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. Author manuscript; available in PMC: 2023 Jan 1.
Published in final edited form as: Int J Sport Exerc Psychol. 2020 Nov 20;20(1):283–301. doi: 10.1080/1612197x.2020.1849353

The Effects of Acute Exercise on Driving and Executive Functions in Healthy Older Adults

Jean-Charles Lebeau 1,2, Justin Mason 2,3, Nelson Roque 4, Gershon Tenenbaum 5
PMCID: PMC8992970  NIHMSID: NIHMS1648217  PMID: 35401070

Abstract

The benefits of exercise on cognitive functioning in older adults are well recognized. One limitation of the current literature is that researchers have almost exclusively relied on well-controlled laboratory tasks to assess cognition. Moreover, the effects of a single bout of aerobic exercise in older adults have received limited attention. The proposed study addresses these limitations by assessing the effects of a single bout of exercise on a more ecologically valid task - driving. Seventy-one participants (Mage = 66.39 ± 4.70 years) were randomly allocated to 20min cycling at moderate intensity or sitting and watching driving videos. Participants were then tested on their driving performance using a driving simulator. Driving performance was measured with three different scenarios assessing decision making, driving errors, reaction time, and attention. On a subsequent session, all participants were tested on executive functioning before and after a fitness test. Non-significant effects of exercise were observed on driving performance. However, participants performed better on the Trail Making Test (Cohen’s d = 0.25) and Stroop test (d = 0.50) after the fitness test compared to their baseline. These results suggest that post-exercise cognitive improvements do not transfer to improved driving performance among healthy older adults. This study also highlights the importance of assessing expectations as a possible moderator of the effects of acute exercise on activities of daily living. Future studies must examine other relevant ecologically valid tasks and ensure similar expectations between experimental and control groups to further advance the knowledge base in the field.

Keywords: physical activity, cognitive functioning, ecological validity, elderly

The effects of exercise on cognitive functioning are now well established. Results from meta-analyses and reviews demonstrated that acute exercise benefits participants’ cognitive performance (Chang, Labban, Gapin, & Etnier, 2012; Sprague et al., 2019). One particular cognitive function that benefits from acute exercise are executive functions (Chang et al., 2012). Executive functions are higher order cognitive functions related to the management of emotions and attention (Etnier & Chang, 2009). Some examples of executive functions investigated in the exercise psychology literature include planning, task switching, attentional control, inhibition, and working memory (Hillman et al., 2006; Northey, Cherbuin, Pumpa, Smee, & Rattray, 2018).

Despite a large body of evidence for the benefits of chronic exercise on executive functioning in older adults (e.g., Erickson et al., 2011; Leckie et al., 2014; Northey et al., 2018), the research on the effects of acute aerobic exercise on cognitive functioning for this population has received less attention. Investigations of the acute effects of exercise have scientific merit as it offers insights into transient modulations of information processing related to exercise and could inform chronic exercise studies design. Meta-analytic results have consistently provided support for the positive effect of acute exercise on executive functions, especially for older adults (Chang et al, 2012; Ludyga et al., 2016). Specifically, those reviews suggest that executive functions are more sensitive to acute exercise interventions in individual undergoing developmental changes. As such, older adults might strategically use acute exercise to perform tasks that rely on executive functions. However, current acute exercise studies in older adults rely primarily on laboratory measures of executive functions such as the Stroop test (Barella, Etnier, & Chang, 2010; Chu, Chen, Hung, Wang, & Chang, 2015; Hyodo et al., 2012), alternate use of familiar objects (Netz, Tomer, Axelrad, Argov, & Inbar, 2007), or Trail Making Test (Etnier & Chang, 2009). Although laboratory tasks provide strong internal control, they offer limited transferability to the sort of dynamic cognitive functioning in everyday life. For this reason, there has been a call from researchers to extend the effect of exercise on cognition to more ecologically valid, real-life tasks (Etnier, 2012; Sprague et al., 2019). One such ecologically-valid task especially relevant for older adults is driving.

Older Adults and Driving

Executive functions are among the cognitive functions impaired in higher age, affecting older adults’ cognitive functioning and capacity to execute instrumental activities of daily living such as driving (Karthaus & Falkenstein, 2016; Wechsler et al., 2018). Driving is a task that combines visual perception, sensorimotor skills, and cognitive abilities (Karthaus & Falkenstein, 2016; Mathias & Lucas, 2009). Specifically, driving a vehicle relies on higher-order cognitive abilities (i.e., attention switching, divided attention, executive functions) because it requires observing and responding to traffic demands and pedestrians, along with planning, implementing, and regulating one’s own behavior (Karthaus & Falkenstein, 2016). Hence, driving performance relies on both higher-order and lower-order cognitive skills. Specifically, driving requires higher-order skills such as executive functions (e.g., attention switching, planning, inhibition, working memory) to work in combination with lower-order cognitive skills (e.g., processing speed, visual perception, sensorimotor skills; Karthaus & Falkenstein, 2016).

Cognitive functions, and executive functions in particular, are important predictors of safe driving and on-road driving performance (Adrian, Moessinger, Charles, & Postal, 2019). For example, executive functions scores (as measured by the TMTs) predicted the ability to stay centered in the lane while driving in older adults (Aksan et al., 2017). Further support comes from neurocognitive studies who have identified links between brain networks related to executive functions and those related to driving (Rupp et al., 2019). In particular, Rupp and colleagues found that event-related potentials specifically located in frontal regions, the part of the brain responsible for executive functions, were associated with driving performance. Other driving tasks differentiating older drivers from their younger counterparts have also been identified. For example, when tested against a young population on a driving simulator, healthy older adults (age 60 years and above) performed worse on variables such as reaction time (RT), collision, braking time, and ability to maintain a constant distance behind a car (Alonso et al., 2016; Doroudgar et al., 2017). Hence, these variables seem important to include in driving research when assessing the efficacy of strategies aiming at reducing the risk of crashes among older drivers.

Older drivers have a higher rate of intersection and left-turn crashes than younger drivers (Chandraratna & Stamatiadis, 2003) due to having difficulty estimating the distance from oncoming vehicles (Horswill, Helman, Ardiles, & Wann, 2005). One variable which has been associated with collision in left-turns at an intersection is the useful field of view (UFOV). The UFOV designates the visual area where stimuli can be identified with a brief glance without head or eye movement (Sanders, 1970). The UFOV paradigm is sensitive to aged-related decline and can be easily administered via a computerized test (Edwards et al., 2005). Performance on the UFOV has been shown to predict driving ability and crash risk among older drivers (Wood & Owsley, 2014).

To test older adults’ performance while driving, driving simulators have been designed and reported to be reliable predictors of on-road driving (Casutt, Martin, Keller, & Jancke, 2014). Driving simulators allow the collection of variables such as distance from oncoming vehicle when turning left at an intersection, driving errors (e.g., driving through a red light), distance from preceding cars and from pedestrians crossing at an intersection, and RT to a leading car braking. Therefore, driving simulators offer a reliable and safe way to measure driving performance, and could be used to test the efficacy of strategies to improve executive functions involved in driving, such as a single bout of aerobic exercise.

The Current Study

Ample evidence suggests that older adults benefit from chronic aerobic exercise interventions (Erickson et al., 2011; Leckie et al., 2014), but very few studies have investigated the acute effects of exercise on cognition for this population. Specifically, in a review of 40 studies by Ludyga and colleagues published in 2016, only three studies with participants over 65 years old were included. Moreover, cognitive performance has been predominantly assessed via laboratory tasks and the effects of exercise on everyday life activities such as driving have not been investigated.

This study aims at addressing those limitations by examining the effects of an acute bout of aerobic exercise on driving performance and executive functions in healthy older adults. To ensure our sample was comparable to previous studies, we sought to replicate the acute effects of exercise on executive functions and investigate driving performance in the same study. Consistent with meta-analytic results (Chang et al., 2012; Pontifex et al., 2019), exercise duration was set at 20 min to allow sufficient time interval for the exercise to elicit improvements in cognitive performance. The exercise intensity was moderate (i.e., 60% ± 3% of heart rate reserve), in line with the Inverted-U perspective in exercise-cognition research (Hillman, Kamijo, & Pontifex, 2012). Additionally, because cardiorespiratory fitness levels have been found to moderate the relationship between exercise and cognition (Chang et al., 2012; Chu et al., 2015), participants performed a submaximal exercise test for 15min at a mild-to-moderate intensity, following which participants were immediately tested on their executive functioning. Finally, in line with Boot et al. (2013) and Chu et al. (2015)’s recommendations about measuring expectations in exercise studies, we assessed participants’ expectations about the effect of exercise (or watching driving videos) on their driving performance. Drawing from past experimental findings supporting that acute exercise improves executive functions and that driving performance relies on executive functions, it was hypothesized that (1) participants in the acute exercise condition will perform better than participants in the video (control) condition on the driving scenarios, and (2) scores on the executive functions tests (Stroop test and TMT) will be enhanced following the fitness test compared to baseline scores measured before the fitness test.

Method

Participants

Two power analyses using G*Power 3 (Faul, Erdfelder, Lang, & Buchner, 2007) were employed with α level set at .05 and power at .80 to determine the number of participants required for the study. For the executive function variables (i.e., TMT and Stroop test), the effect size was set at d = 0.65 (Chu et al., 2015). The power analysis using a Repeated Measures (RM) ANOVA with two conditions (i.e., exercise and control) and two measurements (i.e., pre and post) revealed a required sample size of 22. The power analysis using a MANOVA test with two conditions and five response variables revealed a required sample size of 70 for a moderate effect size, f 2(v)= .20.

Seventy-one older adults (46 females, 25 males; Mage = 66.39 ± 4.70 years) participated in this study. Participants were recruited by phone from a local database of senior citizens and a local senior center. The criteria selected for age was 60 years and older, consistent with previous studies. Exclusion criteria included having history and/or current conditions against performing an exercise task (e.g., heart disease), and/or cognitive assessment (e.g., brain injury, depression), and/or not currently driving. Participants were screened for depression, cognitive impairment, and colorblindness via the Geriatric Depression Scale (GDS, Sheikh & Yesavage, 1986), the Short Portable Mental Status Questionnaire (SPMSQ; Pfeiffer, 1975), and the Color Blindness Test (Ishihara, 1966), respectively. Participants were informed of these exclusion criteria before coming to the lab, and no participant was excluded, missing, or dropped out.

Participants were randomly allocated to the exercise (i.e., cycle ergometer) or video condition according to randomization function in Microsoft Excel (2010) and each participant signed an informed written consent before participating in the study. The Institutional Review Board approved this study and the authors complied with APA ethical standards.

Covariates

Driving variables.

The Adelaide Driving Self-Efficacy Scale (ADSES; George, Clark, & Crotty, 2007) and the Driving Habits Questionnaire (DHQ; Owsley, Stalvey, Wells, & Sloane, 1999) were used to assess driving self-efficacy and driving habits respectively. These two variables have been shown to be positively correlated with driving performance (George et al., 2007) and were assessed to identify potential covariates.

Expectations.

In line with recommendations by Boot et al. (2013) and Chu et al. (2015), expectations for performance on the driving simulator and executive functions tasks were measured. Two separate stems were developed to measure expectations for the participants in the exercise and video conditions. Participants in the exercise condition were asked to what degree they believe exercising on the cycle will help their performance on the driving task. The video condition participants responded to a similar stem with “exercising on the cycle” replaced by “watching the video.” Each item was scored on a scale ranging from 0 (none/not at all) to 10 (very much/very well). For the executive functions tasks, two items were created and participants were asked to rate to what degree they believe that exercising at moderate intensity on the treadmill will help their performance on the Stroop test and TMT separately.

Apparatus

Metabolic measurement cart (ParvoMedics Inc., Sandy, UT).

The TrueOne 2400 metabolic measurement cart was used to measure oxygen consumption (VO2) and respiratory exchange ratio (RER). The analysis system synchronizes with a programmable treadmill and Polar HR monitor and has been shown to provide reliable and valid measures of gas exchange variables (Crouter, Antczak, Hudak, Della Valle, & Haas, 2006).

Cycle ergometer (Monark 828E; Monark Exercise, Varberg, Sweden).

The Monark is a mechanically braked cycle-ergometer that uses a pendulum system to adjust the intensity of the resistance. Workload can range from 4 to 1400 Watts. A screen attached to the cycle displays revolutions per minutes (RPM), HR, time, speed, distance, and watts.

Heart rate monitor (HRM; Polar Electro Oy, Kempele, Finland).

The Polar RS 100 HR Monitor provided HR recording during the cardiorespiratory fitness test. HR is measured through a strap worn around the chest connected to a watch that displays HR in beats per minute (bpm).

Driving simulator (DriveSafety, Salt Lake City, UT).

The RS 200 driving simulator includes a dashboard with a virtual panel, steering wheel, accelerator and brake pedals, and three 24” LCD displays that provide the driver with a simulated view of the environment. Driving performance was measured with three different scenarios assessing decision-making, attention to traffic lights and pedestrian, and sustained attention behind a leading vehicle. More specifically, these scenarios required the participant: (1) deciding whether it is safe to turn with oncoming traffic; (2) driving at a constant speed going through several intersections while paying attention to the lights and pedestrians; and (3) maintaining a constant distance with a preceding car that brakes and accelerates randomly.

Measures

Useful field of view (UFOV; Edwards et al., 2005).

The UFOV is a computerized test made of three subtests measuring processing speed and attention. The first subtest assesses processing speed by measuring the time needed to correctly identify a target (i.e., truck or car) presented in the center of the screen. The second subtest measures divided attention and involve the identification of a central target along with the localization of a target in the peripheral vision (i.e., silhouette of a car or truck). The last subtest assesses selective attention and consists of the same subtest, but also includes triangles of the same size as the targets as distractors. The test-retest correlation for healthy older adults was r = .72, r = .81, and r = .80 for the three subtests, respectively (Edwards et al., 2005).

Stroop Test (Scarpina & Tagini, 2017).

The Stroop test is a widely used measure of executive functions, assessing specifically the ability to inhibit a habitual response, selective attention, and shifting (Pachana, Thompson, Marcopulos, & Yoash-Gantz, 2014), cognitive variables also relevant to driving (Karthaus & Falkenstein, 2016). This test requires participants to manually identify the color name printed in the same ink color (e.g., BLUE printed in blue ink; congruent condition) or different ink color (e.g., BLUE printed in red ink; incongruent condition). Each stimulus word (i.e., red, green, and blue) was presented in equal proportions to minimize specific word facilitation. A total of 126 stimuli (i.e., 2 blocks of 63 stimuli) were presented to the participants to have a number of stimuli consistent with Chu et al. (2015). Reaction time and accuracy were used as indices of performance.

Trail Making Test (TMT; Reitan & Wolfson, 1993).

The TMT measures cognitive speed and agility with high validity and reliability (Wolf, 2000). The TMT includes two parts: part A and part B. Part A requires the participant to connect 25 numbers in ascending order (i.e., 1-2-3-4, etc.) as quickly as possible. In part B, participants are presented with numbers and letters and are asked to connect them by alternating between numbers and letters (i.e., 1-A-2-B-3-C, etc.). Executive functions scores are computed based on the time required to perform Part A and Part B separately. The difference between TMT-B and TMT-A was computed to set apart the executive functions component of the TMT-B, consistent with validated protocols (Arbuthnott & Frank, 2000).

Rating of Perceived Exertion (RPE; Borg, 1988).

The RPE scale measures perceived effort during exercise on a scale ranging from 6 (no exertion at all) to 20 (maximum exertion). RPE is a reliable measure of perceived effort and physical discomfort with both intra-test (r = .93) and test re-test (r = .94) reliabilities being high (Borg, 1988).

Commitment check.

A commitment check was developed to test participants’ commitment to the tasks. The three items asked how committed the participants were while performing (a) the driving task, (b) the paper and pencil task (i.e., TMT), and (c) the color and work task (i.e., Stroop). Each item was rated on a scale ranging from 1 (none/not at all) to 10 (very much/very well). Participants also rated how similar to real car driving was the driving simulator on a continuum from 1 (not at all similar) to 10 (very similar).

Driving Scenarios

Left-hand turn scenario.

This scenario began with the participant’s car situated in the left-hand turn lane at an intersection with oncoming traffic and a car in the opposing left-hand turn lane. This scenario presented a continuous stream of traffic for about 5 min, with varying gaps between cars moving at the same speeds. Participants were asked to press a button on the steering wheel to signal when it was safe to make a left turn. The system computed the gap length as the distance from the oncoming vehicle when the participant indicated it was safe to turn.

Yellow light and pedestrian driving scenario.

In this scenario, participants drove through 14 traffic lights in a straight roadway and were asked to follow a speed of 45 miles per hour. At nine intersections, the light turned yellow and a decision had to be made. Participants decided (1) to brake and stop at the light, or (2) to continue driving through the yellow and possibly red light, as they got closer to the intersection. When a participant reached the final intersection of the scenario after about 5 min, a pedestrian was triggered to cross the road unexpectedly. Participants either had to drive around the crossing pedestrian or stop and let the pedestrian cross the intersection.

Follow the lead vehicle scenario.

In this last scenario, the participants were asked to follow a car. Driving research commonly uses this type of scenario in order to assess vehicle control and RT (Andrews & Westerman, 2012). Participants were asked to follow the car and maintain a distance of approximately 100 feet for the entire drive (about 18 min). During the scenario, the lead vehicle braked and accelerated randomly, and the participant was required to maintain a safe distance from the lead by responding appropriately.

Perceptually Regulated Exercise Test (PRET; Eston et al., 2012)

The PRET is a submaximal cardiorespiratory fitness test which has been reported to be a valid and reliable test for estimating peak oxygen consumption (VO2peak) in both sedentary and active older adults (Eston et al., 2012; Smith, Eston, Norton, & Parfitt, 2015). Specifically, VO2peak from PRET and maximal graded exercise test were strongly correlated (r = .91), and test-retest reliabilities were r = .76 and r = .94 for active and sedentary older adults, respectively (Eston et al., 2012).

The protocol involves four consecutive stages, each lasting 3 minutes at RPEs of 9, 11, 13, and 15 on Borg’s 6–20 RPE Scale (Borg, 1988). The participant began with a warm-up of walking on a treadmill for 3 minutes and was then instructed to adjust the speed and/or the gradient of the treadmill to exercise at RPE of 9 for another 3 minutes. RPE was recorded during the last 15 seconds of each stage. The same protocol was repeated for RPE 11, 13, and 15. The screen of the treadmill was masked and the RPE scale was presented at eye level directly in front of the participant throughout the PRET. The estimated VO2 peak was predicted by regressing the VO2 values averaged in the last 30 seconds of each stage with the RPE values to an endpoint of 20 (Smith et al., 2015).

Intervention

Participants were randomly allocated to a moderate intensity cycling exercise or video control condition. Heart rate reserve (HRR) was used to indicate exercise intensity, and was calculated as the difference between age-predicted max HR and resting HR. For this study, 60% of HRR was selected for moderate intensity, consistent with previous exercise protocols (Chu et al., 2015; Hyodo et al., 2012). In the exercise condition, participants were fitted with the HR monitor then sat for 3 min to record resting HR. Participants were then directed to the cycle ergometer and were given the opportunity to adjust the handlebar and saddle height for their comfort. The exercise started with a 3 min warm up; then, participants were instructed to cycle for 20 min at a HR range value corresponding to 60% ± 3% of their HRR, similar to previous studies (Barella et al., 2010; Chu et al., 2015). The exercise terminated with a 2 min cool down.

Participants in the control condition were seated and watched a video of driving tips for 25 min. The videos were collected from the YouTube video platform and consisted of tips such as how to stay centered in your lane, where to look when driving, and directions for parallel parking. The videos were selected so that they do not cover the specific situations encountered in the driving scenarios. Immediately after each condition (i.e., within 1–2 min), participants performed the driving scenarios.

Procedure

The study consisted of a visit to two different laboratories on the same day to ensure consistency across participants. The whole experiment took about 3 hours to complete, with a break of about 20 min between visit 1 and 2. Adding the break time to the duration of the driving scenarios, the non-active period between the end of exercise in visit 1 and the beginning of visit 2 was about 60 min. Participants were asked and confirmed that they refrained from intense physical activity 48 hours before the day of data collection and avoided caffeine and alcohol ingestion 12 hours prior to laboratory visits.

The first laboratory visit consisted of completing questionnaires, cognitive testing, exercise or watching videos, and driving scenarios. Participants first completed the screening measures (i.e., demographic and medical history forms, GDS, SPMSQ, and colour blindness) and then were administered the DHQ and ADSES. Next, participants completed the UFOV. Prior to performing the exercise or watching the video clips, participants did a practice scenario on the driving simulator to familiarize themselves with the technology and were asked to rate their expectations of the exercise (or video) on their driving performance. Participants in the exercise condition were then fitted with the HR monitor, warmed up for 3 min on the cycle, followed by 20 min at moderate intensity. Participants in the control condition sat and watched videos on driving tips for the same duration as the exercise condition. Immediately after, participants in both conditions performed the driving scenarios in the same order: left-hand turn, yellow light and pedestrian, and follow the lead vehicle. The session ended with the completion commitment check measures, a short debrief, and a brief explanation of the second laboratory visit.

For the second laboratory visit, participants completed the TMT and Stroop tests both before and immediately after the PRET test, which was used as an acute exercise intervention. Two alternate versions of the TMT were developed to avoid any learning effect (for details, see Razon et al., 2019). For the Stroop test, items were presented in a random order in each block. After completing the TMT and Stroop test for the first time, participants rated their expectation of the effects of a 15 min moderate intensity exercise (i.e., PRET test) on their subsequent performance on the TMT and Stroop tests. After completing the TMT and Stroop a second time after exercise, participants rated their commitment for both cognitive tasks.

Data Analysis

A series of t-tests and χ2 tests were first conducted to test condition baseline differences between groups. Independent t-tests were also used to compare levels of commitment and expectations between the exercise and control conditions. Data from the driving simulator were converted using RStudios (R Core Team, Boston, MA) and the driving variables were computed for each participant and collapsed into mean values for the experimental and control conditions. To address the first hypothesis (i.e., effect of exercise on driving performance), a MANOVA was performed across all five driving variables with the experimental condition as a between-subject factor. For the second hypothesis (i.e., effect of fitness test on executive functions), a RM ANOVA was performed for the Stroop scores (i.e., accuracy and mean RT for both congruent and incongruent conditions) and TMT scores (i.e., time for completion). Time (i.e., before and after the fitness test) was used as a 2-level within-subject factor. For all analysis, significance level was set at .05.

Results

Demographic, Exercise, and Baseline Driving Measures

Although females comprised 65% of the sample, the repartition of genders was similar in both conditions. Analyses on demographic variables revealed no condition differences for highest degree obtained, χ2 (6) = 5.78, p = .45, and employment status, χ2 (2) = 5.75, p = .06. Similarly, non-significant differences were observed between experimental and control conditions for age, self-reported exercise, fitness level, driving habits, and driving self-efficacy (all ps > .08).

Finally, the UFOV test revealed that all participants had normal processing speed and divided and selective attention, which classified them as “very low risk” in performing everyday activities, such as driving a motor vehicle.

Commitment Check

Analysis of the commitment checks revealed that participants were highly committed while performing the driving simulator (Mgrand = 9.43/10), TMT (Mgrand = 9.58/10), and Stroop test (Mgrand = 9.63/10). Additionally, participants in the two conditions did not differ on their commitment level to the driving simulator, t(69) = 0.01, p = .99, d = 0.01.

Expectations

A significant condition difference emerged for the expectations on the driving simulator performance, t(69) = 2.50, p = .02. Participants in the video condition reported higher expectations on driving performance than participants in the exercise condition (M = 7.07, SD = 2.15 vs. M = 5.54, SD = 2.92; d = 0.60). As for the executive functions tasks, participants reported moderate expectations of exercise effects on both the TMT (Mgrand = 5.77/10) and the Stroop test (Mgrand = 5.83/10), with no expectations differences observed between these two tests, t(70) = 0.94, p = .35, d = 0.03.

Effects of Exercise on Driving Performance

Entering expectations as a covariate, the analysis revealed a non-significant condition effect on driving variables, Wilks’ λ = .939, F(5, 63) = 0.82, p = .54, ηp2 = .06. Means and SDs for each driving variable are displayed in Table 1.

Table 1.

Means and SDs of the five driving variables for the exercise and control groups

Exercise (n=35) Video (n=36)
Variables Mean (SD) Mean (SD)
Distance from oncoming cars when turning left (meters) 85.33 (36.94) 94.43 (29.80)
Driving errors (driving through yellow or red light) 4.17 (2.83) 4.69 (2.76)
Minimum distance from the pedestrian (meters) 15.67 (8.21) 17.52 (9.20)
RT to braking events (ms) 1390.42 (903.16) 1390.30 (1245.37)
Minimum distance to the lead car (meters) 15.52 (3.48) 16.10 (3.26)
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Note. RT = Reaction Time

Effects of Fitness Test on Executive Functions

The analysis revealed a significant main effect of time on TMT scores, Wilk’s λ = .937, F(1, 69) = 4.68, p = .03, ηp2 = .06. This effect is displayed in Figure 1. On average, participants were about 5 seconds faster completing the TMT after the fitness test compared to the completion time before the fitness test (M = 32.08, SD = 18.19 vs. M = 36.92, SD = 20.30; d = 0.25).

Figure 1.

Figure 1.

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Mean executive functions scores and SEs for the TMT before and after exercise.

Pertaining to the Stroop scores, the results from the RM ANOVA revealed a significant main effect for congruency on RT, Wilk’s λ = .271, F(1, 70) = 188.72, p = .001, ηp2 = .73. Faster RT were observed for the congruent color words compared to the incongruent color words (M = 0.91s, SD = 0.22 vs. M = 1.05, SD = 0.27; d = 0.58; see Figure 2).

Figure 2.

Figure 2.

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Mean RT and SEs for congruent and incongruent conditions.

The effect of time was also significant, Wilk’s λ = .509, F(1,70) = 67.52, p = .001, ηp2 = .49, with faster RT observed after the fitness test compared to RT before the fitness test (M = 0.92s, SD = 0.25 vs. M = 1.04s, SD = 0.23; d = 0.50). This effect is displayed in Figure 3.

Figure 3.

Figure 3.

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Mean RT and SEs on the Stroop words before and after exercise.

Finally, the congruency by time interaction resulted in a non-significant effect, Wilk’s λ = .979, F(1, 68) = 1.48, p = .23, ηp2 = .02. Descriptively however, the difference in average RT between the congruent and incongruent conditions was smaller after the fitness test compared to difference in RT before the fitness test (M = 0.13s, SD = .09 vs. M = 0.16s, SD = .11; d = 0.30). This difference in average RT between both conditions is shown in Figure 4.

Figure 4.

Figure 4.

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Mean RTs and SEs before and after exercise for both congruent and incongruent conditions.

The analysis pertaining to the response accuracy revealed a main effect for congruency, Wilk’s λ = .675, F(1, 70) = 33.63, p = .001, ηp2 = .32. This effect is presented in Figure 5. Participants performed fewer errors in the congruent condition than in the incongruent condition (M = 0.37, SD = 0.69 vs. M = 1.07, SD = 1.41; d = 0.66). However, neither the effect of time, Wilk’s λ = .991, F(1, 70) = 0.64, p = .42, ηp2 = .01, nor the time by congruency interaction, Wilk’s λ = .990, F(1, 70) = 0.72, p = .40, ηp2 = .01, were significant.

Figure 5.

Figure 5.

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Means and SEs for number of errors for both congruent and incongruent conditions.

Discussion

The purpose of this study was to examine the effects of a single bout of aerobic exercise on driving performance and executive functions in healthy older adults. Extant research into the effects of exercise on executive functions among older adults has focused on chronic exercise but has not investigated acute exercise effects on real-life activities (Chu et al., 2015; Etnier, 2012). The present study’s findings revealed that acute aerobic exercise improved executive functioning but failed to affect driving performance in healthy older adults.

Effects of Acute Exercise on Driving Performance

Three scenarios were used to assess driving performance in an effort to capture the complexity of the driving task. The first scenario involved decision-making and specifically whether it was safe to make a left turn through oncoming traffic at an intersection. Results revealed non-significant differences between the exercise and video conditions for the average distance from oncoming vehicles when deciding to turn. Acute exercise did not appear to affect decision-making when turning left, and more specifically, failed to affect the estimation of time to contact an oncoming vehicle. Estimating how much time is available before an oncoming vehicle arrives at an intersection relies mainly on visual attention and anticipation, and the present findings indicate that these basic cognitive skills were not affected by the exercise. In their meta-analysis, Chang et al. (2012) reported that acute exercise affects information processing tasks to a smaller effect (d = 0.09) compared to executive functions tasks (d = 0.20). However, Chang and colleagues also found that decision-making tasks were affected by acute exercise (d = 0.30), but effect of this magnitude was not observed in the present study. It is possible that studies reviewed by Chang et al. used more laboratory-based decision-making tasks or adopted a different design (e.g., other types of exercise with other populations). Recently, O’Brien, Ottoboni, Tessari, and Setti (2017) investigated the effect of a single bout of open-skill versus close-skill exercise on visual-auditory perception and immediate memory in healthy older adults. The authors found that both exercise types benefited memory while only open skill exercise improved audio-visual perception. The present study’s results are in line with O’Brien et al.’s findings as this first scenario involved visual perception skills and the exercise used (i.e., cycling on a stationary bike) is a closed skill exercise.

Regarding the second driving scenario, participants in both conditions conducted a similar number of driving errors (i.e., driving through a yellow or red light), and no difference was observed in terms of minimum distance from the pedestrian crossing the intersection. Several explanations can be put forward to account for these results. First, this scenario involved a variety of cognitive skills (i.e., switching attention between dashboard and road, observing traffic light and pedestrian, and operating and steering the vehicle), which rely predominantly on executive functions (Karthaus & Falkenstein, 2016). A motor component was also involved in this scenario as participants had to operate the brake and gas pedal to stay within the speed limit while steering the vehicle. Nonetheless, the present results suggest that exercise did not affect executive functions involved in this driving situation. Participants in our study were experienced drivers currently driving an average of 143 miles per week. As experienced drivers, driving may have required less cognitive resources from executive functions, and it is possible that the difficulty of this scenario did not allow room for improvement (i.e., ceiling effect).

Non-significant results were also found for the last scenario. The exercise and the video conditions resulted in similar RT to the braking events or their ability to maintain a constant distance from the leading vehicle. This scenario required participants to maintain sustained attention on the preceding vehicle and sustained attention has been reported as a component of executive functions (Budde et al., 2012). A study with healthy older adults reported that 20 min moderate intensity exercise resulted in increased performance on sustained and selective attention tasks (Peiffer, Darby, Fullenkamp, & Morgan, 2015). This is in contrast with the present results, and it might be explained by the motor component involved. Peiffer and colleagues used computerized tasks where participants were required to move fingers only, whereas participants in this study used arms and feet to operate the vehicle. Hence, it might be possible that a larger motor component associated with the attention task (such as driving in this scenario) mitigates the positive effects of acute aerobic exercise on attention.

There is some evidence suggesting that exercise-induced arousal had a positive influence on the motor component of RT, but limited impact on central components (Chang, Etnier, & Barella, 2009). Results from this last scenario did not reflect such an effect on the motor component, but it is possible that the arousal level was not high enough due to the elapsed time from the end of the exercise to the start of this scenario. This final scenario started about 10–12 min after the end of the exercise bout, and cognitive benefits of acute exercise have been reported to disappear 15 min after exercise completion (Chang et al., 2012), which may account for the non-significant results obtained here.

Effects of Acute Exercise on Executive Functions

In an effort to replicate previous findings for the effects of acute aerobic exercise on executive functions in older adults, the fitness test in this study was used as an exercise and execution functions were assessed before and after the fitness test. The present results support the hypothesis that acute aerobic exercise improves executive functions, specifically mental flexibility and inhibition. The two executive functions tests selected for this study were the most commonly used tasks in the exercise psychology literature (Etnier, 2012; Etnier & Chang, 2009), and the present results replicate the current findings with healthy older adults (Barella et al., 2010; Chu et al., 2015; Kimura & Hozumi, 2012). Overall, participants performed better on the TMT and the Stroop test after exercise, compared to baseline. Specifically, participants completed the TMT faster after the exercise bout, suggesting a better ability to switch attention between tasks. A learning effect is very unlikely here as participants completed two parallel versions of the test and were given the opportunity to practice both parts of the TMT test. Also, the size of the effect is similar to Kimura and Hozumi (2012) who reported an increased mental flexibility performance after an acute aerobic exercise in healthy older adults.

To gain a more comprehensive evaluation of executive functioning, the Stroop test was also selected. The large effect size (d = 0.50) observed in the present study is consistent with previous findings in healthy older adults (Chu et al., 2015), and supports the immediate effects of aerobic exercise on executive functions such as inhibition. The interaction effect of time by congruence supports the selective effect of exercise on executive functions, which has been reported in both acute and chronic exercise studies (Chang et al., 2012; Kramer et al., 1999). Hence, the sample used in this study was affected by exercise in a similar manner that in previous investigations using an experimental design, suggesting a limited (if any) learning effect in the present study. It should be noted that participants expected a moderate effect of exercise on executive functions, which could account for some variance in cognitive performance. The non-significant findings observed in the driving task highlight the difficulty of transferring results from laboratory tasks to more ecologically valid tasks. This issue of transfer is discussed next.

Limitations and Future Research Directions

The present study supports the effects of acute aerobic exercise on executive functions as measured by laboratory tasks but failed to support a similar effect on more ecologically valid tasks such as driving. The investigation of more ecologically valid cognitive tasks is difficult as these tasks are typically less “pure” (i.e., involve a variety of skills) than more controlled laboratory tasks. This variety of skills could increase variability in performance, which in turn may have reduced statistical power. To address this issue of reduced power and potential dropout, future studies should consider recruiting a larger sample size. In a similar vein, the driving performance experiment utilized a between subject design, which was less powerful than the pre vs post comparison in the fitness test where significant differences were noted for Stroop and TMT scores. However, the between subject design allowed utilizing a measure of the difference in expectations between conditions, which was not possible for the pre-post design with the executive function tests. Finally, the fact that participants were experienced drivers could have led to a ceiling effect, thus limiting any potential effects of exercise on driving. To address these limitations, researchers in future investigations must select challenging ecologically valid tasks to ensure that participants have some room for improvement, or select less experienced drivers, to enable more definite conclusions. Different tasks with different motor components could also be compared to investigate how and to what extent acute aerobic exercise affects everyday life functioning.

This study provides additional evidence for the acute effects of aerobic exercise on executive functions and opens the door to a set of studies investigating cognitions in real life. With the ever-growing older population, it is of utmost importance to uncover strategies that prolong autonomy and well-being among older adults. Exercise may be the most efficient and cost-effective strategy to maintain physical health and prevent cognitive decline. As such, exercise is a particularly appropriate strategy for the older population.

What Does This Article Add?

The current study addresses two main limitations of the exercise-cognition literature. First, cognitive performance is almost exclusively assessed via well-controlled laboratory tasks. Second, older adults have received limited attention among studies investigating the effects of a single bout of aerobic exercise. The present results suggest that acute aerobic exercise improves executive functions as measured by the Stroop and TMT, but those effects do not transfer to driving performance on a driving simulator. A possible explanation for this lack of transfer is that driving involves a variety of skills that could have mitigated the impact of exercise on driving performance. Driving involves the contribution of motor, visual, attention, and higher-order cognitive processing domains. Because of the selective effect of exercise on executive functions, it is possible that performance on lower-order cognitive skills (i.e., motor and visual domains) confounded the performance in higher-order processes (i.e., attention and executive functions). The investigation of the effects of exercise on ecologically valid tasks requires a careful consideration of the different cognitive domains involved in those tasks. Specifically, attention should be paid to how much lower-order vs. higher-order cognitive skills are required to perform those tasks. Additionally, researchers should consider the role of the motor component, and to what extend this motor component interacts with cognitive performance. Finally, we also found differences in expectations between the exercise and control group, highlighting the importance of systematically assessing expectations in future studies.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Nelson Roque was supported by National Institute on Aging Grant T32 AG049676 to The Pennsylvania State University.

IRB Approval: HSC # 2017.20838

Footnotes

Declaration of interest: none.

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