Original episode & show notes | Raw transcript
This document breaks down the core concepts discussed in the podcast featuring Kolie Moore and Marinus Petersen. The conversation highlights a crucial tension in sports science: the gap between academic research and real-world athletic performance. It encourages a healthy skepticism and a nuanced, critical approach to both scientific literature and coaching dogma.
A central theme is the disagreement, even among top physiologists, about how to define and utilize exercise intensity thresholds. The podcast contrasts the academic view with practical coaching experience.
This model, often taught in universities, simplifies training into three distinct zones based on physiological markers:
Zone 1 (Low Intensity): From rest up to the First Lactate Threshold (LT1).
Physiology: This is a fully aerobic state where lactate production and clearance are in a low-level, stable equilibrium. It corresponds to easy, conversational riding.
Academic Prescription: The consensus is that elite athletes should perform the vast majority of their training volume in this zone to build a robust aerobic base without accumulating excessive fatigue.
Zone 2 (Moderate Intensity / “The Grey Zone”): The area between LT1 and the Second Lactate Threshold (LT2) or Critical Power (CP).
Physiology: Lactate levels rise above baseline but can still be stabilized for a prolonged period. This zone includes what cyclists call “Tempo” and “Sweet Spot.”
Academic Prescription: The podcast highlights that this zone is often controversially dismissed by academics as generating significant fatigue for little additional adaptive benefit compared to high-intensity work. It’s sometimes pejoratively called “pointless rubbish.”
Zone 3 (High Intensity): Any intensity above LT2 or Critical Power.
Physiology: This is the “severe intensity domain.” Oxygen consumption (VO2) will rise to its maximum (VO2max), and metabolic homeostasis cannot be maintained. Fatigue accumulates rapidly, making efforts in this zone unsustainable for long.
Academic Prescription: This is where the “magic adaptations” are said to occur. High-Intensity Interval Training (HIIT) is prescribed to maximize adaptive signaling in minimal time.
The Problem with LT2: Marinus notes that while LT1 is a relatively consistent physiological marker, LT2 is “highly controversial.” There are dozens of different definitions and protocols to measure it (e.g., MLSS, 4 mmol/L fixed value, Dmax method), leading to a wide range of results. This makes it a less reliable anchor for training.
The Power of the “Grey Zone”: Contrary to the academic dismissal, both speakers’ coaching experience shows that training in Zone 2 (Threshold, Tempo) is highly effective for improving performance, particularly an athlete’s “Functional Threshold Power” (FTP) and fatigue resistance.
A Practical Definition of Threshold: The host, Kolie, proposes a simpler, more functional definition derived from an athlete’s power-duration curve: The threshold is the inflection point where fatigue begins to accelerate dramatically. Below this point, you fatigue slowly; above it, you fatigue rapidly.
Critical Power is a theoretical concept representing the highest power output that can be sustained without depleting a finite energy reserve known as W′ (pronounced “W prime”).
The Controversy: The podcast mentions a paper highlighting the flaws in CP calculation. The result is highly dependent on the duration of the test efforts used. When longer test durations are included, the calculated CP value tends to drop, often settling at 95-99% of the power from the longest test. This suggests it may be more of a mathematical artifact than a fixed physiological boundary.
A Useful Analogy: A practical way to view CP is as a theoretical “performance ceiling,” similar to VO2max. In contrast, FTP represents what an athlete can functionally use in the real world. Training, particularly in the middle-intensity zones, can bring FTP closer to the CP ceiling.
The discussion centers on a paper, “The bias for statistical significance in sport and exercise medicine,” which reveals a systemic issue in how scientific results are published.
The Ideal (Normal Distribution): In an unbiased world, the results of all scientific studies on a given topic would form a bell-shaped curve.
The center of the bell would be a large number of studies finding no statistically significant effect. This is the null hypothesis—the default assumption that there is no relationship between the variables being tested.
The thin tails of the curve would represent the small number of studies that, by chance or because of a true effect, find a statistically significant result (typically where the probability, or p-value, is less than 0.05).
The Reality (The “Two Humps” Distribution): The paper shows a completely different picture. The distribution of published results has a huge valley scooped out of the middle (where null results should be) and two large, suspicious spikes located just at the threshold of statistical significance (p < 0.05).
This skewed distribution provides strong evidence for two major problems:
Publication Bias: Journals, funders, and researchers have a strong preference for “positive” results. Studies that find a new supplement “works” or a training method is “effective” are considered more exciting and are more likely to be published. Studies finding no effect (null results) are often shelved and never see the light of day, even though they are scientifically valuable.
P-Hacking: This refers to the conscious or unconscious manipulation of data or analytical methods to achieve a statistically significant result.
How it’s done: A researcher might keep recruiting participants until the p-value dips below 0.05, try different statistical tests, remove “outlier” data points, or retroactively change the study’s hypothesis.
The Result: This practice pollutes the scientific literature with findings that are likely false positives—effects that were found by chance but are presented as real.
The podcast mentions a proposed fix for this problem. With registered reports, scientists submit their research question, methodology, and analysis plan for peer review before they collect any data. If the plan is deemed scientifically sound, a journal agrees in principle to publish the results, regardless of what they turn out to be. This removes the incentive to p-hack and ensures that valuable null results are published.
The final part of the discussion bridges the gap between these academic issues and the practical decisions a coach or self-coached athlete must make.
Science as a Starting Point: Scientific literature is an invaluable tool for forming an “educated guess.” It prevents wasting time on methods with no plausible mechanism and points you in the right direction.
The Lag of Science: If you wait for a concept to be rigorously proven by a meta-analysis, you are likely 10-20 years behind the curve. The example given is creatine: elite athletes were using it to win Olympic medals decades before it was universally accepted by science.
The Primacy of Real-World Results: If a training method is clearly working for you (e.g., “I’m getting 1% stronger every week”), you should continue doing it, even if some studies suggest it’s “sub-optimal.” Conversely, if a scientifically-backed method is not working for you, you must be willing to abandon it. The ultimate test is your own performance.
Be Critical of Everything: Question the methods of studies. Who were the subjects (untrained individuals respond to anything)? Was the sample size large enough to be meaningful? Could there be publication bias at play?
There is No Silver Bullet: Cycling success requires a huge range of physiological abilities. The idea that one type of training (polarized, HIIT, sweet spot) is the “best” is flawed. The most successful athletes, over the long term, do everything.
Understand the “Why”: Don’t just follow trends. When you hear about concepts like “lactate clearance,” dig deeper. Lactate is a fuel, not a fatigue-inducing waste product. The primary driver of its utilization is mitochondrial density, which is best built with high training volume and a strong aerobic base, not fancy “lactate clearance” intervals.
Test, Don’t Guess: To know if your training is working, you need a consistent and controlled method of testing. This helps you break through plateaus by providing objective feedback, moving beyond what theory says should work to what actually works for you.