Quantifying Energy Expenditure and Cognitive Fatigue of Esports Athletes During Competition

Although Esports is undergoing continual and rapid growth, reaching more than 450 million spectators worldwide (Russ, 2019), there is no comprehensive research investigating the physiological demands of Esports, nor any trialed exercise interventions aimed at improving athlete performance. Understanding the energy expenditure, and cognitive load or competitive Esports gaming may inform future research and application in sports performance training.

Energy expenditure can be assessed directly and indirectly using a number of methods. Indirect calorimetry is considered the most valid non-direct assessment of human energy expenditure (Levine, 2005; Haugen, 2007; Rosado et al., 2013). This non-invasive method involves the collection of expired gases during respiration using a one-way valve and facemask, where the gases are analysed to assess changes following cellular respiration, using oxygen and carbon dioxide analysis hardware and software. By use of the Weir formula (Weir, 1949), changes in oxygen and carbon dioxide expired gases can be computed to give an estimate of metabolic rate. From here, total energy expenditure over a set time interval can be calculated. Methods of measurement of energy expenditure, including the use of the Weir formula using indirect calorimetry are discussed at length by Levine (2005).

Prolonged periods of cognitive tasking which require continued visual attention, fast reaction time and short-term processing of stimulus have been shown to induce cognitive fatigue (Kato et al., 2009). Cognitive fatigue is defined as the psycho-physiological state following prolonged cognitive activity, which leads to deterioration of attentional functioning and response readiness (Zhao et al., 2012; Kato et al., 2009). Research in cognitive fatigue has employed subjective assessments, included self-report surveys (Reimer et al., 2006) and objective assessments using functional near-infrared spectroscopy and electroencephalography, or EEG. EEG is a non-invasive (although, some invasive measures exist) method of monitoring and recording the electrical activity within the brain. The device, consisting of multiple channels, measures voltage fluctuations resulting from ionic current within the neurons of the brain (Niedermeyer & da Silva, 2004). Many studies assessing cognitive fatigue using EEG are in the field of motor vehicle accident prevention and research, using driving simulators to elicit driver cognitive fatigue (Zhao et al., 2012; Schmidt et al., 2009). However, there has been researched using EEG in video gameplay. Sheikholeslami and colleagues (2007) investigated the brain responses to playing a competitive video game over a continuous period of 65 minutes. Two subjects played Mario Power Tennis (Nintendo Gamecube) while measures were collected using 128 channel EEG system. The results indicated an increase in frontal theta-wave the longer the subject played the game, which indicates a significantly increased mental load. This has been discussed in other studies, suggesting that increased magnitude of theta-wave (specifically localised in the anterior cingulate cortex), may be appropriate measures of progressive cognitive fatigue (Pellouchoud et al., 1999).

Given the historically positive outcomes of exercise interventions on physical performance and cognitive function, it is imperative that investigations be made on the influence of physical training on Esports players. Before such interventions can be developed, energy expenditure and cognitive function and fatigue during competitive Esports gaming in elite athletes needs to be assessed. Our research aims to investigate changes in energy expenditure and cognitive fatigue from baseline resting values in Esports athletes following competitive gameplay of League of Legends.

References

  • Haugen, H., Chan, L., & Li, F. (2007). Indirect calorimetry: a practical guide for clinicians. Nutrition in Clinical Practice, 22:4, 377-388.
  • Kato, Y., Endo, Y., & Kizuka, T. (2009). Mental fatigue and impaired response processes: event-related brain potentials in a go/nogo task. International Journal of Psychophysiology, 72:2, 204-211.
  • Levine, J. (2005). Measurement of energy expenditure. Public Health Nutrition, 8:7, 1123-1132.
  • Niedermeyer, E., & da Silva, F. (2004). EEG recording and operation apparatus. Electroencephalography: Basic Principles, Clinical Applications, and Related Fields. Lippincott Williams and Wilkins: Baltimore, USA.
  • Pellouchoud, E., Smith, M., McEvoy, L., & Gevins, A. (1999). Mental effort-related EEG modulation during video-game play: comparison between juvenile subjects with epilepsy and normal control subjects. Epilepsia, 40:4, 38-43.
  • Reimer, B., & Sodhi, M. (2006). Detecting eye movements in dynamic environments. Behaviour Research Methods, 38, 667-682.
  • Rosado, E., Kaippert, V., & de Brito, S. (2013). Energy expenditure measured by indirect calorimetry in obesity. In Applications of Calorimetry in a Wide Context: Differential Scanning (Ed. Elkordy, A). Intechopen.
  • Russ, H. (2019). Global esports revenues to top $1 billion in 2019: report. Reuters.
  • Schmidt, E., et al. (2009). Drivers’ misjudgement of vigilance state during prolonged monotonous daytime driving. Accident Analysis & Prevention, 41:5, 1087-1093.
  • Sheikholeslami, C., et al. (2007). A high resolution EEG study of dynamic brain activity during video game play. 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Lyon. 2489-2491.
  • Weir, J. (1949). New methods for calculating metabolic rate with special reference to protein metabolism. Journal of Physiology, 109, 1-9.
  • Zhao, C., Zhao, M., Liu, J., & Zheng, C. Electroencephalogram and electrocardiograph assessment of mental fatigue in driving simulator. Accident Analysis & Prevention, 45, 83-90.