By Samuele M Marcora, Senior Lecturer in Exercise Physiology School of Sport, Health and Exercise Sciences, Bangor University, Wales

To the Editor: I agree with Professor Tim Noakes that the presence of an end-spurt cannot be explained by either the traditional peripheral fatigue model, or the more recent negative feedback model proposed by Amann and Dempsey (1, 4). However, I think that the presence of an end-spurt is also at odds with Noakes’ central governor model.

In fact, a subconscious intelligent system capable of regulating in anticipation the central neural drive to the locomotor muscles on the basis of a known end-point and afferent feedback from a variety of intero- and extero-receptors should not allow for an end-spurt. On the contrary, it should choose from the very start the maximum speed that can be sustained over 4 miles without dangerous homeostatic failure and, in stable environmental conditions, provide very small but frequent adjustments in central motor command/speed during the race in relation to unpredictable small changes in the physiological conditions of the body. This is the typical functioning of subconscious physiological control systems of homeostasis, and this principle should apply to the central governor as well.

On the other hand, the end-spurt is perfectly compatible with an effort-based decision-making model of exercise performance. When the exercise task is simple (constant-workload or incremental exercise tests to exhaustion), the goal is to last for as long as possible and the conscious decision to take is simple: do I keep going or do I stop? In these testing conditions anticipation is not necessary, and time to exhaustion is determined by two psychological factors: i) potential motivation (the maximum effort a person is willing to exert in order to satisfy a motive) and ii) perceived exertion (2-3, 5).

When the exercise task is more complex (time trials in the lab or actual endurance competitions such as the 4-mile races analysed by Noakes), the conscious decision to take is also more complex: at which speed do I run at the beginning, middle, and end* of the race? Again potential motivation and perception of effort play a major role. However, we need additional conscious information to allow for such complex decision-making process. These conscious information are iii) memory of perception of effort during previous exercise bouts of different intensities and duration, and in different environmental conditions, iv) knowledge of total distance to cover, v) knowledge of distance covered/remaining. Precise knowledge of running speed (or, in the field, running time over a certain distance) certainly helps conscious regulation of pacing, but it is not crucial because our kinaesthetic sense gives us good enough information. Conveniently, we also exclude tactical considerations and we assume that the goal (as in time trials) is to finish the race in the shortest time possible.

Because precise conscious anticipation of perceived exertion and running speed at the end of the race is not possible (and because finishing the race is paramount), athletes usually choose a slightly conservative pace for most of the race. Near the end of the race, when the information provided by the conscious sensation of effort at a certain running speed is more reliable, most “conservative” athletes realise that they can significantly increase running speed without reaching exhaustion before the finishing line, and decide to go for an end-spurt. No additional subconscious intelligent system needed, just our conscious brain.

* such simplistic tripartite subdivision is for illustration purposes only. Decisions about running speed may vary in frequency depending on tactical considerations and other factors

References

1. Marcora S. Is peripheral locomotor muscle fatigue during endurance exercise a variable carefully regulated by a negative feedback system? J Physiol. 2008; 586(7): 2027-8.

2. Marcora SM. Do we really need a central governor to explain brain regulation of exercise performance? Eur J Appl Physiol. 2008; DOI: 10.1007 s00421-008-0818-3.

3. Marcora SM, Bosio A, de Morree HM. Locomotor muscle fatigue increases cardiorespiratory responses and reduces performance during intense cycling exercise independently from metabolic stress. Am J Physiol Regul Integr Comp Physiol. 2008; 294(3): R874-83.

4. Noakes TD, Marino FE. Arterial oxygenation, central motor output and exercise performance in humans. J Physiol. 2007; 585(Pt 3): 919-21.

5. Wright RA. Refining the Prediction of Effort: Brehm’s Distinction between Potential Motivation and Motivation Intensity. Soc Pers Psychol Compass. 2008; 2(2): 682-701.