Exercise Can Improve Speed of Behavior in Older Drivers

Abstract

The main aim of this research was to study the effects of a specific exercise program on the speed of behavior of older adults during on-the-road driving. Twenty-six drivers (55-78 yr old) were randomly assigned to either an exercise group or a control group. The exercise program (3 sessions of 60 min/wk for 8 wk) incorporated tasks that induced the participants to respond quickly to challenging situations. On-the-road driving tasks (under single- and dual-task conditions) included measures of simple and choice reaction time, movement time, and response time. Significant positive effects were found at follow-up resulting from participation in the exercise program: Improvements were found for several measures in all driving tasks, and a composite score reflected a better general drivers' speed of behavior. These results show that exercise can enhance speed of behavior in older drivers and should therefore be promoted.

Full text

48
Journal of Aging and Physical Activity, 2011, 19, 48-61
© 2011 Human Kinetics, Inc.
Exercise Can Improve Speed
of Behavior in Older Drivers
José Francisco Filipe Marmeleira,
Filipe Manuel Soares de Melo, Mouhaydine Tlemcani,
and Mário Adriano Bandeira Godinho
The main aim of this research was to study the effects of a specic exercise program
on the speed of behavior of older adults during on-the-road driving. Twenty-six
drivers (55–78 yr old) were randomly assigned to either an exercise group or a
control group. The exercise program (3 sessions of 60 min/wk for 8 wk) incorpo-
rated tasks that induced the participants to respond quickly to challenging situa-
tions. On-the-road driving tasks (under single- and dual-task conditions) included
measures of simple and choice reaction time, movement time, and response time.
Signicant positive effects were found at follow-up resulting from participation
in the exercise program: Improvements were found for several measures in all
driving tasks, and a composite score reected a better general drivers’ speed of
behavior. These results show that exercise can enhance speed of behavior in older
drivers and should therefore be promoted.
Keywords: automobile driving, aging, reaction time, physical activity
Slowing and increasing variability of motor performance during human aging
is a well-demonstrated phenomenon (Der & Deary, 2006; Hultsch, MacDonald,
& Dixon, 2002; Spirduso, Francis, & MacRae, 2005). The negative effect of
age on reaction time (RT) is more pronounced in tasks that have high levels of
complexity (Der & Deary, 2006) and could affect the way people perform daily
functional tasks such as driving a car (Spirduso et al., 2005). Research has shown
that speed of behavior (i.e., RT to environmental stimuli and speed of execution)
can be improved by the practice of physical activity, in both simple and choice
reaction tasks (American College of Sports Medicine [ACSM], 1998; Spirduso,
2006). However, few studies have explored this potential link among older drivers.
Previous studies have established an association between speed of behavior
and on-road tests (McKnight & McKnight, 1999; Odenheimer et al., 1994) or
crashes (Margolis et al., 2002). Driving is a complex and interactive task involving
a variety of skills and requires the ability to make appropriate and timely decisions.
Marmeleira is with the Dept. of Sport and Health, and Tlemcani, the Geophysics Center, University of
Évora, Évora, Portugal. Melo and Godinho are with the Faculty of Human Kinetics, Technical University
of Lisbon, Cruz Quebrada, Portugal.

Exercise Effects on Speed of Behavior 49
The speed at which visual information is processed may be an important factor
for the successful negotiation of difcult or dangerous trafc situations (Anstey,
Wood, Lord, & Walker, 2005). The relevance of peripheral vision to driving has
been noted in subtasks such as lane maintenance (Land & Horwood, 1995) and
hazard detection (Chapman & Underwood, 1998).
Unfortunately, it has been reported that older drivers show signicantly
decreased visual-attention ability, reecting a spatial constriction of the useful
eld of view (Ball, Beard, Roenker, Miller, & Griggs, 1988) or decreased visual
information-processing efciency (Sekuler, Bennett, & Mamelak, 2000). It is
promising that previous studies have found positive effects of physical activity
on visual-processing speed and divided visual attention (Marmeleira, Godinho, &
Fernandes, 2009; Roth, Goode, Clay, & Ball, 2003)
Increases in RT with aging are evident when it is necessary to control attention
while performing concurrent tasks. In driving, dual-task decits have often been
observed in older adults (Bherer et al., 2005; Chaparro, Wood, & Carberry, 2005).
Secondary tasks appear to interfere with driving, affecting the detection of hazards
and of changes in the driving scenery (Recarte & Nunes, 2003). Research has
revealed that dual-task decits can be reduced by either specic cognitive training
(Bherer et al., 2005) or physical activity training (Hawkins, Kramer, & Capaldi,
1992; Marmeleira et al., 2009).
There is now strong evidence that exercise and physical activity have a
signicant impact on several psychological parameters (Chodzko-Zajko et al.,
2009). Important support for this relationship comes from intervention studies. For
instance, it has been shown that exercise promotes greater information-processing
speed (Marmeleira et al., 2009; Rikli & Edwards, 1991), enhancement of attention
capacity in dual-task situations (Hawkins et al., 1992), and better visual-attention
skills (Roth et al., 2003). Research has also indicated that the frontal region of
the brain, a region that mediates executive function, is the primary locus in which
aging-related cognitive decits are found (West, 1996) and also the locus in which
physical tness appears to exert its greatest inuence (Colcombe & Kramer, 2003).
It has been proposed that physical activity is associated with changes in
underlying mechanisms such as cerebral blood ow (Swain et al., 2003), cerebral
structure (Colcombe et al., 2006), brain-derived neurotrophic factor (Zoladz et al.,
2008), neurotransmitters (Meeusen, 2005), and gene expression patterns (Booth,
Chakravarthy, & Spangenburg, 2002). The gains in cardiovascular tness are often
considered the main physiological mediator underlying the cognitive benets of
physical activity (Chodzko-Zajko & Moore, 1994; van Boxtel et al., 1997). Never-
theless, some studies have failed to obtain evidence of the relation between aerobic
tness and cognitive function (Colcombe & Kramer, 2003; Etnier, Nowell, Landers,
& Sibley, 2006). Thus, the underlying mediators of the relationship between
physical activity and cognitive performance have yet to be fully identied (Etnier
et al., 2006).
Few investigations have explored the potential link between physical training
and driving-related abilities. Recent studies have shown that forms of exercise that
require demanding information processing and for which the speed of behavior
is crucial could be positively transferred to driving situations (Marmeleira et al.,
2009; Matos & Godinho, 2009). However, other studies (Hancock, Kane, Scallen,
& Albinson, 2002) have not found any advantage of sport practitioners over

50 Marmeleira et al.
nonpractitioners in a braking-task experiment. In this context, an important ques-
tion is, What type of exercise is more suitable to affect driving-related abilities?
For instance, it seems reasonable to assume that exercise that incorporates activities
intended to enhance speed of behavior could have a greater impact on the indi-
vidual’s capacity to respond quickly to environmental stimuli during actual driving.
This idea is supported in the hypothesis that for positive transfer to occur between
training and transfer tasks they must involve the same cognitive-processing demands
(Magill, 2003). In addition, studies that compared the individual and combined
effects of physical and mental exercise interventions reported cognitive benets
to be greater for combined cognitive and aerobic training (Fabre, Chamari, Mucci,
Masse-Biron, & Prefaut, 2002; Oswald, Rupprecht, Gunzelmann, & Tritt, 1996).
A great deal of research has focused on elderly drivers’ crash-involvement
patterns, but not on developing methods to enhance their driving-related abilities.
Recently, it was reported that an exercise program developed to stress perceptive,
cognitive, and physical abilities was capable of improving speed of behavior among
older drivers, but measures were collected in a simulated scenario (Marmeleira et
al., 2009). In this context, the main aim of this research was to study the effects
of a similar exercise program on the speed of behavior of older adults during on-
the-road driving.
Methods
Participants
Participants were recruited from the local community by posted yers and local
radio and newspaper announcements. The inclusion criteria were being age 55 years
or more, living independently in the community, being healthy without serious
cardiovascular or musculoskeletal disease, and having a valid driving license, 0.5
or greater corrected visual acuity, and normal cognitive status on the Mini-Mental
State Examination (MMSE; Folstein, Folstein, & McHugh, 1975).
Twenty-six participants fullled the inclusion criteria; 1 subject was excluded
because of severe osteoarthritis. Computer-generated random numbers stratied
by gender were used to randomize participants to either a control group (CG; 63.4
± 6.7 years) or an exercise group (EG; 65.5 ± 6.9 years). During the entire 8-week
period, the CG continued to follow normal daily activities. At the 8-week follow-
up, all adults in each research group completed the posttests. The age ranges were
55–76 years and 57–78 years in the EG and CG, respectively; 9 and 8 women were
in the CG and EG, respectively.
Procedures
Two instrumented cars were used in the experiments. Participants drove a Volkswa-
gen Golf, and a research assistant drove a Fiat Uno. In the Fiat a radio-telemetry
transmitter was instantly activated by the car’s electric circuit whenever the rear
brake light was turned on; in the Volkswagen, the testing devices included a radio-
telemetry receiver, microswitches attached to the foot pedals, and six light-emitting
diodes (LEDs). The LEDs were controlled using a laptop and an interface kit. All

Exercise Effects on Speed of Behavior 51
signs were detected by an MP100 Biopac system (interfaced with a laptop) and
processed with Acqnowledge 3.7.2 software. The signal of the accelerator was reg-
istered when it was initially released; the signal from the brake pedal was detected
when it was initially depressed.
Seats, mirrors, and seat belts were adjusted before getting on the road (a rural
road with little trafc). Participants were instructed to follow the leading car and
maintain a close but safe distance of about 30 m (the exception was the peripheral
RT task, in which the participants drove without the other research car in front). The
vehicles’ speed was around 50 km/hr. One investigator seated in the back seat of the
vehicle driven by the participant ensured that the design protocols were followed,
namely that the sequence and time intervals between stimuli (minimum of 5 s and
maximum of 16 s) were identical for all participants and that the required distance
to the leading car was maintained. Participants were instructed to detect stimuli as
fast as possible while keeping their attention on the road. The same investigator and
research assistant conducted both the pre- and the postassessment. The institutional
human research ethics committee approved this study.
Brake RT Task
Participants were instructed to brake as quickly as possible whenever the leading
car’s rear brake lights were activated. The total drive time was about 6 min. Each
participant had to respond to 26 onsets of the rear brake lights (2 for practice and
24 for data acquisition). Three time measures were recorded: (a) RT, measured from
the onset of the leading car’s brake lights to the initial release of the accelerator by
the driver participant; (b) movement time, the period from the initial release of the
accelerator to the initial brake application; and (c) response time, measured from the
onset of the leading car’s brake lights to the participant’s initial brake application.
Peripheral RT Task
Six red LEDs were positioned approximately 10°, 20°, and 30° left (three LEDs)
and right (three LEDs) of the center of the sight line of the driver and approximately
8° above the car’s console. All the LEDs were placed in the front windshield except
one in the left front door window. The LEDs have a light intensity of 10.0 cd.
The participants reacted by depressing with their left thumb a microswitch
attached to the left side of the steering wheel. One LED at a time was illuminated
for 2 s (less time if the microswitch was depressed sooner). The total time of the
task was about 8 min. The participant had to respond to 54 onsets of the LEDs (6
for practice and 48—8 by LED—for data acquisition). Performance was recorded
in the form of number of signal misses and RTs in milliseconds.
This type of task has been used before and depends greatly on divided visual
attention (Wood, 2002), which is a skill frequently associated with driving perfor-
mance in the elderly (Ball, Owsley, Sloane, Roenker, & Bruni, 1993; Wood, 2002).
Choice RT Task
A two-choice task was used. Participants were instructed to follow the leading
car and react as quickly as possible to either of the following stimuli: (a) The

52 Marmeleira et al.
leading car’s rear brake lights were activated, or (b) one of two LEDs placed
in the front windshield (20° left and right) was activated. In the rst condition,
the participant should brake; in the second condition, he or she should depress
the microswitch on the steering wheel. The use of two LEDs instead of one was
intended to target both sides of the visual eld and to avoid any posture adjust-
ments of the driver to position the LEDs in a more central region of his or her visual
eld.
The total time of the task was about 6 min, during which the participant had
to respond to 32 stimuli (4 for practice and 28 for data acquisition). The occur-
rence of the two stimulus types was balanced. Performance was evaluated by the
RTs and number of errors.
Dual-Task Condition
In the dual-task condition, the primary task was similar to the brake RT task (i.e.,
the participant had to brake as fast as possible whenever the leading car’s rear brake
lights were activated). The time intervals between the stimulus onsets were also
the same as for the brake RT task. The secondary task was a mental-calculation
task that required participants to verbally report the result of adding or subtracting
pairs of numbers presented by the researcher. A new pair of numbers was presented
roughly every 5 s. This type of secondary task has been used frequently (Chaparro
et al., 2005; Marmeleira et al., 2009). Performance measurements were similar to
those of the brake RT task. It has previously been demonstrated that drivers’ ability
is negatively inuenced by the interference resulting from performing a nonvisual
task while driving (Lamble, Kauranen, Laakso, & Summala, 1999). Dual-task
paradigms have also commonly been used to investigate executive functioning
(Adcock, Constable, Gore, & Goldman-Rakic, 2000).
Exercise Intervention
The EG participated in a supervised exercise program 3 days/week for 8 weeks.
Each session lasted approximately 60 min. The exercise intervention incorporated
physical tasks that induced the participants to respond to challenging situations
by producing the desirable motor responses. The idea was that physical activities
that make large cognitive demands may inuence some aspects of cognition more
than repetitive and cyclic activities (Spirduso, 2006).
The types of tasks incorporated in the intervention were very similar to
those used in another study (Marmeleira et al., 2009). However, more emphasis
was placed on activities intended to enhance the participants’ speed of behavior;
frequently, the time needed to respond was a criterion of success. Some examples
of the types of activities are tasks that target simple RT (e.g., while walking, an
auditory/visual sign is presented that requires a specic psychomotor response),
tasks that focus on choice RT (e.g., similar to the simple RT but including more
than one auditory/visual sign), dual-task situations (e.g., walking in different
directions while executing another motor task with the arms), activities that work
peripheral vision (e.g., maintaining several balloons in the air), activities focused
on response inhibition (e.g., while maintaining balloons in the air, all auditory
numeric signs except one require rapidly catching specic-color balloons),

Exercise Effects on Speed of Behavior 53
actions that require planning efforts and decision making (e.g., orienteering
in the gymnasium and in an open space), and activities strongly depending on
working memory (e.g., selecting and completing a specic walking course in
the gymnasium after the presentation of the associated auditory signal, the cor-
respondence of auditory cues to walking courses having been previously estab-
lished). Cooperative games requiring a dynamic group behavior were frequently
included.
It is important to emphasize that the intervention in this study could not be
considered multimodal in the common view of a program with two distinct parts
(mental exercise and physical exercise) that are implemented side by side with
the goal of improving cognitive function in older adults. The type of program that
this research advocates is clearly a physical exercise program in which cognitively
challenging tasks are executed by the older adults undertaking physical activities
such as walking, stepping, reaching, throwing, and manipulating objects.
Statistical Analyses
The upper bound of each time-component measurement was established by com-
puting the mean and standard deviation separately for each participant (CG and
EG, baseline and after 8 weeks) and dropping any trial exceeding the mean by 3
or more standard deviations (Hultsch et al., 2002). A lower bound for legitimate
responses was set at 150 ms, and scores below this limit were dropped. To capture
the overall driving performance for each participant compared with the whole
group, a composite driving score was computed for the baseline and follow-up by
standardizing each of the RT measures (calculating z scores) from the four road
tests and summing z scores. To show the main effect of time, the composite driving
score at follow-up was calculated using the mean and standard deviation from the
baseline RT measures.
Data normality was evaluated by a Shapiro–Wilk test. An independent-sample
t test was used to study differences at baseline between the CG and the EG. The
paired-sample t test was used to compare data within each group at baseline and
after 8 weeks. To assess whether the EG and CG showed differential change after
8 weeks, analyses of covariance (ANCOVAs) were conducted on the change scores
(i.e., postintervention minus baseline), with baseline score serving as the covariate.
Effect sizes are reported as partial eta-squared (ηp2), with cut-off values of .01,
.06, and .14 for small, medium, and large effects, respectively (Cohen, 1988). The
results are expressed as M ± SD. Signicance was set at p < .05 for all tests. Data
were analyzed using SPSS 15.0 for Windows (SPSS, Chicago, IL).
Results
The general groups’ characteristics (Table 1) were similar in gender, age, visual
acuity, MMSE score, years with a driver’s license, and weekly physical activity as
measured by the International Physical Activity Questionnaire–Short Form (Craig
et al., 2003). Six participants from each group had practiced some type of exercise
(mainly dance or aquatic exercise) for at least 1 year. Compliance in the exercise
sessions was very good, exceeding 80% for all participants.

54 Marmeleira et al.
At baseline, the EG and CG did not show any statistical difference in the
driving-related variables. However, several within- and between-groups differences
were found after 8 weeks (Table 2).
In the brake RT task, signicant improvements were found in the EG in RT
(-8%, p = .008) and response time (-7%, p = .045) after 8 weeks. Intergroup analysis
indicated signicant differences in the 8-week changes between groups for RT (-8%
for the EG and 3% for the CG): F(1, 24) = 6.91, p = .015, ηp2 = .231.
In the peripheral RT task, signicant improvements were found in the EG
(-8%, p = .045) after 8 weeks. In the choice RT task, signicant improvements were
found in the EG in RT (-7%, p = .018) at follow-up. Intergroup analysis indicated
signicant differences in the 8-week changes between groups (-7% for the EG and
1% for the CG): F(1, 24) = 10.32, p = .004, ηp2 = .310.
In the dual-task condition, intergroup analysis indicated signicant differences
in the 8-week changes between groups for response time (-7% for the EG and 1%
for the CG): F(1, 24) = 5.08, p = .034, ηp2 = .181.
The EG displayed signicant differences compared with the CG for the mag-
nitude of improvement on the composite score from the pre- to posttest measures:
F(1, 24) = 12.80, p = .002, ηp2 = .358 (Figure 1).
Discussion
This study is one of the few to investigate the effects of exercise on the speed of
behavior of older drivers. During the training program, task constraints induced
the participants to increase the speed of central mental processes (e.g., stimulus
identication, response selection, and response programming) to accomplish the
desired responses. Signicant positive effects occurred in the simple, two-choice,
and peripheral RT tasks and in the dual-task condition. Moreover, a composite score
reecting all RT measurements also showed signicant improvements.
Table 1 General Sample Characteristics
Control
group
Exercise
group
p
n13 13
Women, men 8, 5 9, 4 .680
Age (years), M (SD) 63.4 (6.7) 65.5 (6.9) .514
Visual acuity (decimal), M (SD) 1.1 (0.2) 1.1 (0.3) .905
MMSE (points), M (SD) 28.7 (1.2) 28.9 (1.1) .731
IPAQ (MET min/week), M (SD) 1,576 (1,182) 1,759 (1,194) .698
Weekly distance driven (km), M (SD) 80.1 (47.6) 86.4 (52.0) .741
Time with driving license (years), M (SD) 36.0 (5.5) 30.5 (12.2) .183
Note. MMSE = Mini-Mental State Examination; IPAQ = International Physical Activity Questionnaire–
Short Form.

55 55
Table 2 Driving Measurements at Baseline and at 8 Weeks
Variables
Baseline,
M
(
SD
)
8 weeks,
M
(
SD
)
Difference between
means,
M
(95% CI)
p
Brake RT Task
RT .015
control group 421.2 (64.4) 431.8 (34.2) 10.6 (–26.6, 47.8)
experimental group 438.4 (73.5) 401.7 (56.4) –36.7 (–62.0, –11.4)*
Movement time .583
control group 284.1 (53.4) 283.5 (48.6) –0.5 (–19.6, 18.9)
experimental group 306.4 (41.0) 287.5 (55.3) –18.9 (–53.7, 15.9)
Response time .083
control group 707.3 (91.6) 714.6 (71.8) 7.2 (–31.5, 46.0)
experimental group 740.5 (100.5) 686.4 (107.3) –54.1 (–105.4, –2.6)*
Peripheral RT Task
RT .167
control group 446.6 (95.2) 434.8 (96.4) –11.8 (–63.3, 39.6)
experimental group 415.7 (82.5) 381.9 (56.8) –33.8 (–66.6, –0.93)*
Undetected LEDs .763
control group 1.6 (1.9) 1.2 (1.6) –0.5 (–1.83, 0.90)
experimental group 1.3 (1.1) 1.5 (1.2) 0.2 (–0.49, 0.80)
Choice RT task
RT .004
control group 597.1 (68.2) 601.7 (67.7) 4.6 (–43.0, 52.3)
experimental group 560.1 (89.3) 519.1 (53.4) –41.0 (–73.5, –8.5)*
Errors .187
control group 2.4 (1.3) 2.3 (1.4) –0.08 (–0.91, 0.76)
experimental group 3.1 (1.7) 3.3 (1.4) 0.2 (–0.43, 0.89)
Dual Task
RT .059
control group 607.9 (150.7) 615.5 (79.2) 7.6 (–65.3, 80.5)
experimental group 622.4 (173.3) 577.2 (86.2) –45.2 (–110.6, 20.3)
Movement time .264
control group 285.8 (59.3) 292.0 (47.4) 6.2 (–11.4, 23.9)
experimental group 316.6 (40.9) 296.9 (49.3) –19.7 (–46.2, 6.8)
Response time .034
control group 890.9 (189.6) 917.4 (126.8) 26.5 (–47.1, 100.1)
experimental group 941.8 (200.6) 872.1 (100.3) –69.7 (–154.7, 15.3)
Note. CI = condence interval; RT = reaction time; LED = light-emitting diode. All times are given in milliseconds.
The p values are for differences in the 8-week changes between groups. Analysis of covariance.
*p < .05 changes within the group. Paired-sample t test.

56 Marmeleira et al.
The need to quickly choose between different motor responses according to
the stimuli presented was recurrently trained in the exercise sessions, extending
the ndings of a previous study in the choice RT task (Marmeleira et al., 2009).
Considering that driving is carried out in changeable environments, choice RT
paradigms seem particularly important in the assessment of driving-related abili-
ties. A higher association of on-road driving performance with a complex rather
than with a simple RT paradigm has been previously reported (Odenheimer et
al., 1994).
The peripheral RT task performance showed small benets from the exercise
program. Some previous studies have reported that the time to react to peripheral
stimuli is amenable to improvement by the practice of sports or perceptive-motor
programs (Ando, Kida, & Oda, 2001), whereas others failed to demonstrate such
an effect (Helsen & Starkes, 1999). In the driving-related literature, there are
reports of positive effects of cognitive-processing-speed training on visual-attention
paradigms that involve the presentation of simultaneous stimuli in both central and
peripheral vision (Ball, Edwards, & Ross, 2007; Roenker, Cissell, Ball, Wadley, &
Edwards, 2003). However, it is important to note that these studies were conducted
in laboratory settings and did not measure response time but only response accuracy.
One can assume that, when measurements are carried out during actual driving,
some performance decrement in peripheral RT may occur as a result of a probable
increase in anxiety leading to the allocation of more attentional resources to the
central driving task (Janelle, 2002).
The current study did not nd signicant differences in the changes between
groups over the 8-week period in the peripheral RT task. However, considering the
signicant improvement in the EG, it seems realistic to expect larger intergroup
differences if the exercise program were extended in length. It is important to note
Figure 1 — Composite driving scores for the control group (CG) and the experimental
group (EG) at baseline and 8-week follow-up. †p < .01 for differences in the 8-week changes
between groups. Analysis of covariance. Lower composite scores indicate greater speed of
behavior.

Exercise Effects on Speed of Behavior 57
that during the intervention program, several activities were planned to focus spe-
cically on peripheral vision; nevertheless, it is difcult to isolate their particular
contribution to the peripheral RT task performance because previous research has
shown that the practicing on visual stimulus in central vision can shorten the RT to
stimulus in peripheral vision, and vice versa (Ando, Kida, & Oda, 2002).
The dual-task condition reected signicant effects of the training program,
reinforcing previous ndings that improvements in attention performance resulting
from dual-task training are generalizable to new task combinations involving new
stimuli (Bherer et al., 2008; Marmeleira et al., 2009). These results are very promis-
ing considering that dual-task decits are often observed in older adults. Secondary
tasks interfere with driving, affecting the detection of hazards and changes in the
driving environment (Recarte & Nunes, 2003). A recent study demonstrated that
performing mental calculations while driving markedly increased the average RT
of elderly drivers in comparison with younger drivers (Makishita & Matsunaga,
2008). Driving leads to a greater mental workload for older drivers than for younger
drivers, and this effect is exacerbated by more complex driving contexts (Cantin,
Lavalliere, Simoneau, & Teasdale, 2009).
The generalized benet from the exercise program to the on-the-road driving
tests reinforces previous ndings about the potential of exercise and perceptual-
motor training to promote important driving-related abilities (Marmeleira et al.,
2009; Matos & Godinho, 2009). This is a very positive outcome given that motor
learning often shows a great specicity, with little generalization to related tasks
or new environments (for an overview see Green & Bavelier, 2008).
There have been some reports of a positive transfer from cognitive-processing-
speed training to driving behavior among older adults (Edwards et al., 2009; Roenker
et al., 2003). In those studies, speed training was mainly administered on computer
screens, focusing on the ability to identify visual information quickly in a central
or divided-attention format. In the current study, we used physical exercise as the
training strategy and emphasized not only stimulus perception but also adjusted
and quicker motor responses.
Much research has focused on the effects of aerobic tness on measures of
cognitive function (ACSM, 1998); however, there has been a lack of studies con-
cerning the hypothesis that physical activities exerting large cognitive demands
may have an important inuence on cognition (Spirduso, 2006). In addition,
some meta-analysis did not support the cardiovascular-tness hypothesis, which
suggests that physical activity can enhance cognitive functioning only when aero-
bic tness is improved (Colcombe & Kramer, 2003; Etnier et al., 2006). Thus,
it is possible that mechanisms other than aerobic tness may mediate changes
in cognitive functioning obtainable through physical activity. Following this
line of thought, special emphasis was given in the current study to the type of
activities included in the exercise program. An important idea was to stimulate
psychological mechanisms that promote transfer of learning from the exercise
program to the driving tasks. The design of the exercise program was supported
in the hypothesis that positive transfer of learning occurs primarily because of
similarities between the amount and types of cognitive processes required by the
performance situations (Magill, 2003).
Speed of behavior was successfully enhanced using a challenging form of
exercise that simultaneously required physical effort (e.g., aerobic capacity and
range of motion) and mental effort (e.g., speed of processing, visual attention, and

58 Marmeleira et al.
dual-task processing). Larger effect sizes were found in all tasks, with the excep-
tion of the peripheral RT task. The fact that marked improvements occurred in a
sample of which about 50% were already engaged in exercise strengthens the idea
that the perceptive and cognitive specicity of the program was fundamental. It
is important to note that previous research has found that combined physical and
cognitive training produced greater improvements in cognitive function than either
physical or cognitive training alone (Fabre et al., 2002). Future studies should
continue to examine this issue.
At follow-up, the composite score reected better general speed of behavior.
Improvement of information-processing speed is especially promising for its
potential to affect older adults’ functional abilities that maintain independence
and quality of life (Edwards et al., 2005; Owsley, Sloane, McGwin, & Ball, 2002).
For instance, Ball et al. (2007) examined data from six studies that used the same
computer-based processing-speed training program and concluded that participants
maintained benets of training for at least 2 years, which translated not only to
safer driving performance but also to efcient performance of other instrumental
activities of daily living. The relevance of the results from the current study also
cosubstantiates the theory that changes in cognitive function with age can result
from generalized, age-related slowing of processing speed (Birren & Fisher, 1995).
The current study has some limitations, and caution should be taken in general-
izing the ndings. A relatively small sample of drivers participated, and evaluations
were conducted during open-road driving, but drivers were aware of the stimulus–
response correspondence. In addition, possible bias might have been introduced in
the study because the investigator involved in the assessments was not blinded to
the participants’ group and because the control group did not receive any control
intervention. Finally, it was not possible to differentiate between contributions to
the obtained improvements by specic characteristics of the exercise program and
possible training effects of physical tness (physical tness was not measured).
Future research using longitudinal designs is needed to examine whether change
in behavioral speed promoted by the exercise program can prevent motor-vehicle
crashes among older drivers.
Conclusion
The current research showed that older drivers’ speed of behavior can be improved
through exercise. Therefore, training interventions for older drivers should integrate
exercise programs. Furthermore, the greatest functional benets will be achieved
if exercise programs for older adults incorporate activities that stimulate both
perceptive and cognitive abilities.
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