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https://doi.org/10.1177/1745691619850568
Perspectives on Psychological Science
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DOI: 10.1177/1745691619850568
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ASSOCIATION FOR
PSYCHOLOGICAL SCIENCE
Our modern societies are becoming increasingly inac-
tive. On average, people spend 5 to 9 hr of their waking
day engaged in sedentary behaviors (Al-Nakeeb etal.,
2012; Matthews etal., 2008; Ruiz etal., 2011), with
detrimental effects on physiological and psychological
health (Iannotti etal., 2009; Tremblay, Colley, Saunders,
Healy, & Owen, 2010). In contrast, physical exercise
has a positive impact on the body (Warburton & Bredin,
2017) and the brain (Gomez-Pinilla & Hillman, 2013),
including several well-documented neurobiological
effects (for a review, see Moreau & Conway, 2013).
Despite the sizeable benefits associated with active
lifestyles, many individuals do not exercise regularly,
the most common reasons being related to a lack of
motivation or time constraints (Anderson, 2003). In an
effort to propose exercise regimens that are more effi-
cient and engaging, researchers have explored different
ways to shorten exercise regimens. Among these, high-
intensity regimens have recently gained popularity
because of their potential to elicit health benefits
comparable with, and sometimes even surpassing, those
elicited by longer workouts (Gibala, Little, Macdonald,
& Hawley, 2012; Weston, Wisløff, & Coombes, 2014).
These regimens come in various forms but are typically
based on intense cardiovascular exercise interleaved
with rest periods.
The long-term impact of physical exercise, including
the chronic effects of high-intensity regimens, has been
studied extensively (for a general review, see Hillman,
Erickson, & Kramer, 2008). This body of research has
demonstrated that chronic exercise exerts a positive
influence on a wide range of variables, including those
related to cognitive performance (Moreau & Conway,
2013; Moreau, Kirk, & Waldie, 2017). Although long-term
850568PPSXXX10.1177/1745691619850568Moreau, ChouExercise and Executive Function
research-article2019
Corresponding Author:
David Moreau, Science Centre, School of Psychology, 23 Symonds St.,
Auckland 1010, New Zealand
E-mail: d.moreau@auckland.ac.nz
The Acute Effect of High-Intensity
Exercise on Executive Function:
A Meta-Analysis
David Moreau1,2 and Edward Chou1
1School of Psychology, The University of Auckland; and 2Centre for Brain Research,
The University of Auckland
Abstract
High-intensity exercise has recently emerged as a potent alternative to aerobic regimens, with ramifications for
health and brain function. As part of this trend, single sessions of intense exercise have been proposed as powerful,
noninvasive means for transiently enhancing cognition. However, findings in this field remain mixed, and a thorough
synthesis of the evidence is lacking. Here, we synthesized the literature in a meta-analysis of the acute effect of high-
intensity exercise on executive function. We included a total of 1,177 participants and 147 effect sizes across 28 studies
and found a small facilitating effect (d = 0.24) of high-intensity exercise on executive function. However, this effect
was significant only compared with rest (d = 0.34); it was not significant when high-intensity exercise was compared
with low-to-moderate intensity exercise (d = 0.07). This suggests that intense and moderate exercise affect executive
function in a comparable manner. We tested a number of moderators that together explained a significant proportion
of the between-studies variance. Overall, our findings indicate that high-intensity cardiovascular exercise might be a
viable alternative for eliciting acute cognitive gains. We discuss the potential of this line of research, identify a number
of challenges and limitations it faces, and propose applications to individuals, society, and policies.
Keywords
cognitive control, inhibitory control, cognitive flexibility, attention, working memory, acute physical exercise, HIT
2 Moreau, Chou
commitment to exercise is key in inducing profound,
lasting changes, studying the short-term effects of exer-
cise remains important in further understanding the
mechanisms subserving change at different time scales.
In this dynamic, several theories have attempted to explain
the effects of acute exercise on cognition (Tomporowski,
2003), emphasizing in the process the fundamental dis-
crepancies between short-term effects and long-term, or
chronic, outcomes. For example, the reticular-activating
hypofrontality model (Audiffren, 2016) posits that acute
exercise forces the brain to shift metabolic resources away
from specific regions such as the prefrontal cortex to
instead favor structures that support exercise, such as the
reticular formation and motor cortices. This process typi-
cally facilitates performance on sensory and motor tasks,
whereas the associated hypofrontality is thought to tem-
porarily impair executive function (Audiffren, 2016;
Dietrich & Audiffren, 2011). A number of electroencepha-
lography studies support this view, reporting postexercise
alterations in the form of shifts in the amplitude or latency
of event-related potential components thought to reflect
an increased allocation of cognitive resources after exer-
cise (Hillman, Snook, & Jerome, 2003; Kamijo etal., 2009;
O’Leary, Pontifex, Scudder, Brown, & Hillman, 2011).
Exercise also leads to acute changes—typically
increases—in the concentration of several neurochemi-
cals in the brain (for a review, see Moreau & Conway,
2013). Examples of these neurochemicals include the
catecholamines (noradrenaline, dopamine), cortisol,
brain-derived neurotrophic factor, and possibly sero-
tonin (McMorris, Turner, Hale, & Sproule, 2016). These
neurochemicals influence brain function in a complex
manner: Whereas moderate increases in catecholamine
concentrations appear to facilitate performance on most
cognitive tasks, excessive concentrations (from high-
intensity and/or prolonged exercise) can inhibit cogni-
tion instead (McMorris etal., 2016), consistent with the
inverted-U hypothesis (Yerkes & Dodson, 1908). In addi-
tion, physical and emotional fatigue (Barnes & Van Dyne,
2009) may impair cognitive performance at higher inten-
sities of exercise.
Although neurochemical accounts and the reticular-
activating hypofrontality model can help explain the
impact of exercise on cognition while exercising, they
might tell us very little about postexercise effects. This
question has practical importance given that many indi-
viduals may be motivated to exercise during breaks if
it has beneficial effects on both physical health and
cognitive performance. Yet compared with that of low-
to-moderate-intensity exercise (e.g., Ludyga, Gerber,
Brand, Holsboer-Trachsler, & Pühse, 2016), the acute
effect of high-intensity exercise on subsequent cogni-
tive performance is not well understood (Browne etal.,
2017; McMorris, 2016). Exercise-induced impairments
in cognitive performance associated with high-intensity
training could be temporary and thus not representative
of subsequent effects after the bout. For example, one
might posit that detrimental effects would rapidly sub-
side to allow general cognitive improvements similar to,
or even surpassing, those observed after aerobic exer-
cise (Kao, Westfall, Soneson, Gurd, & Hillman, 2017;
Samuel etal., 2017). This would be consistent with the
profound physiological effects but rapid physical recov-
ery typically observed following these types of regi-
mens (MacInnis & Gibala, 2017). In fact, a number of
studies have found facilitating effects of high-intensity
exercise on postexercise cognitive performance (Kao
etal., 2017) despite the well-established impairments
reported during high-intensity exercise (Samuel etal.,
2017). On the other hand, it is also possible that the
debilitating effects of high intensity on cognition remain
after the bout (Ludyga, Pühse, Lucchi, Marti, & Gerber,
2019; Mekari etal., 2015) or that the effect of high-intensity
exercise on cognitive performance depends on modera-
tors such as fitness level or age (Browne etal., 2017). In
line with this idea, several high-intensity exercise studies
have failed to find facilitating effects (Browne etal., 2017;
McMorris & Hale, 2012).
Moreover, more fine-grained data might be required
to truly understand the dynamics of exercise-induced
effects on cognition. For example, a meta-analysis by
Chang and colleagues found impairing effects up to 1
min after the bout and facilitating effects beyond 1 min
after exercise (Y. K. Chang, Labban, Gapin, & Etnier,
2012). To complicate things further, methodological dif-
ferences between studies have possibly contributed to
these discrepancies; for example, the threshold for
high-intensity exercise varies substantially across pro-
tocols (McMorris, 2015). Specifically, the following
thresholds, which are based on either maximum heart
rate (HRmax) or maximum power output (Wmax), are
common in the literature: (a) ≥ 77% HRmax (American
College of Sports Medicine, 2010; Y. K. Chang etal.,
2012) and (b) ≥ 80% Wmax (Borer, 2003; Browne etal.,
2017; McMorris, Hale, Corbett, Robertson, & Hodgson,
2015). Using conversion formulas (Arts & Kuipers, 1994;
Lounana, Campion, Noakes, & Medelli, 2007), (a) can
be converted to approximately 59.5% Wmax, and (b)
corresponds to 88.6% HRmax. In addition, a wide range
of populations is included in these studies; for example,
the age range spans from children to older adults,
whereas fitness levels range from sedentary to highly
active, or even professionally trained, individuals. These
differences in thresholds, units of measurements, and
demographics may account for some of the inconsisten-
cies between studies.
The aforementioned inconsistencies are best
addressed via meta-analytic investigations, yet previous
Exercise and Executive Function 3
reviews and meta-analyses have included only a rela-
tively low number of studies. The most recent literature
search for a meta-analysis on the acute effect of exer-
cise on cognition was conducted in 20101 (Y. K. Chang
etal., 2012). In the 9 years since, many studies have
been published, including several that examined inter-
mittent forms of high-intensity exercise typically shorter
than traditional aerobic exercise (Klika & Jordan, 2013)
and often associated with higher self-reported enjoy-
ment (Thum, Parsons, Whittle, & Astorino, 2017). In
addition, several potential moderators of the influence
of exercise on cognition have been identified (Y. K.
Chang etal., 2012; Etnier, Nowell, Landers, & Sibley,
2006; Etnier etal., 1997; Lambourne & Tomporowski,
2010; McMorris & Hale, 2012; Sibley & Etnier, 2003;
Tomporowski, 2003), providing the basis for a deeper
understanding at the mechanistic level.
Present Study
In the present meta-analysis, we focused on the effect
of cardiovascular high-intensity exercise on executive
function. Although restrictive, our emphasis on execu-
tive performance was motivated by three distinct fac-
tors. First, executive function is a central component of
cognition known to influence many cognitive processes
and with ecological relevance to various domains, rang-
ing from academic to professional (e.g., Diamond,
2013). Second, intervention studies have demonstrated
repeatedly that executive performance is malleable and
that it can be improved given adequate regimens or
suitable environments (e.g., Diamond & Lee, 2011).
Finally—and perhaps most importantly—there are clear
theoretical predictions, generated on the basis of previ-
ous literature (e.g., Audiffren, 2016; Dietrich & Audiffren,
2011), about the effects of exercise on executive func-
tion. In our view, predictions related to other cognitive
domains are less well informed theoretically and thus
more prone to spurious findings.
We aimed to answer two research questions. First,
what is the effect of a single bout of high-intensity
exercise on executive performance? This question is
important given the mixed findings reported in previ-
ous studies. Second, how is the effect of high-intensity
exercise on executive performance moderated by the
characteristics of exercise, cognitive tasks, research pro-
tocols, and participants? We predicted an effect of high-
intensity exercise on executive function but did not
predict a direction because of inconsistencies in previ-
ous studies (Y. K. Chang etal., 2012; Ludyga etal., 2016;
Verburgh, Königs, Scherder, & Oosterlaan, 2014). We
also postulated larger nondirectional effects on the
basis of previous findings for longer exercise durations
(Browne etal., 2017; Y. K. Chang etal., 2012; Ludyga
etal., 2016; Miller, Hanson, Tennyck, & Plantz, 2019)
and a greater facilitating effect for cycling compared
with running protocols (Lambourne & Tomporowski,
2010). Because of the relative scarcity of studies exam-
ining subcategories of high-intensity exercise and exer-
cise rhythm, we did not make predictions for these
moderators. We also hypothesized a greater facilitating
effect for cognitive tasks administered after a minute
following exercise (Browne etal., 2017; Y. K. Chang
et al., 2012). Finally, we predicted larger effects in
within-subjects (vs. between-groups) studies (Y. K.
Chang etal., 2012) and lower-quality (vs. higher-quality)
studies (Etnier etal., 1997), larger effects on executive
function when contrasting high-intensity exercise with
rest rather than with low-intensity exercise (Ludyga
etal., 2016), and a greater facilitating effect for high-
fitness participants and for individuals 14 years of age
or older on the basis of previous literature (Y. K. Chang
etal., 2012). In addition, we ran exploratory analyses
to test potentially meaningful interactions (e.g., trade-
offs, additive effects, confounds) between moderators
thought to be informative given previously unexplored
relationships or prior mixed findings.
Method
This meta-analysis followed guidelines from the Pre-
ferred Reporting Items for Systematic Review and Meta-
Analysis Protocols (PRISMA-P) 2015 statement (Moher
etal., 2015; Shamseer etal., 2015).
Eligibility criteria
Participant, intervention, comparator, and outcome
(PICO) criteria were used to determine eligibility (Moher
etal., 2015). Specifically, studies had to include an acute
bout of high-intensity exercise as an independent variable
and performance on at least one standardized test of
executive function as a dependent variable.2 For a study
to be included, the executive-function task(s) also needed
to be administered at least once after the exercise bout.
Studies also needed to include a control/comparison
group or condition, with random allocation to groups/
conditions, and needed to be in English (see Fig. 1).
High-intensity threshold. Two thresholds have been
used in the past to define high-intensity exercise: ≥ 77%
HRmax (American College of Sports Medicine, 2010; Y. K.
Chang etal., 2012) or ≥ 80% Wmax (Borer, 2003; Browne
etal., 2017; McMorris etal., 2015). When exercise inten-
sity was expressed in terms of Wmax, maximal oxygen
uptake (VO2max), or other units, we converted those val-
ues into HRmax equivalents. Details of this conversion are
presented later in the Data Preprocessing section.
4 Moreau, Chou
Search Features
(through January 25, 2018)
Searching electronic databases (Scopus, PubMed, SPORTDiscus, PsycINFO,
Web of Science, ProQuest, ScienceDirect, Google Scholar), and Google,
using combinations of the following search terms:
• For high-intensity exercise: HI*, (for HIT, HIE, HIIT, HICT, HIIE),
High intensity*, physical activity, strength, strenuous, intense,
sprint interval, exercise intensity, anaerobic exercise
• For cognition: stroop, cogniti* (for cognitive, cognition), memory, learning,
attention, language, “executive func*” (for executive function),
intelligence, “reaction time”, rt, expertise, recall,
mental, processing, perception, psychomotor, “decision making”
• For short-term interventions: acute, short term, immediate, burst,
bout, short duration, single
Identification
Search Features
(outside of Databases)
• Scanning reference lists of previous
reviews and meta-analyses (e.g.,
Browne et al., 2017; Chang et al.,
2012; McMorris and Hale, 2012;
Lambourne and Tomporowski, 2010)
• Scanning reference lists of relevant
studies
Records Identified Through
Database Searching
(n = 2,320)
Additional Records Identified
(n = 383)
Criteria for Study Inclusion
• Must include a high-intensity exercise bout as an independent variable
• Must include a standardized cognitive test as a dependent variable
• High-intensity exercise bout must be performed within 1 day
• Cognitive testing must be performed after the exercise bout
• Must include a comparison group or condition at rest and/or performing exercise at a different level of intensity
• Must have randomized assignment for between-groups experiments
• Must have full-text in English and be published in a peer-reviewed journal
• Must have healthy human participants
Inclusion Criteria
Abstracts Screened
(n = 2,320)
Abstracts Screened
(n = 383)
Abstracts Excluded
(n = 2,224)
Abstracts Excluded
(n = 333)
Full-Text Articles Assessed
for Eligibility
(n = 96)
Full-Text Articles Assessed
for Eligibility
(n = 50)
Full-Text Articles Excluded
(n = 118)
• Exercise not intense enough
• Exercise not on a single day
• No cognitive test after exercise bout
• Data not provided in mean (SD ) format
• No adequate comparison group
EligibilityIncluded
Studies Included in Meta-Analysis (n = 28)
• Within-Subjects Studies (n = 18)
• Between-Groups Studies (n = 10)
49 Independent Samples
147 Effect Sizes
Total N = 1,177
Fig. 1. Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) flow diagram of literature search and study
inclusion.
Exercise and Executive Function 5
We chose our intensity thresholds (high, very high,
and maximal) by integrating the two definitions.3 High
intensity corresponded to 77% to 88.5% HRmax, 59.5% to
79.9% Wmax, or an equivalent—similar to the first thresh-
old. Very high intensity corresponded to 88.6% to 99.9%
HRmax, 80% to 99.9% Wmax, or an equivalent—similar to
the second threshold. Maximal intensity corresponded
to ≥ 100% HRmax, ≥ 100% Wmax, or an equivalent.
Definitions of acute exercise and executive func-
tion. Acute exercise refers to exercise performed on a
single day (American College of Sports Medicine, 2010).
Only standardized cognitive tasks or analogues were
included. For search purposes, we included cognitive
domains that are either subcomponents of, or related to,
executive function, such as attention, cognitive control,
cognitive flexibility, fluid intelligence, inhibitory control,
planning, and working memory. See Table S2 in the Sup-
plemental Material available online for details on the full
categorization process.
Search
Sources. We searched the following databases: Scopus,
PubMed, PsycINFO, ScienceDirect, Web of Science, Pro-
Quest, SPORTDiscus, and Google Scholar. Searches were
performed between January 10 and January 15, 2018; all
studies published up until the date of the search were con-
sidered for inclusion. We also searched relevant reviews
and meta-analyses (Browne et al., 2017; Y. K. Chang
etal., 2012; Cooper, Dring, & Nevill, 2016; Lambourne &
Tomporowski, 2010; McMorris & Hale, 2012; Stork, Banfield,
Gibala, & Martin Ginis, 2017), together with reference lists,
to identify other relevant articles (see details at http://osf
.io/59cgd).
Strategy. In each database, a minimum of two different
searches were performed. We used the following combi-
nation of key terms or phrases across all databases:
“HIIT,” “HICT,” “HIT,” “High intensity interval,” and “High
intensity intermittent,” plus either “exercise” or “training,”
plus any of “stroop,” “cognitive,” “cognition,” “memory,”
“learning,” “attention,” “perception,” “language,” and
“executive func*" (* = truncated). In addition to these two
searches, minor variations were performed to find all rel-
evant articles. Search exemplars in the Scopus database
are provided in Table S1 in the Supplemental Material. A
record of all search terms and procedures is available at
http://osf.io/59cgd.
Selection. E. Chou screened studies for initial inclusion
on the basis of titles and abstracts, with advice on inclu-
sion criteria from D. Moreau. If an initial decision could
not be made, E. Chou examined the full text for eligibility.
Articles unable to be categorized by E. Chou alone on the
basis of the outlined criteria were referred to D. Moreau,
who decided which articles to include. The full selection
process is presented in Figure. 1. All full-text studies con-
sidered and the rationale behind their inclusion or exclu-
sion are available at http://osf.io/kqwyc.
Data collection. E. Chou collected the data. For all
studies, information was collected on a number of vari-
ables. The complete list of these variables is included in
List S1 in the Supplemental Material; full data are avail-
able at http://osf.io/72pfv. E. Chou contacted authors by
e-mail (n = 11) to request unpublished data, with the
deadline for replying set to March 30, 2018. Five authors
provided us with unpublished data; we included data
from four of these authors because they matched all
inclusion criteria.
Moderators
Key variables identified from previous studies were
included as moderators in all analyses. These variables
included research-protocol characteristics (study design,
comparison group, and study quality), exercise char-
acteristics (duration, intensity, mode, and rhythm),
cognitive-task characteristics (timing of cognitive testing,
cognitive-task domain, and baseline cognitive testing),
and sample characteristics (fitness and age). We provide
further details about each of these variables hereafter.
Research-protocol characteristics. We characterized
whether studies included a within-subjects or between-
groups design. This distinction was thought to be particu-
larly important in the context of exercise interventions—a
meta-analysis by Y. K. Chang etal. (2012) found signifi-
cant improvements in cognitive performance following
exercise in within-subjects, repeated measures studies
(d = 0.11) but not in between-groups, single-measure
studies (d = 0.06), although the difference between the
two types of designs was not itself significant. We also
documented whether studies compared cognitive perfor-
mance following high-intensity exercise with performance
after rest or with performance following lower intensities
of exercise. We included the comparison group as a mod-
erator on the basis of previous findings showing that
light-to-moderate exercise elicits cognitive gains that are
quantitatively different from test-retest effects and because
of current discrepancies regarding the effect of high-
intensity exercise on cognition (Browne etal., 2017; Y. K.
Chang etal., 2012; McMorris & Hale, 2012).
Finally, studies were categorized into three levels of
quality (low, average, and high) on the basis of the
design properties. This is important given that previous
studies have identified an increase in the magnitude of
6 Moreau, Chou
reported effect sizes within a study as the number of
threats to internal validity (i.e., the risk of bias) increases
(Etnier etal., 1997). To mitigate the risk of bias from
individual studies, we assessed study quality following
Cochrane guidelines (Higgins etal., 2011). Study quality
was assessed on the basis of the variables described in
List S1 in the Supplemental Material. E. Chou graded
study quality according to whether specific criteria were
met (no = 0, partial = 1, yes = 2). Items that were not
reported or not applicable were not used in the calcula-
tion of the quality score. Criteria deemed more likely
to affect the risk of bias were given greater weighting
than other criteria (minimum weighting = 1; maximum
weighting = 3). Although the two types of designs
(within subjects/between groups) had different assess-
ment criteria, this difference was reconciled by conver-
sions into comparable percentage scores. These
assessments are available in Tables S3 and S4 in the
Supplemental Material.
Exercise characteristics. Exercise duration was cate-
gorized as follows: 0 to 5 min, 6 to 10 min, 11 to 20 min,
and > 20 min. This categorization was based on the
results of Y. K. Chang etal. (2012), who, across all inten-
sities, found exercise durations of 0 to 10 min to have a
small, negative effect on cognition after exercise and
durations of 11 to 20 min and > 20 min to have small,
positive effects on cognition after exercise. In addition,
we characterized exercise intensity as high, very high,
and maximal on the basis of the aforementioned criteria.
Previous findings have been equivocal; Y. K. Chang etal.
(2012) reported cognitive improvements for both hard
and very hard exercise (when tested at least 1 min after
exercise) and McMorris and Hale (2012) finding moder-
ate—but not what they referred to as heavy—exercise to
improve processing speed during and after exercise.
Finally, we distinguished studies on the basis of both the
modality and rhythm of exercise. The former distinction
was motivated by a previous meta-analysis (Lambourne
& Tomporowski, 2010) that found cycling to lead to bet-
ter cognitive performance after exercise compared with
running. The latter variable (continuous vs. intermittent)
was modeled because of the rising number of studies
using interval-training paradigms. Previous studies have
not examined this variable in detail.
Cognitive-task characteristics. We analyzed the effect
of time of testing using the categories of 0 to 1 min, 1 to
10 min, and > 10 min after exercise. This categorization
was informed by a previous meta-analysis (Y. K. Chang
etal., 2012) that found no effect of high-intensity exercise
on cognitive performance when tests were administered
immediately (0–1 min) after high-intensity exercise and
small-to-moderate facilitating effects when tests were
administered > 1 min after high-intensity exercise. Exec-
utive-function tasks were further categorized relative to
the specific cognitive domains they tapped, namely atten-
tion, cognitive flexibility, inhibitory control, and working
memory. When a task targeted more than one domain, it
was labeled on the basis of its main subdomain. Finally,
studies either involved baseline testing and postexercise
testing or postexercise testing only. We added this mod-
erator variable given that studies with both time points
may be less biased than those for which only posttest
performance was available.
Sample characteristics. Participants were character-
ized as low fitness, moderate fitness, or high fitness. Y. K.
Chang etal. (2012) found high-fitness participants to be
the only group with significant cognitive improvements
both immediately following exercise (0–1 min) and after
a postexercise delay (> 1 min), whereas low-fitness and
moderate-fitness participants showed improvements only
immediately after exercise for the former and only after a
postexercise delay for the latter. However, Etnier etal.
(2006) did not find a clear relationship between fitness
and cognitive performance overall. Like previous research-
ers (see Y. K. Chang etal., 2012), we further categorized
age into the following groups: children (6–13 years), ado-
lescents (14–17 years), young adults (18–30 years), middle-
aged adults (31–60 years), and older adults (> 60 years).
Note that Y. K. Chang et al. (2012) found significant
improvements in cognition following exercise for all age
groups except children ages 6 to 13.
Continuous moderators. When studies had continuous
data available, we also conducted continuous-moderator
analyses for the following moderators: exercise duration at
high intensity, exercise intensity of high-intensity condi-
tion, timing of cognitive testing, study quality, and partici-
pant age. Although continuous-moderator analyses help
to increase statistical power compared with categorical-
moderator analyses (MacCallum, Zhang, Preacher, & Rucker,
2002), we chose to primarily report categorical analyses
because they allowed the inclusion of a larger number of
relevant studies.
Analyses
The search strategy identified 2,320 records through
database searching and 383 additional records through
other search methods. Of these, 146 full-text articles
were examined for eligibility. Twenty-eight studies were
included in the final meta-analysis (see Fig. 1). For clar-
ity, we present characteristics of within-subjects and
between-groups studies separately; however, these two
types of protocols were analyzed together in our mul-
tilevel model.4 Tables 1 and 2 present an overview of
7
Table 1. Research Protocol and Sample Characteristics for All Within-Subjects Studies Included in the Meta-Analysis
Participants Condition
Study NMale (%)
Mean age
(years) Mean BMI Fitness 1 (highest intensity) 2 (lowest intensity) 3 4
No. of
effect sizes
Alves etal. (2014) 22 40.91 53.7 25.7 — HIIT Stretching — — 5
Berse etal. (2015) 227 53.2 14.8 — — HIIT Resting — — 2
Budde etal. (2012)a46 56.5 23.11 — — Intermittent
maximal exercise
Resting — — 3
Cooper, Bandelow, etal.
(2016)
44 47.73 12.6 18.9 — Sprints Resting — — 7
Craft (1983)b31 100 8.5 — — 10-min exercise Resting 1-min exercise 5-min exercise 3
Etnier etal. (2016) 16 56.25 23.06 — — VO2max session Vt −20% session Vt +20% session — 4
Kamijo etal. (2004)c12 100 27.5 — — HIE Resting Low-intensity
exercise
Medium-intensity
exercise
2
Kamijo, Nishihira,
Higashiura, &
Kuroiwa (2007)
12 100 25.7 — — Hard exercise Resting Light exercise Moderate
exercise
12
Kao, Westfall, Soneson,
Gurd, & Hillman
(2017)
64 42.19 19.2 23.8 High (VO2max) HIIT Resting Continuous
aerobic
exercise
— 3
Kujach etal. (2018) 25 64 21 — Low (sedentary) HIIT Resting — — 4
Lambrick, Stoner, Grigg,
& Faulkner (2016)
20 45 8.8 18.1 — Intermittent exercise Moderate continuous
exercise
— — 1
Llorens, Sanabria, &
Huertas (2015)d
18 100 22 24.3 — Intense exercise Resting — — 4
Llorens, Sanabria,
Huertas, Molina, &
Bennett (2015)
9 66.67 26 — Low (VO2max) Intense exercise Resting — — 3
Peruyero, Zapata, Pastor,
& Cervelló (2017)
44 52.3 16.39 — — Mainly vigorous
exercise
Resting Mainly light
exercise
— 6
I. Ramos etal. (2017) 9 44.44 10.3 16.2 — 110% of LT Seated drawing 90% of LT — 10
Tsukamoto etal. (2016) 12 100 22.9 22.4 High (physically
active)
HIIT Moderate continuous
exercise
— — 6
Walsh etal. (2018) 22 13.64 20 22.7 — HIIT Resting — — 3
Wohlwend, Olsen,
Håberg, & Palmer
(2017)
27 50 24.27 — High (VO2max) High-intensity
running
Low-intensity
running
Medium-
intensity
running
— 4
Note: ADHD = attention-deficit/hyperactivity disorder; BMI = body mass index; HIE = high-intensity exercise; HIIT = high-intensity intermittent exercise; LT = lactate threshold; — = data not provided; VO2max =
maximal oxygen uptake; Vt = ventilatory threshold.
aWhole-group analyses (not by fitness). bExcluded ADHD group from analysis. cOne outlier effect size. dExperiment 1 only.
8
Table 2. Research Protocol and Sample Characteristics for All Between-Groups Studies Included in the Meta-Analysis
Study k
Total
N Na
Malesa
(%)
Mean
agea
(years)
Mean
BMIaFitnessa
Group No. of
effect
sizes
1 (highest
intensity)
2 (lowest
intensity) 3 4
Basso, Shang, Elman,
Karmouta, & Suzuki
(2015)b
8 85 11 40 20.45 23.63 — Exercise (four
independent
groups)
Rest: video
watching
(four
independent
groups)
— — 8
H. Chang, Kim, Jung,
& Kato (2017)c
2 18 9 0 22.1 21.6 — HIE Resting Moderate-
intensity
exercise
— 4
Córdova, Silva, Moraes,
Simões, & Nóbrega
(2009)
4 48 12 0 63.1 24.3 Low (older
adults)
110% of AT Resting 60% of AT 90% of AT 18
Etnier, Labban,
Piepmeier, Davis, &
Henning (2014)
2 43 19 34.88 11.5 — Moderate
(average
PACER scores)
HIE Resting — — 3
Hwang, Brothers, etal.
(2016)
2 58 29 48.3 22.84 22.63 Moderate
(VO2max)
HIE Resting — — 6
Hwang, Castelli, &
Gonzalez-Limad
(2016)
2 30 15 40 23.4 22.21 Moderate
(VO2max)
Exercise +
sham laser
Control (sham
exercise +
sham laser)
— — 4
Lemmink & Visschere
(2005)
2 16 8 100 20.9 — High (soccer
players)
HIIT Rest:
newspaper
reading
— — 4
Netz, Tomer, Axelrad,
Argov, & Inbar
(2007)
3 58 20 30 56.12 — Moderate
(Baecke
questionnaire)
HIE Rest: movie
watching
Moderate-
intensity
exercise
— 4
Whyte, Gibbons, Kerr,
& Moran (2015)
2 40 20 100 21.05 — High (athletes) HIIT Resting — — 2
Zimmer etal. (2016) 4 121 30 70 23.9 21.984 High (students
studying
sport)
HIE Control (foam
rolling, 35
m)
Low-
intensity
exercise
Medium-
intensity
exercise
12
Note: AT = anaerobic threshold; BMI = body mass index; HIE = high-intensity exercise; HIIT = high-intensity interval training; — = data not provided; PACER = Progressive Aerobic
Cardiovascular Endurance Run; VO2max = maximal oxygen uptake. The Baecke questionnaire is from Baecke, Burema, and Frijters (1982).
aGroup 1 only. bEight independent samples (4 testing delays times 2 conditions). cTwo outlier effect sizes; excluded resistance exercise group. dExcluded laser-therapy groups.
eExcluded results after exercise blocks 2 and 3.
Exercise and Executive Function 9
research protocols and sample characteristics for within-
subjects and between-groups studies, respectively.
Data preprocessing. When exercise intensity was not
expressed in terms of HRmax—for example, %Wmax,
%VO2max, or percentage of heart rate reserve (%HRR,
where HRR = HRmax – HRrest)—it was converted into
HRmax (Arts & Kuipers, 1994; Lounana etal., 2007). In
addition, when average age was available, HRmax was
estimated using the formula HRmax = 208 − (0.7 × Age)
from Tanaka, Monahan, and Seals (2001). Note that based
on these conversion formulas, 77% HRmax corresponded
to approximately 59.5% Wmax. When intensity could not
be converted to an HRmax equivalent, such as with scores
from the Rated Perceived Exertion scale, the article was
examined thoroughly to determine the intensity on the
basis of its description. For example, exercise to fatigue
or exhaustion was coded as maximal intensity. We
excluded articles from our analyses if accurate categori-
zation was not possible. Exercise characteristics are fur-
ther described in Tables 3 and 4.
Studies reported two main types of cognitive test
scores: those with postexercise scores only and those
with both baseline and postexercise scores. For the lat-
ter, we calculated mean-difference (posttest-pretest)
scores. Baseline scores were subtracted from the post-
bout scores for each study, correcting for bias and
adjusting for the direction of each cognitive test—higher
scores indicated better performance in some instances
(e.g., accuracy), whereas lower scores indicated better
performance for other measures (e.g., reaction time).
Pooled standard deviation values (SDpooled) were calcu-
lated for each mean-difference score. Where available,
moderator-variable data related to each mean-difference/
raw score were recorded in additional columns. Note
that when studies included more than one posttest ses-
sion, only the testing session closest in time to the exer-
cise bout was analyzed, given the potential for practice
effects to contaminate subsequent testing.
Because different cognitive tests measured perfor-
mance on different scales, and because the cognitive
test scores within each group or condition were avail-
able in either mean-difference or raw-score format, we
calculated bias-corrected Cohen’s d (Cohen, 1992) to
standardize the differences in mean scores between
groups or conditions. For between-groups studies, we
compared the scores between different groups under
different conditions; for within-subjects studies, we
compared the scores between the same group under
different conditions. Cognitive-task characteristics are
further described in Tables 5 and 6.
Main analyses. We conducted all analyses in the R soft-
ware environment (Version 3.5.1; R Core Team, 2018)
using the metafor (Version 2.1; Viechtbauer, 2010) and
multcomp (Version 1.4; Hothorn etal., 2013) packages. We
ran restricted maximum-likelihood, multilevel, random-
effects meta-analyses to estimate overall effects as well as
the heterogeneity between effect sizes. In addition, we con-
ducted mixed-effects meta-analyses to investigate whether
the observed heterogeneity could be explained by the
effects of moderator variables. The R code for our analyses
is available online at http://osf.io/pg53m.
We conducted a multilevel analysis to account for the
dependency between effect sizes (Assink & Wibbelink,
2016). Multilevel analyses group effect sizes on the basis
of higher-level clustering variables (Konstantopoulos,
2011) to prevent inflation and overconfidence in meta-
analytic estimates (Van den Noortgate, López-López,
Marín-Martínez, & Sánchez-Meca, 2013). We used a pro-
cedure that adds random effects at each level of possible
dependency to reduce inflation while preserving valu-
able information provided by studies that report mul-
tiple effect sizes (Viechtbauer, 2010).
Our multilevel analysis had three clustering variables
(i.e., levels). First, we modeled dependency at the
study-design level (i.e., within-subjects and between-
groups designs). Second, we modeled dependency at
the sample level—within samples reporting results in
multiple tests and/or samples with two or more com-
parison groups. For example, a group undergoing three
sessions (resting, low-intensity exercise, and high-
intensity exercise) included dependency across all dif-
ference scores given that the high-intensity session
score was compared with both the resting and low-
intensity session scores. Finally, we modeled depen-
dency within the cognitive domains for which multiple
test scores were reported (e.g., response time and accu-
racy in a Stroop task) within individual samples.
Outliers. Effect sizes whose residuals were more than
three standard deviations from the mean were considered
outliers and excluded from our main analysis. There were
three effect sizes meeting this criteria (see Fig. S1 in the
Supplemental Material). In addition, Cook’s distance was
used as an exploratory estimate to detect outliers. This
method takes into account the relative influence of each
effect size on the overall estimate. Effect sizes with a Cook’s
distance more than three times the mean Cook’s distance
were labeled as possible outliers, although they were not
excluded from our main analyses. There were 16 effects
meeting this criteria (see Fig. S2 in the Supplemental Mate-
rial); however, because this criterion was not defined a
priori, we provided the full data set before outlier removal,
together with the R code, at http://osf.io/cauxq.
Interaction analyses. To test whether the moderating
effects of individual categorical moderators were dependent
10 Moreau, Chou
on other moderator variables, we conducted interaction anal-
yses. We limited the scope of interactions to two moderators
at a time and chose to include the following six moderators
in the interaction analyses: comparison group, exercise
rhythm, exercise intensity, exercise duration, timing of cogni-
tive testing, and cognitive domain. These moderators were
chosen because they were thought to relate to each other in
terms of their influence on the effect of high-intensity exer-
cise on executive function. As these analyses involved multi-
ple pairwise comparisons between each level of each
moderator, we controlled the familywise error rate using the
Holm method (Holm, 1979).
Heterogeneity and model comparisons. We estimated
heterogeneity (between-studies variability in effect sizes)
in two ways. First, we calculated the I2 statistic, which esti-
mates the percentage of between-studies variation that is
due to true differences (e.g., differences in research-
protocol characteristics, differences in exercise intensity)
rather than error. Second, using Cochran’s Q statistic, we
tested whether the moderators could account for the
between-studies heterogeneity. We tested whether a
model including the significant moderator reduced the
model’s residual heterogeneity to nonsignificance. If
residual heterogeneity was still significant, we combined
Table 3. Exercise Characteristics for All Within-Subjects Studies Included in the Meta-Analysis
Study
Exercise
description Modality Rhythm ƛ (%HRmax) Intensity Ttotal (min) THI (min)
Alves etal. (2014) HIIT Cycling Int 85.5 High 25 10
Berse etal. (2015) HIIT Cycling Int 100 Maximal 12 Variable
Budde etal. (2012) Int maximal
exercise
Running Int 100 Maximal 6 6
Cooper, Bandelow, etal.
(2016)
Sprints Running Int 90.87 Very high 10 1.67
Craft (1983) 10-min exercise Cycling Cont 84.1 High 10 10
Etnier etal. (2016) VO2max
session
Running Cont 100 Maximal 30 30
Kamijo etal. (2004) HIE Cycling Cont 100 Maximal Variable Variable
Kamijo, Nishihira,
Higashiura, & Kuroiwa
(2007)
Hard exercise Cycling Cont 78.6 High 22 20
Kao, Westfall, Soneson,
Gurd, & Hillman (2017)
HIIT Running Int 85 High 9 4.5
Kujach etal. (2018) HIIT Cycling Int 78.3 High 6 4
Lambrick, Stoner, Grigg,
& Faulkner (2016)
Int exercise Running Int 100 Maximala15 5.5
Llorens, Sanabria, &
Huertas (2015) Exp. 1
Intense exercise Cycling Cont 79 High 13 5
Llorens, Sanabria,
Huertas, Molina, &
Bennett (2015)
Intense exercise Cycling Cont 78.84 High 24 15
Peruyero, Zapata, Pastor,
& Cervelló (2017)
Mainly vigorous
exercise
Zumba dancingbInt — cHigh 24 Variable
I. Ramos etal. (2017) 110% of LT Running Cont — dHigh 10 10
Tsukamoto etal. (2016) HIIT Cycling Int 95.32 Very high 36 16
Walsh etal. (2018) HIIT Burpees, jumping
jacks, mountain
climbing, squat
jumpsb
Int 94 Very high 11 5.5
Wohlwend, Olsen,
Håberg, & Palmer
(2017)
HIE Running Int 91 Very high 38 16
Note: HIE = high-intensity exercise; HIIT = high-intensity interval training; Cont = continuous; Int = intermittent; HRmax = maximum heart rate;
LT = lactate threshold; — = data not provided THI = exercise duration at high intensity; Ttotal = total exercise duration; VO2max = maximal oxygen
uptake; ƛ = maximum intensity.
aThis was described as moderate intermittent exercise but included 4.5 min of heavy exercise and 1 min of severe exercise. bThese modalities
were analyzed under “running” in moderation analyses. cVigorous exercise measured with an accelerometer. dNo equation available.
Exercise and Executive Function 11
all moderators in our model (including the nonsignifi-
cant ones) to account for any residual heterogeneity. We
evaluated whether the three models (no moderator, sig-
nificant moderators only, all moderators) were different
from each other in terms of goodness of fit and parsi-
mony using the Akaike information criterion–corrected
(AICc) and the Bayesian information criterion (BIC).
Results
Across 28 studies with a range of protocols, as well as
exercise, cognitive task, and sample characteristics,
acute high-intensity exercise had a small, significant,
facilitating effect on executive function after exercise
compared with other conditions (lower-intensity exer-
cise and rest combined). The meta-analytic estimate of
this effect was d = 0.24, 95% confidence interval (CI) =
[0.13, 0.35]. Our main measure of heterogeneity (I2 =
49.08% across all studies) indicated that about half the
variability between effect sizes was potentially due to
real differences between studies, that is, differences that
are not related to random error. Figure 2 provides a
general overview of our findings.
Moderator analyses
Categorical-moderator analyses. Comparison group
was a significant moderator (p = .002; see Table 7 for
details); that is, high-intensity exercise had a facilitating
effect on executive function compared with rest (d = 0.34)
but not significantly different from that of lower-intensity
exercise (d = 0.07). Study quality was a marginally signifi-
cant moderator (p = .08), with the facilitating effect
decreasing in magnitude as study quality increased. Other
moderator variables did not yield significant moderating
effects despite effect sizes being significantly different
from zero at specific levels of those moderators. All details
are reported in Table 7.
Because executive function is an umbrella term that
encompasses many different subdomains, we also
examined the subcomponents attention, cognitive flex-
ibility, inhibitory control, and working memory sepa-
rately. High-intensity exercise had a small, facilitating
effect on cognitive performance for each of these four
subdomains, a finding that was consistent with our
overall analysis. Figure 3 shows forest plots for each of
these subdomains.
Table 4. Exercise Characteristics for All Between-Groups Studies Included in the Meta-Analysis
Study
Exercise
description Modality Rhythm ƛ (%HRmax) Intensity Ttotal (min) THI (min)
Basso, Shang, Elman,
Karmouta, & Suzuki
(2015)
Vigorous Cycling Cont 85 High 60 50
H. Chang, Kim, Jung, &
Kato (2017)
HIE Running Cont 85.5 High 40 30
Córdova, Silva, Moraes,
Simões, & Nóbrega
(2009)
110% of AT Cycling Cont 85.6 High 25 20
Etnier, Labban,
Piepmeier, Davis, &
Henning (2014)
PACER task Running Int 100 Maximal Variable Variable
Hwang, Brothers, etal.
(2016)
HIE Running Cont 87.9 High 20 10
Hwang, Castelli, &
Gonzalez-Lima (2016)
HIE Running Cont 90.98 Very high 20 10
Lemmink & Visscher
(2005)
Int Cycling Int 86.8 High 27 16
Netz, Tomer, Axelrad,
Argov, & Inbar (2007)
Moderate (60%
heart-rate
reserve)
Running Cont 78.7 High 44 35
Whyte, Gibbons, Kerr,
& Moran (2015)
HIIP Sprinting/jumping/
shuffling circuita
Int 94.6 Very high Variable
(> 10 min)
Variable
(> 5 min)
Zimmer etal. (2016) HIE Cycling Cont 85 High 35 30
Note: AT = anaerobic threshold; HIE = high-intensity exercise; HIIP = high-intensity intermittent-exercise protocol; Cont = continuous; Int =
intermittent; HRmax = maximum heart rate; PACER = Progressive Aerobic Cardiovascular Endurance Run; THI = exercise duration at high intensity;
Ttotal = total exercise duration; ƛ = maximum intensity.
aThis modality was analyzed under “running” in the moderation analyses.
12 Moreau, Chou
Continuous-moderator analyses. None of the planned
continuous moderators yielded a significant effect (see
Table 8 for details); that is, when examined separately,
the effect of high-intensity exercise on executive function
did not change significantly as a function of unit increases
or decreases in any of the moderators. The effect of an
exploratory moderator, percentage of male participants,
was marginally significant (p = .058), with the facilitating
effect of high-intensity exercise on executive function
decreasing as the percentage of male participants increased.
Interaction analyses. Of the 15 pairs of moderators
tested, two potentially meaningful interactions emerged.
The first was the interaction between comparison group
and exercise intensity, and the second was the interaction
between exercise rhythm and timing of cognitive testing.
When lower-intensity exercise was the comparison
group, participants undergoing very-high-intensity exer-
cise (three studies) experienced significantly larger
improvements in executive performance compared with
those undergoing high-intensity exercise (eight studies;
Δd = 0.35, p = .002). When exercise was intermittent,
participants tested 0 to 1 min after exercise (10 studies)
experienced significantly larger improvements in execu-
tive performance compared with those tested > 10 min
after exercise (three studies; Δd = 0.77, p = .009). Most
interaction analyses for which both moderators had three
or more levels (i.e., both moderators belonged to any of
exercise intensity, exercise duration, timing of cognitive
testing, or cognitive domain) yielded nonsignificant pair-
wise comparisons as a result of the low number of stud-
ies for each level and because of the Holm correction.
These analyses are available at http://osf.io/9jz3s.
Heterogeneity and model comparisons. The total
heter ogeneity present in the model without moderators was
significant, Cochran’s Q(146) = 264.151, p < .0001. Only
the categorical-moderator-comparison group significantly
reduced heterogeneity (p < .0001), whereas the effects of
the categorical moderator study quality and the continu-
ous moderator percentage of male participants were mar-
ginally significant (p = .082 and p = .058, respectively).
Although the inclusion of comparison group in the
model significantly reduced heterogeneity, the remain-
ing heterogeneity was still significant, QE(145) =
263.449, p < .0001. The inclusion of additional modera-
tors (i.e., study design, comparison group, cognitive-
task domain, categorical study quality, categorical
exercise intensity, baseline cognitive testing, and per-
centage male) available for most effect sizes decreased
Table 5. Cognitive-Task Characteristics for All Within-Subjects Studies Included in the Meta-Analysis
Study Cognitive test(s)aBaselinebNct (min)
Alves etal. (2014) Victoria Stroop Test, digit-span test Yes 1 0
Berse etal. (2015) Shifting task No 1 0
Budde etal. (2012) d2 Test of Attention No 1 0
Cooper, Bandelow, etal. (2016) Stroop interference test, digit-symbol
substitution task, Corsi blocks
Yes 2 0
Craft (1983) WISC-R digit span, WISC-R coding B, ITPA
visual sequential memory
No 1 0
Etnier etal. (2016) Rey Auditory Verbal Learning Task No 3 0
Kamijo etal. (2004) S1-S2 reaction-time task No 1 3
Kamijo, Nishihira, Higashiura, & Kuroiwa (2007) Modified flanker task No 1 3
Kao, Westfall, Soneson, Gurd, & Hillman (2017) Modified flanker task No 1 20
Kujach etal. (2018) Stroop Color and Word Test Yes 1 15
Lambrick, Stoner, Grigg, & Faulkner (2016) Stroop interference test Yes 3 1
Llorens, Sanabria, & Huertas (2015), Exp. 1 Exogenous spatial-attention task No 2 0
Llorens, Sanabria, Huertas, Molina, & Bennett (2015) Visual-search task No 1 0
Peruyero, Zapata, Pastor, & Cervelló (2017) Stroop Yes 1 5
I. Ramos etal. (2017) Modified flanker task, free-recall test, Trail
Making Test parts A and B, tangram puzzle
No 1 20
Tsukamoto etal. (2016) Stroop Color and Word Test Yes 4 0
Walsh etal. (2018) d2 Test of Attention No 1 10
Wohlwend, Olsen, Håberg, & Palmer (2017) Connors Continuous Performance Test Yes 1 0
Note: N = number of postexercise cognitive test repetitions; t = approximate timing of first postexercise cognitive test; WISC-R, Wechsler Intelligence
Scale for Children–Revised (Wechsler, 1974); ITPA = Illinois Test of Psycholinguistic Abilities.
aIncludes only the tests analyzed in our meta-analysis. bDefined as whether the study has a preexercise cognitive test. cBarring one study on
long-term memory (Etnier etal., 2016), for which we analyzed all timings, we analyzed only the first of these postexercise cognitive tests to
minimize the contribution of test–retest effects (minutes after exercise/control).
Exercise and Executive Function 13
heterogeneity substantially, although it remained sig-
nificant, QE (132) = 230.5262, p < .0001.
Both AICc and BIC criteria provided converging evi-
dence in their assessments of model fit and parsimony.
The best model was the model with comparison group
as the moderator (AICc = 151.7, BIC = 166.2), followed
by the model without moderators (AICc = 158.8, BIC =
170.5). Meanwhile, the model incorporating the most
moderators was least preferred (AICc = 169, BIC =
215.1). Together, these results suggested that the non-
significant moderators did not account for enough
residual heterogeneity to justify their inclusion in the
meta-analytic model.
Assessment of metabias
We assessed bias in three different ways. First, we plot-
ted funnel plots to detect possible publication bias.
Funnel plots help visualize the relationship between
each effect size and their corresponding standard error.
In a meta-analysis unaffected by publication bias, effect
sizes from larger samples, which have smaller standard
errors, should cluster around the mean effect. In
contrast, effect sizes from smaller samples, with larger
standard errors, are expected to be more dispersed
around the mean. In addition, all effect sizes are
expected to be represented somewhat equally on both
sides of the mean, forming a symmetric, inverse, funnel-
like shape. Asymmetry in funnel plots may result from
reporting biases, poor methodological quality, true het-
erogeneity, artifacts, or chance (Sterne etal., 2011). The
funnel plot for our overall analysis (Fig. 4) was fairly
symmetrical and evenly distributed, and few effects fell
outside the 99% CI, suggesting that the present meta-
analysis was not substantially affected by publication
bias.
Second, we analyzed whether study quality (cate-
gorical) affected the estimated effect. According to our
criteria (Tables S3 and S4 in the Supplemental Material),
there were 23 high-quality studies, 4 average-quality
studies, and 1 low-quality study. Study quality had a
marginally significant moderating effect on the effect
of high-intensity exercise on executive function (p =
.082). Whereas high-quality studies reported a small
facilitating effect (d = 0.19, 95% CI = [0.07, 0.31]),
average-quality studies reported a moderate facilitating
Table 6. Cognitive-Task Characteristics for All Between-Groups Studies Included in the Meta-Analysis
Study Cognitive test(s) BaselineaN t(min)
Basso, Shang, Elman, Karmouta, &
Suzuki (2015)b
Hippocampal measures:
Hopkins Verbal Learning Test–Revised (delayed recall,
retention, recognition), modified Benton Visual Retention Test
Yes 4c30
Prefrontal measures:
Stroop test, Symbol Digits Modalities Test, Hopkins Verbal
Learning Test–Revised (total recall), digit-span test
(forward and backward), Trail Making Test parts A and B
H. Chang, Kim, Jung, & Kato
(2017)
Stroop test Yes 1 15
Córdova, Silva, Moraes, Simões, &
Nóbrega (2009)
Simple response time, verbal fluency, Tower of Hanoi, Trail
Making Test parts A and B
Yes 1 8
Etnier, Labban, Piepmeier, Davis,
& Henning (2014)
Rey Auditory Verbal Learning Task (lists A and B) No 2d0
Hwang, Brothers, etal. (2016) Stroop test, Trail Making Test parts A and B Yes 1 10
Hwang, Castelli, & Gonzalez-Lima
(2016)
Psychomotor vigilance task, delayed match-to-sample task Yes 1 13
Lemmink & Visscher (2005) Choice reaction-time test No 1 0
Netz, Tomer, Axelrad, Argov, &
Inbar (2007)
Alternate-uses test, digit-span test (forward) Yes 1 5
Whyte, Gibbons, Kerr, & Moran
(2015)
Symbol Digits Modalities Test, Stroop interference test Yes 1 0.25
Zimmer etal. (2016) Stroop test Yes 1 0
Note: The cognitive tests listed include only those used in our meta-analysis. N = number of postexercise cognitive test repetitions; t =
approximate timing of first postexercise cognitive test (minutes after exercise/control). The Hopkins Verbal Learning Test is a product of PAR
(Lutz, FL). The Benton Visual Retention Test is described in Benton (1992). The Symbol Digits Modalities test is described in Smith (1982). The
Rey Auditory and Verbal Learning Test is described in Schmidt (1996).
aThis indicates whether the study had a preexercise cognitive test. bIn this study, prefrontal and hippocampal measures were recorded in
summary z scores. cAll test sessions were examined because this study examined test timing as an independent variable. dBoth of these were
examined because the first repetition corresponded to working memory and the second to long-term memory.
14 Moreau, Chou
Ramos et al. (2017), 7
Craft (1983), 2
Ramos et al. (2017), 9
Kamijo et al. (2007), 12
Llorens, Sanabria, & Huertas (2015), 2
Córdova et al. (2009), 12
Córdova et al. (2009), 6
Llorens, Sanabria, & Huertas (2015), 1
Ramos et al. (2017), 3
Kamijo et al. (2007), 3
Craft (1983), 1
Llorens, Sanabria, & Huertas (2015), 3
Córdova et al. (2009), 9
Kamijo et al. (2007), 8
Lemmink & Visscher (2005), 3
Kamijo et al. (2004), 2
Kamijo et al. (2007), 7
Alves et al. (2014), 2
Budde et al. (2012), 2
Whyte et al. (2015), 2
Netz et al. (2007), 4
Ramos et al. (2017), 8
Ramos et al. (2017), 10
Kamijo et al. (2004), 1
Córdova et al. (2009), 3
Córdova et al. (2009), 13
Kamijo et al. (2007), 9
Kamijo et al. (2007), 10
Zimmer et al. (2016), 11
Córdova et al. (2009), 10
Wohlwend et al. (2017), 4
Kujach et al. (2018), 3
Córdova et al. (2009), 7
Tsukamoto et al. (2016), 2
Kujach et al. (2018), 2
Córdova et al. (2009), 14
Córdova et al. (2009), 2
Córdova et al. (2009), 8
Etnier et al. (2014), 1
Córdova et al. (2009), 11
Córdova et al. (2009), 4
Tsukamoto et al. (2016), 4
Cooper et al. (2016), 7
Whyte et al. (2015), 1
Chang et al. (2017), 1
Study Name Effect Size (Cohen’s d) and 95% CI
−2 −1
01
a
Fig. 2. (continued on next page)
effect (d = 0.59, 95% CI = [0.26, 0.92]), and there were
too few low-quality studies to reliably estimate an aver-
age effect size. Our finding suggested that studies of
lower quality reported larger effect sizes because of
poor control of confounding variables. This result fits
well with findings from earlier research (Etnier etal.,
1997) and might suggest a biasing effect from average-
quality studies.
To further address metabias, we constructed a
p-curve analysis (Simonsohn, Nelson, & Simmons, 2014)
for significant (p < .05) effect sizes in our studies. The
rationale for the p-curve analysis is that the p-value
Exercise and Executive Function 15
Study Name Effect Size (Cohen’s d) and 95% CI
−1 01
Basso et al. (2015), 6
Wohlwend et al. (2017), 1
Hwang, Brothers, et al. (2016), 5
Zimmer et al. (2016), 7
Hwang, Castelli, et al. (2016), 1
Córdova et al. (2009), 17
Zimmer et al. (2016), 10
Ramos et al. (2017), 2
Tsukamoto et al. (2016), 3
Alves et al. (2014), 5
Basso et al. (2015), 4
Tsukamoto et al. (2016), 5
Budde et al. (2012), 1
Alves et al. (2014), 1
Budde et al. (2012), 3
Kamijo et al. (2007), 4
Kujach et al. (2018), 1
Kujach et al. (2018), 4
Etnier et al. (2014), 2
Kao et al. (2017), 1
Kamijo et al. (2007), 2
Ramos et al. (2017), 5
Zimmer et al. (2016), 3
Zimmer et al. (2016), 1
Wohlwend et al. (2017), 3
Basso et al. (2015), 8
Berse et al. (2015), 1
Kamijo et al. (2007), 1
Zimmer et al. (2016), 9
Netz et al. (2007), 1
Zimmer et al. (2016), 4
Netz et al. (2007), 3
Lemmink & Visscher (2005), 1
Craft (1983), 3
Kamijo et al. (2007), 6
Cooper et al. (2016), 5
Berse et al. (2015), 2
Zimmer et al. (2016), 2
Córdova et al. (2009), 15
Wohlwend et al. (2017), 2
Tsukamoto et al. (2016), 6
Llorens, Sanabria, & Huertas (2015), 4
Cooper et al. (2016), 6
Cooper et al. (2016), 3
Cooper et al. (2016), 2
2
b
Fig. 2. (continued on next page)
16 Moreau, Chou
Study Name Effect Size (Cohen’s d) and 95% CI
−1 012
Llorens, Sanabria, Huertas, Molina, & Bennett (2015), 3
Etnier et al. (2016), 3
Hwang, Castelli, et al. (2016), 3
Peruyero et al. (2017), 6
Hwang, Brothers, et al. (2016), 6
Kamijo et al. (2007), 5
Peruyero et al. (2017), 1
Peruyero et al. (2017), 3
Etnier et al. (2016), 2
Basso et al. (2015), 1
Lemmink & Visscher (2005), 4
Etnier et al. (2016), 4
Zimmer et al. (2016), 8
Hwang, Brothers, et al. (2016), 2
Alves et al. (2014), 4
Walsh et al. (2018), 3
Lemmink & Visscher (2005), 2
Chang et al. (2017), 2
Netz et al. (2007), 2
Walsh et al. (2018), 1
Ramos et al. (2017), 4
Peruyero et al. (2017), 5
Walsh et al. (2018), 2
Kamijo et al. (2007), 11
Basso et al. (2015), 3
Córdova et al. (2009), 16
Ramos et al. (2017), 1
Hwang, Brothers, et al. (2016), 3
Lambrick et al. (2016)
Cooper et al. (2016), 1
Hwang, Brothers, et al. (2016), 4
Córdova et al. (2009), 1
Zimmer et al. (2016), 12
Zimmer et al. (2016), 5
Zimmer et al. (2016), 6
Basso et al. (2015), 5
Basso et al. (2015), 2
Tsukamoto et al. (2016), 1
Hwang, Castelli, et al. (2016), 4
Kao et al. (2017), 3
Hwang, Brothers, et al. (2016), 1
Alves et al. (2014), 3
Kao et al. (2017), 2
Ramos et al. (2017), 6
Basso et al. (2015), 7
Córdova et al. (2009), 18
c
Fig. 2. (continued on next page)
Exercise and Executive Function 17
−2 −1 0123
Llorens, Sanabria, Huertas, Molina, & Bennett (2015), 2
Llorens, Sanabria, Huertas, Molina, & Bennett (2015), 1
Peruyero et al. (2017), 4
Chang et al. (2017), 3
Chang et al. (2017), 4
Cooper et al. (2016), 4
Peruyero et al. (2017), 2
Etnier et al. (2014), 3
Hwang, Castelli, et al. (2016), 2
Etnier et al. (2016), 1
Córdova et al. (2009), 5
Study Name Effect Size (Cohen’s d) and 95% CI
RE Model, d = 0.2377, p < .0001, 95% CI = [.1257, .3496]
d
Fig. 2. Forest plot of effect sizes for all included studies. The sizes of the squares represent relative sample sizes. The diamond at the bot-
tom represents the overall effect. The dotted vertical line represents an effect size (d ) of zero. CI = confidence interval; RE = random effects.
distribution for a true effect that is not influenced by
reporting bias should contain fewer high (p > .04) than
low (p ≤ .01) significant p values (Simonsohn etal.,
2014). To construct a p curve, we first converted Cohen’s
d values to t values, adjusting for study design (within-
subjects or between-groups). We then generated the
curve using an online app (available at http://www
.p-curve.com/app4). The p curve (Fig. 5) was right-
skewed, with fewer large (p > .04) than small (p ≤ .01)
p values. This suggested no clear sign of publication
bias for the significant effect sizes analyzed. The power
estimate of the studies examined was relatively high
(74%, 90% CI = [56%, 86%]).
Discussion
Short but intense bursts of exercise have gained traction
in recent years as a viable substitute to aerobic exercise
to elicit cognitive improvements (e.g., Moreau etal.,
2017). Whether this form of exercise—typically referred
to as high-intensity exercise—is associated with facili-
tating or debilitating effects on cognitive performance
immediately following exercise, however, remains
unclear. The present meta-analysis provides a quantita-
tive answer to this question, together with an analysis
of the specific variables that help explain some of the
past discrepancies. In particular, we focused on the
effect of high-intensity exercise, defined as exercise
above 77% of one’s maximum heart rate, on postexercise
executive performance. We identified 28 studies examin-
ing this relationship, with comparisons to either lower-
intensity exercise or rest, and investigated subdomains
of executive function, including attention, cognitive flex-
ibility, inhibitory control, and working memory.
Overall, high-intensity exercise had a small, signifi-
cant facilitating effect on executive function after exer-
cise. This finding was in line with our prediction of an
effect, although we did not hypothesize a direction.
Importantly, this result was inconsistent with theoretical
predictions of impairing effects, such as the inverted-U
theory (Yerkes & Dodson, 1908), the reticular-activating
hypofrontality model (Audiffren, 2016; Dietrich &
Audiffren, 2011), and neurochemical hypotheses
(McMorris etal., 2016). However, it aligns with a previ-
ous meta-analysis by Y. K. Chang etal. (2012), who
found that high-intensity exercise improved postexer-
cise cognitive performance, and with a meta-analysis
by Ludyga etal. (2016), who reported gains in executive
performance associated with acute bouts of moderate-
intensity exercise. Ludyga etal. (2016) stated that their
meta-analysis “examined effects of aerobic exercise on
executive function based on moderate intensity,” and
further indicate that it therefore “remains unclear how
other exercise intensities influence executive control
and if those effects are further moderated by the
subjects’ characteristics” (p. 1622). Our meta-analysis
fills this gap in the literature, providing corroborating
evidence with more intense but shorter bouts of
exercise.
The general facilitating effect of high-intensity exer-
cise should not, however, obscure the inherently com-
plex dynamics related to the effect of exercise on
cognition. Because studies typically average perfor-
mance on a given cognitive task, a lot of information
18 Moreau, Chou
Table 7. Categorical Moderator Analyses
Moderator category, moderator variable, and planned
levels N d [95% CI]
Exercise
Exercise duration at high-intensitya
0–5 min 6 0.18† [−0.03, 0.40]
6–10 min 60.24* [0.04, 0.44]
11–20 min 5 0.21 [−0.04, 0.47]
> 20 min 6 0.29** [0.09, 0.48]
Exercise intensity (HRmax) of high-intensity conditiona
High (~77%–88.5% HRmax) 16 0.27*** [0.13, 0.42]
Very high (~88.6%–99.9% HRmax) 6 0.22 [−0.06, 0.50]
Maximal (100% HRmax) 6 0.15 [−0.11, 0.41]
Exercise modality
Cycling 13 0.15† [0.00, 0.30]
Running 15 0.32*** [0.17, 0.47]
Exercise rhythm
Continuous 14 0.28*** [0.12, 0.44]
Intermittent 14 0.19* [0.03, 0.36]
Cognitive task
Timing of cognitive testinga
0–1 min 14 0.18* [0.01, 0.34]
> 1–10 min 7 0.30** [0.08, 0.52]
> 10 min 8 0.28** [0.07, 0.49]
Cognitive task domain
Attention 22 0.17* [0.03, 0.32]
Cognitive flexibility 50.30* [0.00, 0.59]
General executive function 1 0.39 [−0.21, 0.98]
Long-term memory 2 0.20 [−0.32, 0.72]
Inhibitory control 12 0.27** [0.08, 0.46]
Working memory 80.32** [0.08, 0.55]
Baseline cognitive testing
Has baseline cognitive testing 16 0.24*** [0.10, 0.38]
No baseline cognitive testing 12 0.23* [0.04, 0.42]
Study design
Within-subjects 18 0.23 [−0.10, 0.57]
Between groups 10 0.25 [−0.10, 0.60]
Comparison group typeb
Resting group 21 0.34*** [0.20, 0.47]
Lower-intensity exercise group 14 0.07 [−0.09, 0.23]
Study qualitya
Low 1 0.27 [−0.46, 1.01]
Average 4 0.59*** [0.26, 0.92]
High 23 0.19** [0.07, 0.31]
Participant fitness
Low 3 0.24 [−0.13, 0.61]
Moderate 4 0.35* [0.04, 0.66]
High 6 0.05 [−0.20, 0.31]
Participant agea
6–13 4 0.13 [−0.15, 0.41]
14–18 2 0.51* [0.12, 0.90]
19–30 19 0.26*** [0.12, 0.40]
31–60 2 0.19 [−0.21, 0.60]
> 60 1 −0.04 [−0.55, 0.48]
Note: Boldface type indicates effects that were both significant and had an adequate (> 5) number
of individual studies supplying the effects. HRmax = maximum heart rate; N = number of independent
studies supplying categorical data.
aThese were also analyzed as continuous moderators. bThis was a significant moderator; the effect of
exercise on cognitive performance after exercise was different on at least two different levels of this
moderator.
†p < .10. *p < .05. **p < .01. ***p < .001.
Exercise and Executive Function 19
Córdova et al. (2009), 8
Córdova et al. (2009), 7
Córdova et al. (2009), 9
Craft (1983), 1
Ramos et al. (2017), 3
Craft (1983), 3
Ramos et al. (2017), 5
Etnier et al. (2014), 1
Alves et al. (2014), 1
Basso et al. (2015), 4
Ramos et al. (2017), 2
Hwang, Castelli, et al. (2016), 1
Ramos et al. (2017), 6
Basso et al. (2015), 2
Cooper et al. (2016), 1
Ramos et al. (2017), 1
Basso et al. (2015), 3
Ramos et al. (2017), 4
Basso et al. (2015), 1
Hwang, Castelli, et al. (2016), 2
Etnier et al. (2014), 2
Working Memory
Study Name Effect Size (Cohen’s d) and 95% CI
RE Model, d = 0.3189, p = .0079, 95% CI = [.0835, .5543]
a
−2 −1
0123
Fig. 3. (continued on next page)
can be lost about the fine-grained relationship between
exercise and cognitive performance (see, e.g., Moreau
& Corballis, 2018). For instance, it is possible that high-
intensity exercise leads to an early dip in performance
as a result of competing physiological resources fol-
lowed by a subsequent increase in performance as a
result of higher levels of activation. If both of these
phases (debilitating effect followed by a facilitating
effect) occur within the course of a cognitive task, aver-
age performance could represent a very coarse measure
of the effect of exercise on cognitive performance. The
lack of systematic analyses investigating cognitive
dynamics after exercise, together with inconsistencies
in defining thresholds of high-intensity, perhaps explain
some of the discrepancies between our results and
those of previous meta-analyses (Y. K. Chang etal.,
2012; Lambourne & Tomporowski, 2010; McMorris &
Hale, 2012). With this caveat in mind, moderator analy-
ses helped to further identify factors influencing the
effect of high-intensity exercise on executive function.
We discuss these factors in detail hereafter.
What characteristics of exercise matter
in eliciting cognitive enhancement?
The type of comparison or control used in the included
studies was a significant moderator. Specifically,
high-intensity exercise was associated with superior
executive performance compared with rest but not with
lower-intensity exercise; in the latter comparison, the
two exercising conditions elicited similar gains in exec-
utive function. These findings are in line with our
hypothesis of a larger effect for comparisons to resting
conditions. In contrast with the predictions made by a
number of models, for example, psychological (Thum
etal., 2017) or neurochemical (McMorris etal., 2016)
theories, high-intensity exercise does not appear to
impair executive function. This is despite some of the
physiological changes induced by exercise (e.g.,
increases in lactate and catecholamines, shifts in blood
flow to cortical regions) being larger following high-
intensity regimens (McMorris etal., 2016), suggesting
that rather than following an inverted-U function, the
facilitating effect of exercise holds at high-exercise
intensities, at least after the bout.
To test this idea, we used common subcategories to
further characterize high-intensity exercise, namely high
(77%–88.5% HRmax), very high (88.6%–99.9% HRmax), and
maximal (100% HRmax). Whereas high-intensity exercise
was associated with a small, facilitating effect, exercise
at very-high and maximal intensities was associated with
nonsignificant effects. However, this moderator did not
significantly affect the relationship between exercise and
executive function in categorical-moderator analyses,
20 Moreau, Chou
Study Name Effect Size (Cohen’s d) and 95% CI
RE Model, d = 0.2734, p = .005, 95% CI = [.0826, .4643]
b
−2 −1
0123
Chang et al. (2017), 1
Whyte et al. (2015), 1
Tsukamoto et al. (2016), 4
Kujach et al. (2018), 2
Zimmer et al. (2016), 11
Kamijo et al. (2007), 3
Zimmer et al. (2016), 2
Cooper et al. (2016), 5
Kamijo et al. (2007), 6
Kamijo et al. (2007), 1
Zimmer et al. (2016), 1
Zimmer et al. (2016), 3
Kamijo et al. (2007), 2
Kao et al. (2017), 1
Kujach et al. (2018), 1
Kamijo et al. (2007), 4
Tsukamoto et al. (2016), 3
Zimmer et al. (2016), 10
Kao et al. (2017), 2
Kao et al. (2017), 3
Zimmer et al. (2016), 12
Lambrick et al. (2016)
Hwang, Brothers, et al. (2016), 3
Chang et al. (2017), 2
Alves et al. (2014), 4
Hwang, Brothers, et al. (2016), 2
Peruyero et al. (2017), 3
Kamijo et al. (2007), 5
Llorens, Sanabria, Huertas, Molina, & Bennett (2015), 3
Cooper et al. (2016), 4
Peruyero et al. (2017), 4
Llorens, Sanabria, Huertas, Molina, & Bennett (2015), 2
Inhibitory Control
Cognitive Flexibility
Study Name Effect Size (Cohen’s d) and 95% CI
RE Model, d = 0.298, p = .0476, 95% CI = [.0032, .5929]
c
−2 −1
0123
Córdova et al. (2009), 14
Córdova et al. (2009), 13
Ramos et al. (2017), 10
Ramos et al. (2017), 9
Córdova et al. (2009), 15
Berse et al. (2015), 2
Netz et al. (2007), 1
Berse et al. (2015), 1
Córdova et al. (2009), 17
Córdova et al. (2009), 16
Netz et al. (2007), 2
Hwang, Brothers, et al. (2016), 6
Córdova et al. (2009), 18
Fig. 3. (continued on next page)
Exercise and Executive Function 21
−2 −1
0123
Cooper et al. (2016), 7
Córdova et al. (2009), 4
Córdova et al. (2009), 11
Córdova et al. (2009), 2
Tsukamoto et al. (2016), 2
Kujach et al. (2018), 3
Wohlwend et al. (2017), 4
Córdova et al. (2009), 10
Kamijo et al. (2007), 10
Kamijo et al. (2007), 9
Córdova et al. (2009), 3
Kamijo et al. (2004), 1
Ramos et al. (2017), 8
Netz et al. (2007), 4
Whyte et al. (2015), 2
Budde et al. (2012), 2
Alves et al. (2014), 2
Kamijo et al. (2007), 7
Kamijo et al. (2004), 2
Lemmink & Visscher (2005), 3
Kamijo et al. (2007), 8
Llorens, Sanabria, & Huertas (2015), 3
Llorens, Sanabria, & Huertas (2015), 1
Córdova et al. (2009), 6
Córdova et al. (2009), 12
Llorens, Sanabria, & Huertas (2015), 2
Kamijo et al. (2007), 12
Craft (1983), 2
Ramos et al. (2017), 7
Wohlwend et al. (2017), 2
Tsukamoto et al. (2016), 6
Llorens, Sanabria, & Huertas (2015), 4
Cooper et al. (2016), 6
Cooper et al. (2016), 3
Cooper et al. (2016), 2
Lemmink & Visscher (2005), 1
Netz et al. (2007), 3
Zimmer et al. (2016), 4
Zimmer et al. (2016), 9
Wohlwend et al. (2017), 3
Kujach et al. (2018), 4
Budde et al. (2012), 3
Budde et al. (2012), 1
Tsukamoto et al. (2016), 5
Alves et al. (2014), 5
Zimmer et al. (2016), 7
Hwang, Brothers, et al. (2016), 5
Wohlwend et al. (2017), 1
Alves et al. (2014), 3
Hwang, Brothers, et al. (2016), 1
Hwang, Castelli, et al. (2016), 4
Tsukamoto et al. (2016), 1
Zimmer et al. (2016), 6
Zimmer et al. (2016), 5
Córdova et al. (2009), 1
Hwang, Brothers, et al. (2016), 4
Kamijo et al. (2007), 11
Walsh et al. (2018), 2
Peruyero et al. (2017), 5
Walsh et al. (2018), 1
Lemmink & Visscher (2005), 2
Walsh et al. (2018), 3
Zimmer et al. (2016), 8
Lemmink & Visscher (2005), 4
Peruyero et al. (2017), 1
Peruyero et al. (2017), 6
Hwang, Castelli, et al. (2016), 3
Córdova et al. (2009), 5
Peruyero et al. (2017), 2
Chang et al. (2017), 4
Chang et al. (2017), 3
Llorens, Sanabria, Huertas, Molina, & Bennett (2015), 1
Attention
Study Name Effect Size (Cohen’s d) and 95% CI
RE Model, d = 0.1735, p = .0215, 95% CI = [.0255, .3214]
d
Fig. 3. Forest plots of effect sizes for working memory (a), inhibitory control (b), cognitive flexibility (c), and
attention (d). The sizes of the squares represent within-domain differences in sample size only. The diamond
at the bottom represents the overall effect. The dotted vertical line represents an effect size (d) of 0. CI =
confidence interval; RE = random effects.
22 Moreau, Chou
whereas in continuous-moderator analyses there was
only a small, nonsignificant decrease in facilitating
effects as intensity increased, particularly at the highest
intensities (> 88.5% HRmax). The inconsistencies between
past theoretical (McMorris etal., 2016) and meta-analytical
(Y. K. Chang etal., 2012) studies are perhaps not surpris-
ing given that they relied on different thresholds. Using
a threshold of > 88.5% HRmax, McMorris and Hale (2012)
did not find facilitating effects, whereas McMorris etal.
(2016) predicted detrimental effects; using a threshold
of > 77% HRmax, Y. K. Chang etal. (2012) reported
improved postexercise performance. Our results,
derived using the lower threshold, align with past meta-
analytic literature (Y. K. Chang etal., 2012; McMorris
and Hale, 2012) and support the idea of an optimal
intensity (i.e., 77%–88.5% HRmax) of high-intensity exer-
cise to induce improvements in executive function.
Other exercise characteristics could potentially affect
executive function. Just as higher exercise intensities
are thought to be associated with greater physiological
Effect Size (Cohen’s d)
Standard Error
0.60
0.45
0.30
0.15
0.00
−1
012
Fig. 4. Funnel plot of effect sizes for studies in the meta-analysis. Each dot represents an individual effect size and is plotted as a
function of standard error. Light gray and dark gray triangles denote 95% and 99% confidence intervals, respectively, for the effect
sizes, given the absence of publication (or small-study) bias. The vertical line represents the random-effects-model estimate (d = 0.24).
Table 8. Continuous Moderator Analyses
Moderator type and variable N d0 [95% CI] Δ [95% CI] ƛ
dproj
at ƛ
Exercise
Exercise duration at high-intensity 23 0.16* [0.00, 0.32] +0.0039 [−0.0036, 0.0115] 50 0.36
Exercise intensity (%HRmax) of high-intensity condition 26 0.27 [−1.01, 1.55] −0.0007 [−0.0151, 0.0137] 100 0.20
Cognitive task
Timing of cognitive testinga27 0.19* [0.04, 0.33] +0.0065 [−0.0057, 0.0188] 30 0.38
Research protocol/participant
Study quality 28 0.93* [0.07, 1.79] −0.0078 [−0.0173, 0.0018] 100 0.15
Participant age 28 0.31** [0.08, 0.54] −0.0030 [−0.0113, 0.0053] 63.1 0.12
Percentage male (exploratory) 28 0.44*** [0.20, 0.68] −0.0037† [−0.0076, 0.0001] 100 0.07
Note: dproj = projected Cohen’s d; d0 = estimated effect when moderator is 0; N = number of independent studies supplying continuous data; Δ =
change in effect size associated with increasing the value of the continuous moderator by 1 on the effect-size estimate; ƛ = maximum value of the
continuous moderator.
aThis does not include testing for long-term memory.
†p < .10. *p < .05. **p < .01. ***p < .001.
Exercise and Executive Function 23
effects, so are longer bouts of exercise (Awopetu, 2014).
We therefore expected greater effects on cognitive per-
formance as exercise duration increased. Although
duration as a continuous moderator trended in this
direction, it was nonetheless nonsignificant. Likewise,
different exercise durations (0–5, 6–10, 11–20, and > 20
min) led to small, facilitating effects (some significant,
some nonsignificant) that were similar in magnitude
to one another. Note that this could be due to the
larger effects associated with higher intensities being
compensated by shorter durations; overall, the cor-
relation between intensity and duration was negative
and marginally significant. Furthermore, whereas the mag-
nitude of the facilitating effect was consistent across
different durations, its latency could potentially vary
independently. For example, some longer exercise
bouts might be associated with a delayed, prolonged
peak of the facilitating effect. We could not examine
this relationship because too few studies examined
duration as an independent variable. Fortunately, the
field is moving toward the continuous measurement of
physiological (e.g., lactate, serum brain-derived neuro-
trophic factor, heart rate) and cognitive changes during
and after exercise, aspects that will help further our
understanding of the complex, dynamic interactions
between physiology and cognition.
We also explored how exercise rhythm—either con-
tinuous or intermittent—affected behavior. Intermittent-
exercise protocols have become increasingly popular,
perhaps because of their cardiorespiratory (Elliott,
Rajopadhyaya, Bentley, Beltrame, & Aromataris, 2015;
Milanovic´, Sporiš, & Weston, 2015; J. S. Ramos, Dalleck,
Tjonna, Beetham, & Coombes, 2015; Weston et al.,
2014) and psychoaffective (Thum etal., 2017) benefits
compared with traditional continuous exercise. We
examined whether this form of exercise had compa-
rable benefits on immediate executive function but did
not find a difference between the effects of intermittent
and continuous exercise compared with rest—both
types of protocols were associated with small, facilitat-
ing effects. This result is consistent with physiological
responses to exercise, which appear to be similar
regardless of whether rest periods are interleaved
throughout the exercise protocol (Arnardóttir, Boman,
Larsson, Hedenström, & Emtner, 2007; Safarimosavi,
Mohebbi, & Rohani, 2018). Note that this could be an
important advantage of intermittent exercise—with less
exercise volume overall but with higher peak intensities
100%
0%
25%
50%
75%
.01 .02 .03.04 .05
p Value
Percentage of Test Results
Observed p Curve
Null of No Effect
Null of 33% Power
Power Estimate: 74%, 90% CI = [56%, 86%]
Tests for Right-Skewness: pFull = .0001, pHalf < .0001
Tests for Flatness: pFull = .9996, pHalf < .9999, pBinomial = .3748
46%
14%
17%
6%
17%
Fig. 5. P curve for all significant (p < .05) effect sizes. The observed p curve includes
35 statistically significant (p < .05) results, of which 23 were p < .025; 112 additional
results were entered but excluded from the p curve because they were p > .05. The 90%
confidence interval is given for the power estimate.
24 Moreau, Chou
compared with continuous exercise, similar effects can
potentially be elicited.
Two caveats need to be acknowledged, however.
First, these short-term similarities may not reflect long-
term equivalence, both in terms of mechanisms and
outcomes. Several studies have investigated the physi-
ological effect of chronic high-intensity exercise
(Maillard, Pereira, & Boisseau, 2018), as well as the
relationship between acute and chronic physiological
effects (Tonoli etal., 2012), yet the extent to which
acute effects translate or even relate to long-term
change at the cognitive level remains unknown. Sec-
ond, the high intensity typically associated with inter-
mittent exercise may not be desirable for specific
populations (e.g., low-fit individuals, older adults). In
this regard, acute exercise at more moderate intensities
(55% to 70% HRmax) might be preferred for individuals
prone to injury or at risk of cardiovascular conditions
(Ludyga etal., 2016). The rhythm and intensity of exercise
remain dissociable, however, and further research will
allow exploring whether lower intensities of intermittent
exercise could elicit similar improvements in executive
function (Kujach etal., 2018; Ludyga etal., 2016).
Finally, an additional characteristic of exercise was
worth investigating in our view—when exercising at
high intensity, does it matter whether one runs, cycles,
or swims? In other words, is the modality of exercise
relevant, or should it just be treated as a personal pref-
erence with no direct impact on cognition? Most studies
examined either running or cycling, as these modalities
were relatively easy to measure and control. Because
the two modalities have important physiological differ-
ences (Millet, Vleck, & Bentley, 2009), it was plausible
that these be reflected in terms of differences in execu-
tive function. However, we did not find differences
attributable to modalities of exercise in the present
meta-analysis—both were associated with small facili-
tating effects, with a marginally significant effect in the
case of cycling and a significant effect in the case of
running. This result differed from our prediction: We
hypothesized, on the basis of Lambourne and Tompo-
rowski’s (2010) meta-analysis, that cycling would elicit
a greater facilitating effect on executive function. Note
that in Lambourne and Tomporowski (2010), the dif-
ference between modalities was more pronounced
when cognitive tests were administered during, rather
than after, exercise, which could suggest that the
observed benefit of cycling compared with running in
their study was due to the difficulty of performing cog-
nitive tasks while running rather than true differences
in the effect of running versus cycling. Nevertheless,
the disparity between the findings of the present study
and those reported by Lambourne and Tomporowski
(2010) points to exercise modality as a potential mod-
erator of interest for future investigation.
How were our results influenced
by testing, protocol, and sample
characteristics?
In addition to variables related to exercise itself, we
tested moderators related to the specific measurements
used to assess executive function, for example, whether
the timing of testing had an effect on cognitive perfor-
mance. We did not find a significant effect of timing;
tests conducted in the ranges 0 to 1, 1 to 10, and > 10
min after a bout were all associated with small, facilitat-
ing effects. These results differed from our predictions,
which were made on the basis of Y. K. Chang etal.
(2012), who found tests administered after 1 min—but
not 0 to 1 min—after a bout to elicit facilitating effects.
Several studies administered the same postexercise
tasks repeatedly to further elucidate the temporal char-
acteristics of exercise-induced cognitive change. We
chose not to analyze tests beyond the first repetition
(i.e., posttest) to reduce confounds from practice
effects. Recent studies examining the influence of test
timing have strived to control practice effects; for exam-
ple, Zimmer etal. (2017) randomly assigned partici-
pants to multiple groups with only one posttest session,
which was scheduled at a different time after exercise
(0, 30, 60, and 90 min). They found a moderating effect
of test timing on cognitive performance, with a detri-
mental effect of high-intensity exercise on executive func-
tion at the earlier (0, 30 min) but not the later (60, 90
min) time points. Furthermore, performance decline was
correlated with blood-lactate concentrations, suggesting
that the observed cognitive effects might be mediated by
physiological variables (Zimmer etal., 2017).
Furthermore, we tested whether our findings were
contingent on a comparison against a baseline measure
of executive performance. Contrary to our prediction,
the presence or absence of baseline cognitive testing
did not significantly moderate the results; studies with
baseline testing (i.e., repeated-measure studies) and
studies without baseline testing (i.e., single-measure
studies) both showed small, facilitating effects of exer-
cise on executive performance. Our findings also did
not differ depending on the subdomain of executive
function we focused on (i.e., attention, cognitive flex-
ibility, inhibitory control, or working memory). These
results matched our general prediction and were in
line with findings from Y. K. Chang etal. (2012), who
found an overall facilitating effect of exercise on exec-
utive function when collapsing all intensities and test
timings.
Exercise and Executive Function 25
Most studies included in the current meta-analysis
were of high quality, with several average-quality stud-
ies and only one low-quality study. Study quality was
a marginally significant moderator of the influence of
high-intensity exercise on executive function, with a
moderate, facilitating effect for average-quality studies
and a small, facilitating effect for high-quality studies.
This finding was in line with our prediction (although
we did not predict the direction of the effect) as well
as previous research (Etnier etal., 1997). The larger
effects in average-quality studies possibly arose because
of poor control of confounding variables, and low- and
average-quality studies generally had small sample sizes
(see Tables S3 and S4 in the Supplemental Material).
Because of the paucity of lower-quality studies in the
present meta-analysis, care should be taken in interpret-
ing the effects of study quality.
Moreover, study design—whether the experiment
included a within-subjects or between-groups design—
did not moderate the relationship between high-
intensity exercise and executive function. Both types
of design were associated with nonsignificant, small,
facilitating effects of high-intensity exercise on execu-
tive function, with substantial heterogeneity in the
results. This result differed from our prediction (which
was based on the results of Y. K. Chang etal., 2012) of
a larger effect for within-subjects studies. Our results
suggest that most within-subjects studies included in
the present meta-analyses controlled adequately for
confounds, leading to noninflated effects, which may
not have been the case in previous meta-analyses.
Finally, we also tested the influence of sample char-
acteristics on the relationship between exercise and
executive performance. These included fitness level,
age, and sex. None of these moderators showed a sig-
nificant effect despite prior studies pointing to the con-
trary (e.g., Y. K. Chang etal., 2012; Etnier etal., 2006).
Note that for fitness level, there were important caveats
that prevented a full examination of this moderator,
which we detail in the limitations section. With respect
to age, few studies examined participants who were
not young adults, limiting our ability to investigate the
effect of this moderator.
Interactions and model comparisons
We tested a number of interactions between moderators
to examine whether the effect of a moderator variable
was contingent on another one. From the 15 pairs of
moderators defined a priori, we found two significant
interactions after correcting for multiple comparisons.
First, when the comparison group was a lower intensity
exercise group, those individuals undergoing very-high-
intensity exercise experienced significantly larger
facilitating effects than those undergoing high-intensity
exercise. Second, when exercise was intermittent, those
who were tested 0 to 1 min after exercise experienced
significantly larger facilitating effects than those who
were tested > 10 min after exercise. However, given the
low number of studies supplying effects at particular
levels (three effect sizes for very-high- vs. lower-intensity
exercise; three effect sizes for intermittent exercise and
testing > 10 min after a bout), these results should be
interpreted with caution.
To further understand relationships between mod-
erator variables, we compared a number of models that
included single or multiple moderators.5 Of all models,
the model with seven moderators had the lowest resid-
ual heterogeneity, although it remained significantly
larger than zero. Model comparisons showed the model
with one moderator (comparison group) to be a better
fit than the model with the largest number of modera-
tors and the model with no moderators. Thus, including
the one significant moderator of our analyses to the
model was beneficial despite the inherent penalty for
model complexity. In contrast, the addition of other
moderators to the model was detrimental to overall fit
despite the resulting decrease in heterogeneity.
Were our results biased?
Our analyses suggested low bias overall, as illustrated
by the funnel plot shown in Figure 4 and corroborated
with the p-curve analysis shown in Figure 5 that indi-
cated a low probability of p hacking for the significant
effect sizes. Furthermore, analyses of study quality sug-
gested that although most studies were not significantly
biased (i.e., high-quality studies), a few were biased
toward large effects (i.e., low- and average-quality stud-
ies) compared with the overall meta-analytic estimate.
Because the inclusion of lower-quality studies did not
substantially alter funnel-plot symmetry, it is reasonable
to assume that the overall meta-analytic estimate was
not substantially affected by biased studies. Importantly,
the meta-analytic estimate of a subset of the meta-
analytic data composed exclusively of high-quality
studies remained significant, showing a small facilitat-
ing effect of high-intensity exercise. Altogether, these
assessments of quality suggested a low degree of bias;
however, given that we could not obtain all the poten-
tially relevant data we requested, it remains possible
that our findings were affected by publication bias, at
least to a certain extent.
Limitations of the present study
We should point out a few limitations to the present
meta-analysis. First, the study was not preregistered,
26 Moreau, Chou
thus possibly increasing the risk of confirmation bias
and providing weaker control over theory-driven
choices in the selection, coding, and interpretation of
studies (Lakens, Hilgard, & Staaks, 2016). With meta-
analyses, errors may arise in the implementation of
inclusion criteria, extraction of data, and coding of
study characteristics; however, to mitigate bias, we thor-
oughly documented each step of the present meta-
analysis and made this documentation publicly available
to increase transparency and allow reproducibility.
Our meta-analysis was also limited by inherent fea-
tures of the studies we included. For example, a major-
ity of the within-subjects studies, and a few of the
between-groups studies, did not include baseline
scores. This is problematic given that baseline testing
reduces error by providing a reference for each indi-
vidual (Higgins etal., 2011). In several instances, it also
proved difficult to precisely quantify participant fitness
because of missing information and typical lack of indi-
vidual data. Complex relationships such as the one
linking exercise and executive function can be under-
stood only with a detailed assessment of the dynamics
of cognitive performance, that is, of the specific char-
acteristics and their evolution in time. This type of
analysis relies on more than summary statistics and
instead requires individual trial-by-trial data. The stud-
ies we reviewed in this meta-analysis did not include
this information, but we believe future studies should
collect, retain, and ideally share these types of data to
allow more detailed analyses.
In addition, the categorizations we made with regard
to exercise intensities and cognitive domains could
have influenced the results. With respect to the former,
we should note that we did not convert intensity scores
into VO2max, arguably considered the best measure for
exercise testing (Fletcher etal., 2013). Rather, we con-
verted intensity scores into %HRmax using conversion
formulas (Arts & Kuipers, 1994; Lounana etal., 2007)
that were possibly not representative of the samples
included in the current meta-analysis. This was our
preference to allow the inclusion of a larger set of stud-
ies, possibly to the slight detriment of overall quality—
the moderator analysis for exercise intensity should be
interpreted with this limitation in mind.
Likewise, our categorization of cognitive tasks under
the umbrella term of executive function helped sim-
plify our analyses and our interpretation of results.
However, there remain important differences in defini-
tions of executive function (see, e.g., Diamond, 2013;
Miyake etal., 2000), and different theoretical accounts
could lead to variations in how the evidence is assessed.
However, we attempted to mitigate this effect by fur-
ther looking into subdomains of executive function to
provide a more detailed analysis at a finer level of
investigation and to increase overall accuracy and
transparency.
Finally, there were particular characteristics of exer-
cise that remain largely unexplored. For example, few
studies explored the distinction between high (77%–
88.5% HRmax), very-high (88.6%–99.9% HRmax), and
maximal (100% HRmax) intensities. There were very few
studies that examined the adolescent, middle-aged, and
older-adult age groups or that investigated sex differ-
ences in the influence of high-intensity exercise on
executive function. Importantly, these additional ques-
tions do not necessarily require additional studies—
many of these analyses could be carried out if
demographic data were openly available.
Concluding Remarks
In a meta-analysis including 147 effect sizes across 28
studies, we found that high-intensity exercise had a
small, facilitating effect on executive performance and
that this effect was comparable with that of more mod-
erate, but longer, forms of exercise. We believe our
findings have important applications for individuals,
society, and policy. Specifically, they demonstrate the
relevance of high-intensity exercise for those seeking
time-efficient ways to induce immediate cognitive
improvements, such as children who are often con-
strained to sedentary learning environments, profes-
sionals whose work conditions involve long hours
sitting at a desk, or older individuals with limited
opportunities to exercise. In these various contexts,
recognizing the benefits of high-intensity exercise could
promote healthier, more productive lifestyles, with a
positive impact on the community at large.
Action Editor
Laura King served as action editor for this article.
Author Contributions
D. Moreau conceived the study, provided statistical and
research expertise; supervised the literature search, data col-
lection, and data analyses; and provided funding for the proj-
ect. E. Chou performed the literature search, collected the
data, and conducted the analyses. D. Moreau and E. Chou
wrote the manuscript, and both authors approved the final
version for submission. E. Chou is the guarantor.
Acknowledgments
We thank Beau Gamble for his help with data analysis, and
three anonymous reviewers for their constructive comments.
Declaration of Conflicting Interests
The author(s) declared that there were no conflicts of interest
with respect to the authorship or the publication of this article.
Exercise and Executive Function 27
Funding
D. Moreau is supported by the Royal Society of New Zealand
(Marsden) and the Neurological Foundation of New Zealand.
E. Chou was supported by a University of Auckland summer
research scholarship (2017–2018).
Supplemental Material
Additional supporting information can be found at http://
journals.sagepub.com/doi/suppl/10.1177/1745691619850568
Notes
1. Note that a more recent meta-analysis (Ludyga etal., 2016)
investigated the acute effect of moderate aerobic exercise—but
not of higher intensities—on executive function.
2. Note that standardization refers to the paradigm (e.g.,
n-back), not necessarily its specific implementation at the task
level (e.g., type of stimuli, number of trials, duration, data-col-
lection software, system specifications).
3. Note that Y. K. Chang etal. (2012) used similar categories
(hard, very hard, and maximal) but with thresholds of 77% to
93% HRmax, 93% to 100% HRmax, and > 100% HRmax, respectively.
4. For the sake of completeness, separate analyses for within-
subjects and between-groups studies are also reported at
https://osf.io/cauxq/.
5. Note that this approach does not consider individual mod-
erator levels; rather, it incorporates all levels of each moderator
included in an attempt to account for some of the heterogeneity
present in the meta-analytic model. Because this makes it diffi-
cult to interpret the contributions of individual moderators, only
model-level statistics, such as residual heterogeneity, model fit,
and parsimony, are meaningful.
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