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FATIGUE MANAGEMENT RECOMMEDATIONS2012030515131111840

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									    FATIGUE MANAGEMENT DISCUSSION POINTS
The following discussion points and recommendations arise as a
consequence of reviewing the following documents:

  a. Procedures for Validation and Calibration of Human Fatigue Models:
     The Fatigue Audit InterDyne Tool. US Department of Transport Federal
     Railroad Administration. Final Report, November 2010.
  b. Transport and Main Roads: Rail Safety Regulation Safety Alert. Notice to
     Accredited Rail Transport Operators. 01/11 – Use of bio-mathematical
     models of human fatigue. 18/1/2011.
  c. Transport and Main Roads: Rail Safety Regulation Safety Alert. Notice to
     Accredited Rail Transport Operators. 04/10 – Use of bio-mathematical
     models managing risks of human fatigue in the workplace. 30/7/2010.
  d. Transport Safety Alert. Independent Transport Safety Regulator. Use of
     Bio-Mathematical Models in Managing Risks of Human Fatigue in the
     Workplace. 27/7/2010.


DISCUSSION POINTS


  1. Rail Transport Operators (RTO) using bio-mathematical fatigue models should make
     necessary adjustments to their practice to minimize over reliance on just the “results”
     (i.e. Fatigue scores) in making risk management decisions pertaining to fatigue.
  2. Current bio-mathematical fatigue models only partially represent the factors that
     impact on fatigue and performance and cannot be solely relied upon to deliver “rules”
     to be applied to fatigue risk management.
  3. Instructions supporting such models are often unclear and should be supplemented to
     provide better guidance to users.
  4. A FAID score of less than 80 does not necessarily mean that a person is not impaired
     by fatigue. FAID scores as low as 63.18 represent the exact “fatigue threshold” for
     FAID.
  5. Sleep estimate output of FAID provides supplementary information to assess average
     opportunity for sleep and provides a general indication of the impact of time of day
     factors on amount of sleep based on group data not on an individual basis.
  6. Factors such as physiological, work environment, social/psychological must also be
     considered.
  7. Fatigue management models need to evolve and include individual direct measures to
     be best practice.
  8. Limitations of bio-mathematical models: (i) In itself it is not a fatigue risk
     management approach. (ii) “Scores” should never be used as the sole basis for risk
     management decisions regarding work hours. (iii) Such models are better at
     predicting fatigue when people are kept awake continuously. They are less effective
     at predicting the accumulation of performance impairment over many days of sleep
     loss. (iv) Model outputs are based on averages and should not be used to make
     decisions on individual worker’s fitness for duty. (v) Such models do not take into
     account other factors that impact on fatigue (e.g. Time pressure, work/team
     environment, skill level, experience, age, health and home life).
  9. RTO in many cases are applying the general statements by FAID model developers in
     industry reports/articles for the validation of FAID/FAID scores of <80. The research
     evidence to support this is not strong.
   10. It has not been established if FAID scores can predict the risk of accidents/incidents.
   11. Fatigue associated with shift work is cumulative due to partial sleep loss over time
       and is different to the effects of being continuously awake – which is what the FAID
       model measurements are made upon.
   12. Simple comparisons between fatigue/FAID scores and blood alcohol content (BAC)
       have limited validity.
   13. Performance on some tasks is more sensitive to sleep deprivation than alcohol
       impairment.
   14. A FAID score of <80 does not mean that a work schedule is acceptable or that a
       person isn’t fatigue impaired.
   15. FAID scores grouped as low, moderate and high are general/qualitative descriptions.
       A person whose score places him/her in the low range can still be fatigue impaired.
   16. FAID scores should not be the sole discriminator to determine the acceptability of
       individual work schedules.
   17. Informing workers of their FAID score is not an effective fatigue management
       control.
   18. FAID scores should not be used retrospectively to determine if fatigue was a factor in
       an incident.
   19. FAID outputs do not reflect extended awake time, time on task, the impact of the
       task/workload on alertness/performance.
   20. A FAID score of <80 does not necessarily means that there is adequate opportunity
       for recovery sleep.
   21. RTO using FAID scores should also use FAID sleep estimate output in their decision
       making. However, FAID sleep estimate appears to be based on average data from
       train drivers and may be less accurate for other types of shift workers.
   22. The default minimum sleep set by FAID is 5 hours in the previous 24, this should not
       be interpreted as being adequate. Scientific uncertainty surrounds this with some
       research suggesting a minimum of 6 hours for highly safety critical tasks.
   23. There is a range of other indicators that can be used to assess aspects of the
       effectiveness of a work schedule and fatigue management program including: (i)
       monitoring performance indicators (e.g. Planned vs. actual hours worked, time on
       task, consecutive shifts, etc.). (ii) Monitoring operational data (e.g. Errors). (iii)
       Monitoring absenteeism. (iv) Monitoring self reported fatigue. (v) Collection of sleep
       data (e.g. Readiband type monitoring).
   24. FAID scores do not assess; age, social/psychological issues, health, competing
       priorities in relation to individual workers.
   25. FAID scores of >80 indicate severe fatigue, 70-80 indicate extreme fatigue. A fatigue
       threshold (level at which fatigue becomes an unacceptable risk) is a FAID score of
       60. FAID scores of <80 do not necessarily indicate a lack of fatigue.
   26. Accurate estimates of mean FAID and FAST/SAFTE (Sleep, Activity, Fatigue and
       Task Effectiveness model) scores can be used at a population level but not on an
       individual basis. Other factors need to be considered.
   27. It has not been established if FAID scores can predict the risk of incidents or
       accidents.
   28. FAID (and similar bio-mathematical tools) are (at best) predictors of fatigue for a
       given work schedule. They are not a direct, objective measure.


BACKGROUND


The Fatigue Science ReadiBand is an easy to use, wrist-worn device that measures day-to-day
sleep quality, quantity and timing. The ReadiBand relies upon the measurement and analysis
of wrist movements to detect and characterize sleep/wake periods.
The system is highly accurate. A recent comparative study showed the ReadiBand, system to
be 93% correlated to sleep lab polysomnography (sleep studies).

ReadiBand data provides a range of sleep statistics, such as sleep efficiency, sleep duration,
and time to sleep onset. Additionally, ReadiBand data can be used to generate actual (rather
than predicted) individual fatigue risk profiles.

A major study funded by the U.S. Federal Railway Administration (FRA) validated
the SAFTE model (Sleep, Activity, Fatigue, and Task Effectiveness model) and FAST
Tool (Fatigue Avoidance Scheduling Tool), upon which the Readiband system is
based, as accurate predictors of fatigue-related accidents. A similar system has been
validated by the US Department of Transportation for use in the aviation industry.

Whilst fatigue can’t be completely eliminated in the workplace, this approach of
measuring, mitigating, and evaluating forms a continuous improvement process that
significantly reduces the impact of fatigue on safety and improves employee
wellbeing.

ReadiBand establishes whether an employee is adequately alert when on duty. The
FAST tool identifies rostering factors within an organization that creates
unnecessarily high levels of fatigue and can also be used on an individual basis to
mitigate fatigue by teaching employees how to better handle lifestyles to attenuate
personal fatigue levels.



HOW IT WORKS

The Readiband device is placed on the participant’s wrist and is worn 24/7 for 2-4 weeks.

The device measures movement only.

When the person is active the device records a movement signature, when the person is at rest
(not moving) the device records a signature for that.

At the end of a 2-4 period the participant returns the device to the facilitator and movement
patterns are downloaded.

It should be acknowledged that the ONLY way to attenuate the effects of fatigue is to obtain
adequate periods of restorative sleep.

Movement during periods of time otherwise designated as “sleep” indicate that the
participant, although in bed for say 7 hours, may have had multiple periods of wakefulness
during that period indicating sleep patterns are less than optimal. The participant (as
necessary), is then counselled as to how to improve sleep hygiene (e.g. Limiting caffeine
intake within 6 hours of bed-time, improving lifestyle habits – eating, exercise, improving the
sleep environment by darkening the room, using a source of white noise, etc.).

The participant then reattaches the Readiband and incorporates the suggested modifications
into their lifestyles. 2-4 weeks later the data is once again downloaded and compared against
the initial baseline data to ascertain; (a) if sleep patterns have been improved and/or (b) to
recommend other interventions (e.g. Polysomnography - sleep studies) be prescribed to
ascertain whether or not the individual is suffering from maladies such as sleep apnoea and
therefore jeopardizing safety from a fatigue perspective.

Devices such as the Readiband provide the link from “predictive” estimations of fatigue to
“actual measures” and in so doing evolve the QRN Bulk East Fatigue Management Program
(FMP).

It should be noted that:

            1. Individual data obtained from participants is only reported to that participant.
               The organisation sees no individual data sets.
            2. De-identified data can be pooled by site, across the organisation, etc. as
               necessary/requested by management, to ascertain which locations, etc. may
               have fatigue related issues/concerns. Resources can then be allocated
               accordingly. For example, if sleep apnoea is indentified as being a significant
               cause of fatigue at a certain site a Health and Wellness initiative could be
               deployed to improve lifestyle habits as sleep apnoea is strongly collated with
               excess body weight.



RECOMMENDATIONS

       A number of sites across Bulk East have been most receptive HSE initiatives such as
        Participative Ergonomics (PE), on-site physiotherapy and the 24 Week Lifestyle
        Behavioural Change Program. Many aspects of these programs dovetail together and
        support/leverage one another. A trial of the Readiband system would compliment a
        number of aspects of the above programs as well.
       Identify a “typical” site (from the above) with supportive management and a 24/7
        operational load. Aim to get a sample size of 20-30 volunteers working across all
        shift combinations and permutations over a 6-8 week test period.
       Have the above 20-30 volunteers involved in a pilot trial to identify and assess: (i)
        The effectiveness of the system in delivery, (ii) the effectiveness of the system in
        identifying fatigue, (iii) adherence of volunteers over the duration of the trial, (iv) the
        effectiveness of suggested behavioural changes in attenuating fatigue and improving
        individual outcomes, (v) the effectiveness of the Readiband reporting system in
        providing detailed group reports capable of elucidating issues and intervention
        strategies on a site by site basis.
       Get the above scenario costed with a view to ultimately, if deemed to be an effective
        intervention, being able assess targeted sites, classes of employees, etc. to ascertain
        trend data and potential global interventions to improve Fitness for Work from a
        Fatigue Management perspective.

								
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