Abstract
of the “central governor” in exercise physiology suggests the brain plays a key role in regulating exercise performance by continuously monitoring physiological and psychological factors. In this case report, we monitored, for the first time, a marathon runner using a metabolic portable system and an EEG wireless device during an entire marathon to understand the influence of brain activity on performance, particularly the phenomenon known as “hitting the wall”. The results showed significant early modification in brain activity between the 10th and 15th kilometers, while the RPE remained low and cardiorespiratory responses were in a steady state. Thereafter, EEG responses decreased after kilometer 15, increased briefly between kilometers 20 and 25, then continued at a slower pace. After kilometer 30, both speed and respiration values dropped, along with the respiratory exchange ratio, indicating a shift from carbohydrate to fat metabolism, reflecting glycogen depletion. The runner concluded the race with a lower speed, higher RPE (above 15/20 on the Borg RPE scale), and reduced brain activity, suggesting mental exhaustion. The findings
25, then continued at a slower pace. After kilometer 30, both speed and respiration values dropped, along with the respiratory exchange ratio, indicating a shift from carbohydrate to fat metabolism, reflecting glycogen depletion. The runner concluded the race with a lower speed, higher RPE (above 15/20 on the Borg RPE scale), and reduced brain activity, suggesting mental exhaustion. The findings suggest that training strategies focused on recognizing and responding to brain signals could allow runners to optimize performance and pacing strategies, preventing premature exhaustion and improving overall race outcomes. Keywords:marathon; running; electroencephalography; brain activity; cardiorespiratory parameters; exercise performance 1. Introduction Although participation in marathon running has significantly increased [1,2], under- standing the physiological responses during a marathon remains a complex challenge. “Hitting the wall“ (HTW) describes the abrupt onset of overwhelming fatigue that often occurs in the later stages of the race, potentially reducing a runner to a walking pace and sometimes preventing them from finishing altogether. Many articles have extensively explored HTW in marathon running [3–7], with the prevailing belief being that runners experience this phenomenon when their glycogen stores become depleted, often due to inadequate race nutrition [8–10]. Additionally, poor pacing, whether starting too quickly or finishing too fast, can contribute to suboptimal finishing times [5,9,11,12]. Physiologically, HTW is linked to glycogen depletion, resulting in a less efficient switch to fat metabolism, leading to hypoglycemia and muscle fatigue [13]. Glycogen is a critical energy source during endurance activities, and when these stores become depleted, the body must rely more on fat as an energy source. This switch is less efficient and slower, leading to a reduced rate of ATP production, which is necessary for muscle contractions. Consequently, this can cause hypoglycemia, a condition characterized by low blood sugar levels, and muscle fatigue, as the muscles do not receive adequate energy Int. J. Environ. Res. Public Health2024,21, 1024.
Int. J. Environ. Res. Public Health2024,21, 1024 2 of 12 to maintain the same level of performance [14,15]. This metabolic shift not only impacts muscle function but also affects overall performance by significantly lowering the body’s ability to sustain high-intensity effort. Psychologically, HTW involves central fatigue, where the brain perceives the effort as excessively high. This perception can lead to a decrease in motivation and an alteration in pacing strategies. Central fatigue is associated with the brain’s attempt to prevent harm to the body by reducing the intensity of physical activity when it senses that the physiological demands are too great. This can manifest as feelings of overwhelming tiredness and an inability to maintain pace, regardless of the runner’s physical condition [16,17]. These physiological and psychological factors interplay, creating the characteristic drop in performance. As glycogen stores deplete, the brain receives signals indicating high levels of effort and potential energy shortages. This triggers central fatigue mechanisms, leading to a reduced ability to sustain the previous level of effort, ultimately causing a significant decline in performance [14,16]. The combined effect of muscle energy deficit and central nervous system regulation ensures that the runner slows down or stops, ostensibly to protect the body from further harm [9]. This multifaceted response highlights the complexity of HTW and the intricate balance between physiological energy supply and psychological endurance during marathon running [18,19]. A recent pilot study has provided insights into the cardiorespiratory function during a marathon, using breath-by-breath gas exchange measurements to analyze oxygen uptake ( . V O2), ventilatory rate ( . V E), and their association with the rate of perceived exertion (RPE) throughout the race [20]. This experiment revealed a systematic drift in cardiorespiratory parameters indexed by the RPE ratio. Specifically, RPE was found to increase for a given fraction of maximal cardiorespiratory parameters, indicating that as physiological demand increased, perceived exertion increased disproportionately. The study highlighted that despite RPE being a potential candidate for controlling marathon pace, the indexation of physiological parameters or speed by RPE showed a consistent increase for all runners. This suggests that perceived exertion is not
RPE was found to increase for a given fraction of maximal cardiorespiratory parameters, indicating that as physiological demand increased, perceived exertion increased disproportionately. The study highlighted that despite RPE being a potential candidate for controlling marathon pace, the indexation of physiological parameters or speed by RPE showed a consistent increase for all runners. This suggests that perceived exertion is not solely dependent on the physiological strain but is also influenced by psychological and experiential factors. Furthermore, this study highlights the multidimensional nature of perceived effort and suggests that even when a runner’s RPE increases more than physiological parameters, it may be associated with a significant change in brain activity during the marathon. The concept of the “central governor” (CGM) in exercise physiology, introduced by Noakes, provides a theoretical framework for understanding how the brain regulates exercise performance [21]. According to the CGM, the brain continuously monitors and regulates exercise intensity to avoid reaching a state of catastrophic failure or harm, using a combination of physiological, sensory, and psychological feedback for real-time adjust- ments [16,20]. Electroencephalography (EEG) allows researchers to observe changes in brain activity related to cognitive processes. The utilization of EEG in sports research has traditionally been more prevalent in less dynamic sports [22–24]. However, recent advancements in mobile EEG technology and overall technological improvements offer an opportunity to delve into the intricate interplay between neuroscience and sporting behavior, allowing researchers to observe changes in brain activity related to cognitive processes during dynamic sports such as running [25]. The frequency bands commonly explored during exercise and sport research are alpha (α: 8–13 Hz) and beta (β: 13–30 Hz) bands, as they are thought to convey distinct aspects related to motor planning, execution, and control [26–30]. Alpha activity, characterized by quite low-frequency oscillations, is associated with perceptual awareness and the inhibition of non-essential processing, thereby facilitating task performance [31,32]. In contrast, beta activity, characterized by high-frequency oscillations, is associated with voluntary contractions, alertness, and arousal, enhancing the perception of stimuli [33–35]. Studies have used the frontal alpha/beta ratio to assess arousal levels, noting that high alpha activity and/or low beta
quite low-frequency oscillations, is associated with perceptual awareness and the inhibition of non-essential processing, thereby facilitating task performance [31,32]. In contrast, beta activity, characterized by high-frequency oscillations, is associated with voluntary contractions, alertness, and arousal, enhancing the perception of stimuli [33–35]. Studies have used the frontal alpha/beta ratio to assess arousal levels, noting that high alpha activity and/or low beta activity are associated with decreased arousal or vigilance [36,37]. In line with these findings, high alpha activity and/or low beta activity in the frontal lobe were observed after running at high intensity [29,38].
Int. J. Environ. Res. Public Health2024,21, 1024 3 of 12 To our knowledge, no study has measured real-time brain activity during a marathon. Despite our knowledge of the physiology of running and exercise in general [9,15,39,40], there are still significant gaps in our understanding of how to transpose these results to real-life marathon conditions, where ecological validity is paramount. This research aims to determine whether the brain undergoes more pronounced activity modifications than physiological parameters, and if observable changes in EEG patterns associated with mental fatigue and cognitive decline manifest as the runner nears exhaustion in the last 10 km of a marathon. Monitoring these specific EEG patterns may offer valuable insights into the brain’s cognitive response to the challenges posed by prolonged exercise. We hypothesize that the brain will undergo more pronounced activity modifications than physiological parameters, and that these changes in EEG patterns will reflect mental fatigue and cognitive decline as the runner nears exhaustion. We anticipate observing a decrease in beta activity and an increase in alpha activity [36,37], and/or high alpha activity and/or low beta activity, commonly linked to manifestations of mental fatigue. 2. Materials and Methods 2.1. Participant The study involved a single male marathon and trail runner (DB) with nearly 5 years of competitive experience. At the time of the event in 2023, he was 27 years old, 176 cm tall, and weighed 69 kg. The subject volunteered to participate in the study and was asked not to modify his usual training regimen or diet. His marathon record is 3 h, 10 min, and 12 s, set in 2019. He was selected for the homogeneity of his physiological and endurance characteristics [41–43]. The subject reported training three to four times per week (50–80 km/week) for over 5 years. His training included high-intensity interval training (6×1000 m at 90–100% of his maximal heart rate) once a week and tempo training (15–25 km) at 90–100% of his average marathon speed. DB received information about the study and gave his written consent to participate. The study’s objectives and procedures were approved by an institutional review board (CPP
for over 5 years. His training included high-intensity interval training (6×1000 m at 90–100% of his maximal heart rate) once a week and tempo training (15–25 km) at 90–100% of his average marathon speed. DB received information about the study and gave his written consent to participate. The study’s objectives and procedures were approved by an institutional review board (CPP Sud-Est V, Grenoble, France; reference: 2018-A01496-49). 2.2. Experimental Design: RABIT ® Test and the Marathon Race 2.2.1. Pre-Race Protocol DB performed the RABIT ® (Running Advisor Billat Training) test to determine his maximal oxygen consumption ( . V O2max) and maximal heart rate (HRmax). The RABIT ® test was conducted outdoors on a hard dirt path. This test has been validated as a reliable field test of maximal and functional aerobic capacity [44,45]. The RABIT ® test, rather than the more commonly used Graded Exercise Test, was chosen because it is based on the RPE pace control. The test consisted of three incremental exercise stages, adjusted to prescribed RPE levels: “light” (RPE = 11/20) for 10 min, “somewhat hard” (RPE = 14/20) for 5 min, and “very hard” (RPE = 17/20) for 3 min [45]. Exercise intensity was assessed subjectively by the participant, with descriptions ranging from “very light” to “very, very hard”. The RPE scale correlates well with cardiorespiratory and metabolic variables such as minute ventilation, heart rate, and blood lactate levels [46]. Each stage was followed by a 1 min rest period. DB was instructed to adjust his running speed continuously to maintain the prescribed RPE, ensuring that RPE, not speed, remained constant for each stage. The test revealed DB’s maximal aerobic speed ( . V O2max) as 18 km.h −1 , with an HRmax of 179 beats per minute, and a . VO2max of 62 mL.min −1 .kg −1 . 2.2.2. Marathon Race and Environmental Conditions Three days after the RABIT ® test, DB ran a marathon inÉvry-Courcouronnes, France, starting at 10 a.m. The unofficial marathon course consisted of a 5km loop on a flat road. The temperature ranged from 8 to 11 ◦ C (between 10
per minute, and a . VO2max of 62 mL.min −1 .kg −1 . 2.2.2. Marathon Race and Environmental Conditions Three days after the RABIT ® test, DB ran a marathon inÉvry-Courcouronnes, France, starting at 10 a.m. The unofficial marathon course consisted of a 5km loop on a flat road. The temperature ranged from 8 to 11 ◦ C (between 10 a.m. and 1 p.m.), with no precipitation and an average humidity of 65%. Blood lactate was measured on the finger (Lactate PRO2
Int. J. Environ. Res. Public Health2024,21, 1024 4 of 12 LT-1730; ArKray, Kyoto, Japan) just after the warm-up (15 min at an easy pace) and three minutes after crossing the finish line. Continuous blood lactate measurement was not performed during the race as the ratio of expiratory measurements allowed estimation of the carbohydrate and lipid contribution [47]. 2.3. Experimental Measurements 2.3.1. Cardiorespiratory Factors and Speed Respiratory gasses (oxygen uptake [ . V O2], ventilation rate [ . V E], and the respiratory exchange ratio [RER]) were continuously measured using a telemetric, portable, breath- by-breath sampling system (K5; Cosmed, Rome, Italy) (Figure). COSMED reusable face masks are ideal for metabolic testing both at rest and during exercise, regardless of the nature, intensity, or duration of the test. These masks are made of silicone (without latex or other allergenic materials) and are anatomically contoured with a strong ribbed support structure and an integrated chin strap to ensure a perfect fit with no leakage, providing maximum comfort. The runner used the mask version with inspiratory valves to reduce inspiratory resistance during high-intensity exercise for improved comfort.Int. J. Environ. Res. Public Health 2024, 21, x 5 of 13 Figure 1. Positioning of different sensors on participant. 2.3.2. The Rate of Perception of Exertion (RPE) Scale DB was accompanied by an experimenter on a bicycle who showed him the Borg’s 6–20 scale [46] at every kilometer and more frequently if requested by the runner. The RPE was recorded by DB using a small microphone that he carried. DB recorded an RPE at least every kilometer or more frequently if he felt the need. To ensure the accuracy of the report, DB was familiarized with the scale during the 2 weeks preceding the race. 2.3.3. Electroencephalography (EEG) Measurements EEG was recorded using the LiveAmp 16-channel wireless mobile amplifier con- nected to gel-based active electrodes (Brain Products, Gilching, Germany) (Figure 1). Due to the straps used to fasten the K5 portable gas exchange system, the posterior electrode sites were not easily accessible. Therefore, referring to the 10–20 extended system, we rec- orded EEG at the Fp1, Fp2,
(EEG) Measurements EEG was recorded using the LiveAmp 16-channel wireless mobile amplifier con- nected to gel-based active electrodes (Brain Products, Gilching, Germany) (Figure 1). Due to the straps used to fasten the K5 portable gas exchange system, the posterior electrode sites were not easily accessible. Therefore, referring to the 10–20 extended system, we rec- orded EEG at the Fp1, Fp2, F7, F3, Fz, F4, F8, FC5, FC1, FC2, FC6, T7, C3, C4, T8, and Pz sites. The REF and GROUND electrodes were placed on the Cz and Fpz sites, respectively. EEG channels were digitized at a rate of 250 Hz, with no filter applied except for the anti- aliasing filter. EEG data were recorded on an exchangeable memory card using the Brain Vision Recorder software (version 1.26.0001, Brain Products, Gilching , Germany). For EEG processing, artifacts were first detected automatically based on signal power criteria in specific frequency bands. Spectral analysis using FFT over 2 s windows was then performed to obtain spectral power in two conventional frequency bands: alpha α (8–13 Hz) and beta β (13–30 Hz). These values were combined as a sum of ratios to pro- duce an EEG criterion for classifying the runner’s brain activity into several states, ranging from the weakest to the strongest in terms of median and high-frequency power. Figure 1.Positioning of different sensors on participant. Additionally, a global positioning system watch (Forerunner 645, Garmin, Olathe, KS, USA) paired with the K5 system was used to measure the heart rate (HR) and the speed response (using 5 s data averages) throughout each trial (Figure). The same cardiac belt was used for both the Garmin and K5 systems due to compatibility. Given that recent research shows marathon performance depends on pacing oscilla- tions [48], runners were encouraged to self-pace their run without focusing on the cardio- GPS, whose dial was hidden. During the marathon, refreshment points (offering water, dry and fresh fruit, and sugar) were located every 5 km as well as at the finish line. At the aid
runners were encouraged to self-pace their run without focusing on the cardio- GPS, whose dial was hidden. During the marathon, refreshment points (offering water, dry and fresh fruit, and sugar) were located every 5 km as well as at the finish line. At the aid
Int. J. Environ. Res. Public Health2024,21, 1024 5 of 12 stations, DB was allowed to remove the mask to drink or eat. DB drank one glass at each refreshment point (with flat water and fruits). 2.3.2. The Rate of Perception of Exertion (RPE) Scale DB was accompanied by an experimenter on a bicycle who showed him the Borg’s 6–20 scale [46] at every kilometer and more frequently if requested by the runner. The RPE was recorded by DB using a small microphone that he carried. DB recorded an RPE at least every kilometer or more frequently if he felt the need. To ensure the accuracy of the report, DB was familiarized with the scale during the 2 weeks preceding the race. 2.3.3. Electroencephalography (EEG) Measurements EEG was recorded using the LiveAmp 16-channel wireless mobile amplifier connected to gel-based active electrodes (Brain Products, Gilching, Germany) (Figure). Due to the straps used to fasten the K5 portable gas exchange system, the posterior electrode sites were not easily accessible. Therefore, referring to the 10–20 extended system, we recorded EEG at the Fp1, Fp2, F7, F3, Fz, F4, F8, FC5, FC1, FC2, FC6, T7, C3, C4, T8, and Pz sites. The REF and GROUND electrodes were placed on the Cz and Fpz sites, respectively. EEG channels were digitized at a rate of 250 Hz, with no filter applied except for the anti-aliasing filter. EEG data were recorded on an exchangeable memory card using the Brain Vision Recorder software (version 1.26.0001, Brain Products, Gilching, Germany). For EEG processing, artifacts were first detected automatically based on signal power criteria in specific frequency bands. Spectral analysis using FFT over 2 s windows was then performed to obtain spectral power in two conventional frequency bands: alphaα (8–13 Hz) and betaβ(13–30 Hz). These values were combined as a sum of ratios to produce an EEG criterion for classifying the runner’s brain activity into several states, ranging from the weakest to the strongest in terms of median and high-frequency power. 2.4. Statistical Analysis Data are reported as means±standard deviations, unless otherwise stated. Given the exploratory nature of this pilot study
Description
The study investigates brain and metabolic responses during a marathon.