Master’s Thesis, 15 ECTS
Master’s Programme (one year) in Cognitive Science, 60 ECTS Spring 2021
Supervisor: Linnea Karlsson Wirebring
Are athletes active in high- contact sports at risk of
impaired executive functioning?
A quasi-experimental study on competitive mixed martial arts
(MMA) athletes.
Candice Roxanne Cattaneo
Abstract
The study of high-contact sport athletes and the implications of repetitive head injury (RHI) associated with these sports has been at the forefront of traumatic brain injury (TBI) research for the last decade. The present study represents a quasi-experimental study exploring whether an experimental group (N=39) consisting of amateur and professional competitive mixed martial arts (MMA) athletes differ in three operations of executive functioning ability (shifting, updating and inhibition) when compared to a control group (N=44) of non-contact sports athletes. Participants completed a self-report measure of executive functioning ability as well as six computerized executive function (EF) tasks. The results from the study demonstrated no statistically significant differences between the experimental group and the control group on the performance of each executive functioning operation. A moderate negative correlation was found between the number of years competing and performance on shifting and updating in the experimental group. A moderate negative correlation between the number of reported competitive fights and all three EF operations within the experimental group was also reported. The results also showed a statistically significant difference in the beliefs of executive functioning abilities between the experimental group and the control group.
The experimental group reported a higher level of belief in poorer executive functioning ability than that of the control group. These findings provide evidence that while competing in MMA does have implications on executive functioning abilities, they are not in line with previous research done on other high-contact sports athletes.
Keywords: High-contact sports, executive functioning, repetitive head injury, concussion Sammanfattning
Att studera hur utövare av fullkontaktsporter påverkas av upprepade huvudskador (RHI), associerat med utövandet av sporten, har varit ett fokusområde för forskningsfältet inom
traumatiska huvudskador (TBI). Denna uppsats utgör en kvasi-experimentell studie som undersöker huruvida en experimentell grupp (N=39) bestående av amatörer och professionella ’mixed martial arts’ (MMA)- atleter skiljer sig i sin förmåga inom tre typer av exekutiva funktioner (skiftning, uppdatering och inhibition) jämfört med en kontrollgrupp (N=44) som inte utövar fullkontaktsport.
Deltagarna fick först fylla i ett formulär där de skattade sin egen förmåga inom exekutiva funktioner, varpå de slutförde sex digitala test som gav ett mått på deras exekutiva funktioner. Studien påvisade ingen signifikant skillnad mellan de två grupperna för någon av de testade exekutiva förmågorna.
Däremot fann studien en moderat negativ korrelation mellan antal år av tävlan och prestation på skifte och uppdatering, liksom en moderat negativ korrelation mellan antal tävlingstillfällen och de tre måtten på exekutiva funktioner, för den experimentella gruppen. Resultaten visade även en signifikant skillnad i självskattningen av exekutiv förmåga, mellan den experimentella gruppen och kontrollgruppen där experimentgruppen rapporterade sämre upplevda exekutiva förmågor, jämfört med kontrollgruppen. Dessa fynd indikerar att tävlan inom MMA har implikationer för exekutiva förmågor, även om de inte är i linje med tidigare forskning gjord på utövare av andra
fullkontaktsporter.
Nyckelord: fullkontaktsport, exekutiva funktioner, upprepade huvudskador, hjärnskakning
Are athletes active in high-contact sports at risk of impaired executive functioning? A quasi- experimental study on competitive mixed martial arts (MMA) athletes.
Previous research exploring whether there is a correlation between high, consistent levels of physical activity, often seen in professional athletes, and performance of executive functions has provided insight into the benefits of physical activity. The requirement to shift goal directed behaviour due to the constant change in variables of competitive sports gives rise to an environment where executive functioning is often used and therefore improved (Bixby et al., 2007; Di Russo et al., 2010;
Krenn et al., 2018; Marchetti et al., 2015 and Soga et al., 2018). From the above research it would appear that partaking in sports would naturally lead to improved performance in executive functioning. A question regarding risks associated with certain sports, namely high-contact sports, and their implication on cognitive functioning has arisen and needs to be addressed. What risks are associated with sports that, by their very nature, include RHI (defined as repetitive blows to the head or repeated episodes of concussion experienced in a short period of time (Buki et al., 2015)) which are known to decrease cognitive ability?
Over the last decade a plethora of research has been done assessing the cognitive implications of high-contact sports, such as American football, rugby, boxing, and ice-hockey. What has been found is that competitive athletes in high-contact sports often present with some form of abnormal cognitive decline and/or neurodegenerative disorders (Bailey et al., 2012; Gallo et al., 2020; Manley et al., 2017;
McKee et al., 2012; Small et al., 2013 and Stern et al., 2013). The causation of these abnormal cognitive abilities and/or neurodegenerative disorders have been linked to single, episodic or repetitive blunt force impacts to the head. These blunt force impacts result in a transfer of acceleration deceleration forces to the brain which ultimately lead to scaled levels of TBI (defined by Buki et al. (2015) as the alteration in brain functioning, or brain pathology as a result of an external force to the head). It has also been reported that this TBI experienced ultimately leads to permanent brain damage (Omalu et al., 2011; Omalu, 2014). While the research listed above has provided much needed information on the effects of high-contact sports, specifically the effects of RHI associated with high-contact sport, there has been little research done involving MMA athletes and their cognitive ability. The present study was performed in order to begin filling this gap in research on the cognitive implications, specifically executive functioning implications, of partaking in competitive MMA.
Risk for cognitive decline in high-contact sports athletes
Sports related injuries that lead to the abnormal decline in cognitive abilities have been labelled as a “silent pandemic”. With over three million sports related TBIs occurring annually in the USA alone it is evident that sports related TBI should be a public health concern. Sports associated with an increased risk of TBI include those that often result in contact and collisions, such as American football, rugby, boxing and ice-hockey. One symptom of these sports related TBIs is that of poor executive functioning.
TBI and executive functioning in high-contact sports athletes
In order to look at the possible risk of high-contact sports on cognitive ability multiple studies have explored the correlation between RHI, TBI, concussions, and executive functioning. In research from Jordan (2000) a reported 20% of professional boxers presented with chronic traumatic brain injury (CTBI). A symptom of CTBI is mild to severe deficits in executive functioning. In a second study by Di Russo and Spinelli (2010) the event-related potential (ERPs) and reaction times (RT) of professional boxers performing a Go/No-Go task were measured. What was found in this study is that the P3 component was delayed and reduced in boxers (a sign of poor executive functioning). Similar findings of delayed and reduced P3 activation have been seen in brain trauma patients. The authors suggested that this impairment derived from repetitive knocks to the head which resulted in visible
damage to the frontal lobe of participants via an MRI scan. In another study Esopenko et al. (2017) characterised retired professional ice-hockey players cognitive functioning in relation to experienced concussions. The results demonstrated that reliable group differences appeared on executive functioning tests. Researchers suggested that this was due to repetitive head injuries, specifically concussions experienced. Lastly, in a study by Lazor (2020) the researcher explored the impact of sport-related concussions on the cognitive abilities of ice-hockey professionals via a systematic literature review. The researcher found that ice-hockey players showed poorer cognitive functioning, including executive functioning.
Executive functioning in high-contact sport athletes
The correlation between declined executive functioning as a response to RHI and TBI has been difficult to empirically explore due to the lack of visible brain injury or brain damage present when brain physiology is studied alongside cognitive functioning. Due to this limitation research looking into executive functioning of competitive athletes has mostly postulated that any decline seen would be a response to RHI experienced within the sport.
This research includes that of Terpstra et al. (2019) who explored the relationship between neuropsychiatric symptoms and executive functioning of former professional football players. Two novel experimental conditions were used, response inhibition and inconsistency of responding on a go/no-go task. The authors found that, compared to a control group, the former professional football players showed significantly poorer executive functioning. In another study, Vos et al. (2018) performed a systematic literature review exploring executive functioning of professional football players. The authors reported evidence (although limited) of executive dysfunction in 21 studies on executive functioning in professional football players. Similar findings can be seen in other studies (Montenigro et al., 2017; Seichepine et al., 2013 and Stamm et al., 2015).
In order to explore the differences in cognitive functioning (including executive functioning) between former rugby and non-contact-sport players Hume et al. (2016) studied the data provided from an elite rugby group, a community rugby group and a control group of non-high-contact sport athletes. The participants were all required to perform the online CNS Vital Signs neuropsychological test battery. The results from this study showed that the elite rugby group performed worse on executive functioning tasks compared to both the community rugby group and the non-high-contact sport group. Results also showed that the community rugby group performed worse on executive functioning tasks than that of the non-high-contact sport group. Further research on executive functioning in high contact sports athletes can be found in similar studies (Basson & Essack, 2017;
Bernick & Banks, 2013; Cunningham et al., 2020; Heilbronner et al., 2009; Kim et al., 2019).
Based on the above research it would seem plausible that high-contact sports outside of the sports listed above, such as MMA, pose a potential cognitive risk to the athletes that partake in them.
More research examining potential abnormal cognitive decline, specifically poorer EFs, of athletes in all high-contact sports is needed. The research offered through these studies could help ensure that athletes are kept as safe as possible throughout their competitive careers.
Diversity and unity in executive functioning abilities
Addressing the possible impairment of executive functioning in high contact sport athletes, specifically MMA athletes, requires a knowledgeable approach in understanding and measuring EF. In order to fulfil this requirement, the three-factor model (Miyake et al., 2000 and Miyake & Friedman, 2012) discussed below was deemed appropriate for the current study.
EF’s have been defined as the general-purpose mental tools or apparatus responsible for the operation of different cognitive subprocesses. These subprocesses regulate the dynamics of human cognition (Miyake et al., 2000). An important issue raised originally by Teuber (1972) and revisited by Duncan et al, (1997) is to what extent can central EF’s be considered unitary? To what extent can
various EF’s reflect the same ability or underlying mechanism? A latent three-factor model of executive functioning, which holds empirical support was proposed by Miyake and colleagues (Miyake et al., 2000 and Miyake & Friedman, 2012). The model suggests three separate operations of executive functioning: shifting between mental sets and/or tasks (shifting), monitoring and updating the content of working memory (updating), and inhibiting preponent responses (inhibition). Shifting has been defined as the ability to interchange between either mental states or tasks. Updating, as the ability to monitor incoming information and identify it as relevant as well as replace old non-relevant information with the new information. Inhibition, as the ability to stop automatic and/or dominant responses when needed (Sandberg et al., 2013). All three operations are frequently suggested as vital EF’s (Baddeley, 1996; Logan, 1985; Lyon & Krasnegor, 1996; Rabbitt, 1997; and Smith & Jonides, 1999).
The three operations of EF showed both unity and diversity with a modest correlation (rs = .40 to .60) that did not fit perfectly with each other. They also demonstrated differential associations with other constructs. The patterns mentioned above have been recorded across ages and abilities in multiple studies (Friedman et al., 2011; Miyake & Friedman, 2012; Rose et al., 2011; and Vaughan &
Giovanello, 2010). In order to apply the three-factor model, an assessment of each executive functioning operation via a group of cognitive tasks is required. The assessment must allow for the inference of separated yet correlated latent variables. A recent conceptualisation of the model used a nested factors approach (Friedman et al., 2008 and Friedman et al., 2011). In this approach the unity of EF tasks is captured by a latent common EF factor which is derived from the performance over all of the executive functioning tasks. Diversity is captured by added “shifting specific” and “updating- specific” latent factors. The lack of an “inhibition-specific” latent factor is due to a statistically insignificant correlation between the inhibition tasks once the variance common to the three operations of executive functioning is accounted for (Friedman et al., 2008 and Friedman et al., 2011).
Findings in support of the nested factor model can be seen in numerous independent data sets that aimed to replicate it (Miyake & Friedman, 2012).
A complementary measurement tool to that of Miyake et al. (2000) three-factor model would be a self-report questionnaire on executive functioning ability. A self-report measure allows participants to provide their own opinion on their ability to perform executive functioning tasks. This information could hold value if a negative correlation between the participants ability to identify poorer executive functioning and their measured executive functioning ability is evident. The correlation mentioned above could potentially lead to self-report measures on executive functioning ability being utilised as a biomarker for poorer executive functioning and cognitive decline.
Self-report on executive functioning
Self-report measurement tools are frequently used in both neuropsychological and psychological assessment. They offer an easy to administer measurement tool which is often self- explanatory in nature and can be implemented in multiple applications. The use of self-report measures alongside behaviour or performance measures provides an objective and novel way to assess a concept such as executive functioning (Mitchell & Miller, 2008). Two common self-report measures of executive functioning include the behaviour Rating Inventory of Executive Function- Adult version (BRIEF-A), originally proposed by Roth et al, (2005) and The Dysexecutive Functioning Questionnaire (DEX), originally proposed by Wilson et al, (1998). The BRIEF-A is classified as a standardized self and informant measurement tool developed in order to assess the executive control functions of adults between the ages of 18-90 years old. The tool includes nine non-overlapping clinical tasks corresponding to the differentiated domains of executive functioning (Rabin et al., 2006). The DEX has been classified as a measurement instrument that assesses everyday signs of executive dysfunction across multiple situations and considers individual differences such as age, gender, and health (Gerstorf et al., 2008).
While both the self-report measurement tools mentioned above are suitable for laboratory research they are not suitable as an online self-report assessment tool. Due to the changing nature of research the development of an online self-report on executive functioning tool was identified and fulfilled by Buchanan et al, (2010) in the form of the WEBexec. The WEBexec is a self-report web-based questionnaire assessing an individual’s belief with regards to their executive functioning. The present study made use of the WEBexec in order to explore whether professional MMA athletes, compared to non-contact sports athletes, reported more problems with their executive functioning ability. This data could provide information on whether athletes in high-contact sports hold insight into the potential cognitive risks involved with the specific sports they partake in.
Overview of the present study
The research above provides some empirical evidence on the dangers of high-contact sport especially where RHI is a common phenomenon. While this research has focused on popular high- contact sports it has not extended itself to a high-contact sport still in its infancy, namely MMA. MMA has been defined as an extreme full-contact combat sport where participants utilise fighting techniques from multiple disciplines such as wrestling, boxing, Brazilian Jiu Jitsu and various other martial arts disciplines. The very nature of MMA calls for violence that elicits mild to severe RHI in both practice (via sparring) and competitive fights. In order to prevent injury it is advised that amateur and professional MMA athletes wear head gear as well as mouth guards. This practice is rarely enforced and often dismissed as an uncomfortable burden that often prevents MMA athletes from performing at their highest level. The rules and regulations around competitive MMA fights require that a referee stop a fight only if a fighter is knocked unconscious or unable to defend themselves.
Due to the lack of regulation around the number and/or intensity of strikes a fighter can experience within competition RHI often occurs.
Due to the nature of the sport and based on the research provided above a plausible claim would be that competitive MMA athletes should experience some form of abnormal cognitive decline, including poorer executive functioning. Very little research has been done to support this claim.
In a study performed by Lim et al, (2019) researchers aimed to determine the incidence of injury and concussion in amateur and professional MMA athletes. Descriptive statistics were able to determine that MMA athletes experience a high risk of injury exposure, especially with respect to concussions. The authors suggested that MMA exposes athletes to inherent risk with the most common location of injury being the head and mild traumatic brain injury (mTBI) being recorded as the most common type of injury. The study did not evaluate the cognitive functioning of these athletes in order to determine if this injury poses a threat to cognitive functioning. Due to the lack of research on the subject a largely open empirical question still remains: are competitive MMA athletes at risk of altered cognitive functioning as a consequence of RHI and/or TBI?
In order to begin adding to the current, limited research on executive functioning in MMA athletes, the current study will apply the three-factor model of executive functioning (Miyake et al., 2000; Fleming, 2015) to the performance of MMA athletes on multiple well-validated measures of shifting, updating and inhibition. The current study offers a few predictions based on previous research which, while not necessarily generalisable, does provide a foundation for what to expect from high-contact sports participants. Firstly, the study predicts that executive functioning on each EF operation of MMA athletes will be significantly poorer than that of the control group. This prediction is in line with evidence from previous research done on high-contact sports athletes which showed poorer executive functioning of these athletes (Bailes & Cantu, 2001; Dompier et al., 2015; Gallo et al., 2020; Manley et al., 2017; McAllister et al., 2012). A second prediction is that MMA athletes that have competed for longer, been involved in more competitive fights and/or have experienced more concussions will present with poorer executive functioning. Previous studies have presented evidence suggesting that there is no correlation between the number of years, competitive bouts or
concussions and poorer cognitive functioning, including executive functioning (Belanger et al., 2010;
Bruce & Echemdia, 2009; Iverson et al., 2006). These studies have been limited in that their methods, cohorts and main findings greatly varied which has made it difficult to come to a uniform conclusion on the matter. There also seems to be a positive correlation between the amount of time competing, competitive bouts and concussions experienced and neurodegenerative diseases such as Chronic Traumatic Encephalopathy (CTE), Dementia Pugilistica and Alzheimer’s disease. All of these neurodegenerative diseases also list poorer executive functioning of patients as a symptom (Graham et al., 2014; McKee et al., 2012 and Corsellis et al., 1973).
In summary, the present study was done in order to begin research exploring the executive functioning abilities of competitive MMA athletes using an empirical model of executive functioning.
The present study aimed to fill a gap in research related to the cognitive implications of partaking in high-contact sports.
Method Participants
A total of 83 competitive athletes between the ages of 18-45 were recruited via multiple platforms to participate in a study on the possible implications of competitive MMA on executive functioning. Recruitment was done through emails sent to professional and amateur competitive MMA gyms, adverts placed on social media groups and through crowdsourcing via Prolific and Mturk.
Interested parties were instructed to follow a link that would take them straight to the main study hosted by Gorilla Experiment Builder (www.gorilla.sc). Compensation was offered to participants at SEK 70 an hour and participation was voluntary. Participants were part of either a control group or an experimental group based on whether they classified themselves as non-contact sport athletes or MMA athletes. Before the start of the online study each participant was informed of the purpose of the study and their right to decline participation in the study at any time. Informed consent was given via an online consent form.
Exclusion criteria
The exclusion criteria applicable to both the control and experimental group included that participants have no history of drug and/or alcohol abuse and did not present with either a psychological and/or learning disorder/disability (such as depression, bipolar disorder, ADHD or dyslexia). All exclusion criteria were stated in the advert and social media posts.
Eligibility requirements
Eligibility requirements for both the control and experimental group included that the participant be between 18-45 years old, be fluent in English and have access to a stable internet connection and a laptop or desktop computer. The control group were required to compete at either an amateur or professional level in a non-contact sport, such as cycling or golf. The experimental group were required to compete at either an amateur or professional level in MMA.
Control group
The control group consisted of participants recruited from multiple locations including South Africa, The United States of America, England, Ireland, Scotland, France and South Korea. Participants reported occupations that include accounting, psychology, nursing, seafaring, software development, teaching, language consulting, entertainment hosting and legal personal assistance. All control group participants competed in non-contact sports reported as running, cycling, squash, Olympic lifting, triathlons, body building, swimming and gymnastics. Complete demographic information for the control group can be seen in table 1.
Experimental group
The experimental group consisted of participants recruited from South Africa, New Zealand, Poland, The United Kingdom, India and The United States of America. Occupations for the experimental participants were reported as professional fighter, sports coach, investor, teacher, personal trainer, small business owner, student, freelancer, personal assistant and supervisor.
Complete demographic information, including the number of years competing, the number of competitive fights partaken in and the reported number of concussions can be seen in table 1.
Table 1
Demographic information of the control and experimental group
Control group Experimental group
N 44 39
Age (M) 29 30
SD 6.5 5.8
Gender
Female/Male (%) 23/21 (52/48) 10/29 (26/74) Level of education
Secondary level 14% 13%
Undergraduate degree/diploma 45% 44%
Postgraduate Degree 39% 43%
Masters degree or higher 2% 0%
Number of years in competitive MMA
M (SD) 4 (3.7)
Median 4
1-5 years 79%
6-9 years 18%
10 + years 3%
Number of fights in competitive MMA
M (SD) 10 (2)
Median 5
1-10 competitive fights 77%
10 + competitive fights 23%
Number of reported concussions
M (SD) 3(5)
Median 1
0-5 reported concussions 82%
6-9 reported concussions 8%
10 + reported concussions 10%
Note. n = Number of participants; M = mean; SD = standard deviation
Instruments and materials
Participants completed a total of eight computerised tasks separated into three distinct stages. All tasks were modified by the author of this report based on source code provided by the
experiment site builder. The study was performed via the online platform Gorilla Experiment Builder (www.gorilla.sc). The first stage required the collection of informed consent and demographic information. Participants were informed of what the study was exploring, the role each participant played in the study, that the study was voluntary and that they could exit the study at any time without repercussion. Demographic information was collected on variables such as age, gender, occupation and level of education in the control group and experimental group. The number of years, fights, and concussions was also collected from the experimental group. The second stage required the participants to provide self-reports on their executive functioning via the use of the WEBexec, a self- report web-based questionnaire adapted from Buchanan et al, (2010). The final stage consisted of six executive function tasks; two tasks tapping into each of the three-component executive functioning abilities that were investigated (proponent response inhibition, working memory updating, and mental set shifting).
Demographic data collection
In order to check that the two samples matched, gender, age, place of residence and level of education was collected from the participants in both the control group and experimental group via a demographic survey. The experimental group participants were required to provide further data pertaining to the number of years they had partaken in competitive MMA, the number of competitive fights they had performed in, and the estimated number of concussions they had experienced. The data collected was used to provide inferential statistics in order to explore possible statistically significant differences between MMA athletes that had i) Competed for more years, and ii) partaken in more competitive fights and iii) experienced more concussions.
Self-report on executive functioning
The WEBexec, a short web based self-report measure of problems with executive functions, adapted from Buchanan et al, (2010), required all participants to answer six items shown in table 2.
Each item aimed to tap into experiences of executive functioning. The instructions as well as the items were presented to participants on a single screen. For each item participants responded on a 4-point scale (1= No problems experienced, 2= A few problems experienced, 3= More than a few problems experience and 4= A great many problems experienced). A global score between 6 and 24 was calculated in order to demonstrate the participants overall experience of their own executive functioning instead of any specific aspect thereof. This score provided data for inferential and descriptive statistics of each participants belief in their executive functioning abilities.
The questionnaire was chosen due to its brief nature so as to reduce the mental load on participants. The questionnaire presents with good internal consistency, with Cronbach’s’ Alpha of .79 recorded in the original study by Buchanan et al, (2010) exploring the reliability and validity of the web-based tool. A Cronbachs Alpha of .76 was recorded in a second study performed by Rodgers et al, (2006) who used the web-based tool in their web-based study examining self-reported sequelae of the use of ecstasy as a recreational drug. In the original study by Buchanan et al, (2010) the scores from WEBexec were positively correlated to scores on the DEX potentially showing that the two measures have a common dependent measure. In the same study the results from the WEBexec questionnaire were negatively correlated to the participants performance on three objective measures of executive functioning (reverse digit span, semantic fluency, and semantic fluency with inhibition). This correlation indicated that participants that performed poorly on the objective measures of executive functioning also reported more problems via WEBexec.
Executive function measures
Participants completed a total of six computerised EF tasks. Each task was selected based on previous research (Fleming et al., 2015; Friedman et al., 2008: Miyake et al., 2000). The six tasks consisted of two tasks as objective measures for each of the three-factor executive functioning
abilities investigated in this study (mental set shifting, working memory updating and proponent response inhibition). The order in which the tasks were displayed was based on the intensity of the tasks determined by eight participants who took part in a pilot study. These participants were able to provide feedback on what they believed to be the most efficient and user-friendly way to perform the six tasks.
Table 2
WEBexec Items
1. Do you find it difficult to keep your attention on a particular task?
2. Do you find yourself having problems concentrating on a task?
3. Do you have difficulty carrying out more than one task at a time?
4. Do you tend to “lose” your train of thoughts?
5. Do you have difficulty seeing through something that you have started?
6. Do you find yourself acting on “impulse”?
Shifting one: number-letter task. In each trial of the number-letter task, adapted from Rogers
& Monsell (1995), a number-letter pair (E.g., 4-K or 13-A) was presented on a screen in one quadrant of a four-quadrant square (numbers ranged from 3 to 99 and letters ranged from A to Z). Participants were required to classify the target character (the number or letter) into one of four categories (odd or even for numbers and vowel or consonant for letters) depending on the position of the pair. If the pair presented in one of the top quadrants in the square participants would be required to categorise the number as odd or even by clicking on an odd or even tab at the bottom of the screen (ignoring the letter). If the pair presented in one of the quadrants in the bottom half of the square participants were required to categorise the letter as a vowel or a consonant (ignoring the number) by clicking on a tab either labelled consonant or vowel at the bottom of the screen. A new pair presented on a new screen once the participant clicked on the tab at the bottom of the screen.
Participants began by completing four practice trials in order to familiarise themselves with the task. The practice trials presented in a predictable switch pattern with the pairs moving clockwise through the quadrants. Participants were given feedback for 600ms via a green tick for a correct answer and a red cross for an incorrect answer. No feedback was given during the main task.
Participants then completed two blocks (10 trials each) in which the stimulus first appeared exclusively in the top half of the square, requiring participants to indicate if the number was odd or even. Then exclusively in the bottom half of the square, requiring participants to indicate whether the letter was a vowel or consonant. No shifting was required for either of these blocks.
In the third block (20 trails) participants were shown pairs presented randomly throughout the four quadrants. Participants were required to shift between categorising either the number or letter.
The difference in response time (RT) to complete the no shifting tasks (block one and block two) and the shifting task (block three) was used as the dependent measure.
Shifting two: plus-minus task. The plus-minus task, adapted from Jersild (1927) and Spector
& Biederman (1976), required participants to either add or subtract the number three from a list of 45 randomised numbers (between 3 and 99). Participants were instructed to type their answers into the space provided and press the enter button. Participants were instructed to do this as quickly and accurately as possible throughout the task. The task consisted of a practice block and three main blocks (15 trials each).
The practice block consisted of six trials. Participants were instructed to record their answers by typing it into the space provided and pressing enter. The first two trials required participants to add three to the number presented on each screen. The third and fourth trial required participants to subtract three from each number presented on each screen. The fifth and sixth trial required
participants to alternate between adding to trail five and subtracting from trial six. Feedback was given for 600ms on whether the participant was correct via a green tick or incorrect via a red cross. No feedback was given in the main task.
The main task consisted of three blocks. In block one (15 trails) the participants were instructed to add three to each of the 15 randomised numbers presented separately on 15 screens. In block two (15 trials) participants were instructed to subtract three from each of the 15 randomised numbers presented separately on 15 screens. No switching was required for the first two blocks. In block three (15 trials) participants were instructed to alternate between adding and subtracting three from 15 randomised numbers (E.g. add three to the first number presented, subtract three from the second number presented, carrying on with this pattern for the rest of the task). Each block was separated by an instruction page informing participant of which task they were expected to perform.
The dependent measure for this task was the difference in RT to complete block three (shifting block) and the average RT to complete block one and block two (no shifting blocks).
Updating one: keep track task. In the keep track task, adapted from Yntema (1963), participants were required to remember a series of exemplar words that belonged to six different categories (animals, colours, countries, relatives, metals and distances). The task was broken up into one practice trial and three blocks of two trials. Each trail represented a difficulty level (block one=three categories, block two= four categories and block three=five categories). In each trial a list of three to five target categories presented at the bottom of the screen and remained throughout the trial along with a list of 15 words. The words were presented for 1500ms each in the middle of the screen. The list of words included three exemplars of the words from the target categories as well as random words not belonging to any category. Participants were instructed to keep track of the last word from each category that was presented on the screen. At the end of each trial the participant was required to type in the last word they could recall from each target category and press next to continue to the next trial. For example, if the target categories were animals, colour and relatives, at the end of the trial participants recalled the last animal, colour and relative presented in the list. This required participants to closely monitor the words presented and update their working memory representations for each category as in the study done by Miyake et al, (2000). No feedback was given in any of these trials
Before participants began the task they were shown the six categories and the words that fell underneath each category to ensure that they knew which exampler word belonged to which category. They were also given one practice trial in which three categories were presented at the bottom of the screen before moving onto the main task.
The number of words correctly recalled and recorded, out of a total of 24 words, stood as the dependent measure for this task.
Updating two: letter memory task. In the letter memory task, adapted from Morris & Jones (1990), a sequence of randomised letters that varied in length were presented in the middle of the screen for 2000ms each. In order to ensure updating from the participant they were instructed to recall out loud the last four letters from each sequence. For example, if the sequence was THBRA, the participant was instructed to recall the letter T, then TH, then THB, then THBR, finally HBRA dropping the first letter (T) and only recalling the last four letters in the sequence.
The number of letters presented in each sequence varied (5,7,9 or 11) in order to ensure the above instructions were followed and continuous updating of working memory representations was utilised until the end of each trial. The task consisted of one practice block and four main blocks (12 trials).
The practice block consisted of three trials. Trial one presented a five lettered sequence, trial two presented a seven letter sequence and trail three presented a nine lettered sequence. After each sequence participants were asked to recall and type in the last four letters in the space provided.
Feedback was given for 600ms via a green tick for a correct answer and a red cross for an incorrect answer. No feedback was given in the main task.
In the main task each of the four blocks consisted of three trials that varied based on the number of letters in each sequence (block one=5 letters, block two= 7 letters, block three= 9 letters and block four= 11 letters). The total number of trials completed in the main task was 12. The 12 trials were all randomly presented with no way for participants to predict the number of letters presented in each sequence. As in the practice trials, participants had to record the last four letters of each sequence by typing these into the space provided and pressing the enter key in order to move onto the next sequence.
The dependent measure for this task was the total number of correctly recalled sequences out of a total of 12.
Inhibition one: Stroop task. In the Stroop task, adapted from Stroop (1935), participants were presented with a total of 80 randomised trials. The task consisted of 40 (congruent) trials of different coloured circles (10 blue, 10 red, 10 green and 10 yellow circles), 30 (incongruent) trials with a colour word printed in a different colour (the word blue printed in green letters), and 10 colour words printed in the same colour as the word (the word red printed in red letters). Participants were instructed to identify the colour of the stimulus as quickly as possible by clicking on one of the colour tabs (blue, green, red or yellow) at the bottom of the screen. A new stimulus was displayed once the participant clicked on a colour tab.
Before they began the main task participants had a practice block in which they were presented with six trials of either a coloured word or a coloured circle. This was done in order for participants to familiarise themselves with the task. Feedback was given in the practice trial for 600ms via a green tick for a correct answer and a red cross for an incorrect answer. No feedback was provided in the main task.
The dependent measure for this task was the difference in RT between the incongruent trials, where the words and colours did not match, and the congruent trial, where the participants were shown a coloured circle.
Inhibition two: stop signal task: The stop signal task, adapted from Van Den Wildenberg et al, (2006), consisted of one practice block and two main task blocks. In block one (40 randomised trials) of the main task the participants were presented with a screen that displayed a fixation cross for 500ms. This was followed by a green arrow within a white circle either pointing to the left or to the right. The participants were instructed to categorise the arrows as quickly and accurately as possible into either a left or right category. The participants did this by clicking on the F key on the keyboard for a left pointing arrow and the J key on the keyboard for a right pointing arrow. A new trial appeared either after the participant clicked the F or J key or after 1500ms. While the task may seem simple participants only had 1500ms to complete the task. This block was used to build a proponent response.
Block two (40 randomised trials) presented in a similar way. A fixation cross appeared on the screen for 500ms followed by either a left or right pointing arrow within a white circle for 1500- 1700ms. Participants were instructed to categorise these arrows into either a left or right category by pressing the F or J key, respectively. A new trial would appear either after a participant clicked the F or J key or after 1500ms-1700ms. The difference in block two is that a stop signal was presented in this block. In the stop signal trials the white circle around the arrows turned red after either 500ms or 700ms alternating. Participants were instructed to give no response to these stop signal trials. 24 of the 40 trials in block two consisted of stop signal trials.
Before the main task participants were given a practice block. The practice block consisted of eight randomised trials where a fixation cross for 500ms and either a left or right facing arrow within a white circle for 1500ms were presented on the screen. four of the eight trials consisted of the standard trial. Participants were required to categorise a left or right facing arrow by clicking on the F key for a left arrow and the J key for a right arrow. Four of the eight trials consisted of the stop signal
task whereby the white circle turned red after 500-700ms and participants were required to give no response.
Feedback was provided in both the practice block and the main task blocks. Participants were given feedback in three different situations. If the participant did not click either the F or J key in a non-inhibition trial a feedback button stating “you should have clicked” appeared for 200ms. If the participant chooses the incorrect category (a left facing arrow was categorised as a right facing arrow or vice versa) a feedback button stating “wrong answer” appeared for 200ms. If the participants clicked too quickly in a stop signal trial (clicked either before the red circle or while the red circle was present) a feedback button stating “wrong answer” appeared for 200ms.
The dependent measure for this task was the number of categorisation responses received for the stop signal trials.
Procedure
Participants began the study once they clicked on the link provided in the advert or email sent during the recruitment process. The study opened with a consent form informing participants of the purpose of the study, that their participation was voluntary and that they could exit the study at any time by closing their browser. Once informed consent had been given by the participant a new screen opened with the demographic survey. Participants were required to answer each demographic question and provide consent once again before moving onto a new screen which displayed the self- report survey (WEBexec). Once all answers had been given in the WEBexec questionnaire the participants moved on to the main six executive functioning tasks. The tasks presented in the following order: letter-number task, plus-minus task, keep track task, letter memory task, Stroop task and stop signal task. Each task had to be completed in that order and fully before the participant moved onto the next task. The end of the experiment provided a debriefing page whereby participants were thanked for their participation and the email address of the lead experimenter was provided if they had any questions or concerns.
Data quality
Based on analysis done by previous studies data quality checks of all executive functioning task data were performed (Fleming et al., 2015; Friedman et al., 2008; Friedman et al., 2011). For each task that utilised mean (RT) as its dependent measure RT times less than 200ms were automatically discarded. This was done in order to ensure that answers provided were testing EF and were not guessed or accidental (Fleming et al., 2015 and Sandberg et al., 2014). In order to ensure participants were providing focused attention to each task accuracy within the tasks was calculated with a 92%
accuracy rate being recorded. A box and whiskers chart identified outliers on each executive functioning task. A conservative approach was utilised with only outliers identified by SPSS statistics for Windows, Version 27.00 (SPSS) as severe being removed from each data set before analysis. See table 3 and 4 for sample sizes after outliers were removed. Due to the nature of the dependent measures being different (some dependent measures recorded RT while others recorded correct responses) the data was transformed so that variables could be averaged across the operations and compared. In order to standardise across data sets variables were transformed into Z-scores via SPSS.
Once transformation of the data was complete, data was tested for normal distribution using the Shapiro and Wilk (1965) test for normalcy with an α = .05 being set. Visual representation of normal distribution was also offered via a Q-Q plot chart. Descriptive statistics for each computerised task, including skewness and kurtosis, can be seen in table 3 for the control group and table 4 for the experimental group.
Table 3
Descriptive Statistics (control group) for executive functioning tasks and WEBexec measure
Measure M (SD) n Skewness Kurtosis
Self-Report
WEBexec 9 (3) 42 0.26 0.51
Shifting
Number Letter 588ms (0.21) 42 0.31 0.07
Plus Minus 819ms (0.41) 41 0.76 0.19
Updating
Keep Track 10(3) 41 -0.96 -0.69
Letter Memory 8 (3) 23 -0.34 -0.81
Inhibition
Stroop 552ms (0.25) 43 0.17 -0.79
Stop Signal 16 (4) 39 -0.97 0.06
Note. M = Mean; SD = Standard Deviation, n = number of participants.
Table 4
Descriptive Statistics (experimental group) for executive functioning tasks and WEBexec measure
Measure M (SD) n Skewness Kurtosis
Self-Report
WEBexec 12 (5) 39 0.2 -1.25
Shifting
Number Letter 520ms (0.23) 35 0.04 1.92
Plus Minus 858ms (0.42) 35 0.27 0.08
Updating
Keep Track 11 (5) 39 -0.18 0.18
Letter Memory 10 (2) 29 -0.94 -0.42
Inhibition
Stroop 590ms (0.41) 34 0.92 1.86
Stop Signal 15 (6) 31 -0.55 -0.89
Note. M = Mean; SD = Standard Deviation, n = number of participants.
Statistical analysis
In keeping with the three-factor model of executive functions inferential statistics were drawn from the average scores of each executive functioning operation instead of each individual executive functioning task. To compare performance on each executive functioning operation only participants who had completed both executive functioning tasks per each operation and whose results were not considered outliers were included in each operations data set. The average scores between the two executive functioning operation tasks were recorded per each participant in order to perform an independent sample t-test and Spearman’s Rank correlation. The individual sample t- test compared the performance of the control group and the experimental group on each EF operation and on the WEBexec. The nature of the different dependent measures saw some measures having a high score as an indication of high EF ability (such as accuracy measures in the
keep track task and letter memory task) and other dependent measures having a low score as an indication of high EF ability (such as the plus-minus task and the number-letter task). To control for this issue the dependent measures that recorded low scores as a measure of high EFs were turned around in SPSS to ensure that a high combined value was equal to high EFs. Equality of variance was ensured before the independent samples t-tests using Levenes test for equality of variances with p=
.05. Bonferroni correction was utilised to control for multiple comparisons and to avoid a type I error with a significance level of .02 being set. Spearman’s correlation explored any correlations between the number of years competing, number of competitive fights reported, number of reported concussions and performance on each EF operation of the experimental group. Spearman’s correlation was selected as an appropriate statistical tool due to the monotonic relationship, as opposed to the linear relationship, of the variables analysed. This allowed the measurement of the strength and direction of association between the two variables based on their rank. Bonferroni correction was implemented in order to control for multiple comparisons with p= .02
Results Analysis overview of executive functioning operations
The primary prediction of this study was that there would be a statistically significant difference between the executive functioning abilities of competitive MMA athletes (experimental group) compared to non-contact sport athletes (control group). The experimental group was expected to display poorer executive functioning abilities due to their risk of experiencing RHI. The results of the independent sample t-test can be seen in table 5. The results concluded that there were no statistically significant differences between the performance of the control group compared to the experimental group on shifting, updating or inhibition, contradicting the original prediction.
Table 5
Individual sample t-Test of control and experimental groups compared performance on EF operations
EF Operations n t-score(df) p d
Shifting Experimental group 37
-0,06(66) .9 .7
Control group 31
Updating Experimental group 21
0,051(48) .9 .7
Control group 29
Inhibition Experimental group 38
0,7(63) .5 .7
Control group 27
Note. n = Number of participants; df = degrees of freedom; d = Cohens d Following Bonferroni correction a significance level of .02 was set
Analysis overview of within-group correlations (experimental group)
The second prediction suggested in this study is that the number of years, number of fights and number of reported concussions would be negatively correlated to the performance on executive functioning operations within the experimental group. The results of the Spearman’s correlation for each EF operation can be seen in table 6.
The analysis showed a moderate negative correlation between the number of years competing and performance on shifting as well as a moderate negative correlation between the number of years competing and performance on updating. There was also a small negative correlation recorded between the number of years competing and inhibition. Statistically significant moderate negative correlations were recorded between the number of competitive fights and performance on all three EF operations. Finally, there were no statistically significant correlations seen between the number of reported concussions and performance on shifting and updating, with a small negative correlation seen between the number of concussions experienced and performance on inhibition.
The results from the above analysis provide three clear pieces of information on the potential implications of competitive MMA. Firstly, they indicate that the more years a participant competes in MMA for the more likely they are to portray moderately poorer executive functioning on both shifting and updating tasks as well as poorer executive functioning on inhibition tasks (although this seems to be less severe). Secondly, the number of competitive fights a participant competes in negatively impacts executive functioning ability on all three operations of executive functioning. Lastly, participants who reported experiencing more concussions portray no difference in their ability to perform executive functioning tasks compared to those who report fewer concussions.
Table 6
Correlation between performance of EF operations and number of years competing, number of competitive fights and reported concussions.
Shifting Updating Inhibition
Number of years competing
N 31 29 27
Spearman correlation (rs) -0.47 -0.3 -0.14
p .02 .01 .04
Number of competitive fights
n 31 29 27
Spearman correlation (rs) -0.23 -0.27 -0.377
p .02 .01 .005
Number of reported concussions
n 31 29 27
Spearman correlation (rs) -0.02 0.05 -0.13
p .09 .07 .05
Note. n= Number of participants
Following Bonferroni correction a significance level of .02 was set
Analysis overview of WEBexec self-report measurement tool
The results from the independent sample t-test demonstrated that the 39 participants in the experimental group ( M= 12, SD= 5) compared to the 44 participants in the control group (M= 10, SD=
3) reported higher levels of belief in poorer EF ability t(81)= 2.73, p= .01. Cohen’s d was reported as 0.6 suggesting a medium effect size. This result indicates that the experimental group have a much higher belief that they suffer from poorer global executive functioning compared to that of the control group.
Discussion
The main objective of this study was to examine whether there is potential for competitive MMA athletes to experience poorer executive functioning compared to non-contact sports athletes.
It was postulated that this is due to the violent nature of MMA that would result in RHI and TBI being prevalent in most competitive MMA athletes. This RHI would result in mild brain damage that would potentially impact cognitive abilities. The analysis produced contradictory results to the hypothesis with the experimental group and the control group demonstrating similar executive functioning across all three executive functioning operations.
The findings from this study also contradict findings reported in other studies looking at the executive functioning abilities of competitive high-contact sports athletes. A majority of these previous studies provide evidence for poorer executive functioning in high-contact athletes due to RHI and/or TBI (Bernick & Banks, 2013; Heilbronner et al., 2009; Kim et al., 2019; Montenigro et al., 2017;
Seichepine et al., 2013 and Stamm et al., 2015). A speculative possible explanation for the differences seen between the present study and other studies exploring executive functioning in high-contact sports athletes is that the type of RHI experienced by competitive MMA athletes differs compared to that of other high-contact sports athletes. The combination of multiple martial arts found in MMA results in numerous ways in which an athlete could experience a head-impact. This differs to other high-contact sports which see athletes experiencing a standard form of RHI consecutively, such as tackling in football and rugby or checking in ice-hockey. A second difference in the type of RHI experienced by MMA athletes compared to other high-contact sports athletes is that of the amount of force behind the impact experienced. Other high-contact sports athletes experience RHI with much greater force, for example being hit with a shoulder or full body contact at high speed, compared to only a fist or foot from a standstill, as seen in MMA. This form of acceleration-deceleration of the head associated with TBI and executive dysfunction in other high-contact sports is not often seen in MMA.
Could the different types of RHI and the smaller force of RHI seen in MMA athletes head-impacts compared to the standard, consecutive, high-force head-impact associated with other high-contact sports be the reason why poorer executive functioning was not evident in MMA athletes but has been reported in athletes of other high-contact sports?
Research by Meaney and Smith (2011) examined the biomechanics of concussions and found that increased pressure within the brain is responsible for neurological dysfunction. They also found that the level of this dysfunction is highly correlated with the peak pressure (the most force) seen during an injury. The findings in this study lead to the proposition that more force experienced during impact leads to more TBI which in turn leads to poorer neurological functioning. The authors also found that a difference in neurological dysfunction is present based on whether the impact is considered to have linear acceleration or rotational acceleration. Rotational acceleration was reported as causing more shear forces throughout the brain which is known to cause shear-induced tissue damage. The shear-induced tissue damage is more prevalent in the brain as the brain is more susceptible to it than other organs. Other studies also exploring the biomechanics of RHI between high-contact sports athletes and control groups have reported similar results (Breedlove et al., 2012;
Talavage et al., 2014). One study utilised Diffusor tensor imaging (DTI) to measure the directionality (fractional anisotropy (FA)) and regularity (mean diffusivity (MD)) of white matter tracts as well as neurocognitive testing (Graham et el., 2014) and found pre-season and post-season FA and MD changes in high-contact sports athletes with self-reported concussions compared to a control group (Bazarian et al., 2012). A similar study comparing the neurophysiological changes between high- contact sports athletes (such as football players and MMA players) could offer valuable insight into the different neurological changes between these athletes and how this could be related to the differences seen in executive functioning ability between high-contact sports athletes.
Further research is required that examines the comparison of multiple high-contact sports athletes (including MMA athletes) their executive functioning ability, the type of RHI experienced by these athletes and any neurophysiological differences seen between these athletes. These studies will help to determine whether the type of high contact sport and RHI experienced by athletes in different high-contact sports is responsible for poorer executive functioning reported in some high-contact sports athletes but not others.
A second objective of this study was to evaluate whether there are differences in the executive functioning abilities within the competitive MMA sample population. A postulation that more years competing, more competitive fights and more concussions would result in poorer executive functioning was made. The results for this second objective demonstrated three important findings.
Firstly, that participants who competed for longer performed worse on all three executive functioning operations. These findings support previous studies who reported that higher levels of head-impact exposure (associated with increased number of years competing in high-contact sports) led to poorer performance of cognitive tasks including executive functioning (Cunningham et al., 2020; McAllister and McCrea, 2017). Secondly, participants who reported experiencing more concussions performed no worse on tasks related to all three EF operations. This finding contradicts previous studies that explored the effects of cumulative concussions on global executive functioning such as those by Sicard et al. (2018), who explored the long-term outcomes of athletes with a history of concussions and reported findings that multiple concussions are responsible for long-term changes in executive functioning abilities. As well as Manley et al. (2017) who reported multiple concussions as a risk factor for cognitive impairment, including executive dysfunction, and De Beaumont et al. (2012) who reported cumulative cognitive alterations after sports related concussions. The lack of cohesiveness from the results in the present study and others is in line with the idea presented above that the type of head impact experienced in MMA is not forceful enough to result in poorer cognitive functioning.
More research on this subject needs to be performed. Third, there were significant correlations reported between the number of fights partaken in and poorer performance on all three of the executive functioning operations. A possible speculation one could make based on the first and third finding is that RHI, and ultimately TBI is experienced in both practice and competition and that the overall competitive career of an MMA athlete is responsible for poorer EF. While the findings above provide a starting point for the study of poorer executive functioning experienced by competitive MMA athletes more research is required in order to solidify these findings. In order to further research exploring whether the number of years, number of concussions and/or the number of competitive fights of MMA athletes is correlated to poorer executive functioning a longitudinal study examining the executive functioning of competitive MMA athletes throughout their competitive career could be valuable.
An interesting question that arises from the findings above is that of whether specific executive functioning operations are controlled by different regions of the brain? Are some regions more susceptible to injury in different high-contact sports? Can we assume that the reason MMA athletes experience poorer performance in some aspects of executive functioning with no evidence of poorer performance in others is due to the fact that executive functioning operations are controlled by different regions of the brain and that the regions of the brain responsible for inhibition, for example, are more susceptible to RHI and TBI in MMA athletes. A study performed by Mace et al.
(2019) explored one aspect of this question using Magnetic Resonance Imaging (MRI) to assess whether the neural architecture of executive functioning could be investigated by the Delis-Kaplan Executive Function Scale (D-KEFS). The findings from the study demonstrated that global, unitary executive functioning was positively associated with superior frontal, rostral middle frontal, and lateral orbitofrontal volumes and negatively associated with frontal pole volumes. The exploration of diverse executive functioning operations found that shifting was positively associated with the dorsolateral prefrontal cortex. Similar results have been seen in other studies (Kopp et al.,
2015; Kortte et al., 2002; Stuss et al., 2001; Varjacic et al., 2018). The study also reported that Inhibition was positively associated with superior frontal regions. The findings from this study could provide a guideline for future studies exploring more localised TBI in competitive MMA athletes and how that TBI influences executive functioning operations.
The final findings of this study demonstrated that MMA athletes believe that they have poorer executive functioning abilities compared to non-contact sport athletes. The MMA group held this belief even though there were no statistically significant differences in the behavioural performance of the MMA group compared to the non-contact sports group. This finding provides relevant support that using self-report measures as biomarkers for poor executive functioning may not be viable which has previously been suggested in other studies (Seichepine et al., 2013 and Vynorius et al., 2016). A possible explanation for the differences reported in the experimental groups EF ability and their belief in their EF ability could be due to what executive functioning represents to each participant compared to the different kinds of EFs tapped into by the six EF tasks used in this study. While executive functioning was explained before the beginning of the study it could be beneficial to add an explanation check in order to see if the participants have a similar understanding of EFs. More research which considers different self-report measures and their validity in predicting executive dysfunction is needed in this field in order to substantiate findings.
While the above findings have contributed valuable insight into the executive functioning abilities of competitive MMA athletes there are certain limitations to the study that need to be discussed. The online nature of the study made it difficult to ensure that participants followed instructions given before the study (such that participants should ensure they are not distracted for the next hour and instructions given at each task, such as answering the question as quickly and accurately as possible). Another limitation of the study is the small sample size. More studies need to be performed with larger sample sizes in order for the findings of the present study to be validated.
The lack of distribution within the experimental groups demographical data on their competitive career provided a challenge in corelating the number of years, number of fight and number of concussions reported with executive functioning abilities. A more evenly distributed sample that includes more athletes that have competed for more than four years and have experienced more than one concussion is necessary in order to validate the findings in the present study.
Future research within this field would benefit from studies focusing on the biomechanics of concussions between athletes in high-contact sports, including MMA athletes. Studies exploring why there seems to be poorer executive functioning recorded in some high-contact sport athletes and not others (including MMA athletes) would add valuable insight to the field. A longitudinal study exploring the EF performance of MMA competitors and whether correlations exist between the number of years, number of fights and/or number of concussions would also add valuable insight into the cognitive implications of the sport. Lastly, studies examining the neurological structure of executive functioning operations and if this structure can explain the localisation of poorer executive functioning seen in competitive MMA athletes are necessary in order to fully understand how RHI in MMA impacts cognitive ability.
Conclusion
Despite the limitations mentioned above the present study makes valuable inferences by providing some of the first research done on the implications of competitive MMA on executive functioning abilities. Through the utilisation of a multifaceted analytic approach the study was able to conclude that there are no statistically significant differences in the executive functioning operations of competitive MMA athletes compared to non-contact sports athletes. There is, however, a moderate negative correlation between the number of years an MMA competitor has competed and two EF operations (shifting and updating) as well as a moderate negative correlation between the number of
reported competitive fights and performance on all three EF operations. A self-report measure on executive functioning abilities also provided valuable information on how MMA athletes view their executive functioning abilities compared to non-contact sports athletes. The results showed that the MMA athletes reported a belief of higher executive functioning problems than that of the non-contact sport athletes.
