Acute Physical Activity on Cognitive
Function: A Heart Rate Variability
Examination
Nicholas P. Murray & Carmen
Russoniello
Applied Psychophysiology and
Biofeedback
In association with the Association
for Applied Psychophysiology and
Biofeedback
ISSN 1090-0586
Volume 37
Number 4
Appl Psychophysiol Biofeedback (2012)
37:219-227
DOI 10.1007/s10484-012-9196-z
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Appl Psychophysiol Biofeedback (2012) 37:219–227
DOI 10.1007/s10484-012-9196-z
Acute Physical Activity on Cognitive Function: A Heart Rate
Variability Examination
Nicholas P. Murray • Carmen Russoniello
Published online: 29 April 2012
Springer Science+Business Media, LLC 2012
Abstract The purpose of this study was to examine the
relationship of physical activity and cognitive function (as
determined by reaction time and the trail-making test) in
active versus non-active participants. Participants were
divided into one of four groups: active experimental, active
control, non-active experimental and non-active control.
All groups completed a complex cognitive task (the trailmaking test) as well as a set of reaction time tasks both
before and after the experimental session. The experimental groups completed a 30-min exercise session while
the control groups monitored the physical activity of the
experimental group. In addition to the measures of cognitive function, heart rate variability was recorded during the
pre- and post-tests. There was significant cognitive performance improvement in tasks with a higher cognitive and
perceptual component. Heart rate variability data indicated
that a moderate level of arousal based on sympathetic
nervous system activity post exercise was associated with
an increase in cognitive performance. The findings are
discussed in light of the inverted-U hypothesis.
Keywords Cognition Acute exercise Executive control Heart Rate Variability HRV
Introduction
During the last several decades, there have been nearly 200
articles (see Brisswalter et al. 2002; Colcombe and Kramer
2003; Luft et al. 2009; Etnier et al. 2006; Etnier et al. 1997;
N. P. Murray (&) C. Russoniello
Department of Exercise and Sport Sciences, East Carolina
University, 166 Minges, Greenville, NC 27858, USA
e-mail: [email protected]
McMorris and Graydon 1997; or Tomporowski 2003 for
reviews) assessing the effects of exercise on cognitive
function. However, even with considerable evidence that
acute exercise improves cognitive performance, there is
still a lack of consensus of the precise relationship. For
some studies in which physically fit participants were
compared to sedentary ones, there was an increase following an acute bout of exercise in perceptual speed, reasoning and working memory for the fit participants (e.g.,
Arcelin et al. 1998; Hancock and McNaughton 1986).
Other studies (e.g., Bard and Fleury 1978; Cote et al. 1992;
Luft et al. 2009; Magnie et al. 2000) found no significant
differences between fit groups and unfit groups on cognitive tests.
Many authors (Etnier et al. 1997; Etnier et al. 2006; Hall
et al. 2001; Tomporowski and Ellis 1986) have described
methodological problems that could explain these divergent findings. For instance, there are large differences in
duration and intensity of exercise as well as differences in
the type of cognitive tasks and the timing of these tasks
(relative to the exercise bout) that contribute to the difficulty in comparing results. For example, if the exercise
session was too short or too long, it may lead to poorer
performance on a cognitive task due to a participant
reaching an over or under-aroused state (Chmura et al.
1998). In addition, many of the exercise studies that do not
support changes in cognitive function do so by requiring
trained and un-trained participants to work at the same
predetermined V02max level for a given period of time (e.g.
Chmura et al. 1998; Fery et al. 1997). While controlling for
many potential confounding factors, the methodology of
requiring participants to exercise at a predetermined
V02max level may result in a higher or lower activation of
the sympathetic nervous system (especially for untrained
participants) than might be expected and may affect
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performance on a cognitive task. Finally, Davranche and
Audiffren (2004) as well as McMorris and Graydon (1997)
have suggested if the task requires complex decisionmaking then improvements are often found following
exercise whereas tasks with only perceptual components or
requiring little attentional demand do not always demonstrate improvements.
Despite extensive research, the knowledge about exact
mechanisms underlying exercise and cognitive function
relationship is limited (Hillman et al. 2003). Although
several conceptual frameworks have been advanced
(Davranche and Audiffren 2004; Etnier et al. 2006;
McMorris and Graydon 1997; Grego et al. 2005; Tomporowski 2003), overall, there is growing support for the
notion that a moderate level of exercise leads to cognitive
improvement (e.g. Brisswalter et al. 1997; Chmura et al.
1998; Fery et al. 1997). However, there is a paucity of
research that assesses arousal level following exercise to
determine the relationship of autonomic nervous system
response and cognitive performance. In previous research,
there was no attempt made to measure the individual’s
arousal state following exercise. The assumption is that the
exercise will lead to improvements in cognition irrespective of the many of the methodological problems described
above. Recent technological advances in physiological
measurement allow for a better understanding of the relationship between acute exercise on cognitive function as
well as assessing the role of arousal in the acute exercisecognition relationship.
As seen recently in physiology and neuroscience literature (see Aubert et al. 2003 or Thayer et al. 2009 for a
review), one measure of autonomic nervous system functioning that has promise is the assessment of heart rate
variability (HRV). HRV provides an index through different frequency components (via a power spectral analysis) of sympathetic and parasympathetic activity. Although
there is some debate within current research, the high
frequency (HF) component of HRV (.15–.4 Hz) corresponds to the respiratory sinus arrhythmia; thus, it is
modulated by the parasympathetic nervous system
(Berntson et al. 1997). The low frequency (LF) component
of HRV (.04–.15 Hz) provides an index of sympathetic
nervous system modulation, particularly when expressed in
normalized units (Berntson et al. 1997; Malfatto et al.
1998; Malliani et al. 1991; Montano et al. 1994; Pagani
et al. 1986; Zhong et al. 2005). Finally, the very low frequency of HRV (VLF; below .04 Hz) reflects several factors including thermal regulation (Berntson et al. 1997),
and is considered by some to be an indicator of sympathetic
activation (Metelka et al. 1999). However, the exact
mechanism of VLF is still unclear.
HRV is an indicator of autonomic nervous system
function that reflects the balance between the sympathetic
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and parasympathetic nervous inputs into the heart. Furthermore, recent research suggests that changes in HRV
have been associated with activity in the prefrontal cortex
as well as cognitive regulation (Thayer et al. 2009). Generally, HRV decreases as arousal increases (Zhong et al.
2005). This is seen as a shift from a balanced HRV in
which relative power is equal across LF and HF to an
imbalance in which the relative power is higher in the LF
band (i.e. the heart rate becomes less variable). Additionally, when LF and HF power are expressed as normalized
units (n.u.), it is possible to demonstrate the relative contribution of sympathetic and parasympathetic modulation
into the heart (Mukherjee et al. 2011). It is expected under
high arousal LF n.u. would increase while HF n.u. would
decrease and vice versa. The normative values of LF and
HF are percentage of the total power thereby providing the
relative contribution of each frequency band. Therefore,
HRV allows for the calculation of multiple levels of
arousal (i.e. low, moderate, or high). By analyzing cognitive performance as a function of arousal, it is then possible
to provide evidence to demonstrate the influence of acute
exercise on cognitive function.
For example, Luft et al. (2009) examined HRV and
cognition in high-level track athletes. Luft et al. utilized an
incremental treadmill test (a max test) until the participants
reached exhaustion. They found no overall difference in
cognitive performance pre and post. As is often the case in
this line of research, the participants were required to
perform a max test. It is our contention that self-determined
exercise intensity will illicit greater cognitive improvement
due to physical activity than an experimenter required max
test.
Thus, the purpose of this investigation was to examine
cognitive performance following a self-paced acute exercise bout utilizing a simple, 2-choice and 4-choice reaction
time task, and a complex cognitive task (the trail-making
test) for both active and inactive participants. To control
for the potential confounds of fitness level, we felt it was
important to include both active and inactive participants.
The goal was to address the relationship between acute
exercise and cognitive function. The simple, 2-choice, and
4-choice reaction time task was utilized to assess the
information processing requirements of the task and to
account relationship between task complexity and exercise.
Furthermore, the trail-making test was chosen as it provides a general indicator of cognitive function including
cognitive flexibility, visual spatial scanning, visual spatial
ability and visual-motor coordination, and is utilized to
assess frontal lobe function (Moll et al. 2002; Stuss et al.
2001). The trail-making test is widely employed as measure of cerebral dysfunction (Stuss et al. 2001). The trailmaking test (version B used in this study) requires the
participant to link a set of numbers (1–13) and a set
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of letters alternating in ascending order (1-A-2-B…).
Recently, Zakzanis et al. (2005), utilizing fMRI, reliability
found increased activity in prefrontal and frontal regions
involved in rapid action, cognitive shifts, and areas of the
motor cortex while completing the trail-making test. Furthermore, Chaytor et al. (2006) demonstrated ecological
validity for the trail-making test when correlated with tests
of everyday cognitive function activities.
It was hypothesized that both active and inactive participants in the exercise group who achieved moderate
physiological arousal levels (as determined by their HRV)
would have a decrease in reaction time from pre to post
exercise as well as a decrease in time to completion of the
trail-making test. It was also expected that the more complex tasks (i.e., 4-choice RT and the Trail-making test)
would demonstrate greater performance improvement following exercise when compared to the simpler tasks
(simple and 2-choice RT tasks).
Since normalized LF represents relative contribution of
the sympathetic nervous system into the heart (Berntson
et al. 1997; Malfatto et al. 1998; Malliani et al. 1991;
Montano et al. 1994; Pagani et al. 1986; Zhong, et al.
2005), it was also hypothesized that the best performance
for the exercise groups would occur around 40–60 % of the
normalized lower frequency (LF n.u.) band of HRV. The
impetus for the previous hypothesis is that the participants
by choosing their own exercise intensity and who achieved
a moderate physiological arousal (as indicated by LF n.u.)
would have improved cognitive function. To facilitate this,
the participants were given instructions to work at a high,
but comfortable level.
Method
Participants
One hundred and twenty volunteers from a university in the
southeastern United States who indicated their willingness
to participate in this study were recruited to be a in this
study. Participants received extra course credit for participation. The participants’ ages ranged from 18 to 25 years.
There were sixty female (mean age 21.05; SD = 2.87) and
sixty male participants (mean age 20.67; SD = 2.80).
Level of physical activity. The 30-day Physical Activity
Recall Questionnaire (Mahar Dawson and Estes 2002) was
used to determine the level of physical activity. The
physical activity questionnaire consisted of a Likert type
scale ranging from zero to seven, where the participants
would identify their activity level for the past 30 days. The
seven choices consisted of descriptors, which allowed the
participants to indicate their activity level for the last
30-days [psychometric properties are demonstrated in
221
(Baumgartner et al. 2003)]. In general, scores of zero and
one indicate that the person does not participate in regular
activity. Participants who score two, three, or four participate in some moderate activity whereas those who score
five or above participate in some vigorous exercise. The
participants’ labeled non-active had a mean score on the
30-day Physical Activity Recall Questionnaire of 2.3 while
the active participants had a mean score of 6.45.
Tasks, Instruments, and Objectives
Reaction Time (RT) Assessment
RT was measured using a standard PC computer with
software created by the authors to measure RT at three
different levels of uncertainty. Although there are many
factors that may affect RT data collection in this manner
(such as hardware, PC communications, and software
activity), our software program and methodology was
designed to reduce the impact of these issues. The software
was designed to achieve precision of one millisecond for
the times that it measures. Response times were recorded
using four keys (D, F, J, and K) on a typical keyboard, and
the software was designed to run continuous scans of these
keys. All other unnecessary computer processes and network connections were terminated prior to data collection.
The data our software produced includes stimulus presentation type, presentation time, key input time, and total RT.
Participants completed a total of thirty trials of all three
levels, which consisted of a simple RT, a 2-choice RT and
a 4-choice RT task, in random order. For all RT trials, a
warning signal was given 1 s prior to the illumination of
the RT stimuli with an inter-stimulus interval of 3 s. The
randomization of the three RT levels reduced anticipation
effects. In the simple RT, the participant would press a key
on the keyboard every time a circle was illuminated on the
computer screen. In the 2-choice or 4-choice RT tasks,
there was the possibility of either one of two circles or one
of four circles being illuminated and the participant was to
press the corresponding key on the keyboard. All times
were measured in milliseconds and each participant completed ten trials of each RT task for a total of 30 trials. Ten
trials for each RT task was an attempt to capture the
immediate response of the ANS on cognitive function due
to exercise.
Trail Making Test
Two versions of the Trail-making test B were utilized to
measure complex cognitive function. The test requires the
participant to connect a series of numbered and lettered
circles, alternating between the two sequences. Scores were
obtained based on time to completion. This test was
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originally developed for the Army Individual Test Battery
(1944) and it has been extensively used as well as widely
accepted as an indicator of cognitive function. The psychometric properties of the Trail-making test (all versions)
were shown to be reliable (.90) and valid by Franzen et al.
(1996).
Heart Rate Variability
HRV was recorded using a lightweight Heart Rate Variability monitor (Biocom pulse wave sensor M-2001;
Poulsbo, WA), which allows for the computation of the
instantaneous changes of heart rate and provides full
analysis of HRV. The Heart Rhythm Scanner (HRS) software (which is founded upon the mathematical procedures
suggested by the Task Force of the European Society of
Cardiology and the North American Society of Pacing and
Electrophysiology 1996) was used to analyze the data and
the following frequency domain parameters: VLF, LF, HF,
LF normalized units (n.u.), HF n.u., LF/HF ratio, and Total
Power (which is a sum of all frequency bands). LF n.u.
represents normalized low frequency that is the ratio
between absolute value of the LF power and the difference
between Total Power and VLF power (LF/(Total PowerVLF). Likewise, HF n.u. symbolizes normalized high frequency that is the ratio between absolute value of the HF
power and the difference between the Total Power and
VLF power. Normalized units are preferred as they account
for the relative contribution of the frequency band within
the total power as well as accounting for individual differences within the HRV. The LF/HF ratio is the ratio
between the Low Frequency and High Frequency bands.
Thus a score of 3/1 (LF/HF ratio) would indicate three
times greater sympathetic activation. Normalized scores for
HRV are utilized in the data analysis to account for variations in means and scaling of the HRV. In addition, more
recently normative values or in terms of LF/HF ratio have
been proposed as measures of sympathovagal balance
(Malliani 2005; Malliani and Montano 2002); even though
this was previously challenged in the 1990s. With recent
improvements in data collection and methodology, these
indicators are often employed (e.g., Ergün et al. 2008).
The Biocom pulse wave sensor utilizes photoplethysmography (PPG) to acquire the HR data. PPG measures
pulse volume or phasic changes that are related to beat
variations in the force of blood flow. These beat-to-beat
changes in peripheral blood flow reflect the heart’s interbeat intervals similar to ECG. PPG provides summary
information reflecting both cardiac and blood vessel components, and recent research shown PPG to be an accurate
measure of cardiac function when compared to electrocardiography (Russoniello et al. 2003). In addition, HRS
software allows for the continuous recording of the heart
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wave as well as the removal of outliers due to noise. The
system accounted for respiration rates by ensuring that
respiration frequency did not influence the calculated frequency bands. The signal correction was completed following the collection of each data set. HRS software
calculates HRV using a hybrid technique that combines
time analysis (Heart rate, R Wave to R wave (RR) interval,
SDNN, RMS-SD) with frequency or a spectral analysis;
total power, very low frequency, low frequency, high frequency, and high frequency/low frequency ratio.
Exercise Protocol
Stationary bicycles with Polaris type heart rate monitors
were used to perform the acute exercise bout. Participants
were to pedal the stationary bike for 30 min at a self-paced
exercise level. For the self-paced exercise, the participants
self-select or control their intensity levels. Participants
were instructed to maintain a level of exercise intensity
such that they worked at a high, but comfortable level.
During the exercise regimens, participants exercised at a
mean HR intensity of 75.46 % (SD = 8.26) of the predicted maximum heart rate with a range of 65–80 %.
Procedure
The participants were randomly assigned to either an
exercise group or a control group and then these groups
were further subdivided contingent on whether they were
physically active or not. The groups were identified as the
(1) Active-exercise group (AE), (2) the Non-active exercise
group (NE), (3) the Active Control group (AC), and the
Non-active control group (NC). Before starting the experiment, each participant completed an informed consent as
well as the 30-day Physical Activity Recall Questionnaire
for placement in the active or non-active category. Participants were scheduled for the experiment in such a way that
at least two were present at one time. Cognitive function
was measured immediately pre and post exercise. During
pre and post testing, the HRV monitor was then comfortably affixed to the left ear of the participant.
These tests were counter balanced by having half the
participants take the RT test first, while the other half took
the Trail-making test first. In addition the two versions of
the Trail-making test were also counter balanced with half
of the participants receiving version 1 on the pre-test with
version 2 on the post and vice versa. All of the participants
were given a practice trial of each RT test (i.e., simple,
2-choice, 4-choice RT) so they could familiarize themselves with the testing process. The participants completed
the test in separate cubicles in which they had no knowledge of their partner’s performance.
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The participants in the experimental condition were
required to pedal the stationary bike for 30 min. Their
partners (the control condition) were asked to monitor the
participants’ heart rate level using a Polaris heart rate
monitor, and make note of dramatic changes in heart rate.
They were also asked to assist in keeping the exerciser
motivated throughout the 30-min exercise bout. Their
instructions were: ‘‘please help us in monitoring the
physical activity of the individual on the bike. Your job
will be to monitor their heart rate using this heart rate
monitor [participant was shown monitor] and provide your
partner encouragement to exercise at a high, but comfortable pace. Please keep in mind it is your job to keep them
motivated’’. The control group only provided encouragement and did not provide feedback about their current heart
rate. The goal was to keep the control group mentally
active while the exercise group completed their bout. Once
the 30-min time limit was complete, the partners moved
immediately back to the original testing location where
their HRV was recorded post exercise. Immediately following HRV recording, the participants took a posttest for
reaction time and cognitive function following the same
testing procedure used for the pretest.
g2 = .24) whereas the Condition 9 Activity (F1, 116 =
.668) and Condition 9 Actvitity 9 Time (F1, 116 = .156)
interactions produced non-significant results.
The results demonstrated that Exercise (Active and Nonactive) groups were significantly different from the Control
groups (Active and Non-active; see Table 1). The Exercise
group (experimental condition) displayed faster trail making times than the non-active control groups. In addition,
Trail-Making performance improved from Trial 1
(M = 69.917, SD = 10.64) to Trial 2 (M = 60.375,
SD = 11.82).
Tukey’s post hoc tests for the Condition 9 Time interaction demonstrated that groups were similar for Time 1,
but significantly differed during Time 2. Furthermore, a
significant reduction of time to completion from Time 1 to
Time 2 was evident for the Active Exercise group
(M = 67.77 s, SD = 8.51 to M = 53.90 s, SD = 11.81)
and the Non-active Exercise group (M = 69.60 s, SD =
6.72 to M = 53.53 s, SD = 2.58). There was no significant
change from Time 1 to Time 2 for the Active control group
and the Non-active Control group.
Statistical Analysis
The ANOVA for simple RT revealed significant main
effects for Activity (F1, 116 = 8.26, p \ .01, g2 = .06), for
Time (F1, 116 = 36.923, p \ .001, g2 = .24) as well as
significant interactions for Condition 9 Time (F1, 116 =
5.96, p \ .05, g2 = .10) and Condition 9 Activity (F1, 116 =
11.53, p \ .01, g2 = .12). Furthermore, a significant Condition 9 Activity 9 Time interaction (F1, 116 = 19.58,
p \ .001, g2 = .14) was found.
Tukey’s Post Hoc test revealed that, while all groups had
a reduction from Time 1 to Time 2, the Active Exercise
group showed a significant decrease in simple RT compared
to the Control groups and the Non-active Exercise group
(see Table 2). In addition, the univariate analysis of
2-Choice RT demonstrated a significant Activity main
effect, F1, 116 = 8.51, p \ .01, g2 = .10, a significant main
effect for Time, F1, 116 = 31.01, p \ .001, g2 = .21 and
more importantly a significant Condition 9 Time interaction, F1, 116 = 5.51, p \ .05, g2 = .05. Neither the
Condition 9 Activity (F1, 116 = .196) nor the Condition 9 Activity 9 Time interaction (F1, 116 = .658) were
To test the hypotheses of interest, alpha was set at p = .05
as the critical level of significance. For all analyses, a 2
(Condition: control or experimental) 9 2 (Activity: active
or inactive) 9 2 (Time) mixed model ANOVA with
repeated measures on the last factor was utilized for the
Trial-making test and the reaction time data. Tukey’s HSD
post hoc analysis was used when necessary to evaluate
significant interactions. A separate hierarchical regression
analysis for each group was utilized to determine the
associations between LFn.u. and trail-making test and
reaction time. The predictor variable [LF(n.u.)] was entered
initially, followed by the moderator(s) (trail-making test or
reaction time task), the interaction, and then the quadratic
equation. At each step, the significance in explained variance for the dependent variable(s) was assessed to determine whether it was included in the equation.
Reaction Time Data
Results
Trail Making Test
The ANOVA indicated main effects for Condition
(F1, 116 = 24.42, p \ .001, g2 = .18), Activity (F1, 116 = 4.42,
p \ .05, g2 = .10), and Time (F1, 116 = 110.580, p \ .001,
g2 = .24). In addition, data revealed a significant interaction for Condition 9 Time (F1, 116 = 35.76, p \ .001,
Table 1 Descriptive statistics for the trail-making test (SD)
Condition
Time 1
Time 2
ActCon
67.96 (9.84)
64.23 (10.54)
ActEx
67.76 (8.51)
53.9 (11.81)
NonCon
74.33 (14.87)
69.83 (10.79)
NonEx
69.6 (6.71)
53.53 (2.58)
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Table 2 Reaction time descriptive statistics (ms)
Group
Simple RT
2-Choice
4-Choice
Pre
Post
Pre
Post
ActCon
258.33 (40.68)
254.93 (46.22)
335.86 (38.42)
318 (38.42)
383.6 (40.45)
371.5 (51.59)
ActEx
256.43 (29.21)
205.9 (34.97)
332.46 (33.16)
278.96 (37.39)
380.26 (43.74)
332.56 (33.49)
NonCon
264.36 (49.32)
264.1 (60.89)
334 (40.47)
329 (26.07)
402.53 (63.29)
378.13 (44.18)
NonEx
272.03 (38.54)
242.83 (65.23)
355.4 (60.23)
319.6 (40.72)
393.96 (54.81)
355 (15.09)
Heart Rate Variability Data
HRV measures are presented in Table 3. The regression
analysis for the HRV normalized units was completed for
the both conditions separately; however, only the exercise
groups (both active and inactive) revealed a significant
quadratic trend for low frequency band (i.e. arousal) and
the post trail making task, F change2, 59 = 3.87 p \ .05.
LF accounted for 34.6 % of the variance in the
Post
performance of the Trail-making test. As illustrated in
Fig. 1, optimal performance on the trail-making task is
predicted to be between 40 and 60 % of maximal arousal.
Likewise, this trend was also found in the exercise groups
for the 4-Choice Reaction time task, F change2, 59 = 5.475
p \ .01 in which LF account for 40.1 % variance (See
Fig. 2). This was not case for the simple RT or the
2-Choice RT tasks (ps [ .5 and ps [ .10, respectively).
Discussion
The purpose of the present study was to determine whether
acute exercise would demonstrate changes in cognitive
function among active and inactive participants. The data
supported the hypothesis that achieving an appropriate
level of arousal as indicated by HRV enables cognitive
Trail Making Performance (sec)
found to be significant. The post hoc test exhibited a significant reduction of 2-Choice RT from Pre to Post for all
groups.
Finally, for the 4-Choice RT task the univariate analysis
indicated a Condition main effect, F1, 116 = 74.78,
p \ .001, g2 = .39, a significant main effect for Time, F1,
2
116 = 168.95, p \ .001, g = .59, a significant interaction
for Condition 9 Time (F1, 116 = 90.43, p \ .001, g2 =
.44), Activity 9 Time (F1, 116 = 53.04, p \ .001, g2 =
.31) and Condition 9 Activity (F1, 116 = 28.02, p \ .001,
g2 = .19) whereas the main effect for Activity was nonsignificant (F1, 116 = .982). However, more informative
was the significant three-way Condition 9 Activity 9
Time interaction (F1, 116 = 37.22, p \ .001, g2 = .24).
Tukey’s post hoc test revealed greater decrease in RT for
both the Non-Active Exercise Group and the Active
Exercise Group. Moreover, the Active Exercise Group RT
was significantly faster than the Non-Active Exercise
Group and the Control Groups in the Time two 4-Choice
RT Task. Demonstrated was an overall significant reduction in RT for the Active Exercise Group for all reaction
time task as well as decrease in reaction time for the Nonactive Exercise Group during the 4-choice RT task.
Pre
0
20
40
60
80
100
120
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
Low Frequency (n.u.)
Fig. 1 Trail-making performance as a function of low frequency
normalized units for both exercise groups. The Y-axis is inverted to
demonstrate the inverted-U relationship
Table 3 Mean heart rate variability statistics at baseline and post exercise trial (SD)
Group
VLF-power (ln)
Pre
LF-power (ln)
HF-power (ln)
Post
Pre
Post
Pre
Post
ActCon
8.9 (3.29)
8.75 (1.18)
6.06 (2.88)
6.04 (2.59)
5.75 (3.24)
5.43 (2.20)
ActEx
9.2 (1.93)
9.07 (3.05)
5.83 (2.76)
6.07 (2.59)
5.83 (3.25)
5.49 (2.15)
NonCon
8.97 (3.09)
8.34 (3.22)
6.00 (3.02)
5.93 (1.92)
5.94 (3.24)
5.31 (1.61)
NonEx
8.85 (2.54)
9.70 (3.23)
5.88 (2.82)
6.25 (2.60)
5.49 (2.37)
5.46 (2.15)
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200
4-Choice RT (ms)
250
300
350
400
450
500
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
Low Frequency (n.u.)
Fig. 2 4-Choice RT as a function of low frequency normalized units.
The Y-axis is inverted to demonstrate the inverted-U relationship
function improvement. This was demonstrated by a
decrease in time to completion for the Trail-making task
pre and post exercise for both exercise groups. In addition,
the exercise groups demonstrated that a moderate arousal
level (as determined by HRV) was associated with
increased performance on the Trail-making test. The benefits of utilizing HRV in this manner allow for the multiple
levels of arousal to be analyzed (as opposed to a general
case of heart rate).
The RT data were less clear in that there were significant
decreases pre to post for all groups. The active-exercise
group had a greater reduction in the post trials for simple
and 4-choice RT conditions whereas the non-active exercise group had greater reduction for the four-choice reaction time task only as compared to the control groups. This
finding is consistent with other RT studies that compare
simple vs choice RT (Brisswalter et al. 1997). The general
finding is that both groups saw improvement on tasks with
less complexity, but a larger impact of exercise is found
when the tasks are more perceptually demanding as seen in
the 4-choice RT task.
One plausible explanation and a conceptual framework
to describe acute exercise and cognitive function relationship is the inverted-U hypothesis (e.g., Brisswalter et al.
1995; Grego et al. 2004; Tomporowski 2003; Yerkes and
Dodson 1908). The inverted-U hypothesis proposes that
simple tasks can tolerate a larger variation of arousal levels. Thus, participants who are experiencing lower arousal
will complete the simple reaction time task equally well as
someone who is experiencing high arousal. However, as
was demonstrated in the regression analyses, when there is
a higher cognitive component as is found the trail-making
test or higher perceptual component as is found in the
4-choice RT task then an extremely low or high level of
arousal resulted in a reduced performance.
Since the inverted-U relationship was observed in the
trail-making test and the 4-choice RT, it could be
hypothesized that cognitive improvements are associated
with frontal lobe functioning. Recent neuroimaging studies
(Moll et al. 2002; Stuss et al. 2001) have demonstrated that
this is clearly the case with the trail-making test. This
study, as well as others (e.g., Hillman et al. 2003; Magnie
et al. 2000; Nakamura et al. 1999), demonstrates that acute
bouts of physical activity facilitate cognitive function post
exercise. Thus, there is a developing picture that moderate
physical activity is associated with a higher cognitive
component tasks. Future research should determine whether the improvements of cognitive task with frontal lobe
demand following exercise are associated with the neural
priming (the preparedness of the brain for cognitive and/or
physical activities) of frontal and prefrontal areas that may
occur during exercise. It would then follow that the neural
priming is best achieved following moderate levels of
exercise.
Final note, the measurement of HRV is well established
as a reliable indicator of the ANS in other areas where
arousal and sympathetic nervous system measurement are
important considerations such as cognitive function
(Thayer et al. 2009), mental workload (Backs 1998; De Vito
et al. 2002), exercise (Aubert et al. 2003), emotion (Kim
et al. 2004), and chronic heart failure (Andreas et al. 2003).
Evidence supports the use of HRV as an index of mental
workload, which could also provide a plausible explanation
to the results found in this study. That is the changes in HRV
could be potentially due to a change in the mental workload
dynamics of the tasks from pre to post. However, we believe
that this is unlikely the case as differences were found
between the experimental and control groups.
In conclusion, HRV data indicated that a moderate level
of arousal based on sympathetic nervous system activity
post exercise led to increased cognitive performance.
Furthermore, the results indicated that there is an invertedU relationship between exercise and cognitive performance, especially in tasks with a higher cognitive or
demanding perceptual component, and provide additional
support for the inverted-U hypothesis as an explanation for
cognitive function improvement following acute bouts of
exercise.
References
Andreas, S., Bingeli, C., Mohaesi, P., Luscher, T. F., & Noll, G.
(2003). Nasal oxygen and muscle sympathetic nerve activity in
heart failure. Chest, 123, 366–371.
Arcelin, R., Delignieres, D., & Brisswalter, J. (1998). Effects of
physical exercise duration on decision-making performance.
Journal of Human Movement Studies, 32, 123–140.
Army Individual Test Battery. (1944). Manual of directions and
scoring. Washington, DC: War Department, Adjutant General’s
Office.
123
Author's personal copy
226
Aubert, A. E., Seps, B., & Beckers, F. (2003). Heart rate variability in
athletes. Sports Medicine, 33, 889–919.
Backs, R. W. (1998). A comparison of factor analytical methods of
obtaining cardiovascular autonomic components for the assessment of mental workload. Ergonomics, 41, 733–745.
Bard, C., & Fleury, M. (1978). Influence of imposed metabolic
fatigue on visual capacity components. Perceptual and Motor
Skills, 47, 1283–1287.
Baumgartner, T. A., Jackson, A. S., Mahar, M. T., & Rowe, D. A.
(2003). Measurement for evaluation in physical education and
exercise Science. New York: McGraw Hill.
Berntson, G. O., Bigger, T., Eckberg, D. L., Grossman, P., Kaufmann,
P. G., Malik, M., et al. (1997). Heart rate variability: Origins,
methods, and interpretive caveats. Psychophysiology, 34,
623–648.
Brisswalter, J., Arcelin, R., Audiffren, M., & Delignieres, D. (1997).
Influence of physical exercise on simple reaction time: Effect of
physical fitness. Perceptual and Motor Skills, 85, 1019–1027.
Brisswalter, J. B., Collardeau, M., & Arcelin, R. (2002). Effects of
acute physical exercise characteristics on cognitive performance.
Sports Medicine, 32, 555–566.
Brisswalter, J., Durand, M., Delignieres, D., & Legros, P. (1995).
Optimal and non-optimal demand in a dual-task of pedaling and
simple reaction time: Effects on energy expenditure and
cognitive performance. Journal of Human Movement Studies,
29, 15–34.
Chaytor, N. S., Schmitter-Edgecombe, M., & Burr, R. (2006).
Improving the ecological validity of executive functioning tests:
Environmental demands and compensatory strategies. Archives
of Clinical Neuropsychology, 21, 217–227.
Chmura, J., Krysztofiak, H., Ziemba, A. W., Nazare, K., & KaciubaUcilko, H. (1998). Psychomotor performance during prolonged
exercise above and below the blood lactate threshold. European
Journal of Applied Physiology, 77, 77–80.
Colcombe, S. J., & Kramer, A. F. (2003). Fitness effects on the
cognitive function of older adults: A meta-analytic study.
Psychological Science, 14, 125–130.
Cote, J., Salmela, J. H., & Papthanasopoloulu, K. P. (1992). Effects of
progressive exercise on attentional focus. Perceptual and Motor
Skills, 75, 351–354.
Davranche, K., & Audiffren, M. (2004). Facilitating effects of
exercise on information processing. Journal of Sports Sciences,
22, 419–428.
De Vito, G., Galloway, S. D. R., Nimmo, M. A., Maas, P., &
McMurray, J. J. V. (2002). Effects of central sympathetic
inhibition on heart rate variability during steady-state exercise in
healthy humans. Clinical Physiology and Functional Imaging,
22, 32–38.
Ergün, U., Demirci, M., Nurlu, G., & Komürcü, F. (2008). Power
spectral analysis of heart rate variability: Normal values of
subjects over 60 years old. International Journal of Neuroscience, 118(8), 1165–1173.
Etnier, J. L., Nowell, P. M., Landers, D. M., & Sibley, B. A. (2006). A
metaregression to examine the relationship between aerobic
fitness and cognitive performance. Brain Research Reviews, 52,
119–130.
Etnier, J. L., Salazar, W., Landers, D. M., Petruzzello, S. J., Han, M.,
& Nowell, P. (1997). The influence of physical fitness and
exercise upon cognitive functioning: A meta-analysis. Journal of
Sport and Exercise Psychology, 19, 249–277.
Fery, Y. A., Ferry, A., Vom Hoffe, A., & Rieu, M. (1997). Effects of
physical exhaustion on cognitive functioning. Perceptual Motor
Skills, 84, 291–298.
Franzen, M. D., Paul, D., & Iverson, G. L. (1996). Reliability of
alternate forms of the trail-making test. The Clinical Neuropsychologist, 10, 125–129.
123
Appl Psychophysiol Biofeedback (2012) 37:219–227
Grego, F., Vallier, J., Collardeau, M., Bermon, S., Ferrari, P., Candito,
M., et al. (2004). Effects of long duration exercise on cognitive
function, blood glucose and counterregulatory hormones in male
cyclists. Neuroscience Letters, 364, 76–80.
Grego, F., Vallier, J. M., Collardeaul, M., Rousseu, C., Cremieux, J.,
& Brisswalter, J. (2005). Influence of exercise duration and
hydration status on cognitive function during prolonged cycling
exercise. International Journal of Sports Medicine, 26, 27–33.
Hall, C. D., Smith, A. L., & Keele, S. W. (2001). The impact of
aerobic activity on cognitive function in older adults: A new
synthesis based on the concept of executive control. European
Journal of Cognitive Psychology, 13, 279–300.
Hancock, S., & McNaughton, L. (1986). Effects of fatigue on ability
to process visual information by experienced orienteers. Perceptual and Motor Skills, 62, 491–498.
Hillman, C. H., Snook, E. M., & Jerome, G. J. (2003). Acute
cardiovascular exercise and executive control function: A P3
study. International Journal of Psychophysiology, 48, 307–314.
Kim, K. H., Bang, S. W., & Kim, S. R. (2004). Emotion recognition
system using short-term monitoring of physiology signals.
Medical & Biological Engineering & Computing, 42, 419–427.
Luft, C. B., Takase, E., & Darby, D. (2009). Heart rate variability and
cognitive function: Effects of physical effort. Biological Psychology, 82, 187–191.
Magnie, M. N., Bermon, S., Martin, F., Madany-Lounis, F., Suisse,
G., Muhammad, W., et al. (2000). P300, N400, aerobic fitness,
and maximal aerobic exercise. Psychophysiology, 37, 369–377.
Mahar, M., Dawson, N., & Estes, S. (2002). Lifetime physical activity
and fitness laboratory manuel (4th ed.). Englewood, CO: Morton
Publishing Company.
Malfatto, G., Facchini, M., Sala, L., Branzi, G., Bragato, R., &
Leonetti, G. (1998). Effects of cardiac rehabilitation and -blocker
therapy on heart rate variability after first acute myocardial
infarction. The American Journal of Cardiology, 81, 834–840.
Malliani, A. (2005). Heart rate variability: From bench to bedside.
European Journal of Internal Medicine, 16, 12–20.
Malliani, A., & Montano, N. (2002). Heart rate variability as a clinical
tool. Italian Heart Journal, 3, 439–445.
Malliani, A., Pagani, M., Lombardi, F., & Cerutti, S. (1991).
Cardiovascular neural regulation explored in the frequency
domain. Circulation, 84, 482–492.
McMorris, T., & Graydon, J. (1997). The effect of exercise on
cognitive performance in soccer-specific tests. Journal of Sports
Sciences, 15, 459–468.
Metelka, R., Weinbergova, O., Opavsky, J., Salinger, J., & Ostransky, J.
(1999). Short-term heart rate variability changes after exercise
training in subjects following myocardial infarction. Acta Universitatis Palackinanae Olomucensis Facultatis Medicae, 142, 79–82.
Moll, J., de Oliveira-Souza, R., Moll, F. T., Bramati, I. E., &
Andreiuolo, P. A. (2002). The cerebral correlates of set-shifting:
An fMRI study of the trail making test. Arquivos de NeuroPsiquiatria, 60, 900–905.
Montano, N., Ruscone, T. G., Porta, A., Lombardi, F., Pagani, M., &
Malliani, A. (1994). Power spectrum analysis of heart rate
variability to assess the changes in sympatho-vagal balance
during graded orthostatic tilt. Circulation, 90, 1826–1831.
Mukherjee, S., Yadav, R., Yung, I., Zajdel, D., & Oken, B. S. (2011).
Sensitivity to mental effort and test–retest reliability of heart rate
variability measures in healthy seniors. Clinical Neurophysiology, 122, 2059–2066.
Nakamura, Y., Nishimoto, K., Akamatu, M., Takahashi, M., &
Maruyama, A. (1999). The effect of jogging on P300 event
related potentials. Electromyography and Clinical Neurophysiology, 39, 71–74.
Pagani, M., Lombardi, F., Guzzetti, S., Rimoldi, O., Furlan, R.,
Pizzinelli, P., et al. (1986). Power spectral analysis of heart rate
Author's personal copy
Appl Psychophysiol Biofeedback (2012) 37:219–227
and arterial pressure variabilities as a marker of sympatho-vagal
interaction in man and conscious dog. Circulation Research, 59,
178–193.
Russoniello, C. V., Mahar, M. T. Rowe, D. A., Pougtachev, V., &
Zirnov, E. (2003). A comparison of electrocardiography and
photoplethesmography in measuring heart rate variability. In
Proceedings of the association for applied psychophysiology.
Stuss, D. T., Bisschop, S. M., Alexander, M. P., Levine, B., Katz, D.,
& Izukawa, D. (2001). The trail making test: A study in focal
lesion patients. Psychological Assessment, 13, 230–239.
Task Force of the European Society of Cardiology and The North
American Society of Pacing and Electrophysiology: Heart rate
variability: Standards of measurement, physiological interpretation, and clinical use. (1996). European Heart Journal, 17,
354–381.
Thayer, J. F., Lane, R. D., & Bernard, C. (2009). The heart-brain
connection: Further elaboration of a model of neurovisceral
integration. Neuroscience & Biobehavioral Reviews, 33, 81–88.
227
Tomporowski, P. D. (2003). Effects of acute bouts of exercise on
cognition. Acta Psychologica, 112, 297–324.
Tomporowski, P. D., & Ellis, N. R. (1986). The effects of exercise on
cognitive processes: A review. Psychological Bulletin, 99,
338–346.
Yerkes, R. M., & Dodson, J. D. (1908). The relation of strength of
stimulus to rapidity of habit formation. Journal of Comparative
Neurology and Psychology, 18, 459–482.
Zakzanis, K. K., Marz, R., & Graham, S. J. (2005). An fMRI study of
the trail making test. Neuropsychologia, 43, 1878–1886.
Zhong, X., Hilton, H. J., Gates, G. J., Jelic, S., Stern, Y., Bartels, M. N.,
et al. (2005). Increased sympathetic and decreased parasympathetic cardiovascular modulation in normal humans with acute
sleep deprivation. Journal of Applied Physiology, 98, 2024–2032.
123