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Control your intensity: daily application of muscle oxygenation monitoring.

How long should I train? How hard should I train? How often should I train and what will the consequences of my training be in terms of successful performance gains or inevitable signs of fatigue. These are important questions an athlete must ask themselves during every phase of training and planning. The summation of these questions is: what training load am I placing on my body, and how much do I need for optimal development? Training load simply stated is the cumulative amount of stress placed on an individual from a single or multiple training sessions over a given period of time; and is represented as a product of two factors: 1) training duration and 2) training intensity. Perhaps, the most widely cited model to address training load in a practical sense is Eric Banisters training impulse or TRIMP model (1991). The model applies training intensity and training duration metrics to assess training load in terms of both performance and fatigue. It is evident that both duration and intensity are relevant and should be monitored. I would recommend a review by Shona Halson (2014), for more insight into all the possible metrics, and a series of papers by Peter Hofmann on the relevant effect of varying both intensity and or duration on training load (2017, 2022). Duration and intensity are both measurable parameters. While by no means, less relevant and often under evaluated (see the 2022 paper by Birnbaumer, Hofmann et al.), duration is generally a much more straightforward measure. Deciding the number of minutes or hours to train can of course be challenging, but the metrics are simple – seconds, minutes, hours. Intensity on the other hand is not as straight forward. It is self-evident that intensity effects performance and fatigue. Simple evaluation of a power or speed duration curve illustrates the non-linearity of the relationship, and thereby the effect of both intensity and duration on performance and fatigue. But how should you measure intensity? A general split can be made using either internal or external metrics. External meaning physical output, for example power or speed. Internal meaning physiological work or input to generate a physical output, for example heart rate or ventilation, or as per subject of this article muscle oxygenation (SmO2). External metrics are objective and extremely useful. They are the measurement that defines success. To clarify, the winner of a running race is the athlete who can run the fastest for the duration of the event – cover the distance in the shortest amount of time. In other words, speed is the defining performance metric. However, the problem with external metrics is that the human body does not function like a machine, and performance can vary significantly from day-to-day, or between environments. This is true for competition, but also for daily training. If I define my training intensity solely on external metrics, for example watts using my power meter, it will function irrespective of my individual physiological state today. My 200-watt intensity ride will always demand 200 watts, but my internal load may vary significantly when attempting to generate the same 200 watts. Coupling this very important external load metric with an individual internal load metric can help us understand our performance (the external load) and potential fatigue. An internal load, understood in context to external load, will optimise intensity control and thereby training load.

The question now is what internal load metrics we should use. Numerous options exist, with pros and cons. When we talk about intensity control, we talk about training in different intensity domains. The well-accepted split, which fits most classic and contemporary models, is the split of exercise intensity into the domains of moderate, heavy, and severe intensity domains, with two relevant thresholds between the three domains. These domains are derived from cardiopulmonary response during exercise to assess exercise sustainability and physiological stability. The thresholds are roughly estimated using general concepts of blood sampling and ventilatory thresholds 1 and 2. So how do we ensure that we are applying the proper intensity to our training using internal metrics. Well, the most obvious choice would be to apply the very tools used to determine the intensity domains and thresholds directly to training. So, we could use metabolic carts and blood sampling during daily training to ensure we are training at the proper intensity. Anyone who uses these tools will quickly identify the countless hurdles involved in this process, not the least being, the excessive discomfort to the athlete which will bring into question its consistent use over an extended period of time. The next internal metric, which checks all the boxes around practicality, is heart rate. Heart rate is a metric that has been used widely and successfully, appropriately so. However, apart from a few tests, not to be discussed here, heart rate is used as a surrogate for the other testing metrics mentioned earlier. This means heart rate is not the actual relevant parameter to differentiate exercise intensities, rather an attempt to use an internal metric when deriving training zones from gas exchange or blood sampling. This is the impasse many trainers and athletes are faced with, and the pitfall of proper intensity control when assessing training load. The tool needed to solve this impasse, needs the practicality of heart rate with the internal metric specificity provided by gas exchange or blood lactate.

 Welcome near-infrared spectroscopy (NIRS) and the SmO2 metric. NIRS monitors in the 21st century come in compact, wireless units providing a non-invasive accurate measure of oxygen supply and demand to the muscle. The dynamic shift of oxygen supply and demand as a relevant performance metric line up both theoretically and empirically to classical intensity domain metrics. The importance of oxygen uptake, and the notion of oxidative dependent and independent energy sources is encompassed by the concept of oxygen supply and demand. More importantly, SmO2 breakpoints have been shown to agree with both gas exchange and blood sampling results for exercise thresholds (Grassi et al., 1999; Feldmann et al., 2022 ;Yogev et al., 2022). SmO2 is an emerging internal metric that can be applied to directly determine intensity of every training, thereby optimising training load. How, exactly does this work? The concept is relatively simple and has to do with the notion of oxygen supply and demand being represented in the SmO2 signal. The basic understanding has to do with SmO2 rate. If you have a positive SmO2 rate, that means your SmO2 value is increasing, you are clearly in a situation of more oxygen supply than demand. If you have a negative SmO2 rate, the opposite, you are clearly in a situation where oxygen demand is greater than supply. And a general zero slope or zero rate of change of SmO2 would imply steady state. This information, on its own, with a NIRS monitor should be enough go out and experiment with NIRS based intensity control. However, as with any physiological metric there will be questions and nuance. SmO2 responds very rapidly, so initial change in SmO2 will almost always be negative at the onset of exercise. Energy demand of the muscle is instantaneous, but cardiopulmonary work to supply oxygen has a start-up delay. So, when assessing SmO2 rate for a given performance, expect a delay. Of course, heart rate also has a delay, relevant is that the SmO2 delay, or response is much more rapid and more accurate than heart rate – I would refer you to the very prudent titled publication: Near-Infrared Spectroscopy: “More Accurate Than Heart Rate for Monitoring Intensity in Running in Hilly Terrain” (Born et al., 2017). Because SmO2 functions on a 0-100% scale, there is an SmO2 cap, at which you can no longer discern between a positive rate or a zero rate of change. In these situations, it is important to understand general body feedback (how “hard” is this speed?), or even better using external metrics like power to relate the SmO2 rate back the relevant external metric. Combining the external and internal metric will provide substantial information about intensity and daily form. Finally, how does SmO2 relate to the intensity domains discussed; isn’t that the most relevant question if SmO2 rate is to be a useful tool for intensity control? A recent paper by Kirby et al. (2021), directly responds to this question. The maximum intensity at which you can maintain a zero SmO2 rate, separates the heavy from the severe intensity domain, a target I call critical oxygenation (Feldmann & Erlacher, 2021). This simple concept allows you to easily identify the shift between the heavy to severe intensity domains on a daily basis using a simple NIRS monitor. This is the first step to applying SmO2 to intensity control. For more information see the new Moxy Monitor eBook on intensity control; and feel free to send me questions at andri@moxymonitor.com.


Birnbaumer, P., Weiner, L., Handl, T., Tschakert, G., & Hofmann, P. (2022). Effects of Different Durations at Fixed Intensity Exercise on Internal Load and Recovery&mdash;A Feasibility Pilot Study on Duration as an Independent Variable for Exercise Prescription. Journal of Functional Morphology and Kinesiology 2022, Vol. 7, Page 54, 7(3), 54. https://doi.org/10.3390/JFMK7030054

Born, D.-P., Stöggl, T., Swarén, M., & Björklund, G. (2017). Near-Infrared Spectroscopy: More Accurate Than Heart Rate for Monitoring Intensity in Running in Hilly Terrain. International Journal of Sports Physiology and Performance, 12(4), 440–447. https://doi.org/10.1123/ijspp.2016-0101

Feldmann, A., & Erlacher, D. (2021). Critical oxygenation: Can muscle oxygenation inform us about critical power? Medical Hypotheses, 150, 110575. https://doi.org/10.1016/j.mehy.2021.110575

Feldmann, A., Ammann, L., Gächter, F., Zibung, M., & Erlacher, D. (2022). Muscle Oxygen Saturation Breakpoints Reflect Ventilatory Thresholds in Both Cycling and Running. Journal of Human Kinetics, 83(1), 87–97. https://doi.org/10.2478/hukin-2022-0054

Grassi, B., Quaresima, V., Marconi, C., Ferrari, M., & Cerretelli, P. (1999). Blood lactate accumulation and muscle deoxygenation during incremental exercise. Journal of Applied Physiology, 87(1), 348–355. https://doi.org/10.1152/jappl.1999.87.1.348

Halson, S. L. (2014). Monitoring training load to understand fatigue in athletes. Sports Medicine (Auckland, N.Z.), 44 Suppl 2(Suppl 2), 139–147. https://doi.org/10.1007/S40279-014-0253-Z

Hofmann, P., & Tschakert, G. (2017). Intensity- and duration-based options to regulate endurance training. Frontiers in Physiology, 8(MAY), 337. https://doi.org/10.3389/FPHYS.2017.00337/BIBTEX

Kirby, B. S., Clark, D. A., Bradley, E. M., & Wilkins, B. W. (2021). The balance of muscle oxygen supply and demand reveals critical metabolic rate and predicts time to exhaustion. Https://Doi.Org/10.1152/Japplphysiol.00058.2021, 130(6), 1915–1927. https://doi.org/10.1152/JAPPLPHYSIOL.00058.2021

Morton, R. H., Fitz-Clarke, J. R., & Banister, E. W. (1990). Modeling human performance in running. Journal of Applied Physiology, 69(3), 1171–1177. https://doi.org/10.1152/JAPPL.1990.69.3.1171

Yogev, A., Arnold, J., Clarke, D., Guenette, J. A., Sporer, B. C., & Koehle, M. S. (2022). Comparing the Respiratory Compensation Point With Muscle Oxygen Saturation in Locomotor and Non-locomotor Muscles Using Wearable NIRS Spectroscopy During Whole-Body Exercise. Frontiers in Physiology, 13, 483. https://doi.org/10.3389/fphys.2022.818733

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