In this webinar, Rob Higdon, VP of Product at Fatigue Science, will go through how to manage fatigue risks in heavy industry through validated science and new technology.

1. Managing On-Duty Fatigue: A Scientific Approach
Using New Technology and Validated Science
to Reduce Fatigue Risk in Heavy Industry
Robert Higdon
Director of Product & Corporate Development
Fatigue Science

2. Institutional Use & Validation Industrial Workforces Military & Elite Sports
Since 2006, we’ve helped leading firms in heavy industry and elite
sports measure, manage and reduce the risks of fatigue.

3. "Nobody is ever fully awake; I was always in a bit of a daze and
that is because the way the shifts work doesn't allow the drivers
to get a regular sleep pattern.”
- Unnamed Former Tram Driver
Croydon Tram Derailment, 9 November 2016

4. Awareness of fatigue is finally growing.
up to 40%
of commercial trucking accidents
NTSB
Up to 65%
of surface mining haul truck accidents
Caterpillar Global Mining, 2011
The single greatest cause of accidents across a wide range of high-risk industries:
“Fatigue has been identified as the leading
accident risk in the construction industry.”
School of Civil Engineering, University of Sydney, 2010
Exxon Valdez
Oil Tanker
March 1989
Chernobyl Nuclear Power
Plant
April 1986
BP Texas City
Refinery
March 2005

5. What exactly
is fatigue?

6. Fatigue is a physiological
condition that we all face when
confronted with a
lack of sleep or wakefulness
outside of
normal daylight hours.

7. US Department ofTransportation,Analysis of the Relationship between Operator Effectiveness Measures and Economic Impacts of RailAccidents, May 2011
Fatigue Models for Applied Research inWarfighting, Hursh SR, et al.,Aviat Space Environ Med 2004
Fatigue hinders the brain’s
ability to interact with the
body.
Physiologically, it has
similar effects
to alcohol impairment.

8. EMOTIONAL
Increased irritability
Mood fluctuations
Increased anxiety
Depressed mood
Increased frustration
Bouts of Anger
Increased impulsivity
Increased stimulant use
Alcohol use / misuse
COGNITIVE
Reduced concentration
Reduced communication
Reduced attention
Reduced multitasking
Reduced recall of events
Decreased memory
Reduced socialization
Reduced creativity
Reduced decision-making
Reduced performance
PHYSICAL
Loss of reaction time
Metabolic abnormalities
Bodily sensations of pain or cold
Risk of cardiovascular disease
Risk of cancer
Microsleeps
Weight gain
Risk of diabetes
Reduced immunity
Fatigue’s wide range of effects:

9. The facts are unequivocally clear.
Worker fatigue materially impacts:
HEALTH.
SAFETY.
PRODUCTIVITY.

10. SAFTE™ Alertness Model
Exclusive to Fatigue Science
YEARS OF RESEARCH
AND DEVELOPMENT
25
ESTIMATED
DEVELOPMENT COST
$37M
The US Army Research Lab spent over 25 years
researching how sleep influences human fatigue.

11. The time of day
Your sleep quantity
Here’s what factors influence your fatigue,
according to the US Army’s research:
The timing & consistency of
your sleep
Your sleep quality

12. a) Last night
b) 1 – 2 nights ago
c) Over 1 week ago
d) Over 1 month ago
From how long ago can poor sleep affect the
fatigue levels you experience today?

13. The US Army research found that sleep’s effects
are cumulative.
• It’s not just about last night’s sleep.
• Your whole past week of sleep influences
your fatigue.
• Sleep debt is a real thing, and it can build
up over time.
SLEEP
Tues
Mon
Sun
Sat
Fri
Thurs
Weds

14. 11:00 pm 7:00 am
QUANTITY
QUALITY
CONSISTENCY
Ideally 7+ hours / night
Ideally 95%+ efficiency
Ideally < 30 minutes of variance
For many, it’s rare to get consistently ideal sleep.
SLEEP
Tues
Mon
Sun
Sat
Fri
Thurs
Weds

15. Here’s a more typical example of what sleep looks
like for many industrial workers.
x InadequateAmount of Time
in Bed
x High Variability in Bedtimes &
WakeTimes
x SignificantTime Lost to
Awakenings
Tues
Mon
Sun
Sat
Fri
Thurs
Weds

16. Using US Army research, it’s possible to analyze
one’s past week of sleep data, and predict one’s
fatigue levels for the day ahead.

17. Fatigue prediction,
powered by US Army Research
MORE ALERT
Quickest Reaction Time
Best Mental Performance
Less Likely Microsleeps
100
90
80
70
60 Slower Reaction Time
Lower Mental Performance
More Likely Microsleeps
Sleep Data Fatigue Prediction
SAFTE™
Alertness
Model

18. LESS FATIGUE
SAFER
OPERATION
Lapse
Index
Reaction
Time
1.5x+11%
3.7x+30%
5.2x+43%
8.0x+67%
100
90
80
70
60
0.08 BAC
Equiv.
1.0x+0%
SAFTEAlertnessScale
Exclusive to Fatigue Science
SAFTE™ Alertness Model:
Predicting fatigue in terms of
its objective impacts on
safety.
The SAFTE Model has been validated and relied on in studies by:

19. Alertness Score vs. Incident Likelihood
(SAFTE Model Study in Railroad Industry,
US Dept. of Transportation)
SAFTE Alertness Score
US Department of Transportation, Validation and Calibration of a Fatigue Assessment Tool, for Railroad Work Schedules, Summary Report, October 2006
Studies confirm a direct relationship between one’s SAFTE
Alertness Score and their incident likelihood.
SAFTE Alertness Score
Alertness Score vs. Avg. Incident Cost
(SAFTE Model Study in Railroad Industry,
US Dept. of Transportation)

20. 0.43
0.24
0.14
0.10
0.04 0.05
y = 0.6196e-0.471x
R² = 0.9362
SAFTE Alertness Score vs.
Instances of Excessive Speed
(average # of instances > 105 km/h
per drive-hour in given SAFTE Score range)
Similarly, a study showed fatigued drivers to be 9x more likely to
excessively speed and 4x more likely to brake harshly.
N = 5,462 drive-hours analyzed from 12 drivers; Fall 2016; Conducted by Fatigue Science using client telematics data with permission
< 100< 90< 80< 70< 60< 50
SAFTE Alertness Score
0.19
0.11
0.09
0.08
0.05 0.05
y = 0.2142e-0.267x
R² = 0.9419
0 - 49 50 - 59 60 - 69 70 - 79 80 - 89 90 - 100
SAFTE Alertness Score vs.
Harsh Braking Incidents
(average # of harsh braking incidents
per drive-hour in given SAFTE Score range)
< 100< 90< 80< 70< 60< 50
SAFTE Alertness Score

21. Sample Data

22. HourlyAlertness
100
90
80
70
60
SHIFT
START
SHIFT
ENDLow fatigue risk during work hours
A decent sleeper’s Alertness Scores will generally fluctuate in the 80s
or 90s during Day Shift hours, before dipping at night.

23. a) Nearly impossible
b) Possible but unlikely
c) Somewhat likely
d) Very likely
Assuming you are a consistently good sleeper,
how likely are you to dip below 70 when working
an overnight shift?
100
90
80
70
60
0.08 BAC Equivalent
SAFTEAlertnessScale

24. On Night Shifts, it’s normal for anyone–even a good sleeper–to fall below
70 after midnight, due to the body’s circadian rhythm.
High fatigue risk around 1:00am
HourlyAlertness
100
90
80
70
60
SHIFT
START
SHIFT
END
12am

25. Benchmarking Your
Organization’s Fatigue Risk

26. SAFTE Alertness Curve
Individual
Fatigue Risk Profile
Worker 005
Percent ofTime On Duty,
by Alertness Level
By capturing data in a fatigue study, it’s possible to generate an
anonymized Fatigue Risk Profile for each individual.
Night Shift
Worker 005
< 60 < 70 < 80 < 90 < 100
SAFTE Alertness Score

27. Organizational
Fatigue Risk Profile
Site XYZ at Company ABC
Percent ofTime On DutyIndividual Fatigue Risk Profiles
With a sufficient sample size, individual risk profiles can be combined to
form a benchmark for organizational fatigue risk.
< 60 < 70 < 80 < 90 < 100
SAFTE Alertness Score
20%

28. Organizations will set targets for fatigue risk exposure as a percentage
of duty hours spent in each Alertness Score range.
Day Shift Night Shift
< 60 < 70 < 80 < 90 < 100
SAFTE Alertness Score
Example shown. Actual targets vary according to time of day, operational design, and many other factors.

29. Reducing
Fatigue Risk

30. 1st Wave
Science-Based Scheduling
Scheduling
2nd Wave
Reactive Detection & Data Collection
3rd Wave
Predictive Guidance
Birth of
trucking
industry, and
first fatigue-
related
collisions
Awareness of
fatigue grows
with dawn of
circadian
rhythm-based
fatigue research
The science of
quantifying &
predicting fatigue
arrives, led by
SAFTE™ Alertness
Model
First
mainstream
consumer
sleep
trackers
arrive
Mobile tech
enables
personal
fatigue
predictions
Evolution of Fatigue Risk Management
Reactive fatigue
detection tech
arrives,
detecting
“nodding off”
1930’s
ICC
implements its
first Hours of
Service (HOS)
Regulation
1938 1970’s 1990’s 2000 2009 2016
First devices to
pair sleep data
with fatigue
modelling,
enabling
quantification of
personal fatigue
2012
Personal fatigue
predictions tech
becomes smarter,
alerting to risks in
context of work
hours, and guiding
risk reduction
2019
Fleets begin using
the science of
fatigue to
optimize
schedules

31. Readiband
is a tool designed to help workers
and organizations measure, manage
manage and reduce the risks of
fatigue.
™

32. Built for Heavy Industry
Splashproof and durable
Validated 92%+ accuracy
sleep detection
Doesn’t track location
Tracks only wrist motion,
not heart rate
30+ day battery life

33. Fatigue
Awareness
Sleep
Improvement
Z
Tools for Individuals

34. Fatigue Awareness
ReadibandApp 3.0 provides predictive fatigue alerts to workers in the context of their shift hours, enabling advance visibility into
personal fatigue levels before beginning their day on duty.

35. ReadibandApp 3.0 also empowers users to predict the impact of a personalized sleep plan on their fatigue for the week ahead,
enabling them to set targets for personal fatigue risk reduction.
Sleep Improvement

36. Tools for Management
Aggregated fatigue insights that protect worker privacy
View workforce fatigue levels, trends,&
comparisons across sites and groups
Track utilization of investment, while
preserving privacy of sleep & fatigue data

37. Thank You
wwww.fatiguescience.com

38. Q & A
wwww.fatiguescience.com