Keywords
executive function - memory - brain - sport - concussion - soccer
Introduction
Football, also known as soccer, is the world’s most popular sport. It is played by
over 265 million people [1] and enjoyed
by five billion fans worldwide [2].
Football differs from other pitch-based team sports by the use of a player’s head
to
redirect the ball, a technique known as heading. Since its emergence over 150 years
ago, the technique has developed into an integral part of offensive and defensive
play [3]. Studies have shown on average
a player heads the ball between 6 and 12 times per match [4]
[5]
[6]
[7], and it has been estimated
professional players could be exposed to over 50,000 headers throughout their career
[8].
Previous research in football has focused on the effects of concussion [9]
[10]
[11]
[12]
[13]. While this is an important area of research, concussion usually
arises due to uncontrollable collisions with other players and incidence in football
is low at 0.004–2.44 concussions per 1,000 player-hours [14]. Studies have reported impaired
neurological functional and microstructure changes after repetitive head impacts
[15], leading to a growing interest
in the effects of subconcussive impacts in sport [16]
[17]
[18]
[19]
[20]. The term ‘subconcussive’ has been used to describe head impacts that
do not induce typical concussion symptoms [21]
[22]. However, cortical
dysfunction has been observed in athletes after subconcussive impacts, even in the
absence of clinical symptoms [18].
Football players are regularly exposed to multiple subconcussive impacts, as a
result of heading the ball, although the clinical significance of this remains
unclear.
The most concerning evidence regarding the safety of football has emerged from large
retrospective cohort studies conducted in Scotland [23] and Sweden [24], which both reported a significantly
increased neurodegenerative disease mortality risk in former professional players
compared to age, sex and socioeconomic status matched controls. Additionally, both
studies observed an increased risk in outfield players and those with the longest
careers [24]
[25], indicating that risks are higher in
those with the greatest heading exposure. If these findings are attributed to
heading alone, estimates have predicted the yearly global economic burden of heading
could be $2.1 billion [26], providing a
real cause for concern.
Due to concerns over players’ welfare, the Football Association (FA) has issued
guidance limiting heading in training in professional [27] and youth football [28]. A recent trial banned heading in
players under the age of 12, making the technique a punishable offence [29]. Subsequently, the FA has decided to
phase out deliberate heading in grassroots youth football in players under age 12
over the next three seasons [30]. Amid
concerns that heading could present a public health risk, researchers have not yet
provided conclusive evidence to confirm whether use of this technique is indeed
detrimental to players’ neurological function in the long term.
Systematic literature reviews have previously attempted to study the association
between heading and cognitive function among players of all ages and levels [3]
[7]
[31]
[32]
[33]. However, due to the high variability in age and experience in the
study populations included, and the quality and heterogeneity of studies, existing
reviews have concluded that there is no definitive evidence to prove an association.
The inconclusive findings may have arisen due to the effects of heading in those
with the highest exposure being masked by those who rarely head the ball, generating
conflicting results.
Large scale observational studies have shown heading exposure increases with age in
children’s and youth football [34]
[35]. It has also been reported that
heading exposure is higher in adults than adolescents [36], with elite-level male players shown
to have the greatest exposure [37]. If
an association between heading and cognitive function is to be identified, it might
be most likely observed in professional players who present the greatest heading
exposure. A review focused only on professional football players could provide this
insight. Therefore, the aim of this systematic review was to evaluate the
association between heading exposure and cognitive function in current and former
professional football players.
Materials and Methods
This systematic review conforms to PRISMA guidelines [38] and was conducted in line with the
PERSiST [39] guidance (i. e.
implementing Prisma in Exercise, Rehabilitation, Sport medicine and SporTs science).
The protocol for this review was registered with PROSPERO (CRD42023404209) and can
be assessed at https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023404209
[40].
Information sources
The literature search was conducted using seven online databases: MEDLINE (OVID),
Embase (OVID), Web of Science, PsycINFO (OVID), CENTRAL, SportDiscus (EBSCOhost)
and PEDro. The final search was conducted on 5 April 2023. Two additional
records that met the eligibility criteria were identified and were included in
the review. One identified via handsearching, and one published after the final
search date.
Eligibility criteria
The following inclusion criteria was set: (1) the study investigated heading in
football; (2) cognitive function was quantitatively measured; (3) study
participants were current or former adult professional football players; (4) all
observational or experimental study types; (5) published in English.
The following exclusion criteria was set: (1) participant age, sex, and level of
play were not described; (2) study participants were under the age of 18; (3)
non-peer reviewed studies; (4) full-text manuscript not available; (5)
systematic, narrative or scoping reviews; (6) non-human studies.
Search strategy
The search strategy consisted of a combination of Medical Subject Headings (MeSH)
terms and keywords listed in titles or abstracts. Four search blocks were
designed to include articles related to: (1) heading (2) professional football
(3) cognitive function and similar neurological outcomes and not related to (4)
American football. Search terms were separated using the ‘OR’ Boolean operator.
The first, second and third search blocks were separated using the ‘AND’ Boolean
operator. The fourth search block was separated using the ‘NOT’ Boolean
operator. The MEDLINE (OVID) search strategy was translated for each database
where possible. The full search strategies for each database are available (see
Appendix A–G).
Selection process
Records identified from the literature search were exported to EndNote 20
(Clarivate, Philadelphia) alongside the two additional records. Duplicates were
automatically collated and manually checked before removal. The title and
abstracts of the remaining reports were screened by two reviewers independently
and reports that were not deemed relevant were excluded. The full-text
manuscripts for remaining records were retrieved. The remaining reports were
assessed by two independent reviewers for eligibility by applying the inclusion
and exclusion criteria. Reports that did not meet the criteria were excluded,
and the reason for exclusion was reported. Two independent reviewers reviewed
the full-text manuscripts of reports that met the eligibility criteria to
determine their inclusion in the review. The list of studies to be included in
the review was agreed by the two reviewers. A third reviewer was available to
resolve potential discrepancies by a simple consensus.
Risk of bias assessment
The studies included were subject to a risk of bias assessment. The JBI Checklist
for Analytical Cross-Sectional Studies was used to critically appraise each
study [41], which is the preferred
assessment tool for analytical cross-sectional studies [42]. As all studies were
cross-sectional, they all underwent the same assessment. Two reviewers
independently assessed each study with no conflicting scores. The risk
assessment tool critiques studies in eight areas based on their methods,
measurements, interpretations, and statistical analysis. Studies meeting 0–4
conditions were deemed low quality, 5–6 conditions were deemed moderate quality,
and 7–8 conditions deemed high quality. Poor studies were to be excluded from
the review. The results of the risk of bias assessment are available (see
Appendix H).
Results
Study selection
A total of 563 records were identified. The literature search identified 561
records alongside two studies identified outside of the search, and 172 (31%)
duplicates were removed. The titles and abstracts of the remaining 391 (69%)
records were screened. 342 (87%) records were excluded after screening, as they
were not relevant to the review topic. The full-text manuscripts of the
remaining 49 (13%) records were sought. Two (4%) records were available only as
conference abstracts and were therefore excluded. The eligibility of the
remaining 47 (96%) reports was assessed by applying the inclusion and exclusion
criteria. Thirty-seven (79%) reports did not meet the eligibility criteria and
were excluded alongside a reason for their exclusion. Ten (21%) reports met the
criteria and were eligible for the review. However, two of these reports [43]
[44] were identified as being from the
same study. To prevent the duplication of data, the report [43] with the smaller sample size was
excluded. Nine studies [8]
[44]
[45]
[46]
[47]
[48]
[49]
[50]
[51] were selected to be included in
the review. A flowchart illustrating the study selection process is shown in
[Fig. 1].
Fig. 1 PRISMA flowchart displaying study selection process [38].
Study characteristics
Nine cross-sectional studies (n=925) were included in the review. Six studies
[8]
[45]
[46]
[47]
[48]
[51] (n=595) reported evidence for an
association between heading and impaired cognitive function, while three studies
[44]
[49]
[50] (n=330) reported no association.
The studies were conducted in six countries and were published between 1998 and
2023. Study characteristics for each study are listed in [Table 1].
Table 1 Study characteristics of the nine studies
included.
|
Study
|
Study design
|
Location
|
n
|
Sex
|
Age (mean±SD)
|
Playing status
|
Heading exposure setting
|
Length of observation
|
Method of measuring heading exposure
|
Study quality grade
|
|
Bruno & Rutherford, 2022
[8]
|
C-S
|
UK
|
60
|
M
|
68±10
|
Former
|
Matches and training
|
Professional Career
|
Self-reported estimate
|
High
|
|
Downs & Abwender, 2002
[ 45]
|
C-S
|
USA
|
6
|
M
|
42±10
|
Former
|
Matches and training
|
Since youth
|
Heading exposure index
|
Moderate
|
|
Espahbodi et al., 2023
[ 46]
|
C-S
|
UK
|
326
|
M
|
63±10
|
Former
|
Matches and training
|
Professional career
|
Self-reported estimate
|
High
|
|
Koerte et al., 2016
[ 44]
|
C-S
|
Germany
|
15
|
M
|
49±5
|
Former
|
Matches and training
|
Lifetime
|
Self-reported estimate
|
Moderate
|
|
Matser et al., 1998
[47]
|
C-S
|
Netherlands
|
53
|
M
|
25±4
|
Current
|
Matches only
|
1 season
|
Self-reported estimate
|
High
|
|
Matser et al., 2001
[ 48]
|
C-S
|
Netherlands
|
84
|
M
|
24*
|
Current
|
Matches only
|
1 season
|
Self-reported estimate
|
High
|
|
Prien et al., 2020
[51]
|
C-S
|
Germany and Netherlands
|
66
|
F
|
37±5
|
Former
|
Matches and training
|
Lifetime
|
Self-reported frequency
|
High
|
|
Rodrigues et al., 2019
[49]
|
C-S
|
Brazil
|
44
|
M
|
25±5
|
Current
|
Matches only
|
Professional career
|
Self-reported estimate with observed sample
|
High
|
|
Straume-Naesheim et al., 2005
[50]
|
C-S
|
Norway
|
271
|
M
|
26**
|
Current
|
Matches only
|
Since youth
|
Self-reported estimate with observed sample
|
High
|
C-S: cross-sectional; M: male; F: female; *median age, **standard
deviation not provided.
Participant characteristics
The nine studies included 859 male and 66 female professional football players
(mean age=43). The studies investigated a mean sample size of 107.4±113.5
football players. Four studies [47]
[48]
[49]
[50] reported data from current
professional players (n=452, mean age=25) and five studies [8]
[44]
[45]
[46]
[51] from former professional players
(n=473, mean age=59).
Study quality
The risk of bias assessment did not identify any studies of ‘poor’ quality. Seven
studies [8]
[46]
[47]
[48]
[49]
[50]
[51] were graded ‘high’ (n=838) and
two studies [44]
[45] ‘moderate’ (n=21). In the five
studies with a control group, four studies [44]
[45]
[47]
[51] compared against appropriately
matched athletes with similar fitness levels (n=140). One study [49] inappropriately compared football
players to a control group consisting of guards and doormen (n=44). All but one
study [44] adjusted for confounding
factors, including age, past head injuries, level of education, and alcohol
consumption (n=910).
Study methods
Various methods of measuring heading exposure were employed. Five studies [8]
[44]
[46]
[47]
[48] relied on estimated data
collected from self-reported questionnaires (n=538). A sample of matches were
observed in two studies [49]
[50] to confirm self-reported
estimates aligned closely to the actual values (n=315). One study [51] recorded heading frequency, and
participants reported whether they were rare, moderate or frequent headers of
the ball (n=66). A heading exposure index based solely on the number of seasons
played at various levels of competition provided estimates for cumulative
heading exposure in one study [45]
(n=6).
Each study, apart from the two studies [47]
[48] conducted in the
Netherlands adopted a different method of cognitive testing. A paper or
interview based cognitive test battery was used by five studies [8]
[44]
[46]
[47]
[48] (n=538) and two studies [49]
[50] (n=315) used solely a
computerized test battery to measure cognitive outcomes. Two studies [44]
[45]
[51] (n=72) used a combination of
paper or interview and computerized cognitive testing.
Presentation of results
The presentation of results differed greatly between studies due to the
differences in methods of cognitive testing. [Table 2] displays the details of
cognitive testing and results from each study.
Table 2 Cognitive testing details and results from
studies included.
|
Study
|
Test (n)
|
Age (mean±SD)
|
Playing status
|
Method of cognitive testing
|
Cognitive sub-domains tested
|
Main findings
|
Statistical results (95% CI)
|
|
Bruno & Rutherford, 2022
[8]
|
60
|
68±10
|
Former
|
Test Your Memory (TYM) self-administered paper-based
cognitive test
|
Global executive function score (includes cognitive
subdomains of: orientation, calculation, visuospatial
ability, episodic memory, visual memory)
|
Negative correlation between estimated career headers and TYM
scores.
|
Bayesian linear regression analysis: Per 100,000 headers TYM
scores decrease by 3.2 points (1.6–5.3)
|
|
Downs & Abwender, 2002
[45]
|
6
|
42±10
|
Former
|
Paper and interview based cognitive test battery: PASAT, FTT,
WCST and computerized CPT
|
Sustained attention, motor speed, selective attention,
cognitive flexibility
|
Significant negative correlation between estimated heading
exposure and all five WCST test scores.
|
Age-partialled correlation coefficient between heading
exposure and number of categories completed on WCST: − 0.51
(p<0.001)
|
|
Espahbodi et al., 2023
[ 46]
|
326
|
63±10
|
Former
|
Paper and interview based cognitive test battery: TICS-m,
HVLT, VFA, TYM
|
Global executive function score, verbal memory, verbal
fluency
|
Both match and training heading frequency were associated
with a risk of cognitive impairment in later life and the
associations were dose-dependent.
|
Linear regression coefficient for match heading frequency and
test scores on TICS-m: − 1.09 (p=0.002) and TYM: − 1.05
(p=0.003)
|
|
Koerte et al., 2016
[44]
|
15
|
49±5
|
Former
|
Paper and interview based cognitive test battery: TMT A and
B, ROCF
|
Visual attention, task switching, visual memory
|
No significant correlation between cognitive or behavioral
measures and lifetime estimates of heading. Football players
scored worse than matched controls on delayed recall.
|
Football players performed worse on the long delay recall
condition of the ROCF: mean T score 47.7±14.7 versus
56.9±8.8 (p=0.04)
|
|
Matser et al., 1998
[ 47]
|
53
|
25±4
|
Current
|
Paper and interview based cognitive test battery: RPM, WCST,
PASAT, DST, TMT A and B, Stroop test, BWT, subtests of the
WMS, CFT, 15-Word Learning Test, BFRT, Figure Detection
Test, VFT, Puncture Test
|
Abstract reasoning, cognitive flexibility, sustained
attention, visual perception, visual attention, task
switching, inhibitory control, working memory, visual
memory, episodic memory, verbal fluency, fine motor
control
|
Significant negative correlation between heading exposure and
short and long term memory CFT scores.
|
Adjusted regression coefficient for number of headers in a
season and test scores on CFT short term memory: − 0.17
(p=0.048) and CFT long term memory: − 0.19 (p=0.01)
|
|
Matser et al., 2001
[48]
|
84
|
24*
|
Current
|
Paper and interview based cognitive test battery: RPM, WCST,
PASAT, DST, TMT A and B, Stroop test, BWT, subtests of the
WMS, CFT, 15-Word Learning Test, BFRT, Figure Detection
Test, VFT, Puncture Test
|
Abstract reasoning, cognitive flexibility, sustained
attention, visual perception, visual attention, task
switching, inhibitory control, working memory, visual
memory, episodic memory, verbal fluency, fine motor
control
|
Estimated heading frequency observed was inversely associated
to test scores assessing verbal and visual memory and
focused attention.
|
Adjusted regression coefficients for impairment per 1,000
headers. CFT short term memory: − 3.24 (p=0.02); CFT long
term memory: − 3.34 (p=0.01); TMT A 2.57 (p=0.048); 15-Word
Learning Test: − 3.76 (p=0.03)
|
|
Prien et al., 2020
[51]
|
66
|
37±5
|
Former
|
Paper and interview based test battery: CFT, DST, TMT A and
B, PASAT and computerized CPT, FTT, SAT, SDC, Stroop Test,
VBM, VMT
|
Motor speed, simple reaction time, complex attention,
cognitive flexibility, processing speed, verbal memory,
visual memory, verbal fluency, working memory, visual
attention, task switching, sustained attention
|
Players with frequent heading exposure had significantly
worse verbal memory scores than players with rare heading
exposure.
|
Mean difference between frequent and rare headers verbal
memory test scores compared to control group: − 9.166
(p=0.041)
|
|
Rodrigues et al., 2019
[49]
|
44
|
25±5
|
Current
|
Computerized cognitive test battery: Simple Reaction Time
Test, Immediate Memory Test, Attention Test, Executive
Functioning Tests (Number-letter-test, Two-back-test,
Stroop-test), Delayed Memory Test
|
Simple reaction time, verbal memory, selective attention,
task switching, working memory, inhibitory control, delayed
memory
|
No significant difference in the cognitive test scores
between football players with high and low levels of heading
frequency. No correlation was identified between cognitive
performance and estimated career heading frequency.
|
Correlation coefficients for estimated career heading
exposure and test performance. Attention: 0.06 (p=0.665);
Memory: − 0.10 (p=0.521)
|
|
Straume-Naesheim et al., 2005
[50]
|
271
|
26**
|
Current
|
Computerized cognitive test battery: Simple reaction time,
choice reaction time, congruent reaction time, monitoring,
one-back, matching, learning
|
Simple reaction time, decision making, sustained attention,
divided attention, working memory
|
No association between estimated lifetime heading exposure or
number of headers per match and cognitive test scores.
|
|
PASAT: Paced Auditory Serial Addition Test; FTT: Finger Tapping Test;
WCST: Wisconsin Card Sorting Test; CPT: Continuous Performance Test;
TICS-m: Telephone Interview for Cognitive Status-modified; HVLT: Hopkins
Verbal Learning Test; VFA: Verbal Fluency Assessment; TMT A and B: Trail
Making Test A and B; ROCF: Rey Osterrieth Complex Figure; RPM: Raven
Progressive Matrices; DST: Digit Symbol Test; BWT: Bourdon-Wiersma Test;
WMS: Wechsler Memory Scale; CFT: Complex Figure Test; BFRT: Benton’s
Facial Recognition Task; VFT: Verbal Fluency Test; SAT: Shifting
Attention Test; SDC: Symbol Digit Coding Test; VBM: Verbal Memory Test;
VMT: Visual Memory Test; *median age; **standard deviation not
provided.
Paper or interview based cognitive assessment
Out of the five studies that adopted solely a paper or interview based cognitive
assessment, four studies [8]
[46]
[47]
[48] reported an association and one
study [44] did not. A Bayesian
linear regression model reported that per 100,00 headers, Test Your Memory
scores decreased by 3.2 points, providing evidence for a negative association
between heading and the performance of subdomains of executive function [8]. A linear regression analysis was
conducted by another study [46] that
assessed the same subdomains: after adjusting for confounding factors, a
significant correlation was reported showing that Test Your Memory performance
decreased as heading exposure increased in both matches (β=− 1.05 [− 1.75,
− 0.35]) and training (β=− 1.11 [− 1.77, − 0.41]). This study also assessed
executive function using the modified Telephone Interview for Cognitive Status,
and reported a correlation showing that increased heading exposure led to worse
interview performance. Test scores were again significantly correlated with
heading exposure in both matches (β=− 1.09 [− 1.77, − 0.41]) and training
(β=− 0.95 [− 1.61, − 0.29]). The two studies [47]
[48] utilizing the same 15 cognitive
tests both reported that heading frequency was inversely associated with
performance on cognitive tests assessing visual memory. One study [48] also reported a decline in both
visual attention and episodic memory as the number of headers increased. In the
one study [44] that reported no
association, visual attention, task switching and visual memory test scores were
not correlated with lifetime estimates of heading. However, in this same study
football players displayed significantly worse delayed memory performance
compared to control athletes.
Two studies [45]
[51] used a combination of paper and
interview based assessment and computerized assessment. One study [45] used a predominately paper and
interview based assessment for subdomains of complex attention, perceptual motor
function and executive function. They showed that football players with high
heading exposures performed worse on the Wisconsin Card Sorting Test [45], reporting cognitive flexibility
performance to be inversely correlated with heading frequency. The second study
[51] conducted four paper based
tests but did not report any difference in cognitive performance assessed by
paper or interview based tests between frequent and rare headers of the
ball.
Paper or interview-based testing was conducted by seven studies. Five studies
reported an association between at least one of subdomain of cognitive function
and heading. All five of these studies conducted linear regression analysis and
reported heading exposure and cognitive test performance to be inversely
correlated in at least one subdomain [8]
[45]
[46]
[47]
[48].
Computerized cognitive assessment
The two studies [49]
[50] that used only computerized
cognitive assessment reported no association between heading and cognitive
performance on all 14 computerized cognitive tests. One of these studies
reported reaction times but not commission errors [50]. However, both studies analyzed
current players with a mean participant age of 26 years. One study [49] reported no correlation between
heading exposure and test performance assessing subdomains of executive
function, memory and complex attention. The other study [50] reported no mean difference
between players with high and low heading exposure in seven subtasks. Both
studies [49]
[50] observed no association between
simple reaction time or working memory test scores and heading.
In the two studies [45]
[51] that used a combined method of
cognitive assessment, one study [45]
only used the computerized Continuous Performance Test to assess selective
attention and reported a significant correlation showing CPT test performance to
worsen as heading exposure index scores increased. The second study [51] used seven computerized tests to
assess seven different subdomains. They reported that participants who headed
the ball more frequently performed significantly worse on tests assessing verbal
memory compared to players who rarely headed the ball.
In summary, two [45]
[51] of the four studies that used a
computerized assessment did report an association between at least one cognitive
subdomain and heading, namely selective attention and verbal memory. However,
the two studies [49]
[50] that exclusively used
computerized assessment both reported no association.
Studies reporting no association vs. association
[Table 3] compares the study and
participant characteristics between studies reporting an association between
heading and cognitive function [8]
[45]
[46]
[47]
[48]
[51] and studies reporting no
association [44]
[49]
[50]. Eight studies investigated male
players, with five studies [8]
[45]
[46]
[47]
[48] reporting an association and
three studies [44]
[49]
[50] reporting no association. The one
study [51] investigating female
players reported an association between heading and cognitive function.
Table 3 Comparison of study and participant
characteristics between studies showing an association between
heading and cognitive function and studies not showing an
association.
|
Reporting association
|
Reporting no association
|
All studies
|
|
Study Characteristics
|
|
Number of studies (n)
|
|
|
Studies included
|
6 (595)
|
3 (330)
|
9 (925)
|
|
Participant sex
|
|
|
|
|
Male
|
5 (529)
|
3 (330)
|
8 (859)
|
|
Female
|
1 (66)
|
0
|
1 (66)
|
|
Playing status
|
|
|
|
|
Current
|
2 (137)
|
2 (315)
|
4 (452)
|
|
Former
|
4 (458)
|
1 (15)
|
5 (473)
|
|
Location
|
|
|
|
|
Europe
|
5 (589)
|
2 (286)
|
7 (875)
|
|
North America
|
1 (6)
|
0
|
1 (6)
|
|
South America
|
0
|
1 (44)
|
1 (44)
|
|
Study quality
|
|
|
|
|
High
|
5 (589)
|
2 (315)
|
7 (904)
|
|
Moderate
|
1 (6)
|
1 (15)
|
2 (21)
|
|
Heading exposure setting
|
|
|
|
|
Matches only
|
2 (137)
|
2 (315)
|
4 (452)
|
|
Matches and training
|
4 (458)
|
1 (15)
|
5 (473)
|
|
Length of observation
|
|
|
|
|
1 season
|
2 (137)
|
0
|
2 (137)
|
|
Professional career
|
2 (386)
|
1 (44)
|
3 (430)
|
|
Since youth
|
1 (6)
|
1 (271)
|
2 (277)
|
|
Lifetime
|
1 (66)
|
1 (15)
|
2 (81)
|
|
Method of measuring heading exposure
|
|
|
|
|
Self-reported estimate
|
4 (523)
|
1 (15)
|
5 (538)
|
|
Self-reported estimate with observed sample
|
0
|
2 (315)
|
2 (315)
|
|
Self-reported frequency
|
1 (66)
|
0
|
1 (66)
|
|
Heading exposure index
|
1 (6)
|
0
|
1 (6)
|
|
Method of measuring cognition
|
|
|
|
|
Paper or interview based cognitive assessment
|
4 (523)
|
1 (15)
|
5 (538)
|
|
Computerized cognitive assessment
|
0
|
2 (315)
|
2 (315)
|
|
Paper or interview based and computerized cognitive
assessment
|
2 (72)
|
0
|
2 (72)
|
|
Controls
|
|
|
|
|
Appropriate
|
3 (125)
|
1 (15)
|
4 (140)
|
|
Inappropriate
|
0
|
1 (44)
|
1 (44)
|
|
NA
|
3 (470)
|
1 (271)
|
4 (741)
|
|
Adjusts for confounding factors
|
|
|
|
|
Yes
|
6 (595)
|
2 (315)
|
8 (910)
|
|
No
|
0
|
1 (15)
|
1 (15)
|
|
Participant Characteristics
|
|
Mean±SD
|
|
|
Number of participants
|
99±104.2
|
110±114.5
|
103±107.8
|
|
Mean group age
|
52
|
27
|
43
|
Two [47]
[48] of the four studies conducted on
current professional players report an association, while two studies [49]
[50] report no association. In the
five studies conducted on former professional players, four studies [8]
[45]
[46]
[51] report an association between
heading and cognitive function and one study [44] reports no association. The mean
participant age of football players in studies reporting no association was 27
years, while in studies reporting an association the mean age was 52 years.
Four [8]
[45]
[46]
[51] of the seven studies that
measured heading exposure over multiple seasons report an association, while
three studies [44]
[49]
[50] report no association. However,
the two studies [47]
[48] that observed heading for only
one season report an association.
Generally, the studies that report an association between heading and cognitive
function investigate older, former professional players and use non-computerized
methods of testing alongside self-reported estimates of heading. Conversely, the
studies reporting no association broadly studied younger, current professional
players using computerized testing and used observed samples to verify the
accuracy of self-reported estimates.
Cognitive Domains
Each study assessed a variety of cognitive domains through the use of paper or
interview based and computerized cognitive assessment. A comparison between the
cognitive subdomains assessed between studies reporting and not reporting an
association between heading and cognitive function is displayed in [Table 4]. The cognitive subdomains
have been grouped into the domains: complex attention, global executive
function, language, learning and memory, and perceptual-motor function, as
defined by the Diagnostic and Statistical Manual of Mental Disorders (DSM-5)
[52].
Table 4 Methods of cognitive testing and the cognitive
domains reported to be associated or not associated with
heading.
|
Reporting association
|
Reporting no association
|
|
Cognitive Domains Tested
|
Number of studies (n)
|
|
Complex Attention
|
|
|
|
Paper or Interview Based Cognitive Assessment
|
|
|
|
Sustained Attention
|
0
|
4 (209)**††[45]
[47]
[48]
[51]
|
|
Visual Attention
|
1 (84)*[48]
|
3 (134)*††[44]
[47]
[51]
|
|
Computerized Cognitive Assessment
|
|
|
|
Divided Attention
|
0
|
1 (271)†[50]
|
|
Selective Attention
|
1 (6)†[45]
|
1 (44)†[49]
|
|
Sustained Attention
|
0
|
2 (336)*†[50]
[51]
|
|
Global Executive Function
|
|
|
|
Paper or Interview Based Cognitive Assessment
|
|
|
|
Abstract Reasoning
|
0
|
2 (137)**[47]
[48]
|
|
Cognitive Flexibility
|
1 (6)†[45]
|
2 (137)**[47]
[48]
|
|
Inhibitory Control
|
0
|
2 (137)**[47]
[48]
|
|
Global Executive Function Score
|
2 (386)††[8]
[46]
|
0
|
|
Task Switching
|
0
|
4 (218)**††[44]
[47]
[48]
[51]
|
|
Working Memory
|
0
|
3 (203)**†[47]
[48]
[51]
|
|
Computerized Cognitive Assessment
|
|
|
|
Decision Making
|
0
|
1 (271)*[50]
|
|
Cognitive Flexibility
|
0
|
1 (66)†[51]
|
|
Inhibitory Control
|
0
|
1 (44)*[49]
|
|
Task Switching
|
0
|
1 (44)*[49]
|
|
Working Memory
|
0
|
2 (315)**[49]
[50]
|
|
Language
|
|
|
|
Paper or Interview Based Cognitive Assessment
|
|
|
|
Verbal Fluency
|
0
|
4 (209)**††[45]
[47]
[48]
[51]
|
|
Learning and Memory
|
|
|
|
Paper or Interview Based Cognitive Assessment
|
|
|
|
Episodic Memory
|
1 (84)*[48]
|
1 (53)*[47]
|
|
Verbal Memory
|
0
|
1 (6)†[45]
|
|
Visual Memory
|
2 (137)**[47]
[48]
|
1 (15)†[44]
|
|
Computerized Cognitive Assessment
|
|
|
|
Delayed Memory
|
0
|
1 (44)*[49]
|
|
Verbal Memory
|
1 (66)†[51]
|
1 (44)*[49]
|
|
Visual Memory
|
0
|
1 (66)†[51]
|
|
Perceptual-motor Function
|
|
|
|
Paper or Interview Based Cognitive Assessment
|
|
|
|
Fine Motor Control
|
0
|
2 (137)**[47]
[48]
|
|
Visual Perception
|
0
|
2 (137)**[47]
[48]
|
|
Computerized Cognitive Assessment
|
|
|
|
Motor Speed
|
0
|
2 (72)††[45]
[51]
|
|
Processing Speed
|
0
|
1 (66)†[51]
|
|
Simple Reaction Time
|
0
|
3 (381)**†[49]
[50]
[51]
|
* study conducted on current players; † study conducted on former
players.
Complex attention
Complex attention was assessed by seven studies [44]
[45]
[47]
[48]
[49]
[50]
[51]. In the four studies [45]
[47]
[48]
[51] using paper or interview based
cognitive assessment sustained attention was not associated with heading. One
study [48] did report an association
between visual attention and heading but three studies [44]
[47]
[51] report no association.
Four studies [45]
[49]
[50]
[51] assessed complex attention using
a computerized cognitive assessment. One study [50] reported divided attention to not
be associated with heading and two studies [50]
[51] reported sustained attention to
not be associated. Selective attention was shown to be associated with heading
in one study [45]; however, another
study [49] reported no
association.
Visual attention and selective attention were the only subdomains reported to be
associated with heading; however, all six subdomains of complex attention were
reported not to be associated with heading in at least one study.
Global executive function
All nine studies assessed at least one subdomain of executive function, with
seven studies [8]
[44]
[45]
[46]
[47]
[48]
[51] using a paper or interview based
cognitive assessment. Abstract reasoning, cognitive flexibility, inhibitory
control, task switching and working memory were all reported to not be
associated with heading by two or more studies. However, one study [45] did show cognitive flexibility
scores to be negatively correlated with heading frequency. Two studies [8]
[46] reported a global executive
function score, comprising of several executive function subdomains, that was
also negatively correlated with heading frequency.
Computerized testing conducted in three studies [49]
[50]
[51] reported decision making,
cognitive flexibility, inhibitory control, task switching and working memory
were all not correlated to heading frequency.
In summary, global executive function score and cognitive flexibility were
associated with heading, the other nine tested subdomains of executive function
were not.
Language
Four studies [45]
[47]
[48]
[51] assessed verbal fluency using a
paper or interview based cognitive assessment and all reported that function of
this subdomain was not associated with heading.
Learning and memory
Four studies assessed three subdomains of learning and memory using paper or
interview based cognitive assessment. One study [45] reported verbal memory to not be
associated with heading. Episodic memory performance was shown not to be
correlated to heading frequency in one study [47] but in another study [48] was reported to be negatively
correlated to heading frequency. Additionally, two studies [47]
[48] reported that heading frequency
was negatively correlated with visual memory performance; however, one study
[45] reported no
correlation.
Computerized cognitive assessments were conducted in two studies [49]
[51]. One study [49] reported that both delayed and
verbal memory were not associated with heading and another study [51] reported visual memory not to be
associated with heading. However, the second study [51] did report that verbal memory
performance was associated with heading.
In summary, there are conflicting findings with respect to episodic memory,
visual memory, and verbal memory, as these subdomains were reported to either
have an association in some studies, and no association in others.
Perceptual-motor function
Two subdomains of perceptual-motor function were assessed by two studies [47]
[48] using paper or interview-based
assessment. Both reported no association between fine motor control and visual
perception and heading. Two studies [45]
[51] assessed motor
speed, one study [51] assessed
processing speed, and three studies [49]
[50]
[51] assessed simple reaction time
using a computerized cognitive assessment. All three subdomains were shown not
to be associated with heading. In total, all five subdomains of perceptual-motor
function tested were reported to not be associated with heading.
Discussion
This systematic review aimed to investigate the association between heading and
cognitive function in professional football players. From the studies included in
this review, six studies reported an association between heading and cognitive
function, while three reported no association. Studies conducted on current
professional players were equally as likely to report an association between heading
and cognitive function as they were to not report an association. However, the
majority of the studies conducted on former professional players reported an
association between heading exposure and cognitive function. Of the studies that did
report an association, the cognitive domains that appeared most likely to be
affected were global executive function, complex attention and memory. This suggests
that there may be specific domains of cognition that could be negatively associated
with increased heading frequency in football players, and that these associations
are more likely to present themselves in retired players.
Cognitive testing
Broadly, the subdomains that were reported to be associated with heading exposure
included visual attention, selective attention, cognitive flexibility, episodic
memory, visual memory, verbal memory and global executive function. However, the
studies reviewed implemented a variety of test methods to assess similar aspects
of cognitive function, including both paper or interview-based cognitive
assessment and computerized assessments, making it challenging to compare study
outcomes. These processes are largely directed by the prefrontal cortex, located
beneath the forehead where players typically head the ball [53]. It remains to be understood
whether sub-concussive impacts to this region could potentially be directly
causing localized microtrauma leading to neurodegeneration.
The only two studies that utilized solely computerized tests reported no
significant association between heading exposure and cognitive test performance.
However, it should be noted that both studies only included young current
players averaging 25 years of age. This is important to note as deficits to
cognitive function caused by concussive and subconcussive impacts are typically
observed in the long term due to a possible accelerated decline in cognitive
function [18]. A ceiling effect in
the younger players, further impacted by the low difficulty of some of the tasks
that were implemented [54], could
potentially also be masking any early evidence of possible impaired cognitive
performance [55]. Furthermore, one
of the studies [50] only reported
analyses on reaction time measures of the tasks, not accuracy, and neither study
reported commission errors. If heading exposure is to impact prefrontal cortex
function, it would be plausible to expect an increase in commission errors on
executive function tasks (such as early key presses on a simple reaction time
task, and no-go errors on an inhibition or attention task) [56]. Further studies that implement
computerized tasks should consider commission errors as an outcome measure and
should investigate the effects of lifelong heading exposure on retired
athletes.
Lifetime heading exposure and previous concussions were significantly correlated
in the two studies that presented this data [48]
[50]. Crucially, headers and
concussions had differential and unrelated effects on cognitive domains [48], indicating that headers might
induce neurocognitive changes that are different to those sustained during
concussions. However, when interpreting the results, it is important to note
that previous concussions sustained while playing football may influence reduced
cognitive performance, which the studies did not control for.
Current vs. former professional players
Findings between current and former professional football players differed. Half
of the studies conducted on current professional players reported an association
between cognitive function and heading exposure. In contrast, four of the five
studies conducted on former professional players concluded that heading was
associated with cognitive outcomes. This could suggest that the detrimental
effects of heading are more likely to present themselves in later life. It is
possible that heading could be accelerating cognitive ageing however it is
difficult to exclude the effects of concussions and player to player collisions,
which are common in elite football and could contribute to a decline in
function. Additionally, it is important to recognize that some players who
perform well on cognitive tests may exhibit structural changes in the brain that
may not manifest as cognitive impairment for many years to come. White matter
microstructure alteration has been observed in both male and female amateur
players under age 30 [57], which
suggests that cognitive testing may not be best method to identify neurological
changes in current players. Therefore, some cognitive tests implemented by
studies in this review may not have been sensitive to specific structural
changes that could have been occurring.
Two large cohort studies in Scotland [23] and Sweden [24]
provide evidence to question the safety of heading the ball, and are the only
studies to date to analyses thousands of former professional players. Football
players were shown to have an increased risk of mortality [23] and development of
neurodegenerative disease [24].
Although, the majority of players included in these studies played before the
1970s and are likely to have played with leather balls. These balls have been
shown to increase in weight by up to 20% in wet conditions [58] increasing head acceleration
forces during heading. It is likely that in six of the studies included in this
review [8]
[44]
[45]
[46]
[47]
[48] the players studied played the
majority of their professional careers in the 20th century, when balls were
heavier. Interestingly, five of these six studies report an association between
cognitive function and heading. The heavier balls may have contributed to the
increased risk, although it is not known if the synthetic balls currently in use
completely mitigate these effects.
In contrast to the evidence provided by these two large cohort studies, Vann
Jones et al. reported that the prevalence of cognitive impairment among former
professional players is not significantly different from the general population
[59]. Despite our review raising
concerns about former professional players, it is difficult to attribute the
decline in function to heading specifically rather than an age-related decline
in cognitive function or other possible confounding factors. Further
longitudinal studies are needed to confirm a causal relationship between heading
and cognitive decline in football players.
Female players
In this review, 93% of the players investigated were male, making the findings
mostly representative of male professional players. Only one study was conducted
on retired female players, which reported a significant association between
heading frequency and verbal memory performance. Female participation in
football is rapidly rising with FIFA aiming to have over 60 million female
players by 2026 [60] and incidence
of heading in the female game being of a similar level to males [37]. Previous research has
highlighted that rates of head injuries are higher in female players, which
emphasizes the need to conduct more studies on female players and possible
gender differences in such effects [13]
[61]
[62].
In line with this, Rubin et al. [57]
investigated the effects of heading between male and female football players on
white matter microstructure alteration, identifying a fivefold greater volume of
affected white matter in females compared to males in response to similar
exposure to heading throughout their careers. Together, these results suggest
that females might be more susceptible to neurological damage following
subconcussive head impacts, but more research is needed to corroborate this and
identify potential mechanisms involved.
Measuring heading exposure
Eight of the nine studies measured heading exposure using self-reported estimates
or frequencies, which have been described as an inaccurate method of measurement
[33]
[37]. Although there is a correlation
between self-reported and actual values, footballers have been shown to
overestimate their heading frequency [63]
[64]. Self-reporting
in other disciplines has also been reported as unreliable [65]
[66]
[67]. Samples of observed heading in
two of the studies showed no significant difference between self-reported values
and actual observed headers [49]
[50], which suggests
that estimated values may be reliable in certain populations. Self-reporting can
reliably group participants into low and high heading exposure groups, but
should not be used to measure individual exposure due to recall bias [64], which is particularly likely to
arise in former professional players attempting to remember their heading
history from potentially many decades ago. It is also important to consider the
accuracy of self-reporting is likely to be lower in players suffering from
cognitive impairment, potentially resulting in type I and II errors.
Limitations
The studies in this review display significant clinical diversity and
methodological heterogeneity. The variability in study design, participant
characteristics, and methods of measuring heading exposure and cognitive
function, makes comparison between observations in different study populations
challenging. Although the risk of bias assessment described all studies to be
‘moderate’ or ‘high’ quality, methodological issues were highlighted in all
studies, such as with the reporting of heading frequency. Additionally,
different cognitive tests and methods were adopted across studies. The papers
commonly did not control for key confounding factors such as previous concussion
history, education level, participation in other contact sports, and exposure to
neurodegenerative disease risk factors. To reach more reliable conclusions,
further studies could consider adopting a longitudinal design against matched
control groups, quantifying heading exposure based on observed measures, target
areas of cognitive function most likely to be affected by frontal impacts and
obtain a more comprehensive history of player demographic information and other
neurological risk factors.
Conclusion and future direction
This review provides some evidence for an association between heading and
cognitive function in professional football, although a strong conclusion could
not be drawn due to the high heterogeneity in methods implemented. These
findings suggest that the decision to impose a ban on heading at youth level is
well reasoned; however, it is unknown whether or not the benefits of this will
be diminished when players subsequently start heading the ball.
Considering that studies looking at former professional players were more likely
to report an association than those on current players, future studies should
consider longitudinal monitoring of neurocognitive outcomes in athletes, with
objective monitoring of heading exposure. The cognitive domains that appeared
most likely to be affected included complex attention, executive function, and
memory, and should therefore be considered in future testing.
