Original episode & show notes | Raw transcript
This document provides a detailed exploration of the key topics discussed in the Empirical Cycling Podcast featuring Dr. Andy Coggan. The concepts are organized for clarity and depth, aimed at an educated student audience interested in the science of endurance performance.
Guiding Philosophies and Models of Training
The Nature of Scientific Models: “All Models Are Wrong, But Some Are Useful”
Core Training Proverbs and the Primacy of RPE
The Simplicity of Adaptation: The Two Fundamental Goals of Training
Specific Physiological Concepts
Nitrate Supplementation: Diminishing Returns in Elite Athletes
The Physiology of Aging in Endurance Athletes
Metabolic Regulation: Beyond Simple Thresholds
Fat Metabolism, “FatMax,” and Low-Carbohydrate Strategies
Signaling Pathways vs. Functional Outcomes
Practical Application and Training Theory
VO2 Max Training: The Classic Approach
The “Adaptation Chart”: Understanding Stimulus and Response
The Glycogen Budget: A Framework for Managing Training Load
Race-Day Preparation: A Physiological Hypothesis for “Openers”
The Power Profile Chart: Purpose, Origins, and Evolution
At the heart of the discussion are several overarching principles that frame how we should approach training data and physiological adaptation.
Dr. Coggan repeatedly emphasizes the famous quote by statistician George Box. This principle is critical for understanding the tools used in endurance coaching.
Models are Simplifications: Whether it’s the Performance Management Chart (PMC), WKO’s Power-Duration model, or TSS (Training Stress Score), these are mathematical representations of complex biological processes. They are inherently incomplete and therefore “wrong.”
The Goal is Utility: Their value lies not in being perfectly predictive, but in being useful for making better training decisions.
Model Structure vs. Input Function: A key distinction is made between the input to a model (like TSS) and the model structure itself (the equations that relate the input to a predicted output, like Bannister’s impulse-response model). Dr. Coggan argues that the greatest limitation in predicting performance lies in the model structure, not the specific metric used to quantify training load (TSS, TRIMP, etc.).
The Danger of Over-Parameterization: Complex models with too many adjustable parameters can be made to fit any dataset (the “draw an elephant” analogy). However, they lack predictive power and statistical validity without an enormous number of independent measurements, which is impractical for a single, dynamically changing athlete. This is why a simple model, while “wrong,” is often more useful.
Dr. Coggan champions a pragmatic, athlete-centered approach, encapsulated in two key proverbs:
“If it feels hard, it is hard.”
“All’s you can do is all’s you can do.”
Despite the immense complexity of molecular biology, Dr. Coggan boils down the goals of physiological adaptation to two primary outcomes:
Increase Maximal Force/Power: Making the muscle stronger or more powerful. This is the primary goal of sprinting and resistance training.
Improve Fatigue Resistance: Allowing the muscle to generate a given submaximal force/power for a longer duration.
This second category can be subdivided into fatigue resistance for high-intensity, non-sustainable exercise and for lower-intensity, sustainable exercise. Most endurance training falls into a vast “shade of gray” between pure sprinting and very long, slow distance, where the adaptations are qualitatively similar. This principle argues against overcomplicating training structure, as many different types of workouts can stimulate the desired adaptations.
The conversation delves into several specific areas of exercise physiology, clarifying common misconceptions.
The Mechanism: Dietary nitrate (from sources like beetroot juice) is converted in the body to nitrite and then to nitric oxide (NO), a key signaling molecule that can improve blood flow and metabolic efficiency.
The “Saturation” Effect: Highly trained endurance athletes often see little to no benefit from nitrate supplementation. The theory is that their training itself—which creates shear stress on blood vessels—has already upregulated their endogenous nitric oxide synthase activity. They have higher baseline levels of nitrate/nitrite and have essentially “saturated” the system, meaning external supplementation provides no additional benefit.
Analogy to Creatine: It’s similar to creatine, which the body produces endogenously but can be “topped up” with exogenous supplementation. However, with nitrates, elite athletes appear to have already maxed out their system through training.
Target Populations: The primary beneficiaries are clinical populations (e.g., heart failure patients) or less-trained older individuals, whose nitric oxide bioavailability is often reduced.
“Step-Down” vs. Smooth Decline: While population data shows a smooth, gradual decline in physiological function with age, an individual’s experience is often a series of “step-downs” caused by events like injury, illness, or major life changes (e.g., a new job).
Central Limitation is Key: The primary reason for the decline in aerobic performance with age is attributed to the cardiovascular system (the “central pump”), not the skeletal muscle (the “periphery”).
VO2 Max Declines Most: VO2 max decreases more significantly than the lactate threshold. This means an older athlete’s threshold occurs at a higher percentage of their VO2 max (e.g., 85% in an older athlete vs. 75% in a young one). They can still “grind,” but they have a much smaller reserve capacity above their threshold. This was illustrated by the extreme example of heart failure patients whose threshold could be at 100% of their VO2 peak.
A Continuous, Exponential Process: Dr. Coggan argues strongly against the idea of distinct metabolic “thresholds” (like LT1 and LT2). He posits that metabolic responses to increasing exercise intensity—such as blood lactate accumulation—are best described by a smooth, exponential curve, not a series of breakpoints.
“Threshold” is a Concept, Not a Point: The term “lactate threshold” is a useful mental convenience that exercise physiologists understand conceptually, but there is no single, universally agreed-upon definition because a true physiological “threshold” doesn’t exist in the way it’s often portrayed.
Interconnected Responses: Many physiological markers show this same exponential behavior around the same intensity range: blood lactate, plasma glucose, and epinephrine all begin to rise non-linearly. They are all interconnected parts of the same systemic stress response.
FatMax is a Redundant Concept: The intensity at which fat oxidation is maximal (“FatMax”) is inversely related to carbohydrate oxidation. Since rising carbohydrate use (and thus lactate production) is correlated with threshold, FatMax is also highly correlated with it. Therefore, measuring FatMax provides little new information.
You Don’t Train Fat Burning by Burning Fat: The popular idea that you must train at low intensities to “teach” your body to burn fat is a fallacy. The primary driver for improving the muscle’s capacity to oxidize fat is training volume and overall fitness. High-intensity training is a potent stimulus for improving mitochondrial respiratory capacity, which enhances the ability to oxidize all fuels, including fat.
The Downside of Fuel Restriction: Deliberately restricting carbohydrates (e.g., fasted training, “sleep low”) may compromise the ability to perform high-quality training. The primary goal is to support the highest possible training load to drive performance. Dr. Coggan’s advice is clear: “Train for performance, let your physiology sort itself out.”
Don’t Mistake the Signal for the Adaptation: A common mistake in modern sports science is to focus on acute changes in molecular signals (like PGC-1α or muscle protein synthesis) and assume they will predict long-term performance gains.
The Correct Approach: The scientific process should start with a functional outcome (e.g., improved performance from a specific training program). Only then should one investigate the underlying signaling pathways to understand why it worked. Acute signaling responses are not reliably predictive of chronic adaptation.
The discussion concludes with the application of these physiological principles to real-world training scenarios.
The Hickson Protocol: The discussion references the classic study by Hickson et al., which produced a massive 44% increase in VO2 max in subjects. The protocol was brutally simple: 6x5 minute intervals at a constant, maximal-sustainable power (isopower), three days a week.
RPE as the Guide: The intensity is determined by what is repeatable. Dr. Coggan’s personal metric for finding the right intensity was that he would “crack” on the last interval about half the time. This is a maximal, but repeatable, effort.
Origin and Purpose: This well-known chart was created to visually represent which types of training provide the most potent stimulus for specific physiological adaptations per dose.
Example: Capillarization: Contrary to old myths that high intensity would “damage” new capillaries, the chart correctly reflects the modern understanding that high-intensity exercise—which creates high shear stress—is the most potent stimulus for angiogenesis (new capillary formation). This is supported by the fact that middle-distance runners (1500m) tend to have higher VO2 max values than marathoners.
Still Valid: Dr. Coggan asserts that he would not change anything on the chart today, as it is a conceptual guide based on a vast body of scientific literature.
A Finite Capacity: Athletes have a limited ability to handle physical strain. This can be conceptualized as a “glycogen budget.”
Spend it Wisely: The goal of a smart training plan is to “spend” this budget on the most effective and specific training possible. You want to do as much of the right type of training as you can handle, but no more. Doing too little leads to suboptimal performance; doing too much leads to breakdown.
The Problem: Athletes often experience sluggish, “puffy” legs if they go hard at the start of a race without an adequate warm-up.
The Hypothesis: Glycogen Phosphorylase Regulation:
At the onset of intense exercise in rested muscle, there is a massive, calcium-driven “flash activation” of the enzyme glycogen phosphorylase, leading to a huge burst of glycogenolysis (glycogen breakdown) and lactate production.
Very quickly, this system becomes refractory (less responsive). A subsequent sprint will not elicit the same dramatic spike in glycogenolysis.
“Openers”—a short session with high-intensity bursts the day before a race—or a thorough warm-up on race day serves to “trim the tree.” It triggers this initial flash activation and makes the system refractory, preventing an overwhelming metabolic shock at the start of the race. This leads to smoother sensations and better performance from the gun.
The Need: With the advent of power meters, athletes needed a way to contextualize their numbers.
The Method: The chart was created by:
Pegging the top of each column (5s, 1min, 5min, FT) with data from world-class athletes and world record holders.
Pegging the bottom with data from average, untrained but healthy individuals.
Spreading the values equally in between.
The True Purpose: Identifying Strengths & Weaknesses: The chart was never intended to predict an athlete’s race category. Its purpose is to show an athlete’s relative strengths and weaknesses by looking at the shape of their profile. A rider high on the 5-second column and lower on the FTP column has a sprinter’s profile. The advice remains: “If you want to know how good a bike racer you are, go race your bike.”
Evolution and Obsolescence: Dr. Coggan views the original tables as largely obsolete, having been supplanted by the full Power-Duration Curve generated by modeling software like WKO. This modern approach provides a complete profile rather than just a few discrete points.