Fatigue effects on simulator driving in older and younger adults

1. Fatigue Effects on Simulator Driving in
Older and Younger Adults: Comparing
Complex with Monotonous Drives
Carol Holland and Versha Rathod
Psychology, Aston University, Birmingham,
UK

2. Maintaining safe mobility – a priority for an
ageing society
A very significant part of maintaining independence is maintaining
safe mobility, both as drivers and as pedestrians.
The percentage of adults aged over 70 in the UK with a valid driving
licence has increased, from 39% (1998) to 59% (NTS 2011).
In the year 2000, Maycock (2000) projected that the age at which
everyone had ceased driving would increase to 95 by 2020. This
has been exceeded.
In 2010, there were 621 applications for licence renewals from
people aged 95 to 101. However, numbers of drivers over 80 Killed
or seriously injured did not increase between 2004 and 2010. (DfT
Statistics)

3. Older drivers’ habits have changed but they
remain a relatively safe group of drivers.

4. How do people stay as safe as they do?
The most important factors in older drivers staying as safe they
largely do on the roads, even in the context of some impairments,
are related to:
►ability to regulate their driving in accordance with any
impairments (e.g. Nasvadi & Wister 2009),
►their awareness of their limitations,
►and their willingness to make adaptations to compensate for
them (Holland & Rabbitt, 1992).
Providing guidance to older drivers on self-regulation is
increasingly seen as a priority.
►internal (e.g. cognitive, vision, health, fatigue)
►external (e.g., road categories, lighting)

6. Self-regulation depends on good executive
function
Attentional (executive) control :
►ability to control or shift allocation of attention between tasks
►Inhibition of processing of irrelevant information,
►updating ongoing tasks
►flexibly planning solutions in the context of the changing environment
or impairments.
Examples from health fields:
Hall, et al., (2008) executive function contributes to relationships
between intentions to perform healthy behaviours and actually doing so
Hall, et al., (2010) for those living with chronic illnesses that have
heavy self-regulatory demands, survival time was longer for those with
stronger executive function.

7. Effects on Executive Function relevant to
older drivers
► Executive function declines in older age (e.g. West, 1996)
► It is related to driving in older (Adrian et al, 2011; Parasuraman &
Nestor, 1991) and younger drivers (Mantyla et al, 2009)
► Executive function, especially control of behaviour and ability to
inhibit, is commonly impacted by complex prolonged demanding
situations (van der Linden, et al., 2003)
► Effects are not necessarily on accuracy or speed of performance,
but on factors such as planning or inhibition of inappropriate
responses

8. How do these factors work together?
Looking at Fatigue.
This suggests that in the context of heavy driving demand, the impact
of reduced executive function may be greater, therefore having a
specific impact on older drivers, in a manner related to their executive
function capabilities,
That is, fatigue effects may be different for older drivers.
this study sets out to examine this issue in older drivers.

9. What might we expect to happen in fatigue
situations?
► Chaparro et al. (2005): although older drivers may compensate by
driving more slowly in a complex dual task condition, they did not make
driving or secondary task errors.
► If such self-regulatory capacity becomes exhausted we would expect a
reduction in the control exerted over driving speed with increasing
duration of a complex drive.
Hancock and Desmond (2001) distinguish between
► active fatigue, resulting from demanding situations including overload of
attentional resources, and
► passive fatigue, resulting from low demand, or underload of attention.

10. So…
This study compares the effects of passive versus active fatigue in
older and younger drivers,
►monitoring the effects of perceived sleepiness,
►and examining the contribution of standardised measures of
underlying executive function, in comparison with other cognitive
and perceptual functions, to any fatigue effects in driving
measures.

11. AIMS
►The primary aim is to assess the relative and combined
contributions of sleepiness, fatigue, and cognition on the ability
to maintain safe driving in older adults, as follows:
►(i) Compare effects of monotonous versus complex demanding
simulator driving on sleepiness for older and younger adults.
►(ii) Compare effects of monotonous versus complex demanding
driving on change across the simulator drives in terms of driver
performance measures (fatigue effect) for older and younger drivers,
►(iii) To examine the contribution of cognitive function indices to
simulated driving performance indices and fatigue effects, contrasting
control and adaptation performance measures (speed control) with
errors and reaction time to hazards.

12. Methods
Participants
10 older (>60 years) mean age 65.38 years, SD 4.97) and
19 younger drivers (19-22 years) (21.74 years, SD=5.08).
Cognitive assessments
The CANTAB battery:
►processing speed (CRT)
►executive function (IED (task switching and inhibition) and Stockings of
Cambridge (strategic planning), affective go/no go (inhibition) and rapid
visual information processing
►Verbal recognition memory was also assessed.
The Addenbrookes Cognitive examination, revised. (ACE-R)
Sleep questionnaires
Stanford Sleepiness scale (SSs) alertness,
Karolinska Sleepiness scale (KSs)

13. Driving simulator
The Aston Research Centre for Healthy Ageing (ARCHA) STISIM Drive
simulator (Systems Technology Inc) was used to measure driving data
and associated driving related paradigms:
(i)Hazard response
(ii)speed limit exceedances
(iii)Collisions (pedestrian, vehicle, off road)

14. Copyright C.Holland,
Aston University

15. The scenarios - monotonous
Long monotonous drives (40mins),
14 hazards (0.35 per minute on
average)
long periods of very little complexity
(straight roads with plain “countryside”)
and short periods of urban environment
or traffic features in order to present
comparable hazards.

16. The scenarios - complex
► attentionally demanding
complex drives (10
minutes),
► 11 hazards (1.1 per
minute on average)
► through an all urban
environment

17. Procedure
Participants completed cognitive measures first, and then had a break.
They completed the sleepiness and alertness (Karolinska and Stanford
scales) before and after each drive. Drive orders were counterbalanced
Participants were given a break between the drives in which they were
given non-caffeine beverages and a light snack.

18. Results
(i) Comparison of the effects of monotonous versus complex demanding
simulator driving on sleepiness for older and younger adults.
Karolinska
Before-after F(1,33) = 51.48,
p<0.001,
drive x before-after F(1,33) =
44.10, p<0.001,
monotonous drive was more tiring
than the complex drive overall. No
age group effect, and no 3 way
interaction between the type of
drive and the before after effect
with age.
0
1
2
3
4
5
6
7
Before
complex
After
Complex
before
monotonous
after
monotonous
young
old

19. Results
Stanford
before-after the drives F(1,33)
= 17.90, p<0.001;
drive x before-after F(1,33)
14.45, p<0.001.
Again, monotonous was more
fatiguing
age group F(1,33) = 4.95,
p<0.05.
No 3 way interaction,
0
0.5
1
1.5
2
2.5
3
3.5
4
Stanford
before
complex
drive
Stanford
before
monotonous
drive
Young
Old

20. Aim 2: Compare the effects of monotonous versus complex
drives on driver performance measures.
Each drive was subdivided into two halves. Data were analysed by type of errors
across the two drives. Errors were relatively rare except for speed exceedances
(i) speed control
Younger drivers made
marginally more excursions
over the speed limit than older
drivers (F(1,28)=3.45, p<0.07).
Older drivers’ exceedances
increased with duration in the
complex drive (age group x
drive interaction F(1,28)=5.11,
p<0.05), but the three way
interaction was not significant.
0
1
2
3
4
5
6
complex 1 complex 2Monotonous
1
Monotonous
2
Young
Old

21. Aim 2: Compare the effects of monotonous versus
complex drives on driver performance measures
Reaction Times to
matched Hazards
Significant effect for
complex drive for old, but
not for monotonous t(13) =
2.37 p<0.05.
For young, similar
significant effect for both
drives t (18) = 2.26, p<0.05 0
1
2
3
4
5
6
7
8
Complex1 Complex2 Mon1 Mon2
Young
Old

22. Aim 3: Contribution of cognitive function to
fatigue effects (in RTs,speed control, collisions)
Calculated fatigue effects (performance at end of the runs minus
performance at the beginning).
Complex drive
RT – no relationships
Speed control - RVP (hits) –sustained updating task,
- Go/no go task latency (inhibition processing)
Collisions - CRT
Monotonous drive
RT – no relationships
Speed control - RVP false alarms (failure to inhibit)

23. Conclusions
►Although RT to hazards was faster amongst older
drivers than younger drivers overall, older adults
showed more slowing in the complex scenario.
►The fatigue effect (difference between RTs at start and
end of the drives) was greatest in the monotonous
drive for the younger drivers.
►These findings together suggest that younger, less
experienced drivers were more affected by a long
monotonous drive and older drivers more affected by
the complex drive.

24. Conclusions cont…
►The fatigue effects (as opposed to actual
performance) were predicted by inhibition and updating
components of executive function, but not task switching
or planning.
►This mainly affected control of driving such as speed
control, rather than response to hazards
►Increase in collisions in the complex drive with fatigue
was predicted by underlying processing speed (but there
weren’t many collisions!)

25. Take home point and limitations
We need to increase the numbers of older drivers in our sample.
simulator sickness in the longer drives a real issue.
Response to Hazards assessment needs fine tuning
But - Within high functioning experienced older drivers, complex,
demanding drives are more fatiguing in terms of performance than
long monotonous drives,
Variations in cognition, specifically inhibition and updating, are
important predictors in complex conditions.
High demand may be exhausting self-regulation because of its
effect on already potentially depleted EF, on which SR depends.

26. Thank you!