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Trained athletes and cognitive function: a systematic review and meta-
analysis
Article  in  International Journal of Sport and Exercise Psychology · June 2022
DOI: 10.1080/1612197X.2022.2084764
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International Journal of Sport and Exercise Psychology
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Trained athletes and cognitive function: a
systematic review and meta-analysis
Nicole E. Logan, Donovan A. Henry, Charles H. Hillman & Arthur F. Kramer
To cite this article: Nicole E. Logan, Donovan A. Henry, Charles H. Hillman & Arthur F.
Kramer (2022): Trained athletes and cognitive function: a systematic review and meta-analysis,
International Journal of Sport and Exercise Psychology, DOI: 10.1080/1612197X.2022.2084764
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RESEARCH ARTICLE
Trained athletes and cognitive function: a systematic review
and meta-analysis
Nicole E. Logan a*, DonovanA. Henrya, Charles H. Hillman a,b and
Arthur F. Kramer a,c
aDepartment of Psychology, Northeastern University, 635 ISEC, 360 Huntington Ave, Boston, MA, USA;
bDepartment of Physical Therapy, Movement, & Rehabilitation Sciences, Northeastern University, Boston,
MA, USA; cBeckman Institute, University of Illinois, Urbana, IL, USA
ABSTRACT
As trends in physical inactivity continue to increase throughout the
lifespan, one prominent area of interest is the cumulative benefits
of participating in physical activity. Recent literature has
demonstrated the cognitive and brain benefits associated with
one such population, elite athletes. However, it is unclear which
aspects of the athlete experience drive this athlete–cognition
relationship, which is the objective of the current study. In this
study, we examine, in a quantitative meta-analysis (k = 41), the
relationship between athlete experiences and laboratory-based
measures of cognitive function in the following domains:
attentional allocation, inhibitory control and cognitive flexibility.
Athlete groups outperform control groups on a battery of
cognitive tasks, including attentional allocation (g = 1.18) and
cognitive flexibility (g = 0.31). Moreover, athlete type and
experience are important factors to consider when evaluating this
relationship. Moderator analyses revealed that athletes trained in
aerobic (g = 0.93) or HIIT team-based sports (g = 0.65), as well as
child (g = 0.26), elite (g = 0.94) (semi-professional, professional,
national and international) or older adult athletes (g = 0.91) were
responsible for driving this effect. At a time when physical
inactivity levels are increasing, these results have important
societal considerations. Participating in sport-related physical
activity may be a beneficial influence on cognitive development
throughout the lifespan.
ARTICLE HISTORY
Received 12 November 2021
Accepted 19 May 2022
KEYWORDS
Cognition; elite athletes;
exercise; physical activity;
sport
1. Introduction
As trends in physical inactivity continue to increase throughout the lifespan (Piercy et al.,
2018), the influence of physical activity on cognition has been increasingly investigated,
such that evidence now indicates a beneficial effect of physical activity on cognitive and
brain function (Buckley et al., 2014; Drollette et al., 2014; Erickson et al., 2015; Esteban-
Cornejo et al., 2019; Hillman et al., 2009, 2011; Voss et al., 2019). A single bout of acute
aerobic exercise can enhance cognitive and brain function (Chaddock-Heyman et al.,
© 2022 International Society of Sport Psychology
CONTACT Nicole E. Logan nicolelogan@uri.edu Kinesiology Department, University of Rhode Island, Indepen-
dence Square, 25 W. Independence Way, Kingston, RI 02881, USA
*Present address: Kinesiology Department, University of Rhode Island, Independence Square, 25 W. Independence Way,
Kingston, RI, USA
INTERNATIONAL JOURNAL OF SPORT AND EXERCISE PSYCHOLOGY
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http://orcid.org/0000-0003-4016-2146
http://orcid.org/0000-0002-3722-5612
http://orcid.org/0000-0002-1991-5046
mailto:nicolelogan@uri.edu
http://www.tandfonline.com
2013; Drollette et al., 2014; Hillman et al., 2009; Hsieh et al., 2018; Kao et al., 2017; Yu et al.,
2020) as well as long-term physical activity interventions in specific populations across the
lifespan (Best et al., 2015; Bidzan-Bluma & Lipowska, 2018; Bolandzadeh et al., 2015;
Buckley et al., 2014; Carvalho et al., 2014; Chaddock-Heyman et al., 2013; Chueh et al.,
2017; Davis et al., 2011; Drollette et al., 2014, 2018; Erickson et al., 2015; Esteban-
Cornejo et al., 2019; Gothe & McAuley, 2015; Hillman et al., 2011, 2011; Krafft et al.,
2014; Lautenschlager et al., 2008; Lin et al., 2021; Liu-Ambrose et al., 2010, 2012;
Nangia & Malhotra, 2012; Stroth et al., 2009; Zheng et al., 2016). Additionally, cognitive
abilities can be enhanced with individualised adaptive training, whereby task difficulty
is increased with performance improvements and individuals are encouraged to pursue
different ways to perform a complex task (Voss et al., 2010). However, separate from
the benefits associated with physical activity alone, it remains unclear whether the role
and type of athletic expertise, which results from years of extensive sport and exercise
engagement combined with adaptive training, has on the relationship with cognitive
and brain function. Therefore, the investigation into the role of athletic training, the
influence of experiences that coincides with such training, and how this influences cog-
nitive functioning is important to explore.
Elite athletes represent a specialised population whereby the combination of practi-
cing extreme fitness levels and demanding cognitive skill training represents an
average workday. Such elite athletes perform autonomous physical and mental skills in
continuously adapting to external environments daily, in both practice and competition.
The autonomous nature of this expertise, in accordance with long-term participation in
physical activity and subsequently elite fitness levels, suggests that laboratory-based
measures of cognitive performance would be positively enhanced in such a population
(Tomporowski et al., 2015).
Two broad approaches have been proposed to represent the athlete–cognition litera-
ture: the expert performance approach and the cognitive component skills approach. The
expert performance approach suggests that, in an ecologically valid context (such as
under quantitative sport-specific tasks), whereby the athlete’s expertise is directly involved
in performing tasks that simulate the sport context, experts and novices can be differen-
tiated by means of task performance. Results from studies investigating the expert per-
formance approach, suggest that experts perform better than novices on sport-specific
motor-cognitive tasks of declarativememory, attentional allocation, perception and antici-
pation and decision-making skills (Ericcson, 2003; Singer, 2000; Voss et al., 2010). This
approach strongly differentiates the individual differences in cognitive performance
between the two groups, expert and novice, in ecologically valid contexts. One clear limit-
ation with the expert performance approach, however, is inappropriately determining
cognitive performance on measures external to the sport-specific task. That is, using
this approach in determining laboratory-based fundamental measures of cognitive per-
formance between–groups would be an inadequate and unreliable measure (Voss et al.,
2010). Additionally, even if there are sport-specific cognitive benefits for athletes com-
pared to non-athletes, this does not necessarily imply that athletes excel on more
general (i.e., not sport specific) measures of perception, cognition, and action.
The cognitive component skills approach examines the relationship between sports
expertise and performance measures of cognition, which tap into some of the fundamen-
tal or general cognitive demands (Nougier et al., 1991; Voss et al., 2010). Notably, this
2 N. E. LOGAN ET AL.
approach does not account for the specific cognitive demands of extensive and
competitive athletic training, nor does it account for performance in an ecologically
valid context (Ericcson, 2003). However, this approach is important to consider for the
potential transfer effect of cognitive function: from sport-specific training, to performance
on sport-specific tasks, to performance on more general laboratory-based measures of
cognitive tasks.
Both the expertise and the cognitive skill approaches have been widely, yet vary-
ingly, supported. However, it may be the combination of these two approaches which
drives the athlete–cognition relationship. Voss et al. (2010) suggested there may be
both sport-specific and sport-general cognitive enhancements from competitive
sport training. In particular, the quantifiable relationshipbetween the athlete and
their environment, as well as laboratory-based performance measures of cognition,
that tap into some of the fundamental cognitive sport training, co-exist. Additionally,
a recent meta-analysis has further identified a small to medium effect size, which indi-
cates superior cognitive functions in experts and elite athletes, which is suggested to
be driven by the level of skill expertise (Scharfen & Memmert, 2019). However, pre-
vious research into the relationship between athlete expertise and laboratory-based
measures of attention and cognition has demonstrated inconsistent findings, and
call for the assessment of high-level executive functions (Scharfen & Memmert,
2019; Voss et al., 2010). Much of these previous contradictions arise in part, due to
small sample sizes, lack of inclusion of female athletes, lack of inclusion across age
groups, differences in cognitive task assessments and overall methodological hetero-
geneity of assessing individual differences, resulting in low power and difficulty
detecting specific effects (Scharfen & Memmert, 2019; Schmidt, 1992; Voss et al.,
2010). Specifically, Scharfen and Memmert (2019) call for the use of “elite” rather
than “expert” terminology when describing athlete ability, as well as distinguishing
the difference between high-performance and amateur athletes. They further noted
that heterogeneity among athlete groupings may contribute to the variability
among results, and suggested future studies should create differential subgroups.
These athlete subgroups should reflect the cognitive profiles of individual types of
sport and training type. For example, aerobic high-intensity interval training (HIIT)
for team-based ball sports (i.e., soccer), which requires greater flexibility upon goal-
oriented in-competition decision making, compared to aerobic endurance sports,
such as distance running. Lastly, Voss et al. (2010) noted previous empirical studies
have predominantly included male athletes of the young adult population. As such,
they suggested future studies should try to recruit both male and female athletes,
as well as a range of age groups, to permit more within-study and across-study com-
parisons of gender and age, in attempt to elucidate the athlete–cognition
relationship.
Therefore, the current meta-analysis sought to investigate the relationship between
trained athletes and cognitive function, by expanding upon the combined influence of
fitness, cognitive skill demand, athlete expertise and context specific performance. This
study expands on the previous literature in an attempt to decompose the athlete–cogni-
tion relationship, by specifically examining the variability among the types of trained ath-
letes in their respective sports, as well as the level at which these athletes perform, and
the type of cognition assessed. Notably, we build on the type of sport and the type of
INTERNATIONAL JOURNAL OF SPORT AND EXERCISE PSYCHOLOGY 3
expertise assessed in previous literature, by including a wider range of athletes and their
respective sports in which they are trained in, from a physical attribute standpoint. We
included attributes of the athlete’s physical training (i.e., aerobic, resistance, HIIT, team-
based competition and individual based competition), rather than attributes of the
sport’s context (strategic, interceptive and static (Voss et al., 2010)). This method was
chosen because of the type of physical attributes trained. For example, the energy
systems associated with aerobic training uses aerobic or oxidative metabolism (combus-
tion of carbohydrates and fats in the presence of oxygen), which is commonly used for
distance sports (i.e., distance running and triathlons); HIIT uses the process of glycolysis
(nonaerobic breakdown of carbohydrates to pyruvic acid and lactic acid), which is com-
monly used for team or ball sports requiring repeated short bursts of speed and power
(i.e., basketball and volleyball); and resistance training, which uses phosphocreatine
(the splitting of high-energy phosphagen with stored ATP in the cell), which is associ-
ated with sports that require singular bursts of speed or power (i.e., sprint-distance
running, gymnastics and weight lifting). The characterisation of energy systems may
be more specific and representative of the type of the athlete, rather than the type
of sport in a performance setting (i.e., strategic). This characterisation is an important
and novel addition to the literature in attempt to address mechanistic associations
between involvement in sports and physical activities and cognitive function in
humans. Specifically, the categorisation of athlete type based on energy systems is
novel compared to previous studies. Previous studies have referred to an athletic
skill as “open” or “closed” (Nuri et al., 2013; Wang et al., 2013; Zhu et al., 2020).
Open-team sports involve interaction with direct opponents as well as with team-
mates, and as such, contain situations with high amounts of unstable and seemingly
unpredictable events, however, closed sports do not contain these elements (Elfer-
ink-Gemser et al., 2018). As such, these previous categorisations are not associated
with the type of energy systems used to perform the sport. Distinguishing by type
of energy system trained may provide novel information to the field and complements
categorisation schemes used in previous studies.
Three prominent areas of cognitive function, which are readily examined in the exer-
cise, sport, and cognition literature, were of interest in the current study: attentional allo-
cation (the selective concentration on a discrete stimulus or stimuli), inhibitory control
(the ability to ignore distraction and stay focused) and cognitive flexibility (the ability
to switch or share attention or responses between tasks) (Diamond, 2006). Previous
research has demonstrated how these specific areas of cognitive function have been sen-
sitive to changes in fitness, exercise interventions (McAuley et al., 2011) and thus are of
interest to the athlete population of the current analysis. Unique to this study, trained ath-
letes of varying levels of expertise and sport types were also considered and accounted
for, ranging from college to elite athletes and individual- to team-based sports, and
anaerobic, aerobic and resistance trained athletes. In addition to exploring the moderat-
ing influence of athlete type and athlete experience level, we also examined the influence
of moderating demographic variables, such as age and gender, on cognitive function.
Cross sectional studies that compared cognitive performance (performance accuracy
and reaction time), on tasks which tap into the three cognitive domains (attentional allo-
cation, inhibitory control and cognitive flexibility), between athlete and control groups
were considered.
4 N. E. LOGAN ET AL.
2. Methods
2.1. Literature search
The literature search utilised online databases, primarily Scopus, PubMed, and Google
Scholar. The search terms “athletics”, “athlete” and “sport”, were grouped using the con-
nector “OR” and then were combined using the connector “AND” with search terms
“cognition”, “cognitive function”, “cognitive control” and “executive function”, resulting
in the following search queries: ((“athletics” OR “athlete” OR “sport”) AND (“cognition”
OR “cognitive function” OR “cognitive control” OR “executive function”)). Additionally,
articles were identified from reference lists obtained from articles and dissertations.
The screening and selection of studies were completed by two of the authors in Decem-
ber 2019 and again in May 2021. First, titles and abstracts were examined to identify
studies that met inclusion criteria after the removal of duplicates. Second, the full
text of eligible studies based on the screened studies were read to determine their
final inclusion. Disagreement between the two reviewers was resolved by a consensus
meeting with experts in the field in May 2021. Finally, articles including acuteand
chronic exercise interventions and cognitive function-related tasks were reviewed
further for inspection of baseline analyses. Figure 1 provides an overview of the selec-
tion process. The current analysis followed the Preferred Reporting Items for Systematic
Reviews and Meta-analysis (PRISMA) (Moher et al., 2009) for literature search and
selection.
2.2. Criteria for study inclusion
Studies were considered eligible for inclusion if they were in English, if they involved a
laboratory cross-sectional assessment of cognitive function and if they compared athletes
to a matched group of non-athlete controls. Controls were required to have no self-
reported history of experience at the elite level of the sport of the expert group; some
studies included consistently non-athletes, while others included lower-level social
sport participants as controls. Trained athletes were considered to be those who were
part of a competitive team or level of competition, such as under 18 representative ath-
letes, high school athletes, college athletes, competitive amateurs, semi-professional, pro-
fessional or masters level athletes, and were coded for appropriately. Paediatric
populations between the ages of 11–18, young adults and applicable older adults were
coded and included in the analysis. Studies were excluded if they: were a study of
mental and not cognitive health, review articles or did not include enough information
to calculate the appropriate effect size. Overall, 83 sources with relevant abstracts were
collected. However, 48 studies met the final inclusion criteria (Figure 1). One study
needed supplemental material for both demographic and cognitive outcome data from
the corresponding author to fulfil the analyses, which was not provided and was excluded
(N = 47). One study needed supplemental material for demographic data, which was not
provided, but remained in the analyses with eligible cognitive outcome data (N = 47).
Effect sizes greater than two standard deviations from the mean were considered outliers
and excluded from our main analysis. There were four effect sizes meeting this criteria (N
= 43). Effect sizes where the calculated standard error was equal to zero were excluded
from our main analyses. There were two effect sizes meeting this criteria (N = 41). From
INTERNATIONAL JOURNAL OF SPORT AND EXERCISE PSYCHOLOGY 5
the 41 studies, 176 effect sizes (88 per group) were derived across cognitive measures, and
an overall average effect size was calculated for each study.
2.3. Coding of participant demographics
Moderating variables include: type of population (athlete and control), mean age of
athlete and control groups (continuous), standard deviation of age of athlete and
control groups (continuous), percentage of females in the athlete or control groups (con-
tinuous); athlete type (categorical: resistance trained athlete [i.e., weightlifter], aerobic/
endurance trained athlete [i.e., swimmer], high intensity interval training (HIIT) team
Figure 1. Preferred Reporting items for Systematic Review and Meta-analysis (PRISMA) flow diagram
of literature search and study inclusion.
6 N. E. LOGAN ET AL.
athlete [i.e., basketball player], HIIT individual athlete [i.e., individual table tennis player]
and mixed/other/non-disclosed [i.e., athlete group is comprised of multiple sports]);
athlete experience level (categorical: children, college or amateur, semi-professional/
elite (professional, national and international)); control type (categorical: sedentary
control, social sport playing control or a mixed group of sedentary participants and
those with some social sport experience); and cognitive domain (categorical: accuracy
or reaction time measures for attentional allocation, inhibitory control and cognitive flexi-
bility). Two researchers performed the coding independently, with expertise in the fields
of attention, cognition, perception, executive function and elite sports. When discrepan-
cies between coding were apparent, the coders discussed and resolved the best pro-
cedure for each effect size.
2.4. Outcome measures
The primary outcome of this meta-analysis was behavioural performance on tasks of
cognitive function. Each cognitive task was categorised as one of the three cognitive
domains (Table 1), as either an accuracy measure or a reaction time (RT) measure, or
both. Many studies report several different measures of cognitive function when utilis-
ing the aforementioned tests, such as accuracy and RT, and others. Therefore, an overall
average effect size was calculated for each study, and cognitive domains were also used
as categorical moderator variables. When discrepancies among the allocation of tasks
into the cognitive domain was apparent, the coders discussed and resolved the best
domain best representative for that task. For each study, means and standard deviations
for each cognitive domain, in both the athlete group and control groups were obtained,
in accordance with the athlete and control group sample sizes, to calculate the effect
size as Hedge’s g.
Table 1. Tasks of cognitive function by domain.
Attentional Allocation Inhibitory Control Cognitive Flexibility
Anticipation Task Flanker Incongruent Task Choice Task
Car Brake Reaction Time Identification Task Design Fluency
Detection Task Interference Task Digit Ratio
Digit Symbol Substitute Test NoGo Task Mental Rotation
Dual-Task Stop Signal Task Motor Skill
Flanker Congruent Task Stroop Incongruent Task Switch
Go Task Oddball Task Visuospatial Variability
Local Global Task Redundant-Target Task Wisconsin Card Sorting Task
Non-Delayed Task Anti-Trial Detection Trails Making B Task
Peripheral Reaction Time Verbal Fluency
Stroop Congruent Delayed Task
Trails Making A Task Digit Span
Visual Discrimination Dual-Task
Serial Reaction Time Task N-Back Task
Four-Choice Reaction Time Tower of London
Simple Reaction Time Trails Making B Task
Direct Digit Span Computerised Corsi Blocks
Pro Trial Detection Reverse Digit Span
Psychomotor Vigilance Task Spatial Working Memory
Pro-Anti Trial Detection
Cambridge Gambling Task
INTERNATIONAL JOURNAL OF SPORT AND EXERCISE PSYCHOLOGY 7
2.5. Statistical analyses
Effect sizes and standard deviations were calculated for each cognitive outcome
(accuracy and response time), and for each cognitive domain (attentional allocation,
inhibitory control and cognitive flexibility), for both the athlete and control groups.
If a study included results on multiple outcomes or domains, effect sizes were
averaged to generate arithmetic mean effect size estimate per study (Marascuilo
et al., 1988).
Each task was categorised as providing an accuracy or RT outcome, of one of the
three cognitive measures (attentional allocation, inhibitory control and cognitive flexi-
bility). All statistical analyses were performed in the R software environment (Version
3.6.2; R Core Team, 2019), using the meta (Balduzzi et al., 2019), metafor (Viechtbauer,
2010) and dmatar (Harrer et al., 2019) packages, and the Doing Meta-Analysis in R: A
Hands-on Guide (Harrer et al., 2019). We ran Hartung-Knapp-Sidik-Jonkman, random
effects meta-analyses to estimate overall effects, as well as the heterogeneity
between effect sizes. In addition, we conducted meta-regression and subgroup 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: https://osf.io/
bk2gw/.
Hedges (1982) formula was used to calculate effect sizes for each study:
g = (Mathlete −Mcontrol)/SDP, where Mathlete is the athlete group mean, Mcontrol is the
control group mean and SDp is the pooled standard deviation. All effect sizes were
weighted by Hedge’s sample-size correction: c∗g, where c = 1− [3/(4m− 9)], and
m = 2N− 2. Effect size directions were coded so that positive numbers reflected better
athlete performance. Once effect sizes for all studies were calculated, a random-effects
model was fit to the data to determine the effect oftrained athlete experience on cogni-
tive function. For this analysis, effect sizes for multiple dependent variables within studies
(e.g., accuracy outcomes of various tasks measuring attentional allocation) were averaged
to generate a mean effect size estimate per study. After effect sizes were calculated for
each cognitive domain within each study, they were further averaged together to create
an overall effect size per study. The I² statistic, which describes the percentage of variation
across studies that is due to heterogeneity rather than chance, was used to assess the het-
erogeneity of effect sizes (Huedo-Medina et al., 2006). A random-effects model was also
used to assess the effects of moderator variables in the subgroup (categorical) and meta-
regression (continuous) analyses, which assumed that the effect sizes differed randomly
in the population, with no systematic variation, and that there may be more than one
effect size than that captured by the moderating variables (Overton, 1998; Voss et al.,
2010). Moderating variables were assessed separately by partitioning the effect sizes into
groups based on the moderator variable of interest (number of female and male athletes
and controls, age of athletes and controls, athlete type, control type, athlete level of experi-
ence and type of cognitive domain assessed). Multiple comparisons were corrected using
Bejamini and Hochberg’s false discovery rate (FDR), at a q value of 0.05, after pooling the
p-values from the moderator analyses for each predictor model. Cardiovascular fitness
(ml/kg/min), which has been associated with cognitive function, was only obtained in 7/
41 studies and therefore was not included as a moderator variable. Funnel plots and
Egger’s test of symmetry were used to assess publication bias.
8 N. E. LOGAN ET AL.
https://osf.io/bk2gw/
https://osf.io/bk2gw/
3. Results
3.1. Descriptive statistics of sampled studies
The 41 included studies included 5339 participants (Table 2). Groups were well balanced,
with approximately 42.5% (n = 2267) of all participants being athletes. The average age of
the sample was 25.49 ± 0.91 years, based on a subset of articles that reported descriptive
statistics for age. The average age for athletes (25.33 ± 14.28 years) and controls (25.66 ±
13.47 years) was not statistically different (p > .05). The athlete group included 31.28%
males, and 10.99% females, and the control group included 49.75% males and 7.98%
females. A further breakdown of descriptive statistics by athlete type and athlete experi-
ence can be seen in Table 2. Notably, no studies in the analysis indicated an athlete
sample with resistance training. The majority of studies were described in peer-reviewed
journal articles (97%), and the remaining sources were dissertations. Effect sizes are inter-
preted using Cohen’s (1992) guidelines. Small effects are in the 0.1–0.3 range, medium
0.4–0.6 range and large in the range > 0.7. Funnel plots were inspected for detection
of possible publication bias within each meta-analysis.
3.2. Overall effects
Across 41 studies with a range of cognitive tasks, the effect of athlete status had a signifi-
cant medium facilitating effect on cognitive function measures, compared to non-athlete
controls. The meta-analytic estimate of this effect was g = 0.57, 95% confidence interval
(CI) = [0.27, 0.87]. p≤ .001. Our main measure of heterogeneity (I2) = 99.9% across all
studies indicated that variability between effect sizes was potentially related to random
error, and that moderator analyses are necessary to interpret differences. A general over-
view of these findings can be seen in Figure 2 (Forest plot).
3.3. Moderator analyses:
3.3.1. Cognitive task attributes (Categorical moderators)
Type of cognitive performance assessed (accuracy and reaction time), was not a signifi-
cant moderator (p = .447; see Table 3 for details), despite effect sizes being significantly
different from zero at specific levels of those moderators.
Type of cognitive domain (attentional allocation, inhibitory control, cognitive flexibility
and battery of cognitive tasks) was a significant moderator (p = .002; see Table 3 for
details), such that studies assessing attentional allocation (g = 1.18), cognitive flexibility
(g = 0.96), and a battery of cognitive tasks (g = 0.31), facilitated differences on cognitive
function between athletes and controls, however, inhibitory control measures (g =
0.01), did not.
3.3.2. Athlete and control attributes (Categorical moderators)
The type of athlete (p≤ .001; see Table 3 for details), was a significant moderator, such
that aerobically trained athletes (g = 0.93), HIIT Team trained athletes (g = 0.65) and
mixed or non-disclosed athlete attributes (g = 0.90), facilitated differences on cognitive
function between athletes and controls. However, the type of control (sedentary, social
sport playing and mixed or non-disclosed) was not a significant moderator (p = .224;
INTERNATIONAL JOURNAL OF SPORT AND EXERCISE PSYCHOLOGY 9
Table 2. Descriptive statistics of participant and group attributes in the sampled studies.
N Athlete Age Control Age
Gender (N
female) Group Attributes:
Study All Athlete Control M SD M SD Athlete Control Type of Athlete
Experience of
Athlete Type of Control
Performance
Outcome Task Type
Alves et al. (2013) 154 87 67 19.81 1.90 19.67 1.80 42 31 HIIT team Elite Social-sport RT Battery
Ballester et al. (2019) 66 44 22 23.55 0.90 22.30 0.60 18 11 Mixed or Non-
Disclosed
College or
Amateur
Sedentary ACC & RT Inhibition
Ballester et al. (2015) 75 39 36 13.65 0.45 13.80 0.60 15 18 HIIT team Child Athlete Social-sport ACC & RT Attention
Ballester et al. (2018) 60 40 20 11.00 0.20 11.00 0.20 20 10 Mixed or Non-
Disclosed
Child Athlete Social-sport ACC & RT Attention
Bashore et al. (2018) 308 276 32 19.80 1.50 19.60 1.70 0 0 HIIT team College or
Amateur
Social-sport ACC & RT Inhibition
Batmyagmar et al.
(2019)
99 50 49 66.00 4.00 44.00 5.00 5 4 Endurance Elderly or
Masters
Sedentary ACC Battery
Bianco, Berchicci, et al.
(2017)
24 12 12 30.40 6.20 28.80 5.00 1 5 Mixed or Non-
Disclosed
Elite Sedentary RT Inhibition
Bianco, Di Russo, et al.
(2017)
39 26 13 27.45 1.95 28.50 1.90 7 3 HIIT individual Elite Mixed or Non-
Disclosed
RT Inhibition
Brevers et al. (2018) 52 27 25 19.21 3.58 20.07 3.89 4 3 HIIT individual Elite Social-sport RT Attention
Camponogara et al.
(2017)
23 9 14 22.20 3.08 27.00 9.20 2 8 HIIT team Elite Mixed or Non-
Disclosed
ACC Battery
Chang et al. (2017) 60 40 20 21.18 1.51 21.60 1.35 11 7 Mixed or Non-
Disclosed
Elite Social-sport ACC & RT Battery
Chueh et al. (2017) 48 32 16 20.55 1.75 20.70 1.10 14 7 Mixed or Non-
Disclosed
College or
Amateur
Social-sport ACC & RT Battery
Culpin (2018) 62 43 19 68.55 3.60 69.10 2.70 20 7 HIIT individual Elderly or
Masters
Social-sport ACC & RT Battery
Di Virgilio et al. (2019) 40 20 20 22.00 1.70 22.00 3.00 2 2 HIIT individual College or
Amateur
Sedentary ACC Flexibility
Elferink-Gemser et al.
(2018)
60 30 30 16.00 4.00 16.00 5.00 18 18 HIIT individual Child Athlete Mixed or Non-
Disclosed
ACC Battery
Frick et al. (2017) 221 163 58 25.90 4.53 25.50 5.80 0 0 HIIT team Elite Social-sport RT Flexibility
Hagyard et al. (2021) 154 74 80 Mixed or Non-
Disclosed
Elite Sedentary ACC & RT Inhibition
Heppe et al. (2016) 60 30 30 23.20 4.10 21.70 1.70 30 13 HIIT team College or
Amateur
Social-sport RT Flexibility
Heppe et al. (2016) 54 27 27 24.60 4.00 23.90 3.20 12 12 HIIT team College or
Amateur
Social-sport RT Flexibility
Heppe et al. (2016) 52 26 26 21.90 3.81 22.00 3.15 13 11 HIIT team Social-sport ACC & RT Battery
10
N
.E.LO
G
A
N
ET
A
L.
College or
Amateur
Howard et al. (2018) 30 20 10 22.67 2.84 22.67 2.84 11 5 HIIT team Elite Social-sport ACC Attention
Hüttermann and
Memmert (2014)
16 8 8 24.88 3.27 26.00 4.27 2 4 HIIT individual College or
Amateur
Social-sport ACC Attention
Jacobson and
Matthaeus (2014)
54 39 15 20.12 1.34 20.20 1.27 22 9 HIIT individual EliteSocial-sport ACC & RT Battery
Lesiakowski et al.
(2013)
30 15 15 20.40 5.19 21.09 1.96 5 5 HIIT individual College or
Amateur
Sedentary ACC & RT Battery
Lundgren et al. (2016) 1798 48 1750 23.70 4.96 48.50 0 0 HIIT team Elite Sedentary ACC Flexibility
Marmeleira et al.
(2013)
36 24 12 62.75 6.05 64.10 6.30 0 0 Endurance College or
Amateur
Sedentary RT Flexibility
Notarnicola et al.
(2014)
180 120 60 12.50 0.75 12.50 0.80 60 30 HIIT team Elite Social-sport ACC Flexibility
Pačesová et al. (2018) 98 44 54 18.11 1.35 18.04 1.33 0 0 HIIT team College or
Amateur
Social-sport RT Flexibility
Prien et al. (2019) 693 425 268 18.25 1.90 20.50 2.20 174 134 HIIT team Elite Sedentary ACC & RT Battery
Qiu et al. (2018) 65 42 23 20.44 1.87 20.39 2.29 0 0 HIIT team College or
Amateur
Social-sport ACC Flexibility
Romeas and Faubert
(2015)
59 40 19 21.51 0.32 24.21 0.50 0 0 Mixed or Non-
Disclosed
Elite Mixed or Non-
Disclosed
ACC & RT Attention
Shoemaker et al.
(2019)
18 8 10 19.00 1.00 22.00 5.00 3 6 Endurance College or
Amateur
Social-sport ACC & RT Battery
Tsai et al. (2016) 60 40 20 66.14 4.40 64.33 3.57 13 7 Mixed or Non-
Disclosed
Elderly or
Masters
Sedentary ACC & RT Attention
van de Water et al.
(2017)
24 15 9 24.50 4.00 24.00 4.00 0 0 HIIT individual Elite Social-sport RT Inhibition
Verburgh et al. (2016) 168 117 51 10.55 1.35 10.40 1.20 0 0 HIIT team Child Athlete Social-sport ACC & RT Battery
Vestberg et al. (2012) 57 29 28 25.30 4.20 22.80 4.10 15 11 HIIT team Elite Social-sport ACC Battery
Wang et al. (2016) 34 16 18 21.90 1.72 21.91 1.80 11 14 Mixed or Non-
Disclosed
College or
Amateur
Mixed or Non-
Disclosed
ACC & RT Attention
Wang et al. (2013) 60 40 20 19.77 1.57 20.92 2.33 0 0 Endurance College or
Amateur
Sedentary RT Battery
Wang et al. (2020) 60 30 30 20.10 0.92 21.73 1.43 0 0 HIIT team College or
Amateur
Social-sport ACC & RT Inhibition
Xu et al. (2016) 34 16 18 22.54 5.15 21.09 4.27 5 10 HIIT team College or
Amateur
Social-sport ACC & RT Attention
Yu et al. (2017) 54 36 18 21.10 2.10 21.80 2.10 15 9 Mixed or Non-
Disclosed
College or
Amateur
Sedentary ACC & RT Attention
IN
TERN
A
TIO
N
A
L
JO
U
RN
A
L
O
F
SPO
RT
A
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D
EX
ERC
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PSYC
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11
Figure 2. Forest Plots of effect sizes for cognitive function. The sizes of the squares represent within-
domain differences in the sample size only. The diamond at the bottom represents the overall effect.
The dotted vertical line represents an effect size (g = 0.57). CI = Confidence Interval. PI = Prediction
Interval. SMD = Standard Mean Difference.
12 N. E. LOGAN ET AL.
see Table 3 for details), despite effect sizes being significantly different from zero at
specific levels of those moderators.
The experience of the athlete was a significant moderator (p≤ .001; see Table 3 for
details), such that child athletes (g = 0.26), elite athletes (g = 0.94) and elderly or
masters levels athletes (g = 0.91), facilitated differences on cognitive function between
athletes and controls, however, college or amateur athletes (g = 0.11) did not.
3.3.3. Demographic factors (Continuous moderators)
Planned continuous moderator analyses included assessing the effects of the age of the
athlete and control groups, and the percentage of female and male athletes and control
participants. None of the planned continuous moderators yielded a significant effect on
cognitive function (see Table 4 for details).
Table 3. Categorical moderator analyses.
Model Outcome 95% CI
Categorical
Moderator: Type and
Variable Q df p k g SE p Lower Upper
Cognitive Task
Performance
1.61 2 .447
Accuracy 10 0.527099 0.120945 0.000* 0.29 0.764
Reaction Time 11 1.007798 0.466078 0.031* 0.094 1.921
Accuracy and
Reaction Time
20 0.370085 0.208983 0.077 −0.04 0.78
Cognitive Task
Domain
15.40 3 .002
Attentional
Allocation
10 1.185868 0.469831 0.012* 0.265 2.107
Inhibitory
Control
7 0.019857 0.049142 0.686 −0.076 0.116
Cognitive
Flexibility
9 0.968184 0.405554 0.017* 0.173 1.763
Battery 15 0.313826 0.123821 0.011* 0.071 0.557
Athlete Type 43.08 3 .000
Resistance 0 – – – – –
Aerobic 4 0.932925 0.061988 0.000* 0.811 1.054
HIIT Individual 9 0.065814 0.11714 0.574 −0.164 0.295
HIIT Team 18 0.65533 0.231603 0.005* 0.201 1.109
Mixed or Non-
Disclosed
10 0.904811 0.439616 0.040* 0.043 1.766
Athlete Experience 26.94 3 .000
Children 4 0.263558 0.020059 0.000* 0.224 0.303
College or
Amateur
18 0.110001 0.088356 0.213 −0.063 0.283
Semi-
Professional or
Elite
16 0.938177 0.317948 0.003* 0.315 1.561
Older Adult or
Masters
3 0.90916 0.1461 0.000* 0.623 1.196
Control Type 2.99 2 .224
Sedentary
Control
12 0.279411 0.148159 0.059 −0.011 0.57
Social Sport
Playing Control
24 0.560655 0.195593 0.004* 0.177 0.944
Mixed or Non-
Disclosed
5 1.25274 0.66225 0.059 −0.045 2.551
*Denotes statistical significance at p < .05 and survives FDR multiple comparison correction.
INTERNATIONAL JOURNAL OF SPORT AND EXERCISE PSYCHOLOGY 13
3.4. Assessment of meta-bias
We first plotted funnel plots to assess possible publication bias, and the symmetry of the
relationships between each effect size and the equivalent standard error. Publication bias
reflects the possibility that the studies retrieved for the meta-analysis may not include all
studies actually conducted in the literature, with the most likely omissions being studies
that failed to find statistically significant results (Sterne & Harbord, 2004). The absence of
publication bias should result in effect sizes from larger samples, with smaller standard
errors, clustering around the mean effect in the funnel plot. However, evidence of publi-
cation bias should demonstrate asymmetrical funnel plots. This asymmetry may occur due
to selection bias (bias in publication, location, language, citation and multiple
Table 4. Continuous moderator analyses.
Model Outcome 95% CI
I2 k df g SE t p Lower Upper
Moderator: Type and Variable
Age
Athlete 0.99 41 1, 38 0.008 0.013 0.649 .520 −0.018 0.034
Control 0.99 41 1, 38 0.007 0.015 0.455 .652 −0.024 0.037
Percentage Females
Athlete 0.97 41 1, 38 −0.01 0.006 −1.454 .154 −0.021 0.003
Control 0.97 41 1, 38 −0.009 0.006 −1.422 .163 −0.021 0.004
Percentage Males
Athlete 0.97 41 1, 38 0.009 0.006 1.454 .154 −0.003 0.021
Control 0.97 41 1, 38 0.009 0.006 1.422 .163 −0.004 0.021
*Denotes statistical significance at p < .05 and survives FDR multiple comparison correction.
Figure 3. Funnel Plot of effect sizes for studies in the meta-analysis. Each dot represents an individual
effect size, plotted as a function of standard error. The vertical line represents the random-effects-
model estimate (g = 0.57).
14 N. E. LOGAN ET AL.
publications), true heterogeneity (effect size differing according to study size), data irre-
gularities (poor methodological design and inadequate analysis), artefact (heterogeneity
due to poor choice of effect measure) or chance (Sterne & Harbord, 2004). The funnel plot
in the current meta-analysis (Figure 3) was fairly symmetrical and evenly distributed, with
a few effects falling outside the 99% CI, suggesting that the present meta-analysis was not
substantially affected by publication bias, which was confirmed by Eggers’ test of asym-
metry (p > .5).
4. Discussion
4.1. Summary of results
The present study investigated the nature and extent of the athlete–cognition relation-
ship via a meta-analytic review of 41 studies. The findings provide evidence that there
is a significant medium association between being an athlete and cognitive function,
such that trained athletes have greater effect sizes on measures of cognitive function
in comparison to controls. We found that studies that assessed measures of attentional
allocation, cognitive flexibility and a battery of cognitive tasks had a significant effect
on facilitating the effect size differences between athletes and controls. Notably, the
experience of the athletes was also associated with between–group differences in cogni-
tion, such that differences were found in children,elite and older adult or masters athlete
populations. Further, the type of athlete training was also associated with between–group
differences in cognition, such that differences were found in aerobically trained, HIIT team
trained or athletes of mixed or non-disclosed training attributes. However, the type of
cognitive outcome assessed, the type of control groups and the demographics of the
groups were not associated with between–group differences in cognition.
4.2. General discussion
Our results suggest that despite the heterogeneity of medium-to-large-effect sizes, the
average effects of performance on tasks of attentional allocation cognitive flexibility
and a battery of cognitive tasks, remain statistically in favour of trained athletes, in com-
parison to controls, supporting that of Voss et al. (2010). Further, when accounting for
type and experience of athlete, our results suggest that being a HIIT or an aerobically
trained athlete, being a child athlete, an elite athlete or an older adult or masters
athlete, gives rise to greater effect size performance outcomes of cognitive function
tasks. Research on participation in physical activity throughout childhood has previously
demonstrated positive associations to cognitive and brain function (de Greeff et al., 2018;
Donnelly et al., 2016, 2011; Drollette et al., 2014, 2018; Fox et al., 2010; Hillman et al., 2011;
Pindus et al., 2016; Verburgh et al., 2014). Interestingly, there is also recent evidence advo-
cating for the benefits of HIIT (Kao et al., 2018; Drollette et al., 2018; Hsieh et al., 2021;
Moreau & Chou, 2019) and aerobic exercise (Bidzan-Bluma & Lipowska, 2018; Carvalho
et al., 2014; Chaddock-Heyman et al., 2013; Davis et al., 2011; Drollette et al., 2014,
2018; Hillman et al., 2011; Krafft et al., 2014; Lautenschlager et al., 2008; Stroth et al.,
2009; Zheng et al., 2016) across the life span on measures of cognitive and brain function.
As such, the current findings corroborate the notion that HIIT and aerobic training is
INTERNATIONAL JOURNAL OF SPORT AND EXERCISE PSYCHOLOGY 15
beneficial for cognition. These findings suggest that the mechanisms behind exercise
training, such as increases in BDNF, angiogenesis, synaptogenesis and human growth
factor levels post-exercise, are thus likely to positively influence the brain-cognition
relationship (Basso & Suzuki, 2017; Voss et al., 2019). Specifically, meta-analytic research
suggests that chronic endurance or HIIT exercise protocols improve cognitive functions
in young adult populations, in tasks of processing speed, attentional allocation,
working memory, language skills and cognitive flexibility and (Haverkamp et al., 2020).
This finding is complimentary to the current study, and combined, suggests that
aerobic exercise-induced neural adaptations redistribute neural resources efficiently to
accomplish goal-directed behaviour during cognitive tasks. Although neural findings
provide a biomarker of cognitive function, the coupled cellular and molecular mechan-
isms (e.g., synaptogenesis, angiogenesis and neurotrophins) underlying exercise-
induced effects on cognition provide a more detailed understanding of neural adap-
tations, such as those observed in aerobic based endurance or HIIT exercise protocols
(Basso & Suzuki, 2017).
Notably, the results suggest associations among HIIT-team and aerobic trained athletes
with cognition; however, such associations were not observed with HIIT-individually
trained athletes and cognition. Although not investigated in the current study, the
concept of “open-skill” and “closed-skill” sports may provide further information on this
relationship to cognition. Team sports are considered “open” skill sports, whereby ath-
letes interact directly with opponents, team-mates and unpredictable events; meanwhile,
individual sports are considered “closed” sports, whereby athletes do not interact with
unpredictable environments (Elferink-Gemser et al., 2018). Additionally, open-skill
sports have been associated with superior executive functioning compared to close-
skill sports (Zhu et al., 2020). The mechanism between open-skilled sports and cognitive
ability is suggested to arise from such activities promoting the athlete’s need to activate
more cognitive resources to adapt to variable external information and multi-sensory
environments (Zhu et al., 2020). As such, this explanation could also expand to the
results of the current study, whereby HIIT-team athletes, rather than HIIT-individual ath-
letes demonstrated positive associations with cognition. Future meta-analytic studies
could consider categorising athletes by skill type (open and close), as well as by energy
systems (oxidative metabolism, glycolysis and phosphocreatine). Further, although the
current data do not indicate support for alternatively trained athletes, it is important to
note there are also previously reported benefits to cognitive and brain function from par-
ticipating in other forms of physical activity across the life span, such as resistance training
(Best et al., 2015; Bolandzadeh et al., 2015; Liu-Ambrose et al., 2010, 2012) and yoga
(Gothe & McAuley, 2015; Nangia & Malhotra, 2012).
Further, young adult college or amateur athletes noticeably did not facilitate signifi-
cant differences in effect size between athletes and controls. However, this is not a surpris-
ing result as young adults in this age range commonly perform at the ceiling on tests of
different aspects of cognitive function. Additionally, previous research has demonstrated
that specific populations may be more sensitive to exercise-induced changes in cognitive
function than young adult populations, including children (Hillman et al., 2014), children
with ADHD (Pontifex et al., 2013), children with lower-cognitive performing baseline abil-
ities (E. S. Drollette et al., 2014), children with obesity (Logan et al., 2021) and sedentary
but healthy older adults (Colcombe et al., 2004; Erickson et al., 2011).
16 N. E. LOGAN ET AL.
Additionally, our results also support the notion of evaluating cognitive function, of
both athlete and control populations, with laboratory-based measures, as our methods
support and extend previous findings (Mann et al., 2007; Voss et al., 2010). That is, in
many cases, athletes show superior performance on both sport-related cognition and
measures of aspects of cognition not specifically to sport performance. Lastly, in
attempt to decompose the basis of the athlete–cognition relationship, our results
suggest there is an underlying effect of training type, such that HIIT team and aerobi-
cally trained athletes, as well as child, elite and older adult athletes, demonstrate posi-
tive effects on various measures of cognitive function. Notably, the process of
becoming either a well-trained athlete requires the athlete to extensively train over
a period of many years in order to ascertain competitive performance levels (Baker
et al., 2003; Swann et al., 2015; Williams & Ford, 2008), which suggests that it may
be the physiological adaptations (Hawley, 2002; Hoppeler et al., 1985; Kraemer et al.,
1995) in response to the training which drives, in part, the athlete–cognition relation-
ship; as evaluated with laboratory-based assessments of diverse tasks of cognitive
outcomes.
Overall, the results from the current analysis support the cognitive component skills
approach, which suggests a relationship between sport expertise and performance
measures of laboratory-based cognition. However, it should be noted, our methods did
not aim to analyse the expert performance approach, as sport-specific motor-cognitive
tasks were excluded from the analysis. Therefore, support for the cognitive component
skills approach should be considered carefully. Future analyses should consider a direct
comparison between the two theories.
4.3. Strengths, limitations and future directions
The current analysis was selective to a subset of studies in a 11-year span following the
Voss et al. (2010) analysis.This approach ultimately decreased the available sample size
of studies and is important to consider when addressing the number of studies in the
current analysis. However, selecting studies within this time range enabled the analysis
to identify and compare our results to previous research.
Another key strength of the current study was classifying each of the cognitive per-
formance outcomes (such as accuracy and reaction time) and the cognitive domains
(attentional allocation, inhibitory control and cognitive flexibility). Previous research
has found associations between physiological variables (exercise interventions and
fitness outcomes) and cognitive measures (specifically attentional allocation and inhibi-
tory control). Therefore, identifying specific features of the cognitive tasks enabled us to
detect which tasks are sensitive to group differences. Notably, the result that inhibitory
control was not significantly associated with group differences was surprising, due to
previously established literature demonstrating exercise-induced facilitations.
However, many of the studies that have found inhibitory benefits of exercise, and in par-
ticular aerobic exercise, have not employed trained athletes. Additionally, previous
studies which compared athletes and non-athletes have focused on motor inhibition,
compared to cognitive inhibition (Brevers et al., 2018) As such, future studies which
investigate differences between inhibition modalities (motor and cognitive) among
athlete and non-athlete populations would complement the literature. It should also
INTERNATIONAL JOURNAL OF SPORT AND EXERCISE PSYCHOLOGY 17
be noted that while three robust subdomains of cognitive control were synthesised for
the current analysis, other constructs such as goal-driven decision making and reason-
ing (Mackie et al., 2013), and different types of memory, such as declarative, procedural
and prospective memory (Engle, 2010), should also be included in future studies.
Differentiating between performance on cognitive domains is also important to con-
sider when equating the athlete–cognition relationship, as to not overplay the cognitive
profile of athletes; rather, it enabled us to identify which cognitive domains or skill sets
are associated with a trained athlete’s experience. However, this identification also
raises the question of directionality, or the self-selection (Voss et al., 2010) of this
relationship, which is a limitation of the current analysis. Specifically, there may be a
causation outcome of elite sport participation, such that the cognitive capacities of ath-
letic individuals increase after the experience-dependent learning, and thus enable the
acquisition of a particular cognitive skill set. However, there may also be individuals who
have a greater capacity for specific domains of cognitive performance, who are also
drawn to participate in high levels of elite sport (athletes). Such theoretical comparisons
cannot be answered with the current analysis; however, future research would be well
positioned to examine a controlled longitudinal study, which may be an appropriate
method to provide substance to this question of directionality. Of course, such
studies are very difficult to conduct since they would entail randomising participants
into different sports (or not being involved in sports as a control) and training for exten-
sive periods of time.
4.4. Conclusions
The findings from this meta-analysis indicate that there is a positive association between
athletes and cognitive function. Additionally, this effect is present in reaction time and
accuracy measures on a battery of cognitive tasks. Furthermore, athlete type and experi-
ence are important to consider when evaluating the athlete–cognition relations, as our
results indicate that HIIT team-trained athletes, who compete at an elite level, are facilitat-
ing this effect. Our results indicate a positive relationship between the experiences
derived and physical aspects obtained from being a trained athlete and laboratory-
based measures of cognitive function. As physical inactivity levels continue to increase
throughout the lifespan, these findings demonstrate positive societal considerations for
participation in physical activity and sports.
Data availability
The datasets generated during and/or analysed during the current study are available
from the corresponding author on reasonable request. The R code for our analyses is avail-
able online at: https://osf.io/bk2gw/.
Author contributions
A. Kramer conceived the study and provided statistical and research expertise. N. Logan
supervised the literature search, data collection and data analyses. D. Henry performed
the literature search, collected the data, and conducted the analyses. C. Hillman provided
18 N. E. LOGAN ET AL.
https://osf.io/bk2gw/
research and manuscript expertise. N. Logan wrote the manuscript, and all authors
approved the final version for submission.
Competing interests
The authors report no conflicts of interested associated with the collection, dissemination
or interpretation of this research. No patents, copyrights or royalties are involved or
included in this work. The results of this study are presented clearly, honestly and
without fabrication, falsification or inappropriate data manipulation.
ORCID
Nicole E. Logan http://orcid.org/0000-0003-4016-2146
Charles H. Hillman http://orcid.org/0000-0002-3722-5612
Arthur F. Kramer http://orcid.org/0000-0002-1991-5046
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