4 Alternatives to the Fatigue Risk Index

Biomathematical models (BMM) to predict fatigue are used throughout the infrastructure sector with most organisations, including Network Rail and Highways England, adopting the HSE supported Fatigue Risk Index.

In June 2021, the Fatigue Risk Index was withdrawn from the HSE website for the following reasons;

The software platform on which it runs is an older version of Excel that can no longer be supported and maintained on the HSE website.
The design of the FRI requires improvement to promote better understanding of its outputs, its limitations, and its role in a Fatigue Risk Management System.
In its current format, there have been cases of the FRI being misused in order to justify work patterns that clearly require further action to reduce fatigue-related risk.

With the Fatigue Risk Index potentially being replaced, here is an overview of the alternatives using the research provided by the RRSB and the Australian Civil Aviation Safety Authority.

The Circadian Alertness Simulator (CAS)

Description: CAS is a biomathematical fatigue model that estimates fatigue risk of an individual’s sleep-wake-work pattern in combination with a variety of individual-specific settings.

Objective: The objective of CAS is to estimate risk associated with work-rest-sleep sequences.

Advantages:

Suitable for large-scale application (batch processing has been done with data sets exceeding 10,000 individual employees) and integration with crew planning tools. Graphical representations of work schedule features.
The latest version (CAS-5) is specifically optimised for crew planning and other FRMS applications.
Applicable for use with most job roles including maintenance workers.

Limitations:

Cost may be a consideration for some organisations.
Predictions are based on population averages (as with other models).

The Fatigue Assessment Tool by InterDynamics (FAID)

Description: FAID Quantum, released by InterDynamics in April 2016, uses two BMMs, the FAID Standard BMM and the FAID Quantum BMM (which incorporates sleep prediction together with results using the Karolinska Sleepiness Scale (KSS).

Objective: FAID aims to provide an indication of relative hours of work fatigue exposure associated with the planned or actual hours of work entered. Where task risk has been assigned to work schedule information, FAID facilitates the management of working hours to within defined Tolerance Levels and target Tolerance Level compliance percentages, thereby limiting and auditing the fatigue exposure associated with the hours worked.

Advantages:

The model requires very few inputs in order to make fatigue predictions.
FAID Roster Tool offers the ability to manually build rosters across numerous groups or depots, to compare and store auditable planned and actual hours of work data and to see immediate feedback of FAID scores as shifts are allocated or added.
The model and associated documentation seek to educate users on the limitations and appropriate use of the model, whilst offering practical fatigue risk management frameworks.

Limitations:

FAID is a model of fatigue exposure resulting from hours of work and, as such, mitigation strategies to reduce FAID scores are limited to changing work schedule timing and length.
FAID requires 7 days of history (or 168 hours) to provide meaningful analysis of a given duty. Hence, the first week of analysis will under-estimate the fatigue exposure associated with work hours (incomplete data for the preceding 7 days).

The Sleep, Activity and Task Effectiveness Model and associated Fatigue Avoidance Scheduling Tool (SAFTE-FAST)

Description: Sleep, Activity, Fatigue, and Task Effectiveness (SAFTE) is a biomathematical model of the factors that contribute to fatigue. It is designed to simulate the underlying physiological system that causes degradations in cognitive performance.

The Fatigue Avoidance Scheduling Tool (FAST) implements the SAFTE biomathematical model of performance and fatigue to generate estimates of performance degradation owing to the individual’s level of fatigue.

Objective: The purpose of the SAFTE-FAST system is to provide operators with prospective forecasts of expected fatigue risk so that proactive mitigations can be implemented to eliminate excessive fatigue risk. Retrospective and real-time assessments are also supported.

Advantages:

The FAST software can provide graphical representations of effectiveness and many other outputs (sleep reservoir, circadian phase, and lapse likelihood) along with specific information about the schedule.
The FAST software has been specifically designed for applications in industrial settings and transport, eg, for aviation, rail, and shift workers.

Limitations:

Cost may be a consideration for some organisations, although IBR offers a lower-cost service to evaluate specific schedules and provide a report of findings for organisations that do not wish to licence the model.
Many inputs calculated are based on ‘average worker’ data. This reduces the inputs required but means the model is less customisable to reflec characteristics of individuals.

The Sleep Wake Predictor (SWP)

Description: The Sleep/Wake Predictor model (based on the original Three-Process Model of Alertness) was originally developed by Professor Torbjörn Åkerstedt from the Karolinska Institute in Sweden and Professor Simon Folkard, University of Wales, Swansea. It was developed using several studies with subjective sleepiness ratings.

The SWP model predicts likelihood of sleep onset and sleep termination based on physiological parameters, and has been recently modified to better account for chronic sleep restriction conditions. The software is designed to predict alertness by determining the level of sleepiness associated with changes in circadian rhythms and time awake or asleep. This calculation is used to evaluate the potential for obtaining restful sleep and for a person remaining alert during a specified timeframe.

Objective: The purpose of the Three-Process Model of alertness underlying the SWP is to assist in the evaluation of general effects of work schedules. Specifically, the aims of the model are to:

predict alertness from sleep / wake patterns or from work patterns;
provide quantitative support for the evaluation of schedules;
provide a tool for monitoring alertness and fatigue-related error risk;
assist in education on sleep / wake regulation;
provide a tool for generating research hypotheses on sleep/wake regulation and its consequences.

Advantages:

The model requires very few inputs in order to make fatigue predictions.
The algorithms underlying the model are available for use free of charge.

Limitations:

Duty periods are input manually.
The model is not supplied by a commercial organisation, so support and assistance with interpreting outcomes are not available at this time.

‍

Overall, there are several alternatives to the Fatigue Risk Index that rail operators can consider to help manage the risk of worker fatigue. By taking a holistic approach, leveraging wearable technology, investing in training programs, and prioritizing communication and collaboration, rail operators can help to prevent fatigue-related incidents and ensure the safety and well-being of their workers.