sleep, circadian rhythms, and performance applying sleep science to operational practice gregory...
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Sleep, Circadian Rhythms, and
PerformanceApplying Sleep Science to Operational Practice
Gregory Belenky, M.D.
Hans Van Dongen, Ph.D.
Sleep and Performance Research CenterWashington State University Spokane
Sleep and Performance Research Center Washington State University
The Earth at Night:
The Problem of 24/7 Operations
From www.freemaninstitute.com/nightearth.htm
Sleep and Performance Research Center
Fatigue Degrades Performance• Fatigue degrades performance:
– Simple performance, e.g., reaction time– Complex performance, e.g., accurate situational awareness– Perseverance slips into perseveration– Overall, slowing the Observe, Orient, Decide, Act (OODA) Loop
• Fatigue is a combination of – time awake (sleep loss)– time of day (circadian rhythm)– time on task (workload)
• Simple and complex performance are degraded by – extended time on task– sleep loss– adverse circadian phase
• Performance is impaired by systematic degradation and increased instability
Sleep and Performance Research Center
Components of Fatigue:
Time Awake, Time of Day, Time on Task
Adapted from Wesensten et al., 2004
Sleep and Performance Research Center
Physiology of Sleep
Washington State University
Sleep and Performance Research Center Washington State University
The 24-Hour Sleep/Wake Cycle
Two distinct states of sleep cycling with a 90-100 min periodicity
Recuperation is a function of total sleep time
0000
1200
06001800
Non-Rapid EyeMovement Sleep
(NREM)
Rapid EyeMovement Sleep
(REM)
Waking
Sleep and Performance Research Center Washington State University
Total Sleep Deprivation Imaging StudiesT
hrou
ghpu
t(P
erce
nt o
f B
asel
ine)
120
100
80
60
40
20
0
Sleep Deprivation (Hours)0 24 48 72 86
- Mean Performance (N=17)- Cubic Spline- Linear Regression
PET Scans
From Thomas ML, Sing HC, Belenky G, et al. (2000). Neural basis of alertness and cognitive performance impairments during sleepiness. I. Effects of 24 h of sleep deprivation on waking human regional brain activity. Journal of Sleep Research 9: 335-352.And from Thomas ML, Sing HC, Belenky G, et al. (2003). Neural basis of alertness and cognitive performance impairments during sleepiness. II. Effects of 48 and 72 h of sleep deprivation on waking human regional brain activity. Thalamus & Related Systems 2: 199-229.
Sleep and Performance Research Center Washington State University
Brain Metabolism at 24, 48, & 72 Hours
of Sleep Deprivation
+ 32 mmAC-PC
24 h SD 48 h SD 72 h SD
+ 8 mmAC-PC
Z
1.65
2.33
2.58
3.08
> 4.16
N = 17
Relative to baseline, the prefrontal cortex and the thalamus are mostaffected by sleep loss
From Thomas ML, Sing HC, Belenky G, et al. (2000). Neural basis of alertness and cognitive performance impairments during sleepiness. I. Effects of 24 h of sleep deprivation on waking human regional brain activity. Journal of Sleep Research 9: 335-352.And from Thomas ML, Sing HC, Belenky G, et al. (2003). Neural basis of alertness and cognitive performance impairments during sleepiness. II. Effects of 48 and 72 h of sleep deprivation on waking human regional brain activity. Thalamus & Related Systems 2: 199-229.
Sleep and Performance Research Center
Circadian Rhythm in Core Body
Temperature, Sleep Propensity, and
Performance
Washington State University
Sleep and Performance Research Center
Circadian Rhythm in Body Temperature and
Performance
From Van Dongen HPA, Dinges DF (2005). Circadian rhythms in sleepiness, alertness, and performance. In Kryger MH, Roth T, Dement WC (Eds.), Principles and Practice of Sleep Medicine (4th ed.). Elsevier Saunders, Philadelphia, Pennsylvania: 435-443.
Psy
chom
otor
vi
gila
nce
10%
fast
est R
Ts
(ms)
Cor
e bo
dyte
mpe
ratu
re (
ºC)
Sleep and Performance Research Center Washington State University
Sleep Deprivation vs. Alcohol Intoxication
Dawson & Reid, 1997
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Chronic Partial Sleep Restriction:
Effects on Performance
A Sleep Dose Response Study
Washington State University
Sleep and Performance Research Center Washington State University
From Belenky G, Wesensten NJ, Thorne DR, Thomas ML, Sing HC, Redmond DP, Russo MB, Balkin TJ (2003). Patterns of performance degradation and restoration during sleep restriction and subsequent recovery: a sleep dose-response study. Journal of Sleep Research 12: 1-12.
Psychomotor Vigilance Task Performance
across Sleep Restriction and Recovery
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Recuperative Value of Sleep
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Sleep and Performance Research Center
Consolidated vs. Split vs. Fragmented Sleep• Recuperative value of sleep
depends on total sleep time over 24 hours
• Consolidated sleep– Nocturnal (night) – typically 7-8
hours; facilitated by circadian rhythm
– Diurnal (day) – typically ~ 5 hours; truncated by circadian rhythm
• Split sleep– 5 nocturnal / 2-3 diurnal
• Fragmented sleep– Awakening every 2-3 minutes– Fragmentation to this degree
abolishes recuperative value of sleep
• Sleep interrupted every 20 mins as recuperative as uninterrupted sleep
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Bonnet M & Arand D (2003) Clinical effects of sleep fragmentation vs. sleep deprivation. Sleep Medicine Reviews, 7(4) 297-310
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Objective Measurement of Sleep and
Performance in the Field Environment
&
Integration with Modeling
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Sleep and Performance Research Center
Objective Field Measurement of Sleep and
Performance
• Palm OS Psychomotor Vigilance Test (PVT)• 10 minute test
• Work/Sleep Log• Sleep periods• Start/Stop times of shift
• Actigraph watches• Wear 24hrs/day• Monitors sleep• More reliable than self-
reported sleep • Equivalent to
polysomnography in measuring total sleep time / 24 hours
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Effect of Sleep Loss on Performance on the
Psychomotor Vigilance Test (PVT)
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1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70
1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70
1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70
1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70
0
200 0
400 0
600 0
800 0
RESPONSE NUMBER
60 Hours Awake
36 Hours Awake
12 Hours Awake
84 Hours Awake
0
200 0
400 0
600 0
800 0
0
200 0
400 0
600 0
800 0
0
200 0
400 0
600 0
800 0
12 Hours Awake
36 Hours Awake
60 Hours Awake
84 Hours Awake
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Actigraph Data Scored to Generate
Sleep/Wake History
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Daytime Nap
Nighttime Awakening
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Actigraph Sleep/Wake History Input to
Mathematical Performance Prediction Model
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Nighttime Awakening Daytime Nap
Sleep and Performance Research Center
Fatigue Risk Management System (FRMS)
• Five-tiered defense-in-depth to prevent fatigue-related errors, incidents, and accidents
• Tier 1 – Does system of shift timing and duration allow for adequate opportunity for sleep?
• Computer-based rostering
• Predictive modeling
• Tier 2 – Do employees take advantage of the sleep opportunity?
• Self-report
• Wrist-worn actigraph (sleep watch)
• Tier 3 – In the workplace, do they maintain adequate alertness and performance?
• Self-report & co-worker report
• Palm Pilot-based Psychomotor Vigilance Task (PVT)
• Embedded performance metrics
• Tier 4 – Are there errors, near misses?
• Tier 5 – Are there incidents and accidents?From Dawson D, McCulloch K (2005). Managing fatigue: It’s about sleep. Sleep Medicine Reviews 9: 365-380.
Sleep and Performance Research Center Washington State University
Reprise of Sleep Physiology and Performance
Fatigue is a function of sleep/wake history, circadian rhythm, and workloadSleep loss degrades performanceSleep restores it
All performance degrades with sleep lossSlower to observe, orient, decide, and act (OODA)
Adequate total sleep time sustains performanceNaps add to total sleep timeDivided sleep is as good as consolidated sleep
Performance and sleep propensity follow the 24-hour circadian rhythm in body temperature
Sleep and Performance Research Center Washington State University
The Sleep and Performance Research Center
Gang
Sleep and Performance Research Center Washington State University
Gregory Belenky, MDResearch Professor and DirectorSleep and Performance Research CenterWashington State University P.O. Box 1495Spokane, WA 99210-1495
Phone: (509) 358-7738FAX: (509) 358-7810Email: [email protected]
Point of Contact
Sleep and Performance Research Center
The End
Washington State University
Sleep and Performance Research Center Washington State University
Consequences of Sleep Loss
Short term Minutes, hours
Error, accident, catastrophe
Mid-term Weeks, months, years
Bad planning, inadequate strategizing, poor life decisions
Long-term Years
Possibly promote cardiovascular disease, hypertension, overweight/obesity, type II diabetes, sleep disordered breathing
Triad of factors supporting health, productivity, and well-being Diet
Exercise
Sleep
Sleep and Performance Research Center
Brain Metabolism during Non-REM and REM
Sleep
Frontal areas are deactivated during Slow Wave Sleep; decline in blood flow of ~30% (similar to sleep deprivation)
Frontal areas remain deactivated during REM; increase in flow to waking levels or above except in prefrontal cortex
Frontal areas are slowly reactivated after awakening (sleep inertia)
From Braun AR, Balkin TJ, Wesensten NJ, Carson RE, Varga M, Baldwin P, Selbie S, Belenky G, Herscovitch P (1997). Regional cerebral blood flow throughout the sleep-wake cycle. An H2
15O PET study. Brain 120: 1173-1197.
Sleep and Performance Research Center Washington State University
CDL Volunteers in the Laboratory
• Sleep measured with poly-somnography (electrodes, wires, recorders)
• Performance measured with computer-based tests.
Sleep and Performance Research Center Washington State University
Design of the Study
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
8 hrs in bed 3, 5, 7, 9 hrs in bed
AdaptationPhase Experimental Phase Recovery
Phase
4 5 6 7 8 9 10 11 12 13 14 15
Release from study
8 hrs in bed
1 2 3
Sleep and Performance Research Center Washington State University
Mean Sleep, Baseline, Experimental
Days, & RecoveryMean Sleep Experimental Days9 hr group – 7.9 hrs7 hr group – 6.3 hrs5 hr group – 4.7 hrs3 hr group – 2.9 hrs
Sleep and Performance Research Center
Performance Prediction Modeling Integrated
into Rostering and Scheduling Software
Personal biomedical status monitoring Sleep/wake history (by sleep watch) Circadian rhythm phase (by technology TBD) Predict performance in real time person by person (by
biomathematical performance prediction model) Validate with embedded performance metrics, e.g.,
Lane deviation (trucking) Flight performance (commercial aviation)
Integrate performance prediction into rostering and scheduling software Integrate into the objective function Optimize along with other constraints Potential for automated/turnkey solution for fatigue risk management
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