CRICOS No. 00213J
Obstructive sleep apnoea and daytime driver sleepiness
Dr Ashleigh Filtness
Obstructive Sleep Apnoea (OSA)
Repeated collapse of the airway during sleep blocking air flow.
At least 5 an hour, some up to 60 an hour
Most common in
Middle aged men
Collar >43cm
BMI ≥28
Symptoms
Daytime sleepiness (feeling un-rested after sleep)
Loud snoring
Holding breath during sleep
Gasping for air
Treatment
OSA and Driving
UK law
Group 1 licence holders (car/motorcycle) diagnosed with sleep apnoea must stop driving until the
symptoms have been controlled and confirmed by
medical opinion.
Research Evidence supports OSA return to driving
• Post-treatment, the incidence of road traffic collisions for this group is substantially
reduced. (Yamamoto et al. 2000; George 2001; Tregear et al. 2010;)
• Post-treatment performance at driving
simulator tasks improve. (Orth et al. 2005; Mazza et al.
2006)
– However, not in all studies (Vakulin et al. 2011)
Aim
To investigate the impact of modest sleep restriction and treatment withdrawal on driving ability in long term treated OSA.
1. Sleep restriction (5h) with CPAP treatment 2. Normal length of sleep (≈7h) without CPAP
treatment
6
Vulnerability to sleep restriction
Participants
39 participants male (19 with OSA), age 50 – 75y
OSA Control
Age 63.8 (1.7) 66.6 (1.3)
BMI 34.5 (1.4) 25.5 (0.4)
ESS 5.3 (0.7) 4.7 (0.6)
Usual sleep 7h 40min (8.1 min) 7h 48 min (10.1 min) Treatment
duration
≥1y
Average 7.5y (5.7)
NA
Methodology - Driving Simulator
30 min practice drive Repeated measure
counterbalanced design
• Normal nights sleep (8 h)
• Sleep restriction to (5 h)
9
Simulated road
Monotonous dual carriageway 2 h duration starting at 2pm Occasional slow moving
vehicles to overtake
Measures
Prior sleep was verified using
actigraphy
Measures
• Driving incidents: all four wheels out of the driving lane.
• Subjective sleepiness:
Karolinska Sleepiness Scale (KSS) every 200 seconds.
• EEG: Spectral analysis in the
alpha and theta, 4 – 11Hz
range, and beta, 13 – 30 Hz
range
Results – Driving incidents
Significant group by condition interaction F(1,37) = 9.4, p < 0.05 Significant main effect of condition F(1,37) = 20.8, p < 0.001 Significant main effect of time on task F(1.7,63) = 15.0, p < 0.001
Results - Time to first driving incident
Significant group by condition interaction F(1,37) = 4.2, p < 0.05 Significant main effect of condition F(1,37) = 4.0, p < 0.05
Results - KSS
Significant main effect of condition F(1,37) = 19.9, p < 0.001
Significant main effect of driving time F(1.9,72.1) = 70.0, p < 0.001
Results – EEG alpha and theta
Significant group by time interaction F(1.7, 64.2) = 4.27, p < 0.05 Significant main effect of time F(1.7, 64.2) = 35.0, p < 0.001
Results EEG - beta
Significant group by time interaction F (1.8,65.6) = 4.9, p < 0.05 t(37) = 2.0, p < 0.05
Discussion – Sleep restriction
• No significant difference between driving performance after a normal nights sleep.
• Treated OSA patients show greater vulnerability to sleep restriction
– More incidents
– Shorter safe driving time.
Discussion – Sleep Restriction
• Despite performance decrements no significant
difference in subjective sleepiness groups when sleep deprived.
• Time on task following sleep restriction affected EEG activity differently for OSA compared with control participants particularly during second half of drive
– Alpha and theta
– Beta activity
Vulnerability to CPAP withdrawal
Participants
11 male participants with OSA age 50 – 75y
OSA
Age 65.6 (2.3)
BMI 33.1 (1.8)
ESS 5.2 (0.7)
Usual sleep 7h 45min (3.6 min) Treatment duration ≥1y
Average 7.7y (5.8)
Methodology - Driving Simulator
30 min practice drive
Repeated measure design
•
Normal nights sleep (with CPAP)
•
Normal nights sleep (no CPAP)
Monotonous dual carriageway 2 h duration starting at 2pm
Occasional slow moving vehicles to overtake
22
Measures
Prior sleep was verified using actigraphy
• Driving incidents: all four wheels out of the driving lane.
• Subjective sleepiness: Karolinska Sleepiness Scale (KSS) every 200 seconds.
• EEG: Spectral analysis in the alpha
and theta, 4 – 11Hz range, and
beta, 13 – 30 Hz range
Sleep quality
SDI = number of wake minutes / assumed length of sleep Significant difference in SDI [t (10) = 3.510, p <0.05]
Results –Driving incidents
Significant group main effect of treatment withdrawal F(1,20) = 12.33, p < 0.05
Results - Time to first driving incident
Significant reduction in time to first incident t(10) = 3.8, p < 0.05
Safe driving time
Results - KSS
Significant main effect of condition F(1,10) = 24.12, p < 0.05
Significant drive time by condition interaction F(3,30) = 5.03, p < 0.05
Results – KSS and EEG (alpha and theta)
Results – KSS and EEG (alpha and theta
Significant correlation between KSS and EEG R2 = 0.77, p < 0.05
Discussion – CPAP withdrawal
• Increase in SDI although total sleep time remains similar
• CPAP withdrawal for one night impairs driving performance
– Overall number of incidents
– Time to first incident
Discussion – CPAP withdrawal
• Increased subjective sleepiness
– Aware of the withdrawal
• Although performance is impaired, insight into
sleepiness is apparent.
Conclusions
• CPAP treated OSA patients are more susceptible to the effects of sleep restriction
– Why?
• Missing only 1 night of treatment could be critical
• Patient education
• Control group were also affected
Publications
• Filtness, A.J., Reyner, L.A., Horne, J.A. (2011) Moderate sleep restriction in treated older male OSA participants: greater impairment during monotonous driving compared with controls. Sleep Medicine 12 (9) 838-843
• Filtness, A. J., Reyner, L. A., & Horne, J. A. (2012). One night’s CPAP withdrawal in otherwise compliant OSA patients:
marked driving impairment but good awareness of increased sleepiness. Sleep and Breathing 16(3) 865-871
• Filtness, A. J., Reyner, L. A., & Horne, J. A. (2012). Driver
sleepiness - comparisons between young and older men
during a monotonous afternoon simulated drive. Biological
Psychology 89 (3) 580 – 583
Acknowledgements
Dr Louise Reyner and Prof. Jim Horne,
Loughborough University
Dr Andrew Hall and Dr Chris Hanning,
Leicester General Hospital
Dr Paul Salmon Dr Mike Lenné
Dr Missy Rudin-Brown
Origins of CARRS-Q
Centre for Accident Research & Road Safety –
Queensland (CARRS-Q) was established in 1996 as joint initiative of:
– QUT
– Motor Accident Insurance Commission (MAIC)
Based in the School of Psychology & Counselling, Faculty of Health
Primary role is to undertake research and training to improve safety on Queensland roads and in the
workplace
Research Themes
CARRS-Q currently has approximately 90 staff and students researching 6 themes:
Regulation and Enforcement
School and Community Injury Prevention
Vulnerable Road Users
Occupational Safety
Road Safety Infrastructure
Intelligent Transport Systems
School and Community Injury Prevention
Issues addressed:
School-based education programs
Community-based education programs
Non-enforceable high-risk behaviours e.g. fatigue
Research projects include:
• Understanding sleep in carers (dementia and older adults)
• Postpartum fatigue: Effects on safety-sensitive tasks
• Motivations to drive when sleepy
• Proxy definitions of sleep related crashes
Regulation and Enforcement
Issues addressed:
Drink Driving
Drug Driving
Speeding
Aggressive Driving
Unlicensed Driving
Research projects include:
Development of a brief computer based intervention for first time drink driving offenders
Design and evaluation of anti-speeding messages to target high risk road users' attitudes and behaviours
The road safety implications of unlicensed driving
Vulnerable Road Users
Issued addressed:
Motorcycle riders
Pedestrians
Cyclists
Research projects include:
Development and evaluation of a motorcycle training intervention
Compendium of best practices on motorcycle and scooter safety
Delineating injury patterns and safety behaviours among
cyclist groups
Occupational Safety
Issues addressed:
Work-related driver safety
Fleet safety
Workplace Health and Safety Policy Previous research projects include:
Safety in the heavy vehicle industry
Development and evaluation of an OH&S framework for work related driving
Developing driving risk assessment tools to improve fleet
safety
Road Safety Infrastructure
Issues addressed:
Transportation planning
Safety analysis methodologies (e.g., black spot identification) Research projects include:
Evaluation of High Risk Crash Prediction Methods
Speed Camera Evaluation
Effects of speeding and headway related signs on driver
behaviour
Intelligent Transport Systems
Issues addressed:
Use of Technology and Information Systems
Simulation of future ITS Research projects include:
Predicting vigilance impairment in drivers and operators functioning under monotonous contexts
Integrating driver and traffic simulation to assess in-
vehicle and road-based level crossing safety interventions
Development and evaluation of a novel Driver Training
Assessment Tool
Advanced Driving Simulator
Advanced Driving Simulator
• Officially unveiled in 2010 ; $1.5 million
• Holden Calais, 8 computers, 3 projectors, mobile platform in 3 dimensions
• Replicates real-time traffic conditions
• Many projects – driver, road conditions,
rail projects
CARRS-Q Vision
The CARRS-Q vision is to decrease the local, national and international burden
of trauma-related harm
Questions?
ashleigh.filtness@qut.edu.au
CRICOS No. 00213J
