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Differences in Sustained Attention
Capacity as a Function of Aerobic Fitness
ANTONIO LUQUE-CASADO
1,2,3
, PANDELIS PERAKAKIS
1,4
, CHARLES H. HILLMAN
5
, SHIH-CHUN KAO
5
,
FRANCESC LLORENS
6,7
, PEDRO GUERRA
1,4
, and DANIEL SANABRIA
1,2
1
Brain, Mind, and Behavior Research Center, University of Granada, Granada, SPAIN;
2
Department of Experimental
Psychology, University of Granada, Granada, SPAIN;
3
Department of Physical Education & Sport, University of Granada,
Granada, SPAIN;
4
Department of Personality, Evaluation & Psychological Treatment, University of Granada, Granada,
SPAIN;
5
Department of Kinesiology & Community Health, University of Illinois at Urbana-Champaign, Champaign, IL;
6
Department of Physical Activity & Sport Sciences, Catholic University of Valencia, Valencia, SPAIN; and
7
Universidad
Internacional Valenciana (VIU), Valencia, SPAIN
ABSTRACT
LUQUE-CASADO, A., P. PERAKAKIS, C. H. HILLMAN, S.-C. KAO, F. LLORENS, P. GUERRA, and D. SANABRIA. Differences
in Sustained Attention Capacity as a Function of Aerobic Fitness. Med. Sci. Sports Exerc., Vol. 48, No. 5, pp. 887–895, 2016. Purpose:
We investigated the relationship between aerobic fitness and sustained attention capacity by comparing task performance and brain
function, by means of event-related potentials (ERP), in high- and low-fit young adults. Methods: Two groups of participants (22 higher-
fit and 20 lower-fit) completed a 60-min version of the Psychomotor Vigilance Task (PVT). Behavioral (i.e., reaction time) and elec-
trophysiological (ERP) (i.e., contingent negative variation and P3) were obtained and analyzed as a function of time-on-task. A
submaximal cardiorespiratory fitness test confirmed the between-groups difference in terms of aerobic fitness. Results: The results
revealed shorter reaction time in higher-fit than in lower-fit participants in the first 36 min of the task. This was accompanied by larger
contingent negative variation amplitude in the same period of the task in higher-fit than in lower-fit group. Crucially, higher-fit partic-
ipants maintained larger P3 amplitude throughout the task compared to lower-fit, who showed a reduction in the P3 magnitude over time.
Conclusions: Higher fitness was related to neuroelectric activity suggestive of better overall sustained attention demonstrating a better
ability to allocate attentional resources over time. Moreover, higher fitness was related to enhanced response preparation in the first part
of the task. Taken together, the current data set demonstrated a positive association between aerobic fitness, sustained attention, and
response preparation. Key Words: VIGILANCE, ERP, PHYSICAL ACTIVITY, EXERCISE, COGNITION, REACTION TIME
Over the past decades, growing evidence from various
experimental approaches has shown that aerobic fit-
ness and cognitive-behavioral performance are posi-
tively related (16). A major component of this research has
revealed aerobic fitness-related improvements in a variety of
tasks involving different cognitive functions, that is, from pro-
cessing speed to higher-order cognitive control or memory (34).
Despite the progress on this topic, relatively little is known
about an inherent cognitive process in the majority of these
cognitive tasks that is necessary for optimal performance, that
is, sustained or vigilance attention. Here, we aimed at filling
this gap by providing novel evidence of the positive relation-
ship between aerobic fitness and the capacity to sustain atten-
tion (or to be vigilant) over time during task performance. To
achieve this, we compared a higher-fit and a lower-fit group
of young adults in terms of reaction time (RT) performance
and brain function (by means of event-related potentials
[ERP]) in a 60-min long attentional task.
Sustained or vigilant attention is a higher-order cognitive
function that determines the readiness to respond to relevant
stimuli and the capacity to effectively allocate attentional re-
sources over time. This cognitive function represents a fun-
damental component of the general cognitive capacities of
humans because a reduced ability to monitor significant sources
of information directly affects all cognitive abilities (i.e., slow
responses and/or failures to respond to target stimuli [33]).
In effect, the capacity to sustain attention is highly important
both in specific laboratory contexts and in the completion of
many everyday or professional activities that usually occur
over long periods, such as attending to academic lessons at
school, driving, sports, surgery, or air traffic control (22).
Crucially, our ability to sustain attention is far from stable
and an extended period of attentional demands on a single
task leads to a decrement in performance over time, which is
known as time-on-task effect or vigilance decrement (8). There-
fore, investigation into variables that might contribute against
vigilance-related decrements in attention and performance over
time is highly relevant.
Address for correspondence: Antonio Luque-Casado, M.S., Centro de Investigacio
´n
Mente, Cerebro y Comportamiento (CIMCYC), Campus Universitario de
Cartuja (s/n), 18071, Granada, Spain; E-mail: antonioluque@ugr.es.
Submitted for publication September 2015.
Accepted for publication December 2015.
0195-9131/16/4805-0887/0
MEDICINE & SCIENCE IN SPORTS & EXERCISE
Ò
Copyright Ó2015 by the American College of Sports Medicine
DOI: 10.1249/MSS.0000000000000857
887
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Copyright © 2016 by the American College of Sports Medicine. Unauthorized reproduction of this article is prohibited.
Previous research has examined the relationship be-
tween aerobic fitness and attentional mechanisms (30),
but a relatively scant literature to date has addressed the
association between aerobic fitness and sustained attention
in a direct manner like in the present study. For example,
Bunce (6) evaluated the influence of physical fitness on
age differences in vigilance as a function of the time course
of a task and the level of task complexity, showing an
attenuated vigilance decrement in higher-fit older adults
in comparison with their lower-fit counterpart. Crucially,
no group differences were found in young adults. The re-
sults of the few related studies testing children also point to
a positive relationship between aerobic fitness and sustained
attention. For instance, Pontifex et al. (31) concluded that
poor aerobic fitness was related to impaired vigilance on
the basis of an observed increase in error of omissions and
more sequences of omissions in lower-fit relative to higher-
fit preadolescent participants during a flanker task. Chaddock
et al. (7) investigated the time course of behavioral per-
formance and brain functioning during a flanker task in
preadolescents participants. They showed a decline in per-
formance over time on incongruent trials, but only for
lower-fit participants, who demonstrated a bilateral increase
in activation in frontal and parietal brain regions from early
to late blocks of trials. Higher-fit participants, in contrast to
their lower-fit peers, showed decreased activity as a func-
tion of time-on-task, but greater activity was shown in early
blocks with respect to lower-fit participants. Meanwhile,
Ballester et al. (2) showed a positive relationship between
fitness and vigilance during adolescence, with higher-fit par-
ticipants showing overall shorter RT than lower-fit partici-
pants in the Psychomotor Vigilance Task (PVT).
Despite a growing literature in children and some studies
with older adults, the potential significant relationship be-
tween aerobic fitness and sustained attention in young adults
remains poorly understood. Although cognitive health peaks
during young adulthood (32), which could act by reducing
the room for exercise-related improvement in cognitive func-
tion in this age group, research consistently demonstrates
the importance of physical activity in keeping, and poten-
tially improving, cognitive function throughout life (14).
Additionally, the study of the relationship between sustained
attention and aerobic fitness in this age range is highly rele-
vant because of the disproportionate decline in physical ac-
tivity from adolescence to early adulthood (21). To the best
of our knowledge, the results reported by Luque-Casado
et al. (27) represent the sole evidence of a selective association
between aerobic fitness and sustained attention in young
adults. This study showed better vigilance performance in
higher-fit as compared to lower-fit participants, indexed by
shorter overall RT in a 10 min version of the PVT, whereas
no differences where shown in an endogenous temporal
orienting task and in a duration discrimination task. These
results were taken as evidence suggesting superior sustained
attention capacity in young higher-fit relative to lower-fit
participants. Note, though, that Luque-Casado et al. reported
group differences in overall RT but not in terms of the RT
vigilance decrement.
Thus, convergent evidence suggests the important role of
aerobic fitness on sustained attentional capacity, but research
is scarce, and several important questions remain open. For
example, the few studies to date assessing one of the factors
that have been shown to tax-sustained attention (i.e., the
duration of the task [8]), as a function of aerobic fitness, have
reported inconsistent results. A vigilance decrement of greater
magnitude has been shown in lower-fit individuals relative
to their higher-fit peers both in preadolescents and in older
adults (6,31), whereas no differences were found in young
adults (6,27). Importantly, previous studies have shown that
prolonged sustained attention demands (i.e., 20 to 30 min)
are needed to elicit a significant deterioration in sustained
attention performance in young adults (13). Therefore, given
that all the aforementioned studies used experimental tasks
that typically last for only a few minutes (i.e., 10 min at the
most), the use of a task with extended sustained attentional
demands (i.e., exceeding 30 min of duration) may increase
the likelihood of observing a between-groups difference in
the magnitude of the time-on-task effect in young adults.
Additionally, the underlying neural basis of the aerobic
fitness-related improvements in vigilance performance is another
important issue that remains unknown. Two main ERP compo-
nents of interest with regard to fitness and sustained attention
are the P3 and the contingent negative variation (CNV). On
the one hand, the P3 potential is commonly thought to reflect
the amount of attentional resources directed toward task-
relevant information in the stimulus environment (29). It has
been one of the main indexes of interest in the study of
sustained attention, elucidating a relationship between P3
amplitude and task performance over time (i.e., with RT
increasing and P3 amplitude decreasing [20]). Crucially,
aerobic fitness has also been related to differences in P3
amplitude, with greater fitness related to larger P3 amplitude
(15); thus, fitness may serve as a buffer against vigilance-
related decrements in attention and performance over time.
On the other hand, the CNV is a slow negative wave oc-
curring during the preparatory interval between a warning
signal and an impending stimulus that requires a response,
which appears to reflect sensory, cognitive, and motor prepa-
ration processes (37). Importantly, studies have reported a
positive relationship between aerobic fitness and the magni-
tude of the CNV, leading to improved cognitive performance
in aerobically fit individuals compared with their lesser-fit
counterparts across several cognitive tasks assessing working
memory (19), cognitive control (35), or processing speed (1).
However, although the magnitude of the CNV has been shown
to depend on sustained attention (4), the association between
aerobic fitness and the magnitude of the CNV has not been
investigated on the basis of sustained attentional performance.
Thus, as noted above, the present study stands to provide
novel evidence of the relationship between aerobic fitness,
behavioral performance, and brain function of young adults
in a prolonged sustained attention task during the prestimulus
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response preparation (i.e., CNV) and poststimulus periods
(i.e., P3). Based on previous evidence (1,15,19,35), we
expected the higher-fit group to have larger CNV and P3 am-
plitude values than the lower-fit group, which would also be
related to higher overall performance in the vigilance task.
Further, we predicted that both CNV and P3 amplitudes
would gradually deteriorate as a result of the time-on-task
effect, but with the higher-fit group showing an attenuated
vigilance decrement and maintaining larger overall values
during the course of the task.
METHOD
Participants. An a priori power analysis was conducted
to determine the minimum sample size required for a power
level of 0.80. This analysis was based on data from the pre-
vious study by Luque-Casado et al. (27) where they compared
performance in the PVT of a group of young cyclists and
triathletes (higher-fit) to that of a group of young adults with
sedentary lifestyle (lower-fit). This analysis gave an outcome
of 22 participants per group.
We recruited 50 young male adults to participate in the
present study, 25 undergraduate students from the University
of Granada (Spain) to be included in the lower-fit group and
25 young adults (15 members from triathlon local clubs and
10 from the Faculty of Physical Activity and Sport Sciences,
University of Granada, Spain) to be included in the higher-fit
group. The participants in the higher- and lower-fit groups met
the inclusion criteria of reporting at least 8 h of training per
week or less than 2 h, respectively. Eight of the 50 participants
(three higher-fit and five lower-fit) were subsequently ex-
cluded from the analyses (see data reduction section). Thus,
only data from the remaining 42 participants are reported
(see Table 1).
The experiment reported herein was conducted according
to the ethical requirements of the local committee and com-
plied with the ethical standards laid down in the 1964 Decla-
ration of Helsinki. All participants gave informed consent
before their inclusion in the study, had normal or corrected-
to-normal vision, and no history of neuropsychological
impairment. They were required to maintain a regular sleep–
wake cycle for at least 1 d before the study and to avoid caffeine
and vigorous physical activity before the laboratory visit. All
participants_data were analyzed and reported anonymously.
Procedure. Upon arrival to the laboratory, participants
were seated in front of a computer in a dimly illuminated,
sound-attenuated room with a Faraday cage. First, participants
signed the informed consent and were prepared for electro-
physiological measurement. Participants then received verbal
and written instructions regarding the PVT and practiced the
task for 1 min. The experiment consisted of a single 60-min
block. Once they completed the PVT, all participants per-
formed a submaximal incremental cycle ergometer test to
evaluate their fitness level. The experimental session was
administered during daylight hours, with approximately half
the participants participating in the morning (i.e., 11 higher-
fit athletes, 11 lower-fit nonathletes) and the other half in the
afternoon (i.e., 11 athletes, nine nonathletes). The entire
experimentalsessionlasted2happroximately.
Incremental effort test. A brief preliminary anthropo-
metric study of each participant was performed to measure
height, weight, and body mass index (see Table 1). We used a
ViaSprint 150 P cycle ergometer (Ergoline GmbH, Germany)
to induce physical effort and to obtain power values and a
JAEGER Master Screen gas analyzer (CareFusion GmbH,
Germany) to provide a measure of gas exchange during the
effort test. Before the start of the test, participants were fitted
with a Polar RS800 CX monitor (Polar Electro O
¨y, Kempele,
Finland) to record their HR during the incremental exercise
test and the cycle ergometer was set to the individual anthro-
pometric characteristics.
We used a modified version of the incremental effort test
from the previous study by Luque-Casado et al. (27). The
incremental effort test started with a 3-min warm-up at 30 W,
with the power output increasing 10 W every minute. The
test began at 60 W and was followed by an incremental pro-
tocol with the power load increasing 30 W every 3 min.
Workload increased progressively during the third minute
of each step (5 W every 10 s); therefore, each step of the
incremental protocol consisted of 2 min of stabilized load
and 1 min of progressive load increase. Each participant set
his preferred cadence (60–90 rpm) during the warm-up. They
were asked to maintain this cadence throughout the protocol.
The ergometer software was programmed to increase the
load automatically.
Determination of the ventilatory anaerobic threshold (VAT)
was based on RER [RER = CO
2
production/O
2
consump-
tion]. More specifically, VAT was defined as the ˙
VO
2
at
the time when RER exceeded the cutoff value of 1.0 (9,40).
The researcher knew that the participant had reached his
VAT when the RER was equal to 1.00 and did not drop
below that level during the 2-min constant load period or
during the next load step, never reaching the 1.1 RER. The
submaximal incremental test ended once the VAT was
reached. The oxygen uptake ( ˙
VO
2
,mLImin
j1
Ikg
j1
), RER,
relative load (WIkg
j1
), HR (bpm), and time of the test (s)
were continuously recorded during the entire incremental
test. The fitness level of the participants was determined
from the data set obtained during the incremental physical
test (see Table 1).
TABLE 1. Mean and 95% CI of descriptive and fitness data for the higher-fit and lower-fit
groups.
Higher-Fit Lower-Fit
Anthropometrical characteristics
Sample size
a
22 20
Age (yr) 22 [21, 24] 23 [22, 24]
Height (cm) 1.76 [1.74, 1.78] 1.78 [1.75, 1.81]
Weight (kg) 69.6 [67.1, 72.1] 76.9 [69.0, 85.6]
Body Mass Index (kgIm
j2
) 22.4 [21.7, 23.1] 24.1 [22.1, 26.2]
Incremental test parameters
Time to VAT ( s) 1285 [1180.1, 1386.6] 494 [421.5, 566.5]
V
˙O
2
(mLImin
j1
Ikg
j1
) at VAT 43.7 [40.4, 47.4] 19.5 [17.2, 21.8]
Relative power output at VAT (WIkg
j1
) 3.42 [3.13, 3.73] 1.39 [1.21, 1.58]
a
Only data of the participants included in the analyses are reported.
VAT, ventilatory anaerobic threshold.
VIGILANCE CAPACITY AND AEROBIC FITNESS Medicine & Science in Sports & Exercise
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The PVT. We used a PC with a 19-inch monitor and
E-Prime software (Psychology Software Tools, Pittsburgh,
PA) to control the stimulus presentation, response collec-
tion, and to generate and send triggers indicating the con-
dition of each trial for offline sorting, reduction, and analysis
of electroencephalogram (EEG) and behavioral data. The
center of the PC screen was situated approximately 60 cm
from the participant_s head and at eye level. The device used
to collect responses was a PC keyboard.
The procedure of the PVT was based on the original
version (39). This task was designed to measure vigilance by
recording participants_RT to visual stimuli that occur at
random interstimulus intervals (3,39). Each trial began with
the presentation of a blank screen in a black background
for 2000 ms, and subsequently, an empty red circumference
(6.68-7.82-) appeared in a black background. Later, in
a random time interval (between 2000 and 10,000 ms), the
circumference was filled all at once in a red color. Partici-
pants were instructed to respond as fast as they could once
they had detected the presentation of the filled circle. The
filled circle was presented for 500 ms, and the participants
had a maximum of 1500 ms to respond. They had to respond
with their dominant hand by pressing the space bar on the
keyboard. An RT visual feedback message was displayed for
300 ms after response, except in case of an anticipated re-
sponse (‘‘wait for the target’’) or if no response was made
within 1000 ms after target offset (‘‘you did not answer’’).
After the feedback message, the next trial began. Response
anticipations were considered errors. The task comprised a
single block of 60 min of total duration, and the mean num-
ber of trials per participant was 402 T8.9.
EEG recordings. Continuous EEG data were recorded
using a BioSemi Active Two system (Biosemi, Amsterdam,
Netherlands) and were digitized at a sample rate of 1024 Hz
with 24-bit A/D conversion. The 64 active scalp Ag/AgCl
electrodes were arranged according to the international stan-
dard 10–20 system for electrode placement using a nylon
head cap. The common mode sense and driven right leg
electrodes served as the ground, and all scalp electrodes
were referenced to the common mode sense during record-
ing. The cap was adapted to the individual_sheadsize,
and each electrode was filled with Signa Electro-Gel
(Parker Laboratories, Fairfield, NJ) to optimize signal trans-
duction. Participants were instructed to avoid eye move-
ments, blinking and body movements as much as possible,
and to keep their gaze on the center of the screen during
task performance.
Data reduction. The behavioral data analyses were
performed on the overall participants_mean RT. Trials with
RT below 100 ms (0.03%), anticipations (i.e., responses
prior to the target presentation; 1.49%), and omissions (if
no response was made within 1000 ms after target offset;
0.24%) were discarded from the analysis (3).
We used a combination of bespoke Matlab scripts (Matlab
2013a, Mathworks Inc.), EEGLAB toolbox (version 13.2.2b,
[10]) and ERPLAB toolbox (version 4.0.2.3, [23]) for
processing and analyzing ERP data. Continuous data were
downsampled to 256 Hz, merged offline with behavioral
data, and rereferenced to the average of all electrodes (average
common reference). Noisy scalp electrodes were identified via
visual inspection (only in three of the participants) and were
replaced by an average of the voltages recorded at other
neighbor scalp electrodes (three electrodes on average were
replaced in these subjects). We applied independent com-
ponent analysis (ICA) (10) to correct eye blink artifacts (17).
In order to remove baseline drifts, data were high-pass filtered
(0.1 Hz; 12 dB per octave) before running ICA. Prototypical ICA
components representing eye movements and blinks were
assessed on the raw EEG data before being excluded to cor-
roborate their consistency and temporal match with the ocular
artifacts. The ocular ICA components were removed in a
systematic way for all participants to avoid any bias across
the groups. On average, one independent component was
removed per participant.
Once the ocular artifacts were corrected, separate epochs
were constructed for cues (between j200 and 2000 ms rela-
tive to cue onset) and targets (between j200 and 1000 ms
relative to target onset). The protocol typically used to elicit
the CNV is a two-stimulus (S1-S2) paradigm in which changes
in amplitude between warning (S1) and imperative stimuli
(S2) are measured. Note that 2000 ms is the minimum du-
ration of the random interval between the cue and the target
in the PVT paradigm and therefore, the point of maximal
uncertainty. There is previous evidence showing a reliable
CNV potential even under conditions of high uncertainty
about the onset of S2 (36). Then, the PVT paradigm whereby
participants have to respond to a target stimulus (S2) that
appears in a random interval between 2 and 10 s after the
presentation of a cue stimulus (S1) allows the measurement
of the CNV. The 200-ms prestimulus period was used for
baseline correction both in cues and targets epochs. Sub-
sequently, data were filtered with a 30-Hz low-pass cutoff
(24 dB per octave). Remaining artifacts (EMG, noisy electrodes,
etc.) exceeding T100 KV in amplitude were detected, and
the epochs including those artifacts were excluded from
further analysis. To ensure a sufficient signal-to-noise ratio
and to reduce the possibility that the type I error rate was
inflated by post hoc exclusion of subjects, we set an apriori
criteria of excluding participants for whom more than
25% of trials were rejected (23,26). This resulted in the
exclusion of three higher-fit and five lower-fit partici-
pants. A minimum of 68 trials per condition was main-
tained. Separate grand average waveforms were constructed
across all participants accordingtobothcuesandtargets
categories.
Data measure and electrodes selection. For cue
and target analyses, amplitude was calculated as the mean
voltage in a specified temporal window and electrodes site.
The temporal windows were chosen on the basis of visual
inspection of the grand average waveforms. The electrodes
selection for both cue-locked and target-locked analyses was
a two-stage process. First, several electrodes were selected
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for each potential of interest based on the topographical dis-
tribution of the scalp activity (see Figs. 1 and 2). Next, elec-
trodes for statistical analyses were chosen by their maximal
positive or negative voltage value from each cluster, respec-
tively. Thus, each potential was represented by an average of
the selected electrodes. Specifically, the CNV potential was
represented at frontal and central sites as the maximal
negative mean amplitude between 1500 and 2000 ms after
cue onset at electrode Fz, FCz, Cz, and CPz. The P3 potential
was represented at posterior sites as the maximal mean ampli-
tude between 240 and 440 ms after target onset at Pz and POz.
Design and statistical analysis. Three sets of depen-
dent variables were evaluated in this study: 1) Participants_
descriptive and fitness data (i.e., anthropometrical and incre-
mental exercise test parameters), 2) behavioral data (i.e., overall
mean RT), and 3) ERP data (i.e., CNV and P3 mean amplitude
values). For the behavioral and ERP data, five temporal blocks
of 12 min were considered for the analysis to measure the
time-on-task effect.
Nonparametric permutation testswereusedtoanalyzethe
data. Importantly, these tests are exact, unbiased, and assumption-
free in terms of the underlying distribution of the data
(11,28). We followed a general label exchange procedure
for within-participants factorial designs (12) using a Monte
Carlo approach.
The participants_descriptive and fitness data were analyzed
using one-way between-groups design. For the behavioral and
ERP data, we had a factorial design with the between-groups
variable of group (higher-fit and lower-fit) and the within-
groups variable of time-on-task (block 1, block 2, block 3,
block 4, and block 5). Significant main effects and interactions
were further explored by using post hoc, pairwise comparisons,
FIGURE 1—Grand average waveforms and topogra phic s calp distribution of the CNV as a fu nction of group an d block. Grand average
waveforms are presented at Fz electrode. Time zero represents the cue stimulus appearance. Separate graphs for higher-fit (A) and lower-fit (B)
are shown for clarity. Color lines are used to represent the waveforms as a function of block. Gray marks show the time windows analyzed (i.e.,
1500–2000 ms). Topographic scalp distribution of CNV amplitude (spectrum scale: blue to red) is illustrated for the higher-fit group (C) and
lower-fit group (D) as a function of block. The electrode sites includedintheanalysesarehighlightedin bold in the topographic plots.
VIGILANCE CAPACITY AND AEROBIC FITNESS Medicine & Science in Sports & Exercise
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and separate main effects analyses when appropriate. Mul-
tiple comparisons correction was accounted for by applying
the false discovery rate (FDR) approach. 95% confidence
intervals (CI) and probability threshold values are reported.
RESULTS
Descriptive and Fitness Data
The permutations tests for independent samples revealed
significant differences between groups in all the incremental
test parameters (i.e., time to VAT (s), relative power output
(WIkg
j1
) at VAT and ˙
VO
2
(mLImin
j1
Ikg
j1
) at VAT) (all
P`sG0.01). All data showed evidence of the difference in
fitness level between groups (see Table 1). There were no
statistically significant differences between groups in any of
the anthropometrical parameters (all P`sQ0.11).
Behavioral Results
Participants_mean RT results showed significant main ef-
fects of group (PG0.01) and time-on-task (PG0.01). Cru-
cially, both main effects were better qualified by the significant
interaction between group and time-on-task (PG0.01; see
Fig. 3). Pairwise comparisons (FDR corrected; P-threshold =
0.005) were performed between the higher-fit and lower-fit
group within each temporal block. The comparisons showed
significant differences between groups at blocks 1, 2, and 3
(all P`se0.005) with higher-fit being faster than lower-fit
group (see Fig. 3). There were no significant differences when
comparing both groups in the remaining blocks (all P`sQ0.78).
Electrophysiological Results
Cue-locked ERP. The CNV mean amplitude analyses
revealed significant main effects of group (PG0.01) and
FIGURE 2—Grand average waveforms and topographic scalp distribution of the P3 amplitude as a function of group and block. Grand average
waveforms are presented at Pz electrode. Time zero represents the target stimulus appearance. Separate graphs for higher-fit (A) and lower-fit (B) are
shown for clarity. Color lines are used to represent the waveforms as a function of block. Gray marks show the time windows analyzed (i.e., 240–440 ms).
Topographic scalp distributionof P3 amplitude (spectrum scale: blue to red) is illustrated for the higher-fit group (C) and lower-fit group (D) as a function
of block. The electrode sites included in the analyses are highlighted in white in the topographic plots.
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time-on-task (PG0.01) that were better qualified by the
significant interaction between group and time-on-task (PG
0.01; see Figs. 1 and 4). Further analyses showed a statistically
significant main effect of time-on-task in the higher-fit group
(PG0.01), with the amplitude of the CNV becoming less
negative as time went on. However, this same analysis was
not significant for the lower-fit group (P= 0.19). Further-
more, pairwise comparisons (FDR corrected; Pthreshold =
0.029) showed significant differences between groups at
block 1 (PG0.01), block 2 (PG0.01), and block 3 (P=0.029).
In all cases, the higher-fit group showed greater CNV neg-
ativity than lower-fit group (see Fig. 4). There were no sig-
nificant differences when comparing groups in blocks 4
and 5 (both Ps Q0.08).
Target-locked ERP. The P3 mean amplitude results
showed significant main effects of group (PG0.01) and
time-on-task (PG0.01). Again, the interaction between group
and time-on-task reached statistical significance (PG0.01;
see Figs. 2 and 5). Separate main effect analyses of time-
on-task reached significance both for the higher-fit and lower-
fit group (both P`sG0.01). In order to explain this interaction
further, we performed post hoc comparisons (FDR corrected;
P-threshold = 0.003). For the higher-fit group, P3 amplitude
values peaked in block 3. There were statistically significant
differences when comparing block 3 with respect to blocks 1,
2, and 5 (all P`se0.003). Additionally, the comparison be-
tween blocks 1 and 4 also showed significant differences
(PG0.001). There were no significant differences when com-
paring the remaining blocks (all P`sQ0.06; see Fig. 5). In the
case of the lower-fit group, the comparisons showed signi-
ficant differences only between blocks 1 and 5 (P=0.003),
with decreasing amplitude over time. None of the remaining
comparisons between blocks reached statistically significant
differences (all P`sQ0.02; see Fig. 5). Additionally, pairwise
comparisons (FDR-corrected; Pthreshold = 0.0001) showed
significant differences between groups in all blocks (all P`se
0.0001), with the higher-fit group showing greater P3 mean
amplitude than the lower-fit group (see Fig. 5).
DISCUSSION
In the present study, we tested the positive relation between
aerobic fitness and sustained attention capacity by comparing
RT performance, the CNV and the P3 amplitude, in a 60-min
attention demanding task of two groups of participants: higher-
and lower-fit young adults.
The results showed that higher-fit participants responded
faster than lower-fit participants during the first three blocks
of the task (i.e., 36 min). This was accompanied by larger CNV
amplitude in the same blocks in higher-fit than in lower-fit
adults; however, this difference disappeared in the later blocks.
Crucially, higher-fit participants maintained larger P3 ampli-
tude throughout the task compared with lower-fit participants,
FIGURE 3—Mean and 95% CI of the response time (ms) as a function
of group and block. *Significant differences between groups within
each block (Pe0.005).
FIGURE 4—Mean amplitude and 95% CI of the CNV as a function of
group and block. *Significant differences between groups within each
block (Pe0.03).
FIGURE 5—Mean amplitude and 95% CI of the P3 as a function of
group and block. *Significant differences between groups within each
block (Pe0.0001). †Significant differences between blocks within each
group (Pe0.003).
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who showed a reduction in the P3 magnitude as a function of
the time-on-task.
Concerning performance in the PVT, the results in the first
part of the task are in accordance with the previous study by
Luque-Casado et al. (27), suggesting better vigilance capacity
in higher-fit young adults relative to their lower-fit counter-
parts. However, a greater vigilance decrement was shown
in higher-fit than in lower-fit participants. The ERP data
provided crucial information in order to understand these
apparently contradictory results. The CNV and RT patterns
were closely related, such that the higher-fit group showed
larger CNV amplitude in the first half of the task (blocks 1 to
3) compared with the lower-fit group, but, again, these dif-
ferences disappeared in the later blocks. It is known that
temporal preparation substantially enhances performance by
reducing response times to an imminent signal in simple RT
tasks, (18) and in fact, the magnitude of the CNV has been
shown to depend on sustained attention (4). Thus, the im-
proved performance in higher-fit participants indexed by
shorter RT in the early blocks of the task might be the result
of better cue facilitation despite the high temporal uncer-
tainty of the task, suggesting an enhanced top-down pro-
cessing in terms of endogenous preparation in this group.
This supports previous evidence showing that higher-fit
participants are better at the stage of preparation before tar-
get onset and the behavioral response (19,35) as well as
activating and adapting neural processes involved in cogni-
tive control to meet and maintain task goals (7). However,
important here is that the response preparation benefit could
not be maintained throughout the task by higher-fit partici-
pants and disappeared over time, leading in turn to the loss
of group differences in RT.
Maintaining attention for long periods requires hard
mental work leading to a mental fatigue state (38), which has
been evidenced in our study by the vigilance decrement over
time in both groups. Additionally, it is known that mental
fatigue results in a reduction of top-down attentional ca-
pacity (5). Therefore, even though both groups were affected
by mental fatigue, in the case of higher-fit participants, it
appeared to impact on the enhanced endogenous preparation
as a function of the time-on-task, thus leading to the disap-
pearance of the improved behavioral performance. Indeed,
this would agree with previous studies showing greater dif-
ficulties in maintaining the state of endogenous preparation
in mentally fatigued participants (25) evidenced by the sig-
nificant attenuation of brain activity elicited by cue infor-
mation (i.e., CNV) as a function of time-on-task (24).
In general terms, the P3 potential is thought to reflect the
amount of attentional resources directed to task-relevant in-
formation in the stimulus environment (29), and accordingly,
the P3 should be taken as the relevant index of sustained
attention in our study. In accordance with previous research
(15), we observed larger P3 amplitude for the high-fit par-
ticipants suggesting an enhanced ability to allocate attentional
resources relative to their lower-fit counterparts. Novel to
these previous accounts is the fact that, first, we measured
directly and specifically the ability to maintain attention, un-
like previous studies whose interest was focused mainly on
investigating the relationship of fitness with cognitive control;
and second, the time-on-task effect differentially affected P3
amplitude in higher-fit and lower-fit participants showing a
depletion in the allocation of attentional resources from the
beginning of the task only in the latter group.
Interestingly, higher-fit participants maintained larger P3
amplitude relative to lower-fit participants and demonstrated
maximum amplitude in the third temporal block. The am-
plitude significantly decreased through the end of the task
following the third block, although, importantly, never reach-
ing lower values than in the first block. This apparent de-
pletion in attentional resources allocation coincided in time
with the disappearance of their improved temporal prepara-
tion (indexed by the CNV), which could have led indirectly
to an increase in demands for maintaining the task goal in
the absence of cue facilitation, causing added mental fatigue
and leading to the observed decrease in P3 amplitude from
the peak reached in block 3. In any case, it is noteworthy that
the higher-fit group always showed greater amplitude of
P3 relative to the lower-fit group throughout the task, and
crucially, only the lower-fit group showed a significant re-
duction of the P3 amplitude from the beginning of the task.
All in all, and according to previous evidence (20), these
results can be taken as an index of enhanced ability to maintain
the allocation of attentional resources over time in higher-fit
participants with respect to lower-fit participants.
In conclusion, higher fitness was related to neuroelectric
activity suggestive of better overall sustained attention and
a better response preparation (although only in the first part
of the task). Taken together, the current data set replicates
and extends this area of research by demonstrating an asso-
ciation between higher amounts of aerobic fitness and sus-
tained attention. However, it is important to consider that sport
training context is a stimulating environment where both
cardiovascular fitness and perceptual-cognitive skills are
enhanced, which might in turn influence cognitive function.
Consequently, other factors in addition to fitness might also
account for (at least part) of the group differences reported
here. Hence, future research would benefit from study de-
signs that include specific sport groups and account for the
potential influence of the perceptual-cognitive skills involved
in sport training context to clarify the specific, rather than
combined, effect both of the cardiovascular fitness and the
sport training context on vigilance performance. Finally, be-
cause sustaining attention is a basic requirement for informa-
tion processing and, consequently, a fundamental component
of the general cognitive capacities of humans, our findings
provide additional evidence of the broad relevance for public
health of a physically active lifestyle aimed at improving
aerobic fitness. In effect, this should be considered in envi-
ronments, such as education (i.e., in integrated educational
development plans), or many other aspects of everyday life
and professional activities (e.g., driving, surgery, military and
border surveillance, lifeguarding or air traffic control) because
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this might lead to a reduction of the likelihood of attentional
failures in prolonged high-demand environments.
This research was supported by a predoctoral grant from the
Spanish Ministerio de Educacio´ n, Cultura y Deporte (FPU-AP2010-
3630) to the first author, and research grants from the Ministerio de
Economı´a y Competitividad (PSI2013-46385-P) and the Junta de
Andalucı´a (SEJ-6414) to Daniel Sanabria. The funders had no role in
study design, data collection and analysis, decision to publish, or
preparation of the manuscript. We thank to Enrique Molina for pro-
viding his knowledge and assistance in the statistical data analyses,
and to all the participants who took part in the experiment. We also
thank to Human Psychophysiology and Health Research Group
(University of Granada) for allowing us to use their facilities and assess-
ment instruments. No conflicting financial, consultant, institutional, or
other interests exist. Results of the present study do not constitute
endorsement by the American College of Sports Medicine.
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