Fatigue Risk Management

What is Fatigue?

Fatigue is a term that is used loosely to describe physical or mental weariness. It has a variety of causes:

• Lack of sleep
• The time of day according to the body’s circadian (daily) rhythm
• Performing monotonous tasks for a period of time
• Performing repetitive tasks for a period of time
• Performing mentally demanding tasks for a period of time
• Performing physically demanding tasks for a period of time
• Medical conditions such as Chronic Fatigue Syndrome

While there are a number of causes of fatigue, what they all have in common is that the fatigue can be mitigated by the provision of sufficient sleep. In a work situation, this requires a commitment from both the employer and employee:

• The employer must provide adequate sleep opportunities and facilities.
• The employee must take advantage of the provided sleep opportunities and facilities in a way that will protect against excessive fatigue.

CTI provides a number of tools that can assist the employer with providing adequate sleep opportunities without excessive cost, and can also be used to provide guidance to the employee to assist them to take advantage of the provided opportunities.

Fatigue Risk Management

The concept of Fatigue Risk Management was developed in response to the recognition that prediction of fatigue with 100% accuracy is not possible due to differences between staff members, work tasks and work environment, and because the likelihood of excessive fatigue causing a safety-related accident or incident will also depend on many circumstances related to the criticality of the task being performed.

The aim of Fatigue Risk Management is to minimize the total risk of safety-related accidents or incidents, using Risk Management methodologies. These methodologies are increasingly being mandated or recommended in most forms of transport, for example:

• ICAO and IATA are mandating the use of an FRMS for commercial airlines (see www.iata.org)
• Australian legislation includes recommendations for developing a risk management approach for heavy road vehicle driver fatigue (see www.ntc.gov.au)
• A risk management approach to fatigue is part of the mandatory Safety Management Systems for rail operators in Australia (see NewNatRailSafeAnOverJul2006.pdf)
• The Railways and Other Guided Transport Systems (ROGS) legislation for UK rail operators mandates a Safety Management System to control risks of fatigue in safety critical work (see www.rail-reg.gov.uk)

The management of fatigue risk for transportation crew requires a multi-faceted approach. CTI advocates an approach that involves the concept of defence in depth, as illustrated in the following diagram:

Defence in Depth Concept

CTI provides tools that can assist at all levels of the Defence in Depth concept of fatigue risk management:

Step 1: Planning to minimize fatigue risk

A number of features in CTI’s planning software can be used to minimize fatigue risk:

• When creating crew round trips (known as pairings in aviation), individual crew duties or combinations of crew duties that are likely to be fatiguing can be avoided or minimised. The flexibility of TPAC Rules allows a blend of strategies to be used:
  • Any form of proscriptive maximum duty and minimum rest rules are supported. Proscriptive rules have limitations, however, as they typically do not directly take fatigue into account, and so may unnecessarily classify work as fatiguing, causing increased costs. In rare cases, they may even allow work that has an undesirable risk of fatigue.
  • Bio-mathematical fatigue models such as the CTI BSAFR model may be used in addition to any proscriptive rules. Different levels of acceptable fatigue risk may be configured according to the criticality of the task to be performed – for example, the period of landing at an airport with a difficult approach or a high probability of adverse weather may have more stringent limits than other work times. It is also possible to predict fatigue risk during non-work times – for example during the commute home at the end of a trip. This prediction could then be used in a number of ways – for example it could be used to recommend that transport to the home be supplied by the employer in cases where the expected fatigue risk is high. It is also possible to use different model parameters to investigate the possible effects of natural individual variability, and calculate fatigue risk as the worst-case or average of the modeled fatigue responses.
• When creating crew roster lines, combinations of crew round trips and/or crew base duties that are likely to be fatiguing can be avoided or minimised. In a similar manner to the creation of crew pairings, this can be done using a combination of proscriptive rules and the predictions of a bio-mathematical fatigue model.

Step 2: Managing the fatigue risk caused by changes to the plan

A plan is never set in stone – disruptions caused by staff illness or unavailability or changes to transport schedules are inevitable. The CTI BSAFR model is available as a library for integration into crew tracking systems such as IBS iFlight Crew, and so can be used in a number of situations, including:

• When deciding whether a crew member is capable of safely extending their duty after a schedule delay. The predicted fatigue levels at the end of the extended duty can be used as an input to this process.
• When deciding whether it is necessary to provide a crew member with transport to their home at the end of a trip to avoid safety risks during the commuting.
• When selecting the best standby crew. Comparison of the estimated level of fatigue at the end of the duty for alternative standby crew members can help with the selection of the most appropriate crew member.

Step 3: Identify fatigue as it occurs and mitigate the effects

A major cause of fatigue risk is that a staff member’s fatigue self-rating has been shown to become less accurate as their level of fatigue increases, particularly for monotonous tasks. Strategies to mitigate this include:

• Using the predictions from a bio-mathematical model to inform crew members as to the times during their duty when fatigue risks are likely to be highest. The crew members can then use this information to guide their strategies for mitigating the effects of this fatigue.
• Using the predictions from a bio-mathematical model to provide recommended sleeping practices to crew to enable them to take best advantage of sleep opportunities so as to minimize the risk of fatigue.

Step 4: Learn from reports and incidents to improve steps 1-3

In the event of a fatigue report or fatigue-related incident, it is important to identify the factors that caused this and put in place processes to avoid a repeat of the problem. The CTI BSAFR model can be used to model the situation leading up to the point where a high fatigue risk was present. This modeling can be used to inform changes to parameters and limits used in previous steps to ensure that such a situation is avoided in the future.

Bio-Mathematical Fatigue Models

A large number of studies have been used to create bio-mathematical models that predict fatigue based on the times and durations of an individual’s sleep periods. For these models to be useful in predicting the fatigue of a given work pattern, another step needs to be performed – that is to predict these sleep periods from the given work patterns. Factors such as commuting time, individual differences and cultural differences can result in the sleep patterns of one workforce being different to those of another workforce.

CTI supports two bio-mathematical fatigue models which can be used in different situations:

• The BSAFR (Biological Sleep, Alertness and Fatigue Risk) model was developed to use the latest research in molecular biology and genetics to improve on the standard 3-process models used to predict fatigue from sleep periods, particularly in the presence of time zone shifts and short naps. It also has a highly-configurable model for predicting sleep periods from work periods, enabling the results of a sleep study on the actual workforce being modeled to be used to greatly increase the accuracy of the predicted fatigue. It is suited to situations where detailed fatigue predictions are required.
• The FAID (Fatigue Audit Inter Dyne) model is a simpler model that was developed to predict worker fatigue directly from work periods. It does not directly predict fatigue, but rather predicts a score related to the available sleep opportunity provided by the work periods, and has mainly been validated in situations where time zone shifting is not a large factor. It is suited to situations where it is desired to show that a company has adequately discharged its duty of care, but where predictions of fatigue over the course of a work period are not required.

Integration of Fatigue Models in CTI Products

CTI has integrated the BSAFR fatigue model with TPAC Rules to ensure that it can be used with TPAC Pairing, TPAC Rostering, TPAC Workbench and TPAC Reports in an extremely flexible manner. Visualisations are used to show predicted sleep and effectiveness levels from the BSAFR model along with work periods as shown below:

Fatigue analysis plots

Coloured bars represent work (bright colors are landings). Gray bars represent non-work time. White ribbon areas represent sleep.

The FAID fatigue model is currently integrated into TPAC Rail Crew.