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Modeling Fatigue Predicting Performance

Steven R. Hursh, Ph.D.

Professor, Johns Hopkins University School of Medicine

and

Program Manager, Biomedical Modeling and Analysis

Science Applications International Corporation, 301-785-2341

Hurshs@saic.com

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Outline

Fatigue overview. Drivers of fatigue Biomathematical models of fatigue and the

Sleep, Activity, Fatigue, and Task Effectiveness (SAFTE) Model

Fatigue analysis tools and the Fatigue Avoidance Scheduling Tool (FAST)

Soldier monitoring to assess fatigue Aviation applications

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Operational Definition

Fatigue is a complex state characterized by a lack of alertness and reduced mental and physical performance, often accompanied by drowsiness.

Fatigue is more than sleepiness and its effects are more than falling asleep.

DOT Human Factors Coordinating Committee, 1998

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Symptoms versus Root Causes

Symptoms Operational Consequences Measurable Changes in

Performance Lapses in attention and

vigilance Delayed reactions Impaired logical reasoning and

decision-making Reduced “situational

awareness” Low motivation to perform

“optional” activities Poor assessment of risk or

failure to appreciate consequences of action

Operator inefficiencies

Root Cause Analysis Fatigue is one potential root cause No direct measure, physiological marker, or “blood test” for fatigue

However, the conditions that lead to fatigue are well known and A fatigue model can help evaluation and integrate the specific conditions of an accident to determine if fatigue was involved.

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Major Fatigue Factors

Time of Day: between midnight and 0600 hrs. Cumulative Sleep Debt: more than eight

hours accumulation. Acute Sleep Debt: less than eight hours in

last 24 hrs. Continuous Hours Awake: more than 17

hours since last major sleep period. Time on Task: continuous time doing a job

without a break.

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Major Consequences of Fatigue

Three Mile Island (1979): 4:00 a.m. and involved human error.

Chernobyl Nuclear Reactor Meltdown (1986): 1:30 a.m. and involved human error.

Exxon Valdez (1989): 12:04 a.m. One major cause: “The failure of the third mate to properly maneuver the vessel, possibly due to fatigue and excessive workload.”

Operation Desert Storm (1990): More friendly fire losses than enemy losses, many due to sleep deprivation.

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Benefits of Reduced Fatigue

More capable workforce – “force multiplier” Higher level of performance (higher efficiency , increased

productivity, fewer errors/incidents/accidents) Fewer accidents/incidents Reduced absenteeism, increased availability Improved health Higher moral

Improved safety, reduced workman’s compensation Reduced regulatory pressure Improved labor relations

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ALERTNESS & COGNITIVE PERFORMANCE

CIRCADIAN RHYTHM

CUMULATIVE SLEEP DEBT

ALERTNESS & COGNITIVE

PERFORMANCE

Time of Day Sleep History and Time on Duty

Daily Variations in Effectiveness

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Major Inputs for Predicting Fatigue

Time of Day Amount, quality and timing of sleep Individual factors

Phase of the circadian “pacemaker” Individual sleep need or sensitivity to

sleep loss

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Sources of Information

Time of day: both the clock time and the time zone – inferred from location information

Sleep: Direct measurement Infer from work pattern (AutoSleep)

Duty periods and Critical Events: Drives sleep opportunities Determines critical periods for performance prediction

Individual factors Circadian phase: temperature or hormonal oscillations Sleep need: no simple test at this time

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SAFTE

The Sleep, Activity, Fatigue, and Task Effectiveness (SAFTE) Model is based on 12 years of fatigue modeling experience and over $2.6M of US DOD investment.

Validated against laboratory and simulator measures of fatigue. Work place calibration is underway.

Now accepted by the US DOD as the common warfighter fatigue model.

Independently compared to six models from around the world and judged to have the least error (Fatigue and Performance Workshop, Seattle, 2002).

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POC: Steven Hursh, PhD, Tel: 410-538-290112

Schematic of SAFTE™

Simulation ModelSleep, Activity, Fatigue and Task Effectiveness Model

COGNITIVEEFFECTIVENESS

SLEEP “QUALITY”FRAGMENTATION

SLEEP INTENSITY

SLEEP REGULATION

SLEEP RESERVOIR

SLEEP DEBTFEEDBACK

LOOP

INERTIA

CIRCADIAN OSCILLATORS

SLEEP ACCUMULATION(Reservoir Fill)

PERFORMANCE USE(Reservoir Depletion)

DYNAMICPHASE

PERFORMANCEMODULATION

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Walter Reed Restricted Sleep Study SAFTE Model (red line) Predicts the Average Results with Precision

PVT SpeedChronic Restriction Adaptation

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65

80

95

110

0 T1 T2 B E1 E2 E3 E4 E5 E6 E7 R1 R2 R3Day

Me

an S

pe

ed(a

s a

% o

f Ba

selin

e)

9 Hr

7 Hr

5 Hr

3 Hr

PVT SpeedChronic Restriction Adaptation

50

65

80

95

110

0 T1 T2 B E1 E2 E3 E4 E5 E6 E7 R1 R2 R3Day

Me

an

Sp

ee

d(a

s a

% o

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selin

e)

9 Hr

7 Hr

5 Hr

3 Hr

SAFTE/FAST R2 = 0.94

Restriction RecoveryBaseline

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Driving Simulator AccidentsAccident Likelihood Relative to Baseline vs Predicted Effectiveness

y = 58.184e-0.044x

R2 = 0.8488

0

1

2

3

4

5

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50556065707580859095100

Effectiveness (FAST Prediction)

Ac

cid

en

t L

ike

lih

oo

d (

tim

es

Ba

se

lin

e)

3 hrs sleep/day

5 hrs sleep/day

Expon. (3 hrs sleep/day)

Baseline=1(8 hrs sleep/day)

E7E6

E5

E4

E3

E2

E1

Sleep Dose Response Study (WRAIR Data)

Accident Likelihood Increases with Decreasing Effectiveness

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Practical Software for Implementation

Fatigue Avoidance Scheduling Tool (FAST)

FAST is a fatigue assessment tool using the SAFTE model

Developed for the US Air Force and the US Army.

DOT/FRA sponsored work has lead to enhancements for

transportation applications. Sleep estimation algorithm

Schedule grid data entry tool

Wizards and dashboard

Standard data file format

DOT field calibration underway.

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FAST Graphical Screen Options

Effectiveness

Sleep Periods in BlueWork Periods in Red

Adjustable Criterion Line

Lower Percentile (e.g. 20%)

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y = 11.677x - 11.487

R2 = 0.9757

0

2

4

6

8

10

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0.9 1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9

SAFTE Model Prediction: Response Time [(1/Effectiveness) · 100]

Lap

se

Lik

elih

oo

d (

tim

es

ba

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line

)

3 hrs sleep/day

5 hrs sleep/day

Pooled Data

Linear (Pooled Data)

75% Effectiveness 65% Effectiveness90% Effectiveness

Baseline=1 (8 hrs sleep/day)

Lapses in Attention with Reduced Sleep

Successive days of reduced sleep

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Lapse Index Graph

Lapse Index probably similar to values from PERCLOS drowsiness monitor.

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Individual VariabilityDisplay Lowest 20 percentile, for example

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BAC Scale

Arnedt, J.T., Wilde, G.J., Munt, P.W., MacLean, A.W. “How do prolonged wakefulness and alcohol compare in the decrements they produce on a simulated driving task?” Accid Anal Prev., 2001 May;33(3):337-44.

Dawson, D., Reid, K., 1997. “Fatigue, alcohol and performance impairment.” Nature 388, 23.

Continuous Hours of

Wakefulness

FAST Effectiveness

Blood Alcohol Concentration

18.5 77 0.05

21 70 0.08

Fatigue as predicted by FAST and the effects of alcohol are not identical.

The effects of fatigue may be compared to the effects of blood alcohol to calibrate the severity of fatigue

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Dashboard InformationAnalysis System Could Report Fatigue Indicators

CriteriaValue at pointin schedule

Flags are fatigue indicators

Content based on fatigue analysis workshop hosted by NTSB and conducted by Drs. Mark Rosekind and David Dinges, funded by FRA Office of Safety.

Sleep (last 24 hrs) Chronic Sleep Debt Hours Awake Time of Day Out of Phase Performance Values

Effectiveness Mean Cognitive Lapse Index Reaction Time Reservoir

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Sources of Uncertainty

Incomplete work/rest history, especially sleep history

Differences in personal sleep physiology Bio-rhythms Sleep need

Other personal factors Health Medications

Inaccuracies in our modeling and analysis

Lack of knowledge about specific changes in behavior

Percent of Error

Actigraphy

Temperature Sensing & GPS

Biomedical recordings

Continuous model improvement

Performance Monitoring

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Trip Plan Editor

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Summary of Effectiveness by Waypoints

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Summary of Duty Periods

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B2 Stealth Bomber

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Lounge Chair Solution for In-flight Naps

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Commercial Interest

Two major airlines The two largest business aviation companies Two large oil companies Five largest freight railroads A dozen electric power companies Fatigue consultants Two foreign governments

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If you would like more information, call…………

Steven R. Hursh, Ph.D.Professor, Johns Hopkins University School of Medicine

andScience Applications International Corporation, 301-785-2341

Hurshs@saic.com

Monitoring Fatigue and Predicting Performance

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Actigraph Recording for Sleep Estimation

Actigraph Recording Device: Records whole body activity and permits inferences about sleep timing, quality and quantity.

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Actigraph and Fatigue Assessment Software (FAST)

Technical Concept• Estimates person’s actual sleep and

circadian rhythm based on non-invasive measurement of activity pattern.

• Data could be transferred to computer for fatigue assessment

• Built-in model could gives user real-time estimate of performance effectiveness.

• Allows user to plan future activities to maximize capability using FAST.

• Gives commanders real-time assessment of fatigue status of entire unit

Actigraph Recording Device FAST Performance Assessment Tool

Current status• Fatigue model sufficiently accurate for

generic applications. • Actigraphy devices are now small, reliable,

and highly sensitive. • Planning tool is available today. Used to

plan military operations and training. Used to estimate fatigue in civilian transportation operations.

• Can accept geographic waypoints during schedule to estimate sunlight and jet lag.

Ambulatory Monitoring, Inc.

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Unit Fatigue Analysis System

Sensors Soldier Computer Unit Level Receiver and Computer Aggregate Analysis

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A B C D E F G H I J

Aggregated analysis across individuals and units.

Permits sort of units by aggregated fatigue score.

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Sample Flight Plan AnalysisNot an Actual Flight Plan

Tokyo SIN BKK PEK HKG HOU

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Tools for Aviation

Waypoints and international airport database Trip Planner Zulu time and world-wide local time Waypoint and critical event effectiveness

summary table Duty period summary table Mission Timeline

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Printable Mission TimelineUser Selectable Features