Philip Batterson is a staff physiologist for Moxy Monitor. He is finishing a Ph.D. in molecular exercise physiology where he explores how muscles (more specifically mitochondria) adapt to dietary and exercise interventions. His master's degree is in biology where he focused on applied physiology and predictors of endurance performance. This where he first used near-infrared spectroscopy (NIRS) to show that NIRS derived skeletal muscle oxidative capacity was the single best predictor of 40km cycling performance in highly trained cyclists. 

Andri Feldmann is a staff physiologist for Moxy. He’s been involved with Moxy since the first prototypes were developed as an extension of the work done by his father, Juerg Feldmann. Juerg was a pioneer of using Near Infrared Spectroscopy for training athletes, using medical versions of the technology before Moxy was developed. As a teenager, Andri was often a test subject of Juerg’s and his constant exposure to Juerg’s ideas led him to pursue studies in sports science and human kinetics at the University of British Columbia.

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.

An article published in the Journal of Applied Physiology on April 29^th shed light on some critical applications for the use of near-infrared spectroscopy (NIRS) in endurance sport – The Balance of Muscle Oxygen Supply and Demand Reveals Critical Metabolic Rate and Predicts Time to Exhaustion

Multimodal Fitness or High Intensity Functional Training (HIFT) has gained a considerable following in the past few years. It provides a class based setting that aims to improve metabolic conditioning, strength, and gymnastic ability, amongst other things. Over the first quarter of the year, I have been attending classes. Admittedly, I was a bit skeptical at first but have grown to love the challenge of the different movements and ability to scale any movement in order to complete the workout. One of the major challenges that people going to these classes, myself included, face is the ability to properly pace yourself throughout the entire workout. While some very experienced athletes know that they can complete X number of body weight squats with Y amount of rest, this pacing gets way more challenging when you couple squats with burpees, or other movements.

5-1-5 assessments can be used to estimate the system that is most limiting to an athlete’s performance. These systems include 1) pulmonary, the lung’s ability to uptake, and transfer oxygen to the blood 2) cardiac, the heart’s ability to deliver oxygen rich blood to the muscle and get rid of metabolites and 3) skeletal muscle, the mitochondria’s ability to utilize oxygen in the working muscle Whole-body exercise requires multifaceted integration of biological systems in order to sustain locomotion, if one of these systems is inadequate then fatigue is imminent. In this post, I want to take a deeper dive into what could be responsible for a pulmonary limitation and what could be leading to fatigue in athletes with this limitation, a few papers are cited but the main one spurring this post is by Dempsey et al. 2006.

VO2max, thresholds, and efficiency are thought to be and certainly do have considerable predictive power for endurance performance. They are great global variables to measure and monitor how an athlete is coping with the stress of long endurance athletics. However, most metabolic devices are bulky, require uncomfortable mouth pieces, backpacks, or other restrictive equipment which can interfere with an athlete’s performance. They also fail to directly measure the stress of arguably the most important organ during exercise, the skeletal muscle. Recently, the ability for skeletal muscle to utilize oxygen measured by near-infrared spectroscopy (NIRS) has been shown to the best predictor of endurance performance in trained cyclists. Therefore in order to gain the best picture of an athlete’s physiology, these global measures should be accompanied by devices that can measure local stress.

My journey into physiological testing and program design started approximately 20 years ago when working with ironman triathletes, skiers, cyclists and mountain bikers. All around me were coaches prescribing general triathlon programming without knowing their athlete’s specific performance limitations. How can I prescribe an athlete specific program to improve performance without identifying their limitation(s)?

Introduction. Over the last few weeks a case study of 5 weeks of high intensity interval training has been completed. To refresh your memory, the athlete, a well-trained cyclist, completed five consecutive weeks of 5-10 reps of 30-40s at ~600w (30-60s rest). The athlete was able to complete at least 5 intervals per training session, however during workout 4 only 5 intervals were completed and there was a large discrepancy in how well the athlete could desaturate leading to a truncated workout, and ultimately and much better workout the following week. In this post I want to touch on some things to be aware of as a coach using Moxy, as well as some practical applications using the data. These applications will include recommendations for warm-ups, knowing when to stop an athlete’s workout.

Introduction. In the last blog post I outlined how Moxy was used over the course of five weeks to inform an athletes training. More specifically, the athlete, a well-trained cyclist, completed five consecutive weeks of 5-10 reps of 30-40s at ~600w (30-60s rest). The athlete tracked power output, heart rate, and SmO2. They allowed SmO2 to recover to at least 60% before starting the next interval. While its been suggested that Moxy could be a good tool to help autoregulate workouts there haven’t been too many case studies relating to how Moxy values change over time. In this post, I want to walk-through how SmO2 and power trends changed over the course of five weeks of concerted training. Before diving into the details I want to acknowledge that five weeks may not be adequate time to truly monitor chronic adaptations to exercise in trained athletes, more than likely, what these training sessions will help us understand is how things, like acute training load and stress can affect the outcome of a work.