FAID outputs a single generic variable, i.e., fatigue, rather than a particular quantifiable measure such as neurobehavioral performance (as measured by reaction time tests) or subjective sleepiness (as measured by sleepiness scales). However, a set of secondary transfer functions could be developed to convert the generic fatigue variable output by FAID to these various different output measures. By definition, this would improve FAID s prediction of these measures. Summary of Approach FAID can be conceptualized as a token economy in which employees acquire fatigue tokens during work periods and recovery tokens during non-work periods. The token economy is governed by four main principles: 1. The token value of a particular work or non-work period is dependent on its length and circadian timing; 2. The fatigue/recovery value of tokens that are held decays linearly over time, from a weighting of one for most recently acquired tokens, to a weighting of zero for tokens acquired more than 168 h previously; 3. There is a limit to the total value of recovery tokens that can be held at any point in time such that they cannot be stored beyond full recovery; 4. The overall fatigue score for an individual at any point in time is the net worth of the fatigue and recovery tokens that he/she holds. Benefits of FAID FAID input data is available in existing organizational records: The key characteristics of any good fatigue model are reliability and validity. In addition, models targeted at workplace implementations must also be affordable and relatively simple to use. As discussed above, some early fatigue models may have had difficulty fulfilling these criteria of affordability and simplicity because it is difficult, time consuming, and costly to collect/estimate sleep times and incorporate them as model inputs. In contrast, hours of work, the sole input requirement for FAID, is typically captured in organizational records for scheduling and/or payroll purposes. Thus, there is no additional data capture time/ cost involved in generating input files for FAID. Indeed, FAID can be electronically linked to schedule/payroll records such that input files are automatically generated. Consequently, it is relatively simple and affordable for organizations to implement a fatigue modeling approach based on FAID. Risk management capabilities of FAID: In Australia, the work-related fatigue associated with shiftwork is now recognized in occupational health and safety legislation as a workplace hazard. Consequently, organizations have a duty of care to their employees to minimize the risks associated with fatigue. Importantly then, FAID contains optional features that organizations can incorporate as part of their risk management procedures. Briefly, the level of risk associated with any work task (i.e., low, moderate, high, extreme) can be determined based on the likelihood and consequence of an incident or accident occurring while that task is being undertaken. This risk level can be included as an optional FAID input for each work period. The maximum acceptable level of fatigue for tasks in each risk category can also be specified in FAID, based on the notion that the acceptable level of fatigue for inherently high-risk tasks is lower than the acceptable level of fatigue for inherently low-risk tasks. If this risk information is input, then FAID will not only output fatigue scores for a duty schedule, but it will also identify violations where the acceptable level of fatigue is exceeded. Ideally, any risk management approach should follow a prescribed methodology (e.g., the Australian Standard for Risk Management, AS4360), and should be undertaken as a consultative process with the relevant stakeholders (i.e., employees, labor unions, management, regulator, etc.). MODELING SCENARIOS In June 2002, the Fatigue and Performance Modeling Workshop was held in Seattle, WA. The workshop was attended by invited members of seven research groups from Australia, Europe, and the U.S. that have each developed a model for estimating the work-related fatigue associated with shiftworkers duty schedules: 1. Fatigue Audit InterDyne (FAID); 2. Sleep, Activity, Fatigue, and Task Effectiveness Model (SAFTE); 3. Sleep/Wake Predictor (SWP); 4. Interactive Neurobehavioral Model (INM); 5. System for Aircrew Fatigue Evaluation (SAFE); 6. Circadian Alertness Simulator (CAS); 7. Two-Process Model (2PM) Each group, except the 2PM group, provided predictions for laboratory- and field-based scenarios using their respective fatigue models. At the workshop, an independent party presented analyses that: 1) compared the predictions from each model with the neurobehavioral performance and subjective sleepiness data from the five scenarios; and 2) compared the relative predictive power of the models for each measure from the five scenarios (20). The following sections present the FAID-based predictions for these scenarios. The scenarios are described in detail elsewhere in this issue (20). FAID Inputs While an array of additional information was provided to each group to use as model inputs (i.e., time in bed, assumed sleep times, wake time, light exposure, ambient temperature, geographical location, etc.), the input file created for FAID had a single input: hours of work. Thus, the predictions for each of the scenarios A63

were made solely on the basis of start/end times of work periods. FAID predictions for any hour of work take account of the history of work and non-work periods over the previous 7 d (168 h). However, participants work histories for the 7 d prior to each scenario were not known. For the laboratory-based scenarios (i.e., scenarios 1, 2, and 5), FAID input files were created based on the assumption that participants did not work in the 7 d prior to entering the laboratory. For the field-based scenario (i.e., scenario 3), a baseline week of data was included at the beginning of the FAID input file, reflecting the assumption that each participant worked five consecutive 8-h day shifts (09:00-17:00) followed by 2 d off prior to the first day of data collection. It is important to note that one of the primary aims of the workshop was to compare the predictive power of the various models for data collected in laboratory- and field-based settings. While it would have been a relatively trivial exercise to fit complex curves to the experimental data post hoc, had the data been available, this would have merely demonstrated each models ability to explain, rather than to predict. Consequently, the FAID predictions provided for the workshop, and reported in the current paper, were made without access to the datasets for each scenario, and without post hoc transformations. FAID Outputs FAID outputs fatigue scores in one of five ranges: STANDARD (0 40): the upper limit of this range is similar to the maximum fatigue score produced by the standard 9 5, Monday-Friday work week. MODERATE (40 80): the upper limit of this range is similar to the maximum fatigue score produced by a forward-rotating schedule (morning, afternoon, night) with five consecutive 8-h work periods followed by 2 d off. HIGH (80 100): the upper limit of this range is similar to the maximum fatigue score produced by a forward-rotating schedule (morning, afternoon, night) with six consecutive 8-h work periods followed by 1 d off. VERY HIGH (100 120): the upper limit of this range is similar to the maximum fatigue score produced by a schedule that rotates through two 12-h day shifts, 2 d off, two 12-h night shifts, and 2 d off. EXTREME (120 ): fatigue scores of this magnitude are similar to those produced by a permanent night shift schedule with six consecutive 12-h work periods followed by 1 d off. Protocol Scenario 1: 88 h of Laboratory-Based Sleep Deprivation The participants (25 males, aged 21 48 yr) spent three consecutive baseline nights in the laboratory, then were randomly assigned to one of two conditions: condition 1 88 h of continuous wakefulness; condition 2 88 h Fig. 2. Predicted fatigue scores during wakefulness for scenario 1: a. condition 1; b. condition 2. of wakefulness, except for 2-h nap opportunities starting at 14:45 and 02:45 each day. In both conditions, participants completed a 30-min test battery at 2-h intervals during wakefulness. The test battery included the psychomotor vigilance task (PVT), a measure of neurobehavioral performance capability, and the Karolinska Sleepiness Scale (KSS), a measure of subjective sleepiness. FAID Outputs Condition 1 The predicted fatigue scores for condition 1 ranged from a minimum of 48 (moderate) during the 11 th hour of wakefulness, to a maximum of 195 (extreme) during the 73 rd hour of wakefulness (Fig. 2). There was a general trend for the fatigue scores to increase as the duration of wakefulness increased, reflecting the linear component of the transfer function in FAID. The fatigue scores also followed an approximately sinusoidal pattern, such that they peaked at 06:00 and reached a nadir at 18:00 each day, reflecting the circadian component of the transfer function in FAID. A64

FAID Outputs Condition 2 The predicted fatigue scores for condition 2 followed a similar pattern to those for condition 1, but were generally lower due to the recovery afforded by the 2-h nap opportunities at 02:45 and 14:45 each day (Fig. 2). The predicted fatigue scores ranged from a minimum of 46 (moderate) during the 11 th hour of the condition, to a maximum of 167 (extreme) during the 73 rd hour of the condition. Similar to condition 1, there was a general trend for fatigue scores to increase as the condition continued, but the scores also followed an approximately sinusoidal pattern. Consequently, the highest fatigue score for each day occurred at 06:00 and the lowest fatigue score for each day occurred at 18:00. Relative Predictive Power of FAID For scenario 1 (conditions 1 and 2), FAID ranked fourth for predicting neurobehavioral performance (PVT lapse frequency) and sixth for predicting subjective sleepiness (KSS). Neurobehavioral performance and subjective sleepiness were best predicted by INM and CAS, respectively (20). Scenario 2: 14 d of Laboratory-Based Partial Sleep Deprivation Protocol The participants (24 people, aged 21 48 yr) spent three consecutive baseline nights in the laboratory, then were randomly assigned to one of two conditions: condition 1 14 d of 20 h of forced wakefulness (07:30 03:30) followed by 4 h in bed(03:30 07:30); condition 2 14 d of 18 h of forced wakefulness (07:30 01:30) followed by 6 h in bed(01:30 07:30). Participants remained in the laboratory for three consecutive recovery nights following both conditions, with8hinbedeach night separated by 16 h of forced wakefulness each day. In both conditions, participants completed a 30-min computerized test battery, including the PVT and the KSS, at 2-h intervals during forced wakefulness. FAID Outputs Condition 1 The predicted fatigue scores for condition 1 ranged from a minimum of 48 (moderate) at 17:30 on day 1, to a maximum of 166 (extreme) at 07:30 on days 8 14 (Fig. 3). The predicted fatigue scores followed a similar pattern on each day of the condition. After the nightly 4-h sleep opportunity, fatigue scores fell throughout the day from 07:30, reached a daily minimum at 17:30, then increased until 01:30 prior to the next nightly sleep opportunity. At day 8, fatigue scores reached a plateau that was maintained until day 14, reflecting the fact that the 7-d histories of work and non-work periods prior to each of these days were identical. While following the same pattern as the other days, predicted fatigue scores during the recovery period were generally lower due to the greater sleep opportunity during recovery compared with the other days (i.e., 8 h from 23:30 rather than 4 h from 03:30). Fig. 3. Predicted fatigue scores during wakefulness for scenario 2: a. condition 1; b. condition 2. In both conditions, 14 d of partial sleep deprivation were followed by 3 recovery days. FAID Outputs Condition 2 Despite being generally lower, the predicted fatigue scores for condition 2 followed a similar pattern to condition 1, ranging from a minimum of 48 (moderate) at 17:30 on day 1, to a maximum of 137 (extreme) at 07:30 on days 8 14 (Fig. 3). As in condition 1, the predicted fatigue scores followed a similar pattern on each day. After the nightly 6-h sleep opportunity, fatigue scores fell throughout the day from 07:30, reached a daily minimum at 17:30, then increased until 23:30 prior to the next nightly sleep opportunity. As in condition 1, fatigue scores reached a plateau at day 8 that was maintained until day 14. While following the same pattern as the other days, predicted fatigue levels during the recovery period were generally lower due to the greater sleep opportunity during recovery compared with the other days (i.e., 8 h from 23:30 rather than 6 h from 01:30). Relative Predictive Power of FAID For scenario 2 (conditions 1 and 2), FAID ranked first for predicting neurobehavioral performance (PVT lapse A65

at the end of the sixth work period (at 07:00). Out of 11 work periods, 2 had peak fatigue scores in the high range, 1 had a peak fatigue score in the very high range, and 2 had peak fatigue scores in the extreme range. The fatigue scores for the other six work periods did not exceed the moderate range. The predicted fatigue scores for this work schedule were particularly high because it included seven consecutive work periods during the nighttime and/or early morning. Consequently, the fatigue value of work periods was high because the engineer was required to work during the nighttime, and the recovery value of non-work periods was low because his sleep opportunities occurred during the daytime. FAID Outputs Engineer 2 Fig. 4. Predicted fatigue scores during work periods for scenario 3: a. engineer 1; b. engineer 2. frequency) and second for predicting subjective sleepiness (KSS). SAFTE ranked first for predicting subjective sleepiness (20). Scenario 3: 14 d of Field-Based Work Protocol Ten experienced freight locomotive engineers (all male, aged 36 54 yr) participated in a field-based study for 14 consecutive days while working irregular, unpredictable work schedules. Participants recorded subjective sleepiness on a 4-point sleepiness scale every 1 2 h during work periods. There were 10 participants in scenario 3, but only 2 cases are presented here in the interests of brevity (i.e., those with the highest and lowest fatigue levels). FAID Outputs Engineer 1 The highest predicted fatigue scores in this scenario were associated with the work schedule for engineer 1 (Fig. 4). The predicted fatigue scores ranged from a minimum of 15 (standard) at the beginning of the last work period (at 21:00), to a maximum of 129 (extreme) The lowest predicted fatigue scores in this scenario were associated with the work schedule for engineer 2 (Fig. 4). The predicted fatigue scores ranged from a minimum of 14 (standard) at the beginning of the first work period (at 19:00), to a maximum of 57 (moderate) at the end of the third work period (at 07:00). Out of eight work periods, four had peak fatigue levels in the standard range, and the other four had peak fatigue levels in the moderate range. The predicted fatigue scores for this work schedule were particularly low because there were very few occasions when the engineer had to work at night or during the early morning, and he never had to work on more than two consecutive days. Consequently, the fatigue value of work periods was low because they generally occurred during the daytime, and the recovery value of non-work periods was high because they generally occurred during the nighttime. Relative Predictive Power of FAID For scenario 3 (engineers 1 10), FAID ranked fifth, and SWP ranked first, for predicting subjective sleepiness (4-point sleepiness scale) (see 20). Scenario 4: Ultra-Long Range Flight Operations This scenario was a theoretical schedule for ultralong range (ULR) flight operations between Hong Kong and New York. There is a version of FAID that includes an adjustment factor for the circadian disruption and adaptation associated with rapid time zone transitions. This version of FAID can be used to estimate the fatigue levels associated with work schedules that involve transmeridian flight. However, FAID predictions were not provided for this scenario due to a lack of available data regarding the effects on sleep and circadian rhythms of single time zone shifts of 13 h, as required for ULR flights between Hong Kong and New York. Without such data, we could have only guessed about the rate and direction of phase shifts of the circadian timing systems for aircrew working this ULR flight schedule. A66

Scenario 5: 7dofLaboratory-Based Sleep Restriction Protocol The participants (aged 21 62 yr) spent 3 consecutive baseline nights in the laboratory, then were randomly assigned to one of two conditions: condition 1 8dof 17 h of forced wakefulness (07:00 24:00) followed by 7 h in bed (24:00 07:00); condition 2 8dof21hof forced wakefulness (07:00 04:00) followed by 3 h in bed (04:00 07:00). Participants remained in the laboratory for 3 consecutive nights following both conditions, with 8hinbed each night separated by 16 h of forced wakefulness each day. In both conditions, participants completed a test battery, including the PVT, four times per day at 09:30, 12:30, 15:30, and 21:30. FAID Outputs Condition 1 The predicted fatigue scores for condition 1 ranged from a minimum of 57 (moderate) at 15:30 on the baseline day, to a maximum of 112 (very high) at 21:30 on day7(fig. 5). The predicted fatigue scores followed a similar pattern on each day of the condition: they were highest for the 09:30 and 21:30 test sessions, and lowest for the 12:30 and 15:30 test sessions. Predicted fatigue scores progressively increased from baseline for each of the experimental days. While following the same pattern as the experimental days, predicted fatigue scores on the recovery days declined marginally due to the slightly greater sleep opportunity during recovery compared with the experimental days (i.e., 8 h from 23:00 rather than 7 h from midnight). FAID Outputs Condition 2 Despite being generally higher, the predicted fatigue scores for condition 2 followed a similar pattern to condition 1, ranging from a minimum of 57 (moderate) at 09:30 on the baseline day, to a maximum of 148 (extreme) at 21:30 on day 7 (Fig. 5). As in condition 1, predicted fatigue scores followed a similar pattern on each day: they were highest for the 09:30 and 21:30 test sessions, and lowest for the 12:30 and 15:30 test sessions. Predicted fatigue scores progressively increased from baseline for each of the experimental days. While following the same pattern as the experimental days, predicted fatigue scores during the recovery period substantially and progressively declined due to the greater sleep opportunity during recovery compared with the other days (i.e., 8 h from 23:00 rather than 3 h from 04:00). Relative Predictive Power of FAID For scenario 5 (conditions 1 and 2), FAID ranked fourth, and INM ranked first, for predicting neurobehavioral performance (PVT mean reaction time) (20). Fig. 5. Predicted fatigue scores during wakefulness for scenario 5, a. condition 1; b. condition 2. In both conditions, a baseline day was followed by 7 d of sleep restriction and 3 recovery days. DISCUSSION FAID was primarily developed to be used in workplace settings to quantify the work-related fatigue associated with past, current, and proposed duty schedules. As described above, FAID is a relatively simple fatigue model that can be conceptualized as a token economy in which employees acquire fatigue tokens during work periods and recovery tokens during nonwork periods. Within the token economy, the fatigue value of work periods and recovery value of non-work periods are dependent on their length, circadian timing, and recency. The overall fatigue level for an individual at any point in time is the net worth of the fatigue and recovery tokens that he/she has accrued over the previous 7 d. The major advantage of this approach is that it does not require sleep times as an input, but rather assigns a recovery value to time away from work based on the amount of sleep that is likely to be obtained in non-work periods. Thus, FAID can be used to predict the work-related fatigue associated with any duty schedule using hours of work as the sole input. From an organizational perspective, the two main benefits of FAID are that: 1) it enables estimates of the workrelated fatigue associated with any duty schedule to be made using information that is readily available in schedule and/or payroll records; and 2) its optional A67

features enable fatigue modeling to be incorporated into risk management procedures consistent with occupational health and safety requirements. The current paper provided details of FAID predictions for three laboratory-based scenarios and one fieldbased scenario. These predictions were made without access to the neurobehavioral performance and/or subjective sleepiness data for each scenario, and thus without post hoc transformations of the outputs. The predictions demonstrate that FAID has a linear component, based on hours of duty, and an approximately sinusoidal component, based on time of day. Furthermore, the predictions demonstrate that FAID clearly discriminates between more and less demanding schedules in both laboratory and workplace settings. The analyses by Van Dongen in this issue (20), comparing FAID predictions with the neurobehavioral performance and subjective sleepiness data, indicated that FAID had better predictive power for the field-based scenario than for the laboratory-based scenarios. Interestingly, this is not consistent with previous validations of FAID, which have indicated that the model is better able to predict neurobehavioral performance, sleep latency, subjective sleepiness, and subjective tiredness in the laboratory than it is able to predict neurobehavioral performance and subjective alertness in the field (8,10). Furthermore, Van Dongen (20) found that all of the models presented at the workshop were generally better able to predict the results of the field-based scenario than the laboratory-based scenarios. This was unexpected, given that a multitude of variables that potentially impact on an individual s level of fatigue are controlled to a greater extent in the laboratory than they can be in the field (e.g., caffeine intake, sleeping conditions, social interaction, etc.). A comparison of the six different models predictions for scenarios 1, 2, 3, and 5 indicated that the ranking order of the models varied between scenarios, and even varied between measures within scenarios (20). Depending on the scenario (and measure), FAID ranked as high as first and as low as sixth. Of the other models, SAFTE and INM ranked between first and fourth, SWP ranked between first and fifth, CAS ranked between first and sixth, and SAFE ranked between third and fifth. This indicates that no one model was substantially and consistently better able to predict the actual data than the others. Indeed, the differences in predictive power between the models were relatively small compared with the differences between model predictions and experimental data (20). Taken together, these analyses indicated that FAID, with its relatively simple input requirements (i.e., start/end times of work periods), has predictive power comparable with the other current fatigue models. Given that FAID was primarily created to be used in workplace settings rather than in the laboratory, it is important to briefly note some examples of its practical applications. First, several organizations (e.g., international airlines, class 1 railroads, international mining companies) have used FAID to: 1) consider the likely impact of a particular duty schedule on employees work-related fatigue levels; 2) compare the fatigue levels associated with proposed duty schedules to determine the best alternative; 3) test for risk violations by comparing the fatigue levels associated with proposed duty schedules with the agreed maximum acceptable level of fatigue; 4) assign/offer overtime to employees with relatively low levels of work-related fatigue; and 5) conduct what if? analyses for unscheduled changes to duty schedules. Second, general aviation companies in Australia have used FAID analyses of proposed duty schedules as part of their safety cases submitted to the regulator when applying for exemptions to operate outside of prescriptive hours-of-service regulations. Third, Australian courts and the Australian Transport Safety Bureau have considered FAID analyses of duty schedules when determining whether or not work-related fatigue was a contributing factor in several serious/ fatal accidents. Finally, insurance companies have used FAID to assess the fatigue risk associated with an organization s duty schedules for the purpose of determining insurance premiums. Previous validations of FAID have been published comparing FAID-based estimates with subjective and objective measures collected under conditions of sleep restriction and sustained wakefulness in laboratorybased studies, and in field-based settings by employees working irregular duty schedules (8,10). However, for a fatigue model to be considered robust, it must be tested under a wider variety of circumstances. In recognition of this requirement, three more FAID validation studies are currently being prepared for publication. These studies will: 1) consider the ability of FAID to predict changes in logical reasoning, multiple sleep latency test scores, self-rated alertness, profile of mood state fatigue, visual vigilance, and reaction time in published napping studies; 2) compare FAID predictions with neurobehavioral performance and subjective alertness data collected during a week of simulated night work in the laboratory; and 3) compare FAID predictions with experienced locomotive engineers driving performance during 8-h day and night shifts in heavy-haul freight and urban passenger rail simulators. While the currently available validation studies indicate that FAID provides relatively reasonable estimates of the work-related fatigue associated with duty schedules, they also indicate that there is scope for improvement, particularly for predicting the effects of fatigue outside of controlled laboratory environments (8,10). Consequently, two projects are currently being undertaken to improve the predictive power of FAID. Modifications to FAID are being modeled to reflect the possibilities that: 1) the recovery value of non-work periods during layovers for international aircrew may be considerably disturbed by rapid time zone transitions; 2) shifting the timing of sleep/wake, as occurs with night work, may shift circadian phase; 3) specific populations may have relatively advanced/delayed circadian phase; 4) the relative recuperative value of short sleeps (i.e., naps) and normal sleeps may differ; and 5) the amount of sleep obtained by shiftworkers in time away from work may differ between industries. A68

CONCLUSIONS Simple input requirements were considered a necessary feature of FAID because it was primarily developed to be used by organizations in workplace settings, rather than by researchers in laboratory-based studies. Consequently, FAID was designed so that the workrelated fatigue associated with any duty schedule could be quantified using hours of work as the sole input. The results of the Fatigue and Performance Modeling Workshop in Seattle in June 2002 indicated that the predictive power of FAID was comparable with other current fatigue models. However, the workshop results and previous validation studies suggest that FAID has scope for improvement. Consequently, research efforts are currently being applied to providing further validation of FAID in laboratory, simulator, and workplace settings, and to modifying FAID to improve its predictive power. 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