The Right Pace (EN)

From Metrics to Intuition: Deconstructing Five Intensity Indicators and Your Physiological Map

Wed, 15 Jul 2026 00:00:00 GMT

Most runners have used at least two monitoring tools: heart rate, pace, power, or RPE (Rate of Perceived Exertion).

Many assume they measure different things. In reality, they all answer the same question:

Is my training intensity right now actually landing in the target zone?

The difference is that each tool estimates this from a different angle.

Heart rate reflects the body’s internal response. Pace represents external performance. Power represents mechanical output. Blood lactate directly describes metabolic stress. RPE integrates everything the body is feeling.

No single metric can fully describe training intensity. What actually matters is understanding what each one represents — and when each one lies.

RPE: The Calibration Layer Above Data

RPE is not a fallback for when you have no devices. It’s the calibration tool for all objective data.

Within a matter of minutes, the brain integrates neuromuscular recruitment, respiratory load, sleep quality, accumulated fatigue, and immune status into a composite signal that no sensor can directly measure.

The value isn’t RPE itself — it’s whether RPE aligns with the data.

The same pace feeling unexpectedly hard might indicate fatigue, dehydration, or insufficient recovery. An abnormally high heart rate with a normal perceived effort might just be cardiovascular drift caused by heat. RPE can’t tell you where the fatigue is coming from, but it can tell you today’s body is different from usual.

Heart Rate: Reliable — But Not Always

Heart rate is the most widely used physiological monitoring tool among runners, and the earliest to be systematically applied in the history of endurance training. Continuous, non-invasive, real-time. During steady-state aerobic training, it reliably reflects the body’s internal load — which is why it has remained central to training for decades.

It has two reliable long-term applications: tracking submaximal exercise heart rate (HRex) at the same pace to gauge aerobic adaptation, and using resting HRV (the variation in beat-to-beat intervals, an indirect marker of autonomic recovery) to detect acute fatigue. Both work well in low-intensity steady-state aerobic training.

But heart rate gives false information in three scenarios.

Heat and Heat Acclimatization

The heart is simultaneously managing thermoregulation and oxygen delivery, driving heart rate up. More counterintuitively, once heat acclimatization is complete, plasma volume increases — and HRV may paradoxically rise even when the athlete is more fatigued and pace is declining. Buchheit tracked athletes through a desert ultramarathon: after day four, HRV values looked “well-recovered,” but pace and subjective fatigue continued to deteriorate. Relying on HRV alone would lead to completely wrong decisions.^[1]

Saturation Effect During High Training Volume

In elite athletes during high-volume phases, vagal tone is extremely high, and HRV markers may actually drop due to receptor saturation. That decline doesn’t signal fatigue — it signals that the heart has highly adapted. But almost everyone reads it as “poor condition” and mistakenly reduces training load.

Neuromuscular Fatigue and Glycogen

Neuromuscular fatigue (the repair stress following muscle damage) and glycogen (stored muscle energy) both directly affect running performance, but heart rate has no awareness of either. Post-race leg soreness and serious muscle damage can coexist with completely normal HRV. Running on depleted glycogen gives no warning signal in heart rate either.

There’s also a structural problem: heart rate lags behind intensity changes by 30–60 seconds. A 30-second hill sprint ends, and heart rate only peaks afterward. The number you’re reading no longer represents the intensity you just ran.

Pace: Measures Outcome, Not Stress

Pace tells you how fast you covered the distance, given your current environment and body state. It’s the outcome — not the metabolic stress itself. The same 5:00/km pace on a cool, flat spring road and a hot afternoon climb represent completely different intensities for the body.

On flat, constant-grade terrain, the correlation between pace and power reaches R²=0.97.^[2] With minimal wind and consistent grade, watching pace and watching power are nearly equivalent — which is why pace suffices in flat conditions.

But once grade, headwind, heat, or altitude enters the picture, that equivalence breaks down. Maintaining pace on a climb might push metabolic stress into threshold territory, while pace just shows “slightly slower.” In the second half of a marathon, many runners fade — often not because of pacing strategy errors, but because surface pace in the first half gave the impression of controlled intensity while heart rate and lactate had already crept too high.

Pace is most reliable for flat race simulations (confirming that target finish speed is executable) and recovery run intensity confirmation (ensuring effort stays below threshold). In both cases, environmental variables are minimal, and pace is a trustworthy approximation of power.

Power: The Closest Tool to Real-Time Intensity in Racing

Power bypasses heart rate’s lag, pace’s terrain dependency, and RPE’s subjectivity. It is currently the most objective real-time monitoring tool for training intensity.

Power’s core advantage is its immediate reflection of the mechanical output an individual applies to the ground. It eliminates the 30–60 second lag of the cardiovascular system and is unaffected by terrain grade. In practice, algorithms like Stryd have built grade compensation into their calculations — the moment a runner begins climbing, power immediately captures the additional metabolic burden, and drops back instantly at the crest.

This real-time objectivity matters most during long-distance racing. Heart rate during a race typically follows a characteristic physiological trajectory: low at the start (sympathetic lag), rising to a stable value, then continuing to climb in the second half due to hyperthermia and dehydration-driven cardiovascular drift. Pacing by heart rate zone at this point will force the runner to slow down — even when the heart rate rise is largely unrelated to actual physical output intensity.

Power, by contrast, demonstrates extremely high physiological stability and repeatability. Van Rassel’s research found that during 30 minutes of exercise at MLSS (maximum lactate steady state) intensity, power deviation was just 0.1%, while VO₂ drifted +50 mL/min under the same conditions. Tests repeated days apart showed an intraclass correlation coefficient (ICC) of 1.00 for power.^[3]

This means that once a target power is established through threshold testing, maintaining that output on race day lets pace self-optimize for wind and grade — eliminating the miscalculations of chasing pace or being misled by second-half cardiac drift.

Blood Lactate: Building Your Physiological Map

The previous four tools share the same problem: they estimate intensity. The estimation is indirect. Blood lactate is not.

Draw a drop of blood at a given pace, and you know directly whether the metabolic stress that pace is imposing on your body is high or low — whether you’re approaching a critical threshold. No lag, no environmental noise, no subjective component.

The core value of lactate monitoring isn’t pinpointing a single 4 mmol/L threshold. It’s using samples across multiple pace points to map out your personal lactate curve. Once you know your lactate concentration at each pace, you have a map that precisely links speed to metabolic stress — making daily training intensity execution something measurable rather than guessed.

The 4 mmol/L universal threshold is an oversimplification: untrained individuals may fall at 2–3 mmol/L; elite aerobic athletes can sustain 7–8 mmol/L. Of the 25 threshold definitions in the literature, MLSS correlates most strongly with performance,^[4] but testing it is cumbersome. A complete personal lactate curve makes the debate over which number to use irrelevant — you already have the full picture.

Blood lactate is also the most direct scientific evidence for tracking training adaptation. The same pace showing significantly lower lactate several months later is concrete proof of improved aerobic capacity — more convincing than HRV estimates or VO₂max calculations.

Though the cost of equipment and trained administration limits daily use, it works well as a once- or twice-yearly baseline test: build the curve, calibrate your training zones, then use more convenient tools for daily tracking. That combination delivers both precision and practicality.

The Dimensions of Data, the Gradations of Feel

These five monitoring tools aren’t competing alternatives. They’re a layered, complementary system — each providing a different resolution of the same physiological reality.

Within this system, RPE is the irreplaceable foundation.

RPE is the composite output of the central nervous system, metabolic state, sleep quality, and recovery status — no objective metric can be interpreted without it. The core skill of advanced training monitoring is the ability to detect the gap between perceived effort and data: when power or pace diverges from RPE, the gap itself is a physiological signal. It may foreshadow hidden fatigue, heat stress, or immune disruption. Data provides coordinates; body sense provides context. Neither is sufficient alone.

Building this map takes long-term accumulation — the same principle as lactate testing: establishing a reference between physiological stress and objective output. The difference is only in resolution.

In the high-stakes environment of racing, this map is worth the most. When the first half triggers an adrenaline-fueled sense of effortless momentum, only runners who have deeply internalized this map can make rational intensity decisions at the critical junctures.

Within the overall training architecture: heart rate tracks long-term aerobic adaptation; pace translates to intuitive execution language on flat terrain; power locks in real-time output for threshold training and racing; blood lactate calibrates all zones in one or two annual baseline tests. And RPE runs in the background throughout — continuously checking whether feel and numbers are aligned.

Blind faith in data turns you into a slave to the instruments. It’s only by staying alert to the gap between metrics and sensation that this physiological map gains a soul.

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References

1. Buchheit M. Monitoring training status with HR measures: do all roads lead to Rome? Front Physiol. 2014;5:73. ↗
2. Aubry RL, Power GA, Burr JF. An assessment of running power as a training metric for elite and recreational runners. J Strength Cond Res. 2018;32(8):2258–2264. ↗
3. van Rassel CR, Sales KM, Macpherson REK, et al. Running power is more sustainable than heart rate for monitoring long duration exercise intensity. Front Physiol. 2021;12:682333. ↗
4. Faude O, Kindermann W, Meyer T. Lactate threshold concepts: how valid are they? Sports Med. 2009;39(6):469–490. ↗

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Your Achilles Tendon Isn't Just a Spring

Tue, 07 Jul 2026 00:00:00 GMT

At the moment of landing, your Achilles tendon bears a force roughly six to eight times your body weight. That number usually shows up in injury-prevention discussions — but it points to something more interesting: the Achilles tendon doesn’t just absorb impact. It’s one of the primary determinants of how efficient your running is.

The usual explanation is the spring model: the tendon stores elastic energy on landing and releases it during push-off. That’s true, but it undersells the real story. The Achilles tendon’s most important contribution to running economy isn’t energy recycling — it’s changing how the muscles work, keeping them in a lower-cost operating state at every single step.

The Tendon Doesn’t Generate Force — It Transmits It

The Achilles tendon connects the calcaneus to the soleus and gastrocnemius in a series arrangement. In a series connection, force is equal throughout the chain at any given moment — whatever force the muscle generates, the tendon bears.

The tendon has no contractile tissue. It can’t add force. So how much elastic energy it stores depends on exactly two things: how much force the muscle produces, and how much the tendon deforms under that force.

But chasing maximum energy storage isn’t the goal. The tendon’s more consequential function is absorbing the length change of the entire muscle-tendon unit — so the muscle fascicles themselves barely have to move.

The Tendon Moves So the Muscle Doesn’t Have To

The intuitive picture of running is force transmission: muscle contracts, pulls tendon, rotates joint, pushes body forward. Ultrasound imaging has revealed something more sophisticated.

In 2001, Fukunaga and colleagues used real-time ultrasound to track fascicle behavior in the medial gastrocnemius during walking. Throughout the stance phase, fascicle length barely changed — only minor variation. The Achilles tendon, meanwhile, lengthened roughly 7 mm and snapped back during push-off.

The muscle-tendon unit was changing length. But the tendon was absorbing nearly all of it.

Why does this matter? Because concentric contraction — muscle shortening — is the most metabolically expensive mode of muscle work. If the tendon absorbs the length change, the fascicles can remain near isometric: continuously generating force without extensively shortening. Roberts and colleagues synthesized multi-species data in 2002 to make this explicit: tendons allow muscles to operate at their own preferred velocity even while the whole muscle-tendon unit is changing length rapidly. The muscle generates force; the tendon manages length and stores energy.

Contraction Velocity Determines Metabolic Cost

The reason near-isometric work is so valuable is that muscle metabolic cost is tightly coupled to contraction velocity.

In 1997, Ryschon and colleagues measured efficiency of human skeletal muscle directly in vivo across different contraction modes. Concentric contraction — shortening — had the highest metabolic cost. Isometric contraction had the lowest. Eccentric contraction (lengthening while generating force) was substantially cheaper than concentric.

Faster shortening means more ATP consumed. Running requires generating force repeatedly at every step — if every step demands rapid muscle shortening, the metabolic tab climbs. The Achilles tendon’s function is to keep the muscle in the low-cost operating zone as much as possible.

Optimal Stiffness — Not Maximum Stiffness

“Stiffer tendon, better running economy” — that’s half right. What matters isn’t maximizing stiffness; it’s finding the right range for your own mechanics.

A tendon that’s too compliant deforms too much, reducing elastic energy return efficiency. A tendon that’s too stiff can’t absorb enough length change, forcing the muscle to take on more work. The optimal state is one where the tendon absorbs most of the unit’s length change while the fascicles stay near isometric or low-velocity contraction.

Lichtwark and Wilson built a computational model in 2007 showing that the tendon stiffness that maximizes gastrocnemius efficiency is around 150 N/mm for walking and around 250 N/mm for running. Subsequent research has found a fairly wide optimal plateau — roughly 150–500 N/mm — where efficiency stays near maximum. The target isn’t the hardest possible tendon; it’s a tendon stiff enough to store and return energy effectively while letting the muscle do its job efficiently.

Soleus and Gastrocnemius Work Differently

Both the soleus and gastrocnemius attach via the Achilles tendon and both contribute to propulsion — but they don’t work the same way.

Ishikawa and colleagues tracked fascicle behavior in both muscles simultaneously during walking in 2005, using fiber-optic sensors to measure tendon force directly. The finding: the medial gastrocnemius fascicles remained roughly isometric through the stance phase — continuously generating force without extensive shortening. The soleus, by contrast, was progressively stretched through stance, building up tension, then rapidly shortened during push-off.

The researchers called this the “catapult action.” It isn’t a passive spring. The sequence is: muscle actively generates force → tendon stores energy → energy releases at the right moment. The muscle controls the force; the tendon manages the storage and release. This division of labor is what sustains efficiency across the duration of a long run.

Training Can Change Tendon Stiffness — and Running Economy

If tendon stiffness shapes running economy, can training shift it? Yes.

Bohm and colleagues published a 14-week tendon training intervention in Proceedings of the Royal Society B in 2021. Twenty-three recreational runners were enrolled. Results: tendon stiffness increased roughly 31%, calf strength increased about 10%, and running metabolic cost dropped around 4%.

The mechanism was visible in the data: after training, soleus fascicle shortening velocity during running decreased — the muscle was working closer to isometric, consuming less ATP per step. Tendon stiffness rose, muscle velocity fell, energy cost dropped.

The caveat matters though. More stiffness isn’t always better. If a runner’s tendon is already in the optimal range, pushing stiffness further yields diminishing returns. The training benefit is largest for runners whose tendons are currently too compliant — not for those who are already near the optimal window.

What the Achilles Tendon Is Actually Saving

Running metabolic cost is, at its core, sustained ATP consumption. Every time a muscle generates force, maintains tension, or changes length, it draws on energy supply.

The Achilles tendon’s contribution is letting the muscle do the same mechanical work at lower metabolic cost:

Reducing the demand for rapid muscle shortening
Shifting the muscle away from expensive concentric contractions
Keeping fascicles near isometric or low-velocity operation

Elastic energy storage and return is real — but it’s worth keeping separate from this other function. Energy recycling is a mechanical contribution. Reducing muscle contraction velocity is the metabolic one. Roberts and Azizi catalogued four tendon functions in their 2011 review: improving metabolic efficiency, amplifying power output, absorbing impact, and transmitting force rapidly. For distance runners, the metabolic efficiency function is likely the most significant — the one that compounds across thousands of steps.

Finni and Vanwanseele, reviewing fifty years of Achilles tendon research in 2023, compressed the concept to a single sentence: the tendon reduces the demand on muscles for ATP by lowering the velocity at which they must shorten.

The Achilles Tendon Is a Regulator, Not a Passive Spring

Better running economy isn’t only about more mileage or a higher VO₂max. The coordination between muscle and tendon determines the efficiency of every single step.

Heel raises, eccentric loading, plyometrics, and targeted strength work can progressively improve the tendon’s capacity to bear force and return energy. But tendon adaptation is slow — much slower than muscle. Weeks to months may pass before changes in stiffness translate into measurable shifts in running economy.

The most efficient runners aren’t just stronger. They’re better organized — muscle and tendon each doing their own job, neither compensating for the other.

Muscle generates force.

Tendon stores and returns it.

The Achilles tendon regulates how that force gets used — and it’s doing it at every step.

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References

1. Fukunaga T, Kubo K, Kawakami Y, Fukashiro S, Kanehisa H, Maganaris CN. In vivo behaviour of human muscle tendon during walking. Proceedings of the Royal Society B: Biological Sciences. 2001;268(1464):229–233. ↗
2. Roberts TJ. The integrated function of muscles and tendons during locomotion. Comparative Biochemistry and Physiology Part A: Molecular & Integrative Physiology. 2002;133(4):1087–1099. ↗
3. Ryschon TW, Fowler MD, Wysong RE, Anthony A, Balaban RS. Efficiency of human skeletal muscle in vivo: comparison of isometric, concentric, and eccentric muscle action. Journal of Applied Physiology. 1997;83(3):867–874. ↗
4. Lichtwark GA, Wilson AM. Is Achilles tendon compliance optimised for maximum muscle efficiency during locomotion? Journal of Biomechanics. 2007;40(8):1768–1775. ↗
5. Ishikawa M, Komi PV, Grey MJ, Lepola V, Bruggemann GP. Muscle-tendon interaction and elastic energy usage in human walking. Journal of Applied Physiology. 2005;99(2):603–608. ↗
6. Bohm S, Mersmann F, Santuz A, Arampatzis A. Enthalpy efficiency of the soleus muscle contributes to improvements in running economy. Proceedings of the Royal Society B: Biological Sciences. 2021;288(1943):20202784. ↗
7. Roberts TJ, Azizi E. Flexible mechanisms: the diverse roles of biological springs in vertebrate movement. Journal of Experimental Biology. 2011;214(3):353–361. ↗
8. Finni T, Vanwanseele B. Towards modern understanding of the Achilles tendon properties in human movement research. Journal of Biomechanics. 2023;152:111583. ↗

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Is Training a Science, or a Craft?

Tue, 30 Jun 2026 00:00:00 GMT

We’re used to asking one question in this era: “Is there science behind training this way?”

It’s a good habit. Sport science means we no longer have to rely on remedies passed down master to apprentice—we can use data to test which methods actually work. I spend a lot of time reading research myself, precisely because I trust evidence over intuition.

But the more I read, the clearer one thing becomes: the questions science can answer and the questions a coach has to answer every day are often not the same question.

This isn’t a failure of science. It’s its natural boundary. Seeing where that boundary lies lets us look at training more honestly.

A Study Measures a World Sliced Thin

To get a clean, repeatable answer, a training study has to slice the world thin. Hold one variable, control the rest, observe the result over a not-very-long stretch of time. That’s the price of rigor, and also its limit.

Runners need to understand three of those limits.

The first is time. Most training interventions last only weeks to a few months, but some adaptations simply don’t happen on that timescale. Take running economy: meaningful gains often surface only after more than twenty-four training sessions, and many studies end before that. The long-term effects of speed-endurance training are nearly a blank in the literature. Research can measure “what happened in eight weeks.” It can’t measure “what your body becomes over two years.”

The second is the participants. A huge share of studies recruit untrained or recreationally active people—they’re easy to recruit, have room to improve, and show clear effects. The problem is that how someone who has never trained improves and how an athlete who has trained for ten years improves further are two different things. Staff and colleagues put it bluntly in their 2023 review: studies describing the “long-term development” of elite athletes are pitifully few, and data on those under eighteen barely exist.

The third is sample skew. Many studies recruit only one sex, only a single training group; in that same review, women made up only about a quarter of the participants in long-term development research.

So “research proves it works” carries a hidden string of conditions behind it: that population, that intensity, that duration, that controlled environment. Strip those premises away and the conclusion doesn’t automatically extend into real training.

Building an Athlete Is Measured in Years

Science measures a slice. Building an endurance athlete is a long river.

That Staff review compiled the longitudinal studies: elite endurance performance accumulates across adolescence into adulthood, routinely over a decade. Training volume isn’t added in a straight line—it rises and dips year to year, finally reaching a plateau somewhere between ages twenty-six and thirty. No several-week study can hold that scale.

The more concrete evidence comes from a case study. Solli and colleagues tracked a world-class female biathlete with Olympic and World Championship medals across seventeen full seasons, from age seventeen to thirty-three. Her training volume climbed all the way to a peak, and between ages twenty-two and twenty-seven her VO2max rose by ten percent.

The most interesting part is what came later: in her peak years after thirty, she actually trained with more low-and-moderate intensity and less high intensity. The training philosophy that carried her to the top of the world was no longer the one that brought her up as a junior. What’s right shifts with the person, and with the stage.

A one-off study can never see that turn. It can only take a snapshot, and development is a whole film.

The Better the Athlete, the More Science Around Them

You might think I’m about to say elite training runs on experience, not science. The opposite is true.

The better the athlete, the higher the concentration of sport science around them. National-team-level athletes typically have a full sport-science team—physiological testing, training-load monitoring, nutrition, biomechanics, the whole array—with far more data than any amateur runner could ever access. At the elite level, the problem was never a shortage of science.

But the more data there is, the more one thing stands out: integrating that pile of measurements into the decision of “this athlete, this week, exactly how to train” is still the coach’s job. The sport-science team supplies the map; the one choosing a path across the terrain is the coach.

Sandbakk and colleagues interviewed twelve world-class Norwegian coaches in 2025, whose athletes had collectively won around 380 international medals. They found these coaches “systematically collect training data from their athletes and exhibit an experimental mindset when making individual training adjustments”—and that this entire body of expertise “has been almost untouched in the scientific literature.” Science gave them tools; how to use the tools is another discipline entirely.

The Gap Lives in the Act of Integration

The gap between science and the field isn’t in any single piece of knowledge. It’s in integration.

The questions a coach faces every day look like this: this athlete slept badly last night, there’s a race this week, the rain means moving indoors, and the response to the last session was slower than expected—putting it all together, do we add load today, or pull back and rest? No paper was ever written for “this person at this exact moment.”

Fullagar and colleagues, examining the barriers to translating research into the field, point out that research questions often simply don’t apply to the field setting, and that coaches acquire knowledge differently from researchers. Kirkland and Cowley surveyed over eight hundred endurance coaches and found their most relied-upon source of learning was “learning through experience”—rated far above formal coach education.

It’s not that coaches can’t be bothered to read papers. It’s that the problem they have to solve has no answer in the papers. Integrated judgment can’t be proven by population statistics—the more you emphasize individualization, the harder it is to validate with “on average” research. This isn’t a loophole in science. It’s the part of training that is inherently an art.

Even the skeleton of training still stands on this ground. That Sandbakk study states it plainly: neither traditional periodization nor block periodization has been scientifically verified. Practice has always run ahead of research; what coaches do every day is the thing science hasn’t yet managed to describe.

So, Training Is a Craft

If science can’t give the field every answer, what does a coach decide on?

On a training philosophy they’ve built themselves.

Over a career—the people they’ve coached, their own years as an athlete, the observations stacked up over time, the mistakes made, the bets that paid off—a coach gradually grows a framework of their own. It decides how they view intensity, how they weigh risk against recovery, which side they trust when the data conflicts. That framework is a little like faith: it isn’t adopted because it’s been fully proven, but because it helps a person make a decision under uncertainty.

To call it an art is not to say you can just wing it. Quite the opposite. The judgment it demands is harder than following a script. Art means having to choose where the evidence runs out, and owning that choice.

That’s also why there’s no right or wrong between different coaches’ philosophies. Some prefer the patient accumulation of high volume at low intensity; some are masters of wringing out form with short bursts of high intensity. Some rely on data, some on an eye for reading people. The standard for judging a training philosophy was never “how scientific is it,” but a plainer question:

For this athlete, at this time, under these constraints—did it make them stronger?

There’s no best training method, only one that fits or doesn’t. That’s the starting point of this blog, and the shared premise behind every training article that follows.

Science is the map. The one running is a person,

and the path is something a coach walks out alongside them.

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References

1. Staff, H. C., Solli, G. S., Osborne, J. O., & Sandbakk, Ø. (2023). Long-Term Development of Training Characteristics and Performance-Determining Factors in Elite/International and World-Class Endurance Athletes: A Scoping Review. Sports Medicine, 53(8), 1595–1607. ↗
2. Solli, G. S., Flom, A. H., & Talsnes, R. K. (2023). Long-term development of performance, physiological, and training characteristics in a world-class female biathlete. Frontiers in Sports and Active Living, 5, 1197793. ↗
3. Sandbakk, Ø., Tønnessen, E., Bucher Sandbakk, S., Losnegard, T., Seiler, S., & Haugen, T. (2025). Best-Practice Training Characteristics Within Olympic Endurance Sports as Described by Norwegian World-Class Coaches. Sports Medicine - Open, 11. ↗
4. Kirkland, A., & Cowley, J. (2023). An exploration of context and learning in endurance sports coaching. Frontiers in Sports and Active Living, 5, 1147475. ↗
5. Fullagar, H. H. K., McCall, A., Impellizzeri, F. M., Favero, T., & Coutts, A. J. (2019). The Translation of Sport Science Research to the Field: A Current Opinion and Overview on the Perceptions of Practitioners, Researchers and Coaches. Sports Medicine, 49(12), 1817–1824. ↗

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Three Heart Rate Zone Methods: HRmax, HRR, and LTHR

Thu, 25 Jun 2026 00:00:00 GMT

Many runners use heart rate to track training intensity — but before the numbers mean anything, you need to set your zones.

Set them wrong, and every reading that follows will be off.

The question isn’t how to divide your zones. It’s what you use as the anchor point.

That anchor determines whether the numbers actually reflect your physiology.

HRmax Method: High Cost, One Static Number

The HRmax method is straightforward: find your maximum heart rate, then carve out training zones using fixed percentages.

Zone	Heart Rate Range	Intensity
Zone 1	50–60% HRmax	Very easy recovery activity
Zone 2	60–70% HRmax	Easy aerobic running
Zone 3	70–80% HRmax	Moderate-intensity tempo
Zone 4	80–90% HRmax	High-intensity threshold
Zone 5	90–100% HRmax	VO2max training to anaerobic

If you skip the age formula and go for an actual measurement, the first step is finding your HRmax.

A common field test protocol:

Easy 10-minute warm-up
Run one mile (~1600m) at 10K race pace
Don’t stop — increase pace every minute
Accelerate by roughly 20 sec/km each time
Continue until you can no longer hold the pace
Record the highest heart rate you see

The whole thing takes about 25–30 minutes.

The design logic: use the tempo portion to bring heart rate well up, then use the progressive acceleration to push it toward its ceiling. Compared to a flat-out sprint, this approach tends to get closer to true HRmax.

But reaching true HRmax is harder than most people expect.

The highest number you see at the end of the test is technically HRpeak — it doesn’t necessarily equal your physiological ceiling.

Research shows that approaching true HRmax requires subjects to keep pushing at near-exhaustion, not just heavy breathing.

Most people stop before that point. Breathing has gone out of control, legs have started slowing, or the psychological willingness to keep hurting has run out.

The test ends. But true HRmax was never actually reached.

For experienced runners, this is already a brutal test. For beginners, add safety and physical load to the concerns.

Even if you nail an accurate HRmax, it still can’t answer the more important question:

Where is your threshold?

The HRmax method assumes everyone’s threshold sits at a similar percentage of their maximum. Reality doesn’t work that way.

A well-trained runner’s LTHR might sit at 90% of HRmax. Someone just starting out might only hit their threshold at 80%.

Two people, same HRmax of 180 bpm, completely different ability to sustain high-intensity effort.

Apply the same “Zone 4 = 80–90% HRmax” definition to both, and one person is running comfortably below threshold while the other has blown past it and is accumulating fatigue fast.

Same numbers. Completely different physiology.

There’s another problem: HRmax barely changes with training.

It’s closer to a genetically determined ceiling than a training adaptation.

What does change with training is submaximal capacity.

Same heart rate, faster pace. Same pace, lower heart rate. Lactate threshold appearing at higher intensity.

These are the real markers of endurance improvement.

But a zone system anchored to HRmax can’t capture any of this.

Six months into training, your aerobic capacity might have improved significantly — yet Zone 2’s upper boundary sits exactly where it always did.

The zones didn’t change. That doesn’t mean your fitness didn’t.

The anchor point just can’t see it.

Heart Rate Reserve: A Step Forward, But the Core Problem Stays

Heart Rate Reserve (HRR) was introduced by Finnish physiologist Martti Karvonen in 1957.

The formula:

Target HR = Resting HR + X% × (HRmax − Resting HR)

Compared to a straight HRmax percentage, this method introduces one more variable: resting heart rate.

The logic: your heart isn’t starting from zero — it’s already beating at rest. Factoring in that baseline brings the calculated training intensity closer to your actual metabolic load.

Two people with the same HRmax but different resting heart rates will get different target HRs from HRR. A fitter person with a lower resting HR will actually calculate a lower target HR — which is physiologically appropriate. Compared to everyone using the same HRmax percentage, HRR at least acknowledges that difference exists.

But the Karvonen method carries two fundamental limitations.

First: HRmax is still the upper anchor for the entire system.

The (HRmax − Resting HR) span sets the whole calculation range.

If HRmax is off by 10–15 bpm, that error propagates fully into every zone.

Resting HR adds personalization, but it doesn’t fix a bad upper anchor.

HRR makes zones more individual. It doesn’t solve the problem of HRmax being inaccurate.

Second: resting heart rate is a highly sensitive measurement.

Poor sleep, psychological stress, caffeine, recovery status, mild illness, even yesterday’s training load — all of these can shift resting HR meaningfully.

You test at 45 bpm on a fully recovered morning. The following week, stress at work pushes it to 55 bpm.

Your calculated zones shift. Your lactate threshold didn’t.

Sometimes zone drift reflects life stress, not athletic capacity.

HRR also carries a hidden assumption: that your percentage of heart rate reserve maps to your percentage of oxygen uptake.

For shorter efforts, this holds. But research found that at 45 minutes, heart rate climbs roughly 7% above what the oxygen uptake intensity would predict — and the gap keeps widening (Ferri Marini et al., 2022). Most endurance runners train well past 45 minutes. The method isn’t wrong — it just has conditions.

LTHR: Anchor Directly to the Threshold

Lactate Threshold Heart Rate (LTHR) uses different logic than the other two methods.

HRmax and HRR both start from a maximum value, then try to estimate where the threshold might be.

LTHR anchors directly to the threshold itself.

It’s not interested in your physiological ceiling. It’s focused on the highest intensity you can sustain for a prolonged effort — the heart rate near LT2.

That’s one of the most important physiological turning points in endurance training and racing.

The most common field test is Joe Friel’s 30-minute time trial (Friel, n.d.).

Protocol:

Find a flat route
Complete the test entirely alone
Run all-out for 30 minutes
Press the lap button at 10 minutes
Use the average heart rate for the final 20 minutes as LTHR

This requires a high level of effort, but not a full sprint to exhaustion.

It’s closer to a short time trial than a maximal effort.

McGehee, Tanner, and Houmard (2005) compared four field testing methods against lab lactate measurements.

The 30-minute time trial produced estimates closest to lab results — the best of the four methods tested.

That’s a key reason it’s still widely used today.

Joe Friel’s seven-zone system (Friel, n.d.):

Zone	Heart Rate Range
Zone 1	< 85% LTHR
Zone 2	85–89% LTHR
Zone 3	90–94% LTHR
Zone 4	95–99% LTHR
Zone 5a	100–102% LTHR
Zone 5b	103–106% LTHR
Zone 5c	> 106% LTHR

Most endurance training intensity clusters in the 85–102% LTHR range.

Easy runs, marathon pace, half-marathon pace, threshold work — nearly all of it falls here.

LTHR lives closer to where training actually happens than HRmax does.

For the physiology behind what each zone actually triggers, see Train with a Purpose.

Why LTHR Works Better as an Anchor

The fundamental difference between HRmax and LTHR is what the anchor point means physiologically.

Two runners, both with an HRmax of 180 bpm.

One has trained for ten years, LTHR at 162 bpm. The other just started running, LTHR at 144 bpm.

Under the HRmax method, their zones come out nearly identical.

But their physiological response to the same heart rate is completely different.

The problem isn’t the formula. It’s the anchor.

LTHR’s other major advantage: it moves with your fitness.

When your lactate threshold improves, the next test reflects it.

Your zone system stays current with your actual capacity, rather than freezing in place.

This is why Joe Friel recommends retesting every 4–6 weeks.

Retesting isn’t a flaw. It’s the whole point — the metric responds to training adaptation.

LTHR’s Limitations

LTHR isn’t perfect.

The 30-minute time trial gives you an estimate, not a precise measurement. McGehee et al. (2005) showed it’s the closest field method to lab results — but “closest” isn’t “identical.” Use it as a tool for tracking training status, not as a precise physiological value.

The test also demands running experience. Pacing judgment, intensity control, maintaining output without competition — all of these affect results. Most runners get unstable numbers the first time. Accuracy usually improves after a few tests.

There’s another easy-to-miss limitation: late-training progress doesn’t always show up in heart rate. Fitness gains often appear as running faster at the same heart rate, not a higher heart rate itself. Track LTHR alongside pace — numbers alone can understate how much you’ve improved.

No field test fully replicates a lab. But among the three common methods, LTHR’s anchor is closest to where training actually lives, and most responsive to the fitness changes that training produces.

The purpose of heart rate zones isn’t to categorize numbers. It’s to describe what’s actually happening in your body right now.

An anchor that moves with your fitness is worth more than one that stays fixed.

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References

1. Ferri Marini, C., Federici, A., Skinner, J. S., Piccoli, G., Stocchi, V., Zoffoli, L., Correale, L., Dell'Anna, S., Naldini, C. A., Vandoni, M., & Lucertini, F. (2022). Effect of steady-state aerobic exercise intensity and duration on the relationship between reserves of heart rate and oxygen uptake. PeerJ, 10, e13190. ↗
2. Friel, J. (n.d.). Joe Friel's quick guide to setting zones. TrainingPeaks. ↗
3. Kasiak, P. S., Wiecha, S., Cieśliński, I., Takken, T., Lach, J., Lewandowski, M., Barylski, M., Mamcarz, A., & Śliż, D. (2023). Validity of the maximal heart rate prediction models among runners and cyclists. Journal of Clinical Medicine, 12(8), 2884. ↗
4. McGehee, J. C., Tanner, C. J., & Houmard, J. A. (2005). A comparison of methods for estimating the lactate threshold. Journal of Strength and Conditioning Research, 19(3), 553–558. ↗
5. Shookster, D., Lindsey, B., Cortes, N., & Martin, J. R. (2020). Accuracy of commonly used age-predicted maximal heart rate equations. International Journal of Exercise Science, 13(7), 1242–1250. ↗

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Running Form Is an Outcome: How Movement Is Generated

Tue, 23 Jun 2026 00:00:00 GMT

Running form is not an appearance you can adjust — it’s what happens when your entire movement system operates under a specific set of conditions. Understanding this changes the direction of training completely.

Many runners spend months on squats, lunges, and lower-body strength work. They get stronger. Their stability improves. But their running doesn’t fundamentally change. The easy explanation is that they haven’t learned the technique properly. The more accurate one: running movement isn’t built from understanding or stacking local capacities. It self-organizes from the nervous system operating in context.

A Neglected Question

The logic behind running form coaching goes like this: your movement is flawed, learn the correct way, and you’ll run differently. This assumes running form is a technology that can be directly installed — like learning to ride a bike, something that once acquired stays fixed.

The problem is that running movement doesn’t work that way at all.

In 1947, Soviet biomechanist Nikolai Bernstein published On the Construction of Movements. The book was suppressed for nearly two decades under Soviet political conditions. It wasn’t until 1967, when Pergamon Press released the English collection The Co-ordination and Regulation of Movements, that Western movement science began to reckon with a core question it had long ignored: how is the coordination of human movement actually organized?

Bernstein’s central framework, the “Levels of Construction of Movements,” doesn’t ask how to produce correct movement. It asks why the body — given an enormous number of degrees of freedom — can still generate stable, repeatable motion. The human body has hundreds of joint degrees of freedom; any given movement could theoretically be completed in countless ways. Yet movement isn’t chaotic. The nervous system organizes and constrains those degrees of freedom across multiple levels, making movement controllable and functional.

His answer wasn’t “the brain issues commands.” It was: movement emerges from five neural levels cooperating, self-organizing within the constraints of environment and task. Running form is the result of that process.

Level A: The Foundation Layer

The lowest level in Bernstein’s framework — Level A, the rubro-spinal level — is evolutionarily the most ancient motor layer, present even in fish.

This level governs baseline muscle tone regulation, reflex control, and the immediate responses of elastic tissue. You don’t fall over standing still not because you’re actively trying to stay upright, but because this level continuously adjusts baseline muscle tone throughout your body. The Achilles tendon storing elastic energy at footstrike and releasing it on push-off — that also happens here.

Level A corresponds to the body’s structural capacities: muscle fiber force output, Achilles tendon stiffness, the speed at which muscles respond to load. Strength training that improves force output, tendon stiffness, or local stability is changing the capability base at this level. Squats make the quads stronger. Lunges stabilize hip strength. Calf work increases tendon elasticity. These gains are real, and they affect running performance. But they are not the same as running movement. Level A is the foundation of the system. The foundation isn’t the building.

Level B: The Synergy Layer

One level up is Level B — the level of synergies — corresponding to the thalamus and globus pallidus.

This level organizes multiple muscle groups into stable movement patterns: synergies. Upper and lower limbs form coupled timing relationships. Arms and legs coordinate within a shared rhythm. The trunk maintains balance through continuous weight transfer. This is what we call running form.

Level B has several properties that explain why running form is so resistant to change.

First, it uses the body itself as its coordinate system. The sensory signals this level processes are joint angles, limb velocities, and whole-body proprioception — the body’s internal language. The movement goal is maintaining coordination, not responding to external space. Bernstein called this category of movement “propriomotor.”

Second, Level B patterns are highly stable and largely insensitive to conscious intervention. Once a synergy is established, it’s very difficult to rewrite. A coach says “lift your knees higher,” you respond briefly, but the adjustment doesn’t hold past three kilometers. The layer where conscious commands operate is not the same layer that controls movement stability.

Third, change at this level can only happen within a movement context. Level B synergies self-organize gradually through repeated sensory-feedback loops, and can only be reshaped through actual movement — they can’t be built by proxy through substitute exercises disconnected from the original context.

Strength Training Speaks a Language Level B Doesn’t Understand

This is the core problem of transfer.

Level A’s neural computation language is: how much force does this muscle need, at what speed, with what tension. A squat’s neural language is “lower-limb extension synergy under vertical load.” Level B’s language is: what is the body’s overall state in space right now, what is the coordination ratio between left and right sides, how much output do I need to regulate rhythm for the next ground contact.

These two languages live in different locations anatomically. They have different sensory inputs and different organizational logic. A well-executed squat genuinely improves Level A. But that improvement does not automatically reorganize Level B’s synergy patterns. It’s like installing better engine components without recalibrating the drivetrain — you have more power, but the dynamic coordination of the whole vehicle hasn’t changed.

This isn’t “strength training is useless.” It’s that the effects of strength training are being assigned to the wrong layer.

How Level B Changes

Bernstein described a process in Chapter 8: when a beginner faces a complex movement, they naturally freeze joint degrees of freedom — trading stiffness for controllability. The first time someone runs, the arms barely swing, the shoulders are tight, stride length is short, cadence is irregular. That’s the state before Level B has established any synergy.

As practice accumulates, those frozen degrees of freedom gradually unfreeze and integrate into the overall synergy pattern: the arms begin swinging rhythmically without conscious thought; cadence stabilizes automatically; the trunk finds its forward lean angle. The critical variable is movement practice within a running context — not all forms of practice have equivalent effects.

A 2020 baseball batting study (Gray, 2020) measured this directly. Researchers compared two groups: one practicing under fixed conditions, one under high-variability conditions — different pitch speeds, different locations. The high-variability group showed significantly greater improvement in batting synergies, and higher “good variability” — adaptive fluctuation within task constraints. That’s what Bernstein meant by skill: not more consistent movement, but movement that adapts more flexibly to variation.

The logic transfers directly to running. Practice on hills, descents, turns, and uneven surfaces gives Level B richer sensory feedback material than single-pace flat running. These varied terrain and rhythm changes continuously alter ground contact time, weight transfer patterns, and muscle recruitment ratios — the synergy is repeatedly organized and adjusted across a wider range of constraints.

The variable that matters isn’t training volume. It’s the richness of the movement context. When the body enters “running” repeatedly under varied dynamic conditions, the synergy structure Level B depends on stabilizes across diverse feedback — rather than being locked into a fixed pattern built for one environment.

Do Running Form Classes Actually Work?

To be fair, some things do work. Improving Level A is meaningful — stronger glutes and legs give Level B better hardware to work with. Strength training and gym work accomplish that.

Conscious movement guidance, in Bernstein’s framework, belongs to Level C — the spatial field level. Cues like “land directly under your center of mass” use external spatial coordinates to guide movement, and they have some short-term effect on movement correction. But for Level C instructions to penetrate Level B’s deeper synergy patterns, they require large amounts of repetitive practice under conscious guidance — slowly shaping Level B from above.

The problem isn’t that form classes have no effect. It’s that when the theoretical framework is wrong, the explanation of why things work is also wrong. “I teach you correct form, you learn it, you run better” — that causal chain is backwards. What actually happens: the class provided movement practice within a running context, and Level B gradually self-organized a more efficient pattern through repeated sensory-feedback loops. The driver was “exploring movement in the right context,” not “knowing what good form looks like.”

Understanding that difference tells you where to put your time and energy.

What Your Running Form Is Telling You

Running form analysis still has value — but the value isn’t in “correcting movement.” It’s in understanding the current state of the movement system.

Level A status: is hip drive strong enough? Is Achilles elastic energy storage being utilized? Is landing impact absorbed by muscle or transmitted through bone? These are visible in running form. The intervention is targeted strength training, not “teaching knee lift.”

Level B maturity: is left-right coordination sufficient? Is stride rhythm stable? Are upper and lower limb swings working together? The intervention is varied practice within a running context — different terrain, different speeds, different goals — giving Level B enough rich sensory feedback to self-organize.

What you do after a gait analysis is different from what most form classes teach.

The Logic of Running

The human body is a complex system, not a machine that accepts programming. Running training runs in two different directions: one is improving the body’s output and structural capacity — strength, tendon stiffness, fundamental stability. The other is letting the movement system continuously adjust within actual running contexts, allowing synergy patterns to gradually take shape and stabilize. The first provides the conditions. The second determines the form.

Running form changes primarily in the second process. It isn’t directly taught — it gradually emerges after repeated running at different speeds, on different terrain, under different fatigue states. What you can do is build the Level A hardware, then let Level B self-organize across varied running contexts. Good form appears as the outcome.

Next time someone talks about “improving running form,” the question isn’t whether movement is being corrected. It’s whether the intervention is aimed at the right level.

If the answer is only “teach you correct movement technique,” it may still be at the surface. Because the real question isn’t what the movement looks like — it’s how that movement is being organized inside your body.

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References

1. Bernstein, N. A. (1947/2021). Bernstein's Construction of Movements: The Original Text and Commentaries (M. L. Latash, Ed.). Routledge. ↗
2. Bernstein, N. A. (1967). The Co-ordination and Regulation of Movements. Pergamon Press. ↗
3. Bongaardt, R., & Meijer, O. G. (2000). Bernstein's theory of movement behavior: historical development and contemporary relevance. Journal of Motor Behavior, 32(1), 57–71. ↗
4. Gray, R. (2020). Changes in movement coordination associated with skill acquisition in baseball batting. Frontiers in Psychology, 11, 1295. ↗
5. Hafkamp, M., Casanova, R., & Bootsma, R. J. (2023). Freezing and freeing of degrees of freedom in joint action learning. Frontiers in Psychology, 14, 1287148. ↗
6. Spencer, J. P., Perone, S., & Buss, A. T. (2011). Twenty years and going strong: a dynamic systems revolution in motor and cognitive development. Child Development Perspectives, 5(4), 260–266.

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Running Economy: It's Deeper Than Your Form

Mon, 22 Jun 2026 00:00:00 GMT

How much time have you spent fixing your running form?

Ground contact point, cadence, knee lift angle, trunk lean, arm swing arc. Many runners invest significant time adjusting these external movement details, because the logic behind it is intuitive: find the optimal form, and efficiency will naturally follow.

In 2024, a systematic review integrating 51 studies and data from 1,115 subjects tested this assumption. The results showed that external biomechanical variables — contact time, stride length, joint angles, ground reaction forces — combined explain only 4% to 12% of the variance in running economy.

Most of the efficiency differences come from other factors.

Among Elite Runners, Efficiency Is the Real Dividing Line

Maximal oxygen uptake (VO₂max) has long been regarded as a key indicator of endurance performance. It represents the body’s maximum capacity to deliver and utilize oxygen — the higher the value, the greater the theoretical aerobic output. Elite marathon runners typically have VO₂max values of 70 to 85 ml·kg⁻¹·min⁻¹, far above the general population. Yet among high-level runners, VO₂max differences tend to be small, and lactate thresholds also converge.

Joyner and Coyle, after integrating a large body of research in 2008, proposed that endurance performance is primarily determined by three factors:

VO₂max
Lactate threshold
Running economy

Among these, VO₂max and lactate threshold show relatively limited variation within elite groups, while individual differences in running economy can reach 30 to 40%. Among runners with similar VO₂max, economy often becomes the primary source of performance differences. Improving endurance performance is not just about raising aerobic capacity — it also means reducing the energy cost of running at a given pace.

Efficiency Comes from Coordinative Structure, Not Single-Muscle Strength

Improving efficiency is often understood as strengthening the major muscle groups, but the way muscles work during running is far more nuanced. In 2021, the Bohm team used ultrasound to track fascicle behavior in the soleus and vastus lateralis during running, revealing that the two muscles adopt strikingly different strategies: the soleus operates at a moderate shortening velocity throughout the stance phase, reaching approximately 94% of its theoretical metabolic efficiency — the primary power generator; the vastus lateralis maintains near-isometric contraction, minimizing energy cost to stabilize the leg structure.

Within the same running stride, different muscles operate under different strategies. Some generate force, others maintain stability — together they form a coordinative structure orchestrated by the nervous system. Running economy reflects the overall coordination of this system, not the strength of any single muscle. A strong isolated muscle does not guarantee that the nervous system can deploy it with maximum efficiency during running.

The Achilles Tendon Shapes Soleus Efficiency

A second study by Bohm in the same year explored the relationship between tendon properties and running economy. After subjects completed 8 weeks of tendon-specific training targeting the posterior calf and Achilles tendon:

Achilles tendon stiffness increased by 31%
Soleus shortening velocity decreased
Soleus work efficiency improved by 7%
Running economy improved by approximately 4%

During running, the calf muscles and Achilles tendon form a muscle-tendon unit. Upon landing, the entire system is stretched — the Achilles tendon absorbs most of the length change, storing elastic energy and releasing it at toe-off. With greater tendon stiffness, more deformation occurs in the tendon rather than the muscle itself. The soleus fascicles can operate at a more stable length and lower shortening velocity, placing them closer to their optimal metabolic efficiency range. For the muscle, lower shortening velocity means lower energy cost; for the system as a whole, it means better running economy.

Fletcher and MacIntosh’s review found that the triceps surae accounts for approximately 25% of total oxygen consumption in elite runners, rising to as much as 40% in less-trained individuals. Soleus efficiency has a substantial impact on overall energy use, and the mechanical properties of the Achilles tendon are a key factor.

Gait Correction Cannot Directly Improve Efficiency

Van Hooren et al. systematically analyzed cadence, stride length, contact time, flight time, joint angles, and ground reaction forces in 2024, finding no single external biomechanical indicator capable of meaningfully explaining running economy differences. The highest correlation — cadence — showed only r = −0.20; all external biomechanical variables combined still explained only 4% to 12% of efficiency variance.

The key factors influencing efficiency include how the nervous system recruits muscles, the length and velocity at which muscles operate, and how tendons store and release elastic energy. These processes occur inside the body and cannot be directly observed from external movement patterns. External movement is the output of the internal system — different runners can use different gait patterns while sharing excellent running economy.

Efficiency Can Be Trained — Just Not the Way You Think

Bohm’s research demonstrated that tendon-specific training can alter Achilles tendon mechanical properties and improve running economy. This type of adaptation operates through different physiological mechanisms than general aerobic training — traditional endurance training primarily increases mitochondrial density, capillary density, and oxidative enzyme activity, improving aerobic metabolic capacity and lactate threshold, but not tendon mechanical properties.

Does general running volume effectively modify tendon properties? What training load is required? Does specific strength and elasticity training produce results that running alone cannot? These questions remain open, but the direction is clear: training for efficiency means targeting the operating conditions of the whole system, not isolating individual muscles.

This explains why gait correction usually fails to move the needle: you are trying to rewrite the system’s output without touching the underlying structure that produces it. The point of entry is the system’s constraints — not the shape of the movement.

Dynamical Systems Theory approaches efficiency from exactly this angle — understanding how economy emerges and converges at the system level through self-organization. It remains one of the most promising research frameworks for understanding running economy today.

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References

1. Van Hooren, B., Jukic, I., Cox, M., Frenken, K.G., Bautista, I., & Moore, I.S. (2024). The Relationship Between Running Biomechanics and Running Economy: A Systematic Review and Meta-Analysis of Observational Studies. Sports Medicine, 54(5), 1269–1316. ↗
2. Joyner, M.J., & Coyle, E.F. (2008). Endurance exercise performance: the physiology of champions. The Journal of Physiology, 586(1), 35–44. ↗
3. Bohm, S., Mersmann, F., Santuz, A., Schroll, A., & Arampatzis, A. (2021). Muscle-specific economy of force generation and efficiency of work production during human running. eLife, 10, e67182. ↗
4. Bohm, S., Mersmann, F., Santuz, A., & Arampatzis, A. (2021). Enthalpy efficiency of the soleus muscle contributes to improvements in running economy. Proceedings of the Royal Society B: Biological Sciences, 288(1943), 20202784. ↗
5. Fletcher, J.R., & MacIntosh, B.R. (2017). Running Economy from a Muscle Energetics Perspective. Frontiers in Physiology, 8, 433. ↗

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Train with a Purpose

Tue, 16 Jun 2026 00:00:00 GMT

Training intensities are a recurring source of debate. Someone swears by spending 80% of their time in Zone 2. Someone else says the Norwegian method is the real secret. Another camp insists HIIT delivers the fastest gains. Every claim has data behind it. Every approach has produced good results.

The question isn’t which method is correct — it’s how any of them actually make you stronger.

Training Is Signaling

We covered LT1, LT2, and how the three-zone model divides training intensity in LT1 / LT2 and the Three-Zone Model; Three Heart Rate Zone Methods covers how to anchor those zone boundaries using heart rate. This goes one layer deeper — not the zones themselves, but what each zone triggers inside your body.

Training is fundamentally a signaling process. Apply enough stress, the body detects it, fires off a cascade of molecular responses, and adapts once the stress is gone. Different intensities send different signals and trigger different adaptations. The gene expression pattern activated in muscle cells after a Zone 2 run looks different from what happens after a HIIT session — not as a metaphor, but literally.

Exercise scientist Andrew Coggan systematized this in Training and Racing with a Power Meter (co-authored with Hunter Allen, VeloPress, 2006; third edition 2019): 7 training intensity zones, each with a distinct profile of how strongly it stimulates each physiological adaptation.

Six Physiological Adaptations

Coggan mapped adaptations across several dimensions: mitochondrial enzyme activity, lactate threshold, muscle glycogen storage, plasma volume and cardiac output, muscle capillary density, and fiber type transitions. Each can be improved independently — and each has an intensity zone where it responds most strongly.

Physiological Adaptation	Z1	Z2	Z3	Z4	Z5	Z6	Z7
Plasma Volume ↑	—	●	●●	●●●	●●●●	●	—
Muscle Mitochondrial Enzyme Activity ↑	—	●●	●●●	●●●●	●●	●	—
Lactate Threshold ↑	—	●●	●●●	●●●●	●●	●	—
Muscle Glycogen Storage ↑	—	●●	●●●●	●●●	●●	●	—
Slow-Twitch Fiber Hypertrophy	—	●	●●	●●	●●●	●	—
Muscle Capillary Density ↑	—	●	●●	●●	●●●	●	—
Fiber Type Conversion (IIb → IIa)	—	●●	●●●	●●	●●	●	—
Stroke Volume / Max Cardiac Output ↑	—	●	●●	●●●	●●●●	●	—
VO2max ↑	—	●	●●	●●●	●●●●	●	—
High-Energy Phosphate Stores (ATP/PCr) ↑	—	—	—	—	—	●	●●
Anaerobic Capacity ↑ (Lactate Tolerance)	—	—	—	—	●	●●●	●
Fast-Twitch Fiber Hypertrophy	—	—	—	—	—	●	●●
Neuromuscular Power ↑	—	—	—	—	—	●	●●●

●●●● = Peak stimulus　●●● = Strong　●● = Moderate　● = Minor　— = No significant effect

Two things stand out from this table. First, Zone 1 (active recovery) produces almost no adaptations at all — its function is recovery from the previous session, not building new capacity. Second, and more surprising: Zone 4 (threshold) scores highest for both mitochondrial enzyme activity and lactate threshold — not Zone 2, and not Zone 5.

Mitochondria: The Cell’s Power Plant

Mitochondria are the core of aerobic energy metabolism, converting glucose and fatty acids into ATP. With training, they multiply, grow larger, and increase their oxidative enzyme activity — changes that let you run faster at the same effort, or sustain the same pace with less.

Peak adaptation falls at Zone 4 (threshold), with Zone 3 (Tempo) close behind, and Zone 5 contributing but with diminishing returns. The mechanism: when intensity is high enough, cells burn ATP faster than they can replenish it, triggering the cellular “energy alarm” AMPK, which activates PGC-1α — the master regulator of mitochondrial biogenesis. The higher the intensity, the stronger the alarm. But push too hard and fatigue accumulates too fast to repeat frequently. Zone 4 sits at the sweet spot: signal strong enough, recoverable enough to repeat week after week.

HIIT or steady-state cardio — which is better for mitochondria? In individual studies, HIIT groups show larger increases in citrate synthase (the marker enzyme for mitochondrial oxidative capacity) — high intensity does produce a stronger signal per session. But Abrego-Guandique et al. (2025) showed that when total work volume is matched, the two approaches produce no significant difference in mitochondrial adaptation. The molecular pathway is identical; HIIT fires it with fewer, stronger pulses while steady-state cardio uses more, gentler ones — the accumulated effect is similar.

Lactate Threshold: Turning Lactate into Fuel

An improving lactate threshold means that at higher intensities, your lactate clearance rate keeps pace with production. The core mechanism is the lactate shuttle: lactate produced in fast-twitch fibers is transported through MCT1 carriers to mitochondria in oxidative fibers, where it’s burned as fuel. As training progresses, MCT1 density rises, clearance accelerates, and the accumulation threshold shifts to higher intensities. A 2024 meta-analysis by Benítez-Muñoz et al. integrating 41 studies confirmed that MCT1 upregulation occurs consistently across all aerobic training modes. The full mechanism is covered in Lactate Is Not Your Enemy.

The peak adaptation here is at Zone 4. High enough to continuously stress the lactate clearance system and drive MCT1 expression and enzyme efficiency further — while accumulating fatigue more slowly than Zone 5, allowing more frequent stimulus repetition. This is the physiological logic behind the Norwegian method: Casado et al. (2023) documented the Ingebrigtsen brothers training 3–4 times per week with threshold intervals targeting blood lactate of 2–4.5 mmol/L, precisely calibrated to maximize lactate threshold adaptation while maintaining training frequency.

Muscle Glycogen Storage: How Much Fuel You Can Carry

Peak adaptation falls at Zone 3 (Tempo) — in Coggan’s table, glycogen storage scores highest at Z3, not Z4 or Z5.

The mechanism: moderate-to-high intensity training repeatedly depletes muscle glycogen, and during resynthesis the body supercompensates — rebuilding stores above their pre-training level. GLUT4 transporter expression (which moves glucose into cells) also rises with training, raising the storage ceiling. Zone 3’s advantage comes from sitting between high depletion (enough to drain glycogen) and low fatigue (short enough recovery to repeat frequently), allowing more sessions than Zone 4 or 5. For marathon runners, this directly determines whether the second half falls apart.

Cardiopulmonary Output: The Size of the Pipes

VO2max is determined by two things: how much blood the heart pumps per minute, and how much oxygen muscles can extract from it. In Coggan’s table, both peak at Zone 5.

Helgerud et al.’s 2007 RCT confirmed this directly: 40 subjects were randomized into four groups, and after 8 weeks the Zone 5 interval group showed the largest VO2max improvements — stroke volume increases were tightly correlated with VO2max gains. Zone 2 and Zone 3 groups showed no significant improvement in either metric. Greater stroke volume per beat is the primary driver of VO2max gains, and Zone 5 produces the strongest signal for that adaptation.

Muscle Capillaries: The Last Mile for Oxygen

Blood pumped out still has to reach the muscle fibers. Capillaries are the delivery network. With training, capillary density in muscle increases (angiogenesis), shortening the diffusion distance from blood to muscle cell and improving oxygen utilization.

In Coggan’s table, capillarization peaks at Zone 5. The key molecular signal for angiogenesis is VEGF (vascular endothelial growth factor). Wahl et al. (2014) directly compared VEGF responses between high-intensity and high-volume low-intensity training — VEGF rose significantly in the high-intensity group post-exercise, while the high-volume group showed a decrease. Higher intensity generates a stronger metabolic hypoxic signal, which upregulates VEGF and drives capillary growth. This adaptation scales with intensity — Zone 5 is most efficient, but the effect increases continuously across the range, not just above a fixed threshold.

Fiber Type Transitions: Making Fast-Twitch Fibers More Aerobic

Coggan’s table separates fiber adaptations into two distinct rows. Type IIb → IIa conversion peaks at Zone 3 (●●●), with Zone 2 and Zone 4/5 lower (●●); slow-twitch fiber hypertrophy is different — it peaks at Zone 5 (●●●), with Zone 3/4 moderate. Two seemingly similar adaptations, peak zones two steps apart. Research integrating fiber data across training modes confirms that long-distance training increases slow-twitch fiber proportion, while high-intensity training produces more Type IIa fibers — different intensities build fiber changes in different directions.

Anaerobic Capacity and Lactate Tolerance: Zone 6’s Domain

In Coggan’s table, Zone 6 — near-maximal efforts lasting roughly 30 seconds to 2 minutes — is the peak zone for anaerobic capacity and lactate tolerance (●●●). At this intensity, glycolytic rate far outpaces mitochondrial clearance, causing rapid lactate and hydrogen ion accumulation. Repeated exposure drives buffering adaptations: bicarbonate concentration in muscle and blood rises, intracellular pH drops more slowly, and the ability to sustain output under high-lactate conditions extends.

Zone 6 contributes little to the aerobic adaptations covered above — mitochondrial enzymes, lactate threshold, and VO2max each score only ● — but it’s essential for events like the 800m to 1500m, where maintaining pace under high lactate accumulation is decisive. Zone 6 doesn’t build the aerobic engine; it raises the tolerance ceiling for when that engine overheats.

Neuromuscular Power: Zone 7’s Terminal

Zone 7 — all-out sprints, typically under 10–15 seconds — peaks at neuromuscular power (●●●) in Coggan’s table, while also providing meaningful stimulus for fast-twitch fiber hypertrophy (●●) and high-energy phosphate storage (ATP-PCr, ●●).

The adaptations here run through neural pathways, not metabolic ones: motor unit recruitment increases, motor neuron firing rate accelerates, and inter-muscular coordination improves — enabling rapid mobilization of more fast-twitch fibers in an instant. The phosphocreatine (PCr) system supplies energy in the first few seconds of a sprint; Zone 7 training raises both PCr storage and resynthesis rate. For middle- and long-distance runners, Zone 7’s contribution is concentrated in finishing kick capacity, not the aerobic engine powering the race body.

The Fat-Burning Myth

“You need Zone 2 to burn fat” — this conflates two different things: the energy substrate used during exercise, and long-term metabolic capacity built through training. Fat-burning capacity comes down to mitochondrial oxidative enzyme activity, particularly β-HAD (the key enzyme in fatty acid oxidation).

Talanian et al. (2007) showed that after 2 weeks of HIIT, whole-body fat oxidation capacity increased 36% and mitochondrial enzyme β-HAD activity rose 32%. The mechanism follows the same pathway as mitochondrial biogenesis: AMPK → PGC-1α → elevated mitochondrial enzyme activity → increased fat oxidation capacity. A review co-authored by HIIT pioneer Martin Gibala concluded: “Most studies do not support Zone 2 as the optimal intensity for improving mitochondrial capacity or fat oxidation.” What determines fat-burning capacity is mitochondrial volume — not which intensity zone you’re training in.

Putting the adaptations together, the peak intensity distribution is clear:

Zone 3 (Tempo): Muscle glycogen storage, Type IIb → IIa fiber conversion
Zone 4 (Threshold): Mitochondrial enzyme activity, lactate threshold
Zone 5 (VO2max zone): VO2max, cardiac output, capillary density, slow-twitch fiber hypertrophy
Zone 6 (Near-maximal): Anaerobic capacity, lactate tolerance
Zone 7 (All-out sprint): Neuromuscular power, ATP-PCr storage, fast-twitch fiber hypertrophy

Zone 2 contributes to every adaptation column — but it’s the peak for none of them. Its advantage is low fatigue and high repeatability: with enough volume, gentle stimuli accumulate into meaningful results. Zone 2 is a volume-building tool, not a peak-adaptation trigger.

Training Methods: Different Paths, Same Destination

In three-zone terms: polarized training (the 80/20 framework) puts roughly 80% of time in Zone 1 (low intensity, below LT1) and roughly 20% in Zone 3 (high intensity, above LT2), with minimal time in the middle Zone 2 (tempo to threshold, LT1–LT2). Pyramid training distributes volume in decreasing amounts up the intensity ladder — a large Zone 1 aerobic base, a meaningful slice of Zone 2 threshold work, and a small Zone 3 stimulus at the top.

Which is better? A 2024 meta-analysis by Oliveira et al. integrating 17 RCTs (n=437) compared polarized against other intensity distribution models: time trial performance, time to exhaustion, LT2 speed — no significant differences. Polarized training showed a slight edge in VO2max, but only in short interventions (< 12 weeks) with high-level athletes; the advantage disappeared in longer studies.

Different training methods prioritize different physiological components: polarized targets VO2max and cardiac output, threshold training targets mitochondrial enzymes and lactate threshold, high-volume low-intensity builds fat oxidation capacity. What actually limits your performance determines which components matter — not which pathway gets you there.

Volume and Pace: Not a Trade-Off

Fokkema et al. (2020) tracked nearly a thousand runners (n=997) preparing for half- and full marathons. The finding was clear: weekly mileage and training pace each independently predicted finish time, with no overlap between their contributions. Low mileage with higher pace and high mileage with lower pace activate different proportions of physiological adaptations — but when aerobic load is sufficient, competitive level can converge.

This is why elite runners can train completely differently and still end up on the same Olympic final start line.

Every runner takes a different path. The physiology that makes you better is the same. The path doesn’t determine how fast you run — how much of it you cover does.

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References

1. Allen, H., & Coggan, A. R. (2019). Training and racing with a power meter (3rd ed.). VeloPress. (Original work published 2006)
2. Benítez-Muñoz, J. A., Rubio-Arias, J. Á., Valls-Lacalle, L., González-Ponce, Á., de Vera-Floristán, C., López-Ayala, J. M., & Jiménez, S. L. (2024). Exercise influence on monocarboxylate transporter 1 (MCT1) and 4 (MCT4) in the skeletal muscle: A systematic review. Acta Physiologica, 240(1), e14083. ↗
3. Casado, A., Foster, C., Bakken, M., & Tjelta, L. I. (2023). Does lactate-guided threshold interval training within a high-volume low-intensity approach represent the "next step" in the evolution of distance running training? International Journal of Environmental Research and Public Health, 20(5), 3782. ↗
4. Helgerud, J., Høydal, K., Wang, E., Karlsen, T., Berg, P., Bjerkaas, M., Simonsen, T., Helgesen, C., Hjorth, N., Bach, R., & Hoff, J. (2007). Aerobic high-intensity intervals improve VO2max more than moderate training. Medicine & Science in Sports & Exercise, 39(4), 665–671. ↗
5. Wahl, P., Jansen, F., Achtzehn, S., Schmitz, T., Bloch, W., Mester, J., & Werner, N. (2014). Effects of high intensity training and high volume training on endothelial microparticles and angiogenic growth factors. PLOS ONE, 9(5), e96024. ↗
6. Talanian, J. L., Galloway, S. D. R., Heigenhauser, G. J. F., Bonen, A., & Spriet, L. L. (2007). Two weeks of high-intensity aerobic interval training increases the capacity for fat oxidation during exercise in women. Journal of Applied Physiology, 102(4), 1439–1447. ↗
7. Oliveira, P. S., Boppre, G., & Fonseca, H. (2024). Comparison of polarized versus other types of endurance training intensity distribution on athletes' endurance performance: A systematic review with meta-analysis. Sports Medicine, 54, 2817–2835. ↗
8. Fokkema, T., van Damme, A. A. D. N., Fornerod, M. W. J., de Vos, R.-J., Bierma-Zeinstra, S. M. A., & van Middelkoop, M. (2020). Training for a (half-)marathon: Training volume and longest endurance run related to performance and running injuries. Scandinavian Journal of Medicine & Science in Sports, 30(9), 1692–1704. ↗
9. Storoschuk, K. L., Moran-MacDonald, A., Gibala, M. J., & Gurd, B. J. (2025). Much ado about zone 2: A narrative review assessing the efficacy of zone 2 training for improving mitochondrial capacity and cardiorespiratory fitness in the general population. Sports Medicine, advance online. ↗

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Zone 2 Training: Why All the Talk

Mon, 15 Jun 2026 00:00:00 GMT

Zone 2 training has become something close to a new religion in endurance sports. Andrew Huberman keeps bringing it up on his podcast. Peter Attia lists it as a cornerstone of longevity. After Iñigo San Millán coached Pogačar to the Tour de France title, the Zone 2 system he promotes has been cited everywhere.

Episode 41 of the Inside Exercise Podcast featured host Glenn McConnell interviewing Andrew Coggan. If you’ve spent any time in the power training world, the name should be familiar — he coined the concept of FTP (Functional Threshold Power), co-authored Training and Racing with Power with Hunter Allen (over 100,000 copies sold, translated into eight languages), won time trial championships across five states, and built a small wind tunnel in his basement to study aerodynamics. Power training is a system that measures output in watts — dominant in the cycling world, but conceptually equivalent to pace or heart rate management in running.

In this episode, he said a few things that I think deserve serious attention in the middle of all the Zone 2 noise.

How FTP Came About — and Why He Calls Them “Levels,” Not “Zones”

Coggan introduced the term FTP around 2002 on an online forum called wattage forum. He deliberately chose the word “functional” because he understood that true physiological thresholds don’t have precise tipping points — they’re fuzzy continuums. FTP was never a physiological measurement. It’s a practical anchor: the highest steady-state output you feel you can sustain for about an hour, giving coaches and athletes a shared reference point.

He named his system “seven training levels,” not “zones,” for equally deliberate reasons. Power output in outdoor cycling is inherently volatile — you coast through corners, surge over short climbs, accelerate out of bends. If your mindset is “stay inside a zone,” you end up managing a number instead of responding to the road. Train that way long enough and you become what Coggan calls a diesel engine: smooth at constant effort, useless when the race demands acceleration. “Levels” implies movement within a range. It’s not a box to lock yourself inside.

A Few Things About Zone 2 You May Have Accepted Too Quickly

Coggan has a clear position on why people say Zone 2 works: many of the popular explanations are based on misreading the underlying physiology. He’s not dismissing Zone 2 — but if you don’t understand the real mechanism, your training decisions rest on a shaky foundation.

You Don’t Need to “Train in a Fat-Burning State” to Improve Fat Metabolism

The most widely repeated Zone 2 argument goes like this: low-intensity exercise burns primarily fat, so if you want to get better at burning fat, train at that intensity and teach your body to do it.

Coggan’s point: an untrained person sitting at rest already gets almost all their muscle energy from fatty acid oxidation. Endurance training improves fat metabolism capacity, but the mechanism is an increase in mitochondrial number and quality — mitochondria being the cellular machinery that handles aerobic metabolism regardless of whether the fuel is fat or glucose. The signals that drive mitochondrial biogenesis are changes in the muscle’s energy state and calcium release from contracting fibers. Neither of those signals is “you are currently burning fat.”

This means that through high-intensity interval training, even when the primary fuel being used is carbohydrate, as long as the mitochondrial adaptation occurs, your capacity to oxidize fat during exercise will improve. “You must train in a fat-burning state to improve fat metabolism” doesn’t hold up mechanistically.

Elevated Lactate Doesn’t Switch Off Fat Burning

Another popular claim traces back to a 1960s research hypothesis: high-intensity exercise causes lactate to accumulate, lactate inhibits lipolysis, and so the body switches into carbohydrate-only mode.

The mechanism here is wrong.

During high-intensity exercise, circulation priorities shift — blood flow is redirected from adipose tissue toward working muscles. Fatty acids need albumin as a carrier to leave fat cells and enter the bloodstream, because they’re not water-soluble. With less blood reaching adipose tissue, albumin delivery drops, and fatty acids can’t get out. Blood-borne free fatty acid concentration falls as a result. That’s a physical blood-flow constraint, not a biochemical suppression by lactate.

In 1999, Trudeau and colleagues used microdialysis probes inserted directly into subcutaneous abdominal fat tissue in human subjects, infusing high-concentration lactate into one probe and saline into the other as a control, while measuring glycerol (a direct marker of lipolysis) throughout rest, moderate exercise, and recovery. Result: no difference between the two probes whatsoever. George Brooks’ laboratory’s lactate clamp experiments point the same direction — when blood lactate is artificially elevated under controlled pH conditions, lipolysis is not suppressed; if anything, it trends slightly higher.

Metabolic Reset After High Intensity Is Faster Than You Think

Zone 2 purists sometimes argue that if you include a sprint or a hard climb inside an aerobic session, the low-intensity work that follows “doesn’t count” — because the high-intensity effort has disrupted the hormonal environment and compromised whatever fat-burning benefit the easy portions would otherwise deliver. The practical extension of this belief: keep Zone 2 and hard efforts on separate days, never mix them in the same session.

Data from Coggan’s doctoral research addresses this directly: subjects alternated between time trial intensity for 15 minutes and easy recovery for 15 minutes. In the second half of each recovery block (the final 5 minutes), fatty acid and glycerol metabolism rates had already returned to levels identical to control conditions where no high-intensity effort had been performed at all.

The underlying reason is hormonal clearance rate. Epinephrine and norepinephrine spike significantly during high-intensity exercise and do affect metabolism — but their half-lives are extremely short, cleared within 1–2 minutes of the effort ending. Total metabolic reset window: roughly 10 minutes.

The implication is straightforward: as long as the recovery interval is genuinely easy and long enough, each low-intensity block is independently effective — regardless of what came before it. Outdoor riding rarely stays perfectly flat and steady. Accelerating through a traffic light or rolling over a bumpy climb doesn’t erase the low-intensity training that follows. “Hard efforts contaminate Zone 2 benefit” is a concern the data doesn’t support.

What Coggan Actually Thinks You Should Do

After dismantling those misconceptions, Coggan’s training advice is remarkably practical.

He says the core principles are straightforward: Specificity, Overload, Progression. Your training should match what your race demands, intensity must be sufficient to drive adaptation, and the stimulus must build over time. These hold regardless of which training trend is dominating the conversation.

His concrete example: a college student with six hours a week to train gets more out of spending part of that time at “sweet spot” — roughly 88–93% of FTP — than dedicating all of it to pure Zone 2. That intensity delivers a higher training stimulus in less time. For athletes with limited hours, it’s simply more efficient.

Zone 2 does have a place. For ultra-endurance athletes, high volumes of low-intensity aerobic work have clear justification — because the race is conducted at that intensity, and you need to practice there. But the justification is specificity: “my race demands this capacity.” Not “this is the only intensity that improves fat metabolism.”

He uses the Tokyo Olympic 5000m final as an example: the top three finishers trained in completely different ways — one emphasizing high volumes of easy running, one doing two-a-days with heavy intervals, one taking a mixed approach. All three finished within a meter of each other. What he means by “all roads lead to Rome” isn’t that every method is equally good — it’s that more paths to the goal exist than people assume, and you don’t need to lock yourself into one.

Coggan returns repeatedly to one principle: “The best predictor of performance is performance itself.” He’s skeptical of the belief that you need regular VO₂max or lactate threshold tests to track progress. His reference point is physiologist Ed Coyle’s tracking of Greg LeMond through the 1988 Tour de France season — VO₂max barely changed over the season, yet race performance improved significantly, correlating closely with a roughly 10% increase in mitochondrial enzyme activity in his muscles. The number you most need to track isn’t a lab value — it’s what you can actually do at your target intensity.

On the subject of social media training advice, his stance is clear: stay skeptical. Whoever’s saying it, ask what the evidence is and whether it applies to your situation. Decades of endurance research have already established many of the core principles. You don’t need to restart with a new zone system every two years.

And if you train for health and longevity, his advice is simple: find something you can sustain and genuinely enjoy. Over-rigid intensity control reliably leads to one outcome above all others — losing motivation.

Your body needs time to adapt. But first you have to keep showing up.

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References

1. McConnell G, Coggan A. Zone 2 Training: Why All the Talk. Inside Exercise Podcast, Episode 41. 2023. ↗
2. Holloszy JO. Biochemical adaptations in muscle: effects of exercise on mitochondrial oxygen uptake and respiratory enzyme activity in skeletal muscle. J Biol Chem. 1967;242(9):2278–82. ↗
3. Dudley GA, Abraham WM, Terjung RL. Influence of exercise intensity and duration on biochemical adaptations in skeletal muscle. J Appl Physiol. 1982;53(4):844–50. ↗
4. Issekutz B Jr, Miller HI, Paul P, Rodahl K. Effect of lactic acid on free fatty acids and glucose oxidation in dogs. Am J Physiol. 1965;209(6):1137–44. ↗
5. Trudeau F, Bernier S, de Glisezinski I, Crampes F, Dulac F, Rivière D. Lack of antilipolytic effect of lactate in subcutaneous abdominal adipose tissue during exercise. J Appl Physiol. 1999;86(6):1800–4. ↗
6. Miller BF, Fattor JA, Jacobs KA, Horning MA, Navazio F, Lindinger MI, Brooks GA. Lactate and glucose interactions during rest and exercise in men: effect of exogenous lactate infusion. J Physiol. 2002;544(3):963–75. ↗

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LT1 / LT2 and Training Zone Models: The Map Is Not the Terrain

Fri, 12 Jun 2026 00:00:00 GMT

After meeting more and more runners, I’ve noticed something: a lot of people know about Zone 2, know about lactate thresholds, know about polarized training. They follow the plan. They complete the workouts.

But when the body responds in unexpected ways — when recovery is unusually poor one week, when test numbers come back very different from last time — most people don’t know what to do.

Part of the reason is that these terms make training look like a problem with a right answer: find LT1 and LT2, fill your mileage into the corresponding zones. The framework gives direction, but not enough understanding to handle what falls outside it.

What LT1 / LT2 Are

LT1 (first lactate threshold) is the exercise intensity at which blood lactate begins to rise above resting baseline. Below this point, the body primarily burns fat, lactate production is slow, and breathing is easy. Past LT1, glycolysis kicks in, blood lactate rises gradually, and intensity enters the moderate aerobic range.

LT2 (second lactate threshold) is one level higher. Above this intensity, lactate accumulates faster than the body can clear it, ventilation increases, and breathing becomes labored. This is the upper limit of threshold training intensity, and it sits close to the fastest speed that can be sustained in a time trial.

These two points form the skeleton of the three-zone model: Zone 1 below LT1, Zone 2 between LT1 and LT2, Zone 3 above LT2. The five-zone model subdivides further — splitting the range below Zone 2, and splitting what’s above Zone 3. Polarized training, threshold training — all mainstream training models operate on this same axis, differing only in how they distribute volume.

In recent years, thanks largely to Peter Attia and Iñigo San Millán, “Zone 2” has become enormously popular in mainstream training culture — but the Zone 2 they’re referring to is Zone 2 of the five-zone model: easy aerobic intensity below LT1, roughly corresponding to blood lactate staying under 2 mmol/L. In the three-zone model, this same intensity is called Zone 1. The same label “Zone 2” refers to completely different intensity ranges depending on which system you’re using. Before discussing training zones, confirming which system you’re both using matters more than debating the numbers.

The framework does have real value — it gives training a shared language, a common foundation on which coaches, athletes, and researchers can all discuss intensity.

But the term “lactate threshold” itself implies a switch — one state before you cross it, another after. Human metabolism has no such switch. Lactate is continuously produced and cleared at every exercise intensity. LT1 and LT2 are statistical description points for lactate dynamics under specific conditions, not hard physiological boundaries.

In the 1970s, a German sports medicine research group in Cologne systematically developed lactate testing methods and established 4 mmol/L as the benchmark for anaerobic threshold. That number was a meaningful population average at the time — but it has since been widely applied to individual training, well beyond the scope it was originally designed for.

The Reality of Measuring LT1 / LT2

Finding “your own” LT1 and LT2 is far more complicated than the numbers suggest.

There are currently more than ten methods for determining lactate threshold: visual inspection, D-max, fixed concentration (using 2 mmol/L for LT1 and 4 mmol/L for LT2), ventilatory threshold (VT1/VT2), individual anaerobic threshold (IAT). All of them are in use. None of them give exactly the same results.

How large is the gap between methods? Take D-max — a method that defines the threshold by finding the point of maximum geometric distance on the lactate curve — it ranks last for reliability in published research, with a coefficient of variation reaching 10.3%. That means the same person tested at different times can see results shift by more than 10%. The fixed concentration method has a coefficient of variation of 3–4%, relatively more stable, but only if that fixed value actually has physiological meaning for you.

The bigger problem is that 2 mmol/L and 4 mmol/L are population statistics, not individual physiological truths. One study had subjects run steadily at their own individually determined lactate threshold pace, then measured actual blood lactate — results ranged from 1.90 to 3.80 mmol/L. Everyone was running at “their threshold pace,” but the actual physiological states varied considerably. This means the concept of threshold pace does not point to a consistent physiological state across different people.

LT2 and maximal lactate steady state (MLSS) are often used interchangeably in practice, but most research shows MLSS corresponds to a slightly lower intensity than LT2. The two point in the same direction, but they’re not the same point. If your training is based on LT2, you may be running above MLSS for extended periods without realizing it.

Pre-test conditions are a major factor in results, and one that’s typically underestimated. Muscle glycogen is the primary substrate for blood lactate. Carbohydrate intake in the days before testing, training load, and sleep quality all change the shape of the lactate curve. With depleted glycogen, the entire curve shifts systematically, making the measured threshold intensity appear higher than it actually is. Residual high-intensity fatigue within 48 hours of the test similarly distorts readings.

Differences in sampling site (earlobe vs. fingertip) and reduced peripheral blood flow in cold environments — each detail adds more uncertainty to the measurement. Portable blood lactate analyzers tend to show systematic error at values above 6 mmol/L. Smartwatch algorithms for estimating lactate threshold perform reasonably well for people near the population average, but errors amplify considerably at the extremes — athletes who are very highly trained or very untrained.

For most recreational runners, lactate threshold heart rate (LTHR) estimated from a 30-minute time trial is a more practical field option. Three Heart Rate Zone Methods compares HRmax, Heart Rate Reserve, and LTHR as anchor points.

Training Models: No Clear Winner

Suppose you’ve completed a rigorous test and have relatively reliable LT1 and LT2 values. The next question: which training model produces the best results?

A 2024 systematic review that pooled multiple randomized controlled trials compared polarized training, threshold training, and pyramidal training in recreational athletes — and found no significant difference between the three.

A 2018 meta-analysis did find a moderate advantage for polarized training on time trial performance, but that advantage appeared primarily in studies with interventions shorter than twelve weeks. When the intervention extended beyond twelve weeks, the VO₂max improvements from polarized training converged with those of other models. Short-term data supports polarized training; long-term data has no clear winner.

The relative advantage of polarized training in existing research shows up mainly among elite and world-class athletes — people whose aerobic base is already so strong that more precise intensity distribution is needed to continue driving adaptation. For recreational runners, execution consistency predicts training outcomes better than model selection.

A 2025 review stated directly: each training intensity distribution model has contexts where it applies best; the most effective approach is to adjust dynamically based on training phase and individual needs, rather than committing rigidly to any single framework.

For most recreational runners, debating “polarized vs. threshold” is energy spent in the wrong place.

Training Is an Art

Many runners keep highly detailed training logs — every run tagged with a zone, heart rate data fully analyzed. Following a plan is fine, and many people genuinely do improve doing it.

The problems usually appear outside the framework — when the plan and the body’s state don’t match, when test results come back unexpected, when training stalls and it’s unclear where to adjust. If your understanding of zone models stops at “fill in the numbers,” you’ll have very little room to maneuver when these situations arise.

Research shows that training plans dynamically adjusted based on individual recovery status and training state outperform any fixed plan set in advance. Two recreational runners with the same LT1/LT2 values but different training ages — say, one year versus five — need different types of stimulus, recover at different rates, and have different intensity tolerance. Even if their threshold numbers are identical, the appropriate training plan is not.

Newer runners have the most room to grow from neural adaptation and movement efficiency. At this stage, almost any training produces results; threshold precision matters relatively little, and volume alone is enough to drive adaptation. More experienced runners have saturated their neural adaptation ceiling and need more precise intensity design to continue pushing adaptation — their tolerance for measurement error is correspondingly lower.

These two situations call for fundamentally different training prescriptions. But zone models don’t tell you which category you’re in.

Translating all these factors into an actual training plan involves judgment calls: current training history, past injury record, this week’s stress and sleep, how far out the season target is. None of that can be replaced by a test result.

LT1 and LT2 are useful maps. They describe how the body operates at different intensities and give a shared language for discussion. But the map is not the terrain.

The hard part starts after you have the number.

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References

1. Faude, O., Kindermann, W., & Meyer, T. (2009). Lactate threshold concepts: How valid are they? Sports Medicine, 39(6), 469–490. ↗
2. Urhausen, A., et al. (2022). Lactate thresholds and the simulation of human energy metabolism. Frontiers in Physiology, 13, 899670. ↗
3. Gronwald, T., et al. (2023). From incremental test to continuous running at fixed lactate thresholds: Individual lactate responses. PLOS ONE. ↗
4. Layne, A. S., et al. (2025). Accuracy of fixed intensity anchors to estimate lactate thresholds in recreational runners. PLOS ONE. ↗
5. Mujika, I., et al. (2026). Validity and reliability of portable blood lactate analyzers: Systematic review. Sports Medicine – Open. ↗
6. Boullosa, D., et al. (2025). Accuracy of wearables for determining VO₂max and lactate threshold. Frontiers in Sports and Active Living. ↗
7. Stöggl, T. L., & Sperlich, B. (2018). Polarized versus threshold training intensity distribution. Frontiers in Physiology. ↗
8. Rosenblat, M. A., et al. (2024). Comparison of polarized versus other types of endurance training in recreational to elite athletes: Meta-analysis. Journal of Strength and Conditioning Research. ↗
9. Casado, A., et al. (2025). Recent advances in training intensity distribution theory for cyclic endurance sports. Frontiers in Physiology. ↗
10. Vesterinen, V., et al. (2022). Individualized endurance training based on recovery and training status in recreational runners. International Journal of Sports Physiology and Performance, 17(12), 1776–1783. ↗

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The Mechanism That Actually Makes Training Work

Fri, 12 Jun 2026 00:00:00 GMT

Training itself doesn’t make you better.

When you finish a long run or a set of high-intensity intervals, your body’s capacity actually drops in that moment — muscle micro-damage accumulates, glycogen gets depleted, fatigue builds up. What actually makes you better is the recovery that follows.

Supercompensation: Your Body’s Defensive Upgrade

In 1950, Hans Selye proposed the General Adaptation Syndrome (GAS): the body’s response to stress follows three stages — alarm, resistance, exhaustion. Exercise science borrowed this framework: an appropriate training stress pushes the body into the resistance phase, and after recovery, it doesn’t just return to baseline — it surpasses it.

That “surpassing” is supercompensation.

You apply a training stimulus. Your body’s capacity temporarily drops. During recovery, your body rebuilds the damaged structures and “over-repairs” — preparing itself for a stimulus of similar intensity next time. That window of over-repair is when supercompensation occurs. If your next training session lands in this window, you’re applying pressure from a higher baseline, and capacity spirals upward.

The problem: the timing of this window can’t be measured precisely. No device can tell you in real time that “right now is the supercompensation peak, go train.” HRV and resting heart rate are the most practical proxy indicators available, but both have limited precision. This is the biggest practical limitation of the supercompensation model.

Overloading: Forcing the Body to Adapt

The prerequisite for supercompensation is that you’ve given your body a stimulus worth adapting to. That’s the core logic of Overloading.

Overloading means making training stress exceed your current adaptation level, forcing a higher-order adaptation. It’s a tool for progress, not a warning label.

The ACSM’s 2009 resistance training position statement lists the overload variables you can manipulate: load, sets, reps, frequency, movement tempo, rest intervals. “Train more” is not the only way to overload — increasing intensity, shortening rest, or adding frequency are all ways to increase physiological stress without changing total volume.

Training volume (Volume) is itself the primary source of physiological stress. A “smart program” with insufficient training stress has no supercompensation driver; a basic program with sufficient stress and sufficient recovery will still produce adaptation.

The critical point: overloading must advance within the limits of your recovery capacity. When stress accumulates faster than recovery, supercompensation doesn’t happen — instead, you get the next state on the spectrum.

Overreaching: The Line You Only Know You Crossed After the Fact

Overreaching refers to a state where training stress exceeds current recovery capacity, causing a short-term performance decline. The most widely cited classification comes from the 2013 joint consensus statement from the ECSS and ACSM, which organized related states into three tiers [Meeusen et al., 2013]:

Functional Overreaching: Recoverable within days to weeks; capacity typically ends up higher than before training. Elite training cycles often deliberately push into this state in the weeks before competition, then pair it with a taper so supercompensation surfaces before race day.

Non-functional Overreaching: Takes weeks to months to recover. Symptoms go beyond “tired” — hormonal abnormalities appear, with blunted stress responses in growth hormone, ACTH, and prolactin, along with clear deterioration in psychological markers. The training structure has already gone wrong.

Overtraining Syndrome (OTS): Recovery takes months, sometimes more than a year. Multiple systems fail simultaneously — neurological, endocrine, immune. This is a clinical diagnosis, not something that can be self-assessed.

This framework has one fundamental practical problem: the boundary between Functional Overreaching and Non-functional Overreaching is invisible in the moment. All you know is your training load and how tired you feel. Fast recovery gets labeled Functional in retrospect; slow recovery gets labeled Non-functional. There’s no way to know before you start whether today’s training is pushing you past that line.

Halson and Jeukendrup noted as early as 2004 that direct experimental evidence for “overreaching necessarily precedes overtraining” is lacking [Halson & Jeukendrup, 2004]. The spectrum model’s boundaries have never been sharp in practice.

Press Harder, Bounce Higher — This Intuition Is Wrong

If Functional Overreaching paired with a taper brings out supercompensation, is pushing deeper always better?

In 2014, Aubry et al. answered this directly with an experiment [Aubry et al., 2014]. 33 trained male triathletes were split into a normal training group and an overtraining group. Within the overtraining group, 11 were confirmed to have reached Functional Overreaching; the other 12 only reached Acute Fatigue. All subjects then completed a four-week taper.

Results: The Acute Fatigue group showed a supercompensation gain of 2.6% (± 1.1%); the normal training group showed 2.6% (± 1.6%) — similar magnitudes, but the Acute Fatigue group was more consistent. The Functional Overreaching group showed lower supercompensation than the Acute Fatigue group, and had a 70% upper respiratory infection rate during the taper — compared to roughly 10–20% in the normal training and Acute Fatigue groups.

The optimal pre-taper state is Acute Fatigue, not Overreaching. Crossing that line doesn’t make you bounce higher — it makes your system start to break down.

Overtraining Syndrome: It’s Not Just About Training Too Much

You might think that controlling training volume prevents Non-functional Overreaching or Overtraining Syndrome. That assumption doesn’t hold either.

The EROS research series (Endocrine and Metabolic Responses on Overtraining Syndrome) is the most systematic study of Overtraining Syndrome in recent years. The EROS-DISRUPTORS findings were surprising: between Overtraining Syndrome athletes and healthy athletes, training volume showed no statistically significant difference [Cadegiani & Kater, 2019].

The actual independent trigger for Overtraining Syndrome? Diet.

Insufficient carbohydrate intake, insufficient protein intake, insufficient total caloric intake — these three factors can independently induce Overtraining Syndrome without any change in training volume. The effect of insufficient protein is particularly striking: statistically, each unit of protein deficit corresponds to an odds ratio (OR) of 16.7 for developing Overtraining Syndrome.

Overtraining Syndrome patients show what the authors called “Paradoxical Deconditioning”: their physiological markers don’t resemble athletes — they resemble sedentary individuals. Metabolic rate drops, body fat increases, muscle mass decreases, hormonal responses blunt. They keep training, but the adaptive benefit of that training has completely disappeared.

Not enough sleep: recovery capacity drops. Not enough calories: repair materials run short. High life stress: the same training load creates more effective physiological stress. Volume hasn’t increased, but the denominator of recovery capacity is shrinking — relative stress rises.

What This Means for Training Design

Program design isn’t just about arranging training stimuli — it’s about designing the recovery rhythm. Supercompensation needs both adequate stress and adequate recovery. High weekly intensity without planned recovery days or recovery weeks leaves no space for supercompensation to occur.

Overloading is a conscious choice, not a byproduct of “training hard.” The question isn’t whether to apply stress — it’s how much and how much recovery follows. This is a variable that needs active management.

The Functional Overreaching boundary is unpredictable; conservative accumulation is more reliable than aggressive escalation. Aubry 2014 made it clear: Acute Fatigue is the optimal pre-taper state; pushing into Functional Overreaching is counterproductive — and you can’t tell in the moment which side of the line you’re on.

Overtraining Syndrome risk depends on recovery capacity, which is jointly determined by diet, sleep, and life stress. The same training program produces completely different effective stress loads under different life conditions.

The essence of program design is managing the rhythm of stress within the limits of what recovery can handle. When recovery isn’t designed seriously, training stimuli are just stress accumulation.

It doesn’t matter how polished your program looks. If recovery doesn’t happen, it’s all wasted effort.

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References

1. Selye, H. (1950). Stress and the general adaptation syndrome. British Medical Journal, 1(4667), 1383–1392.
2. Bompa, T. O., & Haff, G. G. (2009). Periodization: Theory and Methodology of Training (5th ed.). Human Kinetics.
3. Meeusen, R., Duclos, M., Foster, C., Fry, A., Gleeson, M., Nieman, D., ... & Urhausen, A. (2013). Prevention, diagnosis, and treatment of the overtraining syndrome: Joint consensus statement of the European College of Sport Science and the American College of Sports Medicine. Medicine & Science in Sports & Exercise, 45(1), 186–205. ↗
4. Halson, S. L., & Jeukendrup, A. E. (2004). Does overtraining exist? An analysis of overreaching and overtraining research. Sports Medicine, 34(14), 967–981. ↗
5. Issurin, V. B. (2008). Block periodization versus traditional training theory: A review. Journal of Sports Medicine and Physical Fitness, 48(1), 65–75.
6. Kreher, J. B., & Schwartz, J. B. (2012). Overtraining syndrome: A practical guide. Sports Health, 4(2), 128–138. ↗
7. Aubry, A., Hausswirth, C., Louis, J., Coutts, A. J., & Le Meur, Y. (2014). Functional overreaching: The key to peak performance during the taper? Medicine & Science in Sports & Exercise, 46(9), 1769–1777. ↗
8. Cadegiani, F. A., & Kater, C. E. (2019). Novel causes and consequences of overtraining syndrome: The EROS-DISRUPTORS study. BMC Sports Science, Medicine and Rehabilitation, 11(1), 21. ↗
9. Cadegiani, F. A., & Kater, C. E. (2020). Diagnosis of overtraining syndrome: Results of the Endocrine and Metabolic Responses on Overtraining Syndrome study—EROS-DIAGNOSIS. Journal of Sports Medicine, 2020. ↗
10. American College of Sports Medicine. (2009). Progression models in resistance training for healthy adults. Medicine & Science in Sports & Exercise, 41(3), 687–708.
11. Kenttä, G., & Hassmén, P. (1998). Overtraining and recovery: A conceptual model. Sports Medicine, 26(1), 1–16. ↗

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Your Muscles Never Run on Just One Engine

Thu, 11 Jun 2026 00:00:00 GMT

Running 100 meters is “anaerobic,” running a marathon is “aerobic” — you’ve heard this before. It’s convenient, intuitive, and almost completely wrong.

Inside real muscle, three energy systems never take turns. They all run simultaneously, with proportions shifting as intensity changes.

What Is ATP

Muscles need energy to contract, but they can’t burn food directly. Food must first be converted into ATP (adenosine triphosphate) — think of it as the body’s universal energy currency. Muscles only accept this format.

The problem is that muscles store very little ATP — all-out effort exhausts the supply in seconds. The job of all three energy systems is to continuously replenish the ATP that gets used. The faster and more abundantly they replenish it, the higher the intensity you can sustain for longer.

Three Systems, Each with Its Role

Phosphocreatine System: 0 to 10 seconds

The fastest system. Alongside ATP, muscles store phosphocreatine (PCr) — think of it as a backup battery. The moment ATP runs low, PCr plugs in and recharges it almost instantly, with no oxygen required.

The tradeoff: very limited reserves. Within 10 seconds of high-intensity effort, more than half is depleted; within a few minutes, nearly all of it is gone. The 100-meter sprint relies primarily on this system, but it can’t sustain prolonged effort.

Lactate System: 10 seconds to about 2 minutes

As PCr reserves fall, the body shifts to glycolysis — breaking down glucose into lactate at a rate roughly 100 times faster than the aerobic system, quickly bridging the energy gap when intensity spikes.

The tradeoff: low efficiency. Each glucose molecule yields only 2 ATP; the aerobic pathway produces 32. Fast, but costly. The 400- to 800-meter races are where this system does most of the work.

Aerobic System: Active from the Start, Dominant after 75 Seconds

The slowest to ramp up, but with near-unlimited capacity. Glucose and fat are fully oxidized in the mitochondria — one glucose yields about 32 ATP, fat yields even more, and it runs continuously as long as there’s oxygen and fuel. Marathon pace is almost entirely sustained by this system.

Its weakness is slow activation. When high-intensity demand spikes suddenly, the aerobic system can’t keep up — that’s why the first two systems step in to bridge the gap. What limits the aerobic system isn’t fuel; it’s the rate at which oxygen can be delivered.

Three Systems Running at the Same Time

None of these systems ever “turn off.”

Research has measured the energy contribution of each system at different exercise intensities:

Duration	Phosphocreatine	Lactate System	Aerobic System
10 sec (full sprint)	53%	44%	3%
30 sec	23%	49%	28%
75+ sec	Declining	Peaking then declining	Above 50%, still rising

During a full-effort 10-second sprint, the aerobic system is still contributing 3% — it never fully stops. By 30 seconds, the aerobic share has already jumped to 28%.

This means there is no such thing as “purely anaerobic running” — only “running with a higher anaerobic proportion.” Conversely, even at marathon pace, the phosphocreatine system never fully shuts down; it contributes to every push-off step.

Intensity changes the ratio — not the switch.

Fast-Twitch and Slow-Twitch Muscle

Before talking about lactate, there’s a foundational concept to establish: not all muscle fibers are the same. Because lactate circulates between fiber types, the next section won’t make sense without this.

Slow-twitch fibers (Type I): aerobically powerful, packed with mitochondria, fatigue-resistant, built for sustained output. The workhorse of long-distance running.

Fast-twitch fibers (Type II): high force, high power, but weaker oxidative capacity, relatively quick to fatigue. Sprinting, jumping, and explosive movements rely on these.

This difference directly shapes how the lactate system behaves.

Lactate Is Fuel

Many people believe running fatigue is caused by lactate accumulation. That picture needs updating.

The lactate system in fast-twitch fibers continuously produces lactate — not only at high intensity, but even during easy jogging, fast-twitch fibers are running glycolysis and generating lactate. What happens then is that this lactate travels through the blood to neighboring slow-twitch fibers, where it’s taken up into the mitochondria and oxidized as fuel for the aerobic system. At low intensity, the rate of lactate production roughly matches the rate of utilization, so blood concentration doesn’t build up.

The heart also preferentially takes up lactate as fuel. During high-intensity exercise, lactate can supply up to 60% of cardiac muscle’s energy needs.

Lactate is not a waste product — it’s a circulating fuel. Rising blood lactate is a signal that the body is running efficiently, with fast-twitch fiber production outpacing slow-twitch consumption. The mechanism that makes your legs give out in the late stages of a race is a different story, and its relationship to lactate itself is far less direct than traditionally assumed.

(A full research breakdown on this topic is in Lactate Is Not Your Enemy.)

The Design of Fatigue

When muscles reach exhaustion during all-out effort, ATP doesn’t actually hit zero.

At rest, ATP concentration is around 8 mmol/kg. At the point of complete collapse, the minimum is roughly 5 mmol/kg. Energy demand may spike 1000-fold, but ATP drops by only a few units. This isn’t accidental — it’s a protective mechanism. If ATP truly ran out, actin and myosin in the muscle fibers would lock up, unable to relax, and the muscle would enter rigor.

The point at which your legs stop working is the body applying the brakes before an ATP crisis can occur. It stops you — before it breaks you.

From this perspective, fatigue is a precision protection system, not a sign of failure.

Training Is Mostly About Upgrading the Aerobic Engine

Of the three systems, training produces the most visible adaptations in the aerobic system.

Mitochondrial density, efficiency, and the electron transport chain capacity within each mitochondrion all change with endurance training. Six months of endurance training can increase mitochondrial maximum ATP production rate (ATPmax) by 32%, with a corresponding 17% increase in leg power output. This improvement is roughly evenly distributed across three directions: more mitochondria, higher efficiency per mitochondrion, and greater electron transport chain capacity.

From rest to all-out effort, the aerobic system’s ATP output can span a 50-fold range. The larger that range, the more energy is available across every intensity level.

Once the aerobic foundation improves, it’s not just easy running that gets faster — high-intensity capacity also advances. This is because the lactate system is under less pressure, and the phosphocreatine system recovers faster. The three systems are not independent: build the aerobic base solidly, and both of the other systems perform better.

Once You Understand This, Training Logic Follows Naturally

Why Is Zone 2 the Foundation of Endurance?

Zone 2 is the intensity range where the aerobic system is highly engaged, but the rate of lactate accumulation hasn’t yet exceeded the rate of utilization — roughly the pace where you can still form complete sentences, but have started to feel the effort. At this intensity, slow-twitch fibers work continuously, and mitochondria receive sustained, stable stimulus, gradually increasing in both number and efficiency.

Many runners find Zone 2 “too easy — nothing’s happening,” and push the intensity up. But once the lactate system is driving, the direction of adaptation shifts. Zone 2 is about upgrading the mitochondrial factory, and that adaptation requires accumulated time — not acute suffering.

Rest Interval Length in Interval Training Determines Which System You’re Training

Full recovery of phosphocreatine requires 5 to 15 minutes. Short sprint intervals (6 to 10 seconds all-out) paired with sufficient rest allow the phosphocreatine system to start each rep with full reserves — the training target is that system’s explosive power.

If the goal is to stimulate the lactate system, shortening rest is deliberate — keeping blood lactate elevated and forcing the lactate system to operate under sustained metabolic pressure. That’s the logic behind 400- to 800-meter pace intervals.

Rest intervals aren’t “longer is better” — they’re part of the training target. You design the combination that puts maximum stress on the system you want to strengthen.

Why Does Training Only at Race Pace Lead to a Plateau?

Without raising the ceiling of the aerobic engine, you hit a speed wall quickly. You can push through with willpower, but the cost is a lactate system that’s overloaded every session — slower recovery, and cumulative fatigue that outpaces adaptation. With a weak aerobic base, speed training can only deliver gains within a narrow window.

This is also why many runners who have trained seriously for years start improving only after they add a large volume of easy running — it’s not that slow running makes you faster. It’s that upgrading the aerobic engine raises the returns on training across the board.

Energy is not three separate lines. It’s a flame that always burns simultaneously, with the proportions constantly in motion.

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References

1. Spriet, L. L. (2012). Interaction among skeletal muscle metabolic energy systems during intense exercise. Journal of Nutrition and Metabolism, 2012, 905612. ↗
2. Adeva-Andany, M. M., et al. (2014). Biochemistry, anaerobic glycolysis. StatPearls. ↗
3. Layec, G., et al. (2016). Mitochondria to motion: optimizing oxidative phosphorylation to improve exercise performance. Journal of Physiology, 594(18), 5231–5241. ↗
4. Harms, M., & Johansson, A. (2022). A century of exercise physiology: key concepts on coupling respiratory oxygen flow to muscle energy demand during exercise. European Journal of Applied Physiology. ↗

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Lactate Is Not Your Enemy

Wed, 10 Jun 2026 00:00:00 GMT

The Norwegian Double Threshold method, Zone 2, the LT1-to-LT2 continuum, lactate-guided pacing — modern endurance training vocabulary is built almost entirely on the concept of lactate. Elite athletes get blood drawn regularly to calibrate every training intensity against blood lactate values rather than heart rate or pace; Norwegian runners anchor key sessions in the “2 to 4 mmol/L” range; even the term “threshold pace” was originally defined by the lactate threshold as a physiological marker.

Without understanding lactate, it’s hard to truly understand what these training frameworks are saying — or to judge whether your own training is in the right place.

The problem is that most runners have the role of lactate exactly backwards.

Energy Isn’t Three Lines — It’s a Spectrum

Introductory running physiology usually comes with a three-system chart: the phosphocreatine system (PCr), the glycolytic system, and oxidative metabolism, mapped respectively to sprints, middle distances, and endurance. It’s a useful teaching framework — but inside real muscle, these three systems never take turns.

They run simultaneously. Only the relative contributions shift with intensity. The aerobic system is still working during a 400-meter sprint; the phosphocreatine system never fully shuts down at marathon pace. More importantly: the glycolytic system doesn’t need high intensity to “turn on” — even at easy jogging pace, fast-twitch fibers are continuously running glycolysis and continuously producing lactate. That lactate is immediately taken up and oxidized by neighboring slow-twitch fibers; it just never accumulates in the blood.

Lactate isn’t an alarm that goes off at some threshold intensity. It’s the baseline state of metabolism.

Lactate Is Fuel

Since the 1930s, lactate carried the reputation of a waste product in exercise physiology — a dead-end byproduct of glycolysis that acidified the muscle environment, made you slow down, and signaled fatigue. That understanding was wrong for decades.

In 1985, George Brooks at UC Berkeley proposed the lactate shuttle hypothesis, arguing that lactate moves continuously between cells and tissues as an actively transported, actively used energy source — not a waste product waiting to be cleared. The hypothesis has since been supported repeatedly across different laboratories and methodologies.

Specifically: the heart preferentially selects lactate as an energy substrate, and during high-intensity exercise lactate can account for up to 60% of cardiac fuel. The brain also takes up blood lactate as exercise intensity rises. Oxidative slow-twitch fibers continuously absorb lactate from fast-twitch fibers and oxidize it under aerobic conditions — not by accident, but by design.

Lactate transport depends on a family of membrane proteins called MCTs (monocarboxylate transporters). MCT1 primarily moves lactate into high-oxidative cells (slow-twitch muscle, cardiac muscle); MCT4 primarily exports lactate out of fast-twitch muscle. One of the adaptations from endurance training is increased MCT1 expression density in muscle — more and faster channels for lactate to enter oxidative cells.

In 2004, Gladden published a 26-page literature review in the Journal of Physiology synthesizing thirty years of research: lactate is not a byproduct of oxygen deprivation, but a highly active fuel molecule. During moderate-intensity exercise, the turnover rate of lactate in the blood can even exceed that of glucose.

Brooks pushed further. His 2018 paper in Cell Metabolism showed that lactate is not only a fuel but a cellular signaling molecule — regulating gene expression, promoting wound healing and angiogenesis, acting as an energy-regulatory signal for the brain. He called this the “lactormone” concept: a metabolite that functions simultaneously as fuel and hormone. From “waste product” to “critical fuel” to “signaling molecule” — few updates in exercise physiology have covered that much ground.

Lactate Isn’t What Makes You Slow

So why do your legs start to fall apart at high intensity?

The old explanation was hydrogen ions (H⁺) — lactate dissociates and releases H⁺, acidifying the intramuscular environment and inhibiting enzyme activity and muscle contraction. This framing worked its way deep into training language: “clear lactate,” “train the body to tolerate high lactate,” “build acid tolerance.”

The problem: this mechanism is far weaker in living tissue at physiological temperature (37°C) than in the low-temperature, in vitro experiments that generated it. Westerblad et al. (2002) showed that at normal working muscle temperatures, H⁺ inhibition of force output is negligible. Many of the early experiments that put lactate on trial were run on isolated muscle at low temperature — then extrapolated to the whole human. The lactate ion itself (La⁻) has even less direct effect on muscle contraction; Posterino et al. (2001) found the impact to be under 5%.

The more likely culprit for late-race form breakdown is inorganic phosphate (Pi). When phosphocreatine (PCr) breaks down to supply energy, it releases Pi. Pi enters the sarcoplasmic reticulum and binds with calcium ions (Ca²⁺), forming calcium phosphate precipitate and reducing the amount of Ca²⁺ available for release. Muscle contraction is triggered by calcium — less calcium means each neural signal recruits less contractile force, turnover falls apart, form collapses. You feel like you’re “too acidic to run,” but what’s actually happening is that calcium can’t get out.

Rising lactate is a signal that your body is working efficiently. What makes you slow is a different mechanism entirely.

The Flawed Logic of “Lactate Clearance” Training

Jack Daniels’ T-pace workout — 6 × 1.6 km with one-minute recoveries — I’ve run it many times. The first three reps are usually fine. By the fourth, my cadence starts to break down. Fifth rep, calves tighten. Sixth rep is pure willpower. The next day’s fatigue feels like I raced.

I used to think this was what lactate threshold training was supposed to feel like. Hard means effective. You’re training the body to “tolerate high lactate,” to “clear lactate faster.” The logic sounds clean. But it’s physiologically backwards.

“Clearing lactate” isn’t flushing it out — it’s oxidizing it. Burning it. Building that capacity requires: more mitochondria (more oxidative sites), higher MCT1 density (faster lactate entry into oxidative cells), stronger oxidative enzyme activity (more efficient combustion). These adaptations need a specific intensity range — one where lactate is moving but not rapidly accumulating, where the body has enough time to oxidize what it produces. Above that range, production outpaces oxidation, blood lactate spikes, and what the body experiences is depletion, not productive adaptation.

This is precisely why the Norwegian Double Threshold method anchors quality sessions in the 2–4 mmol/L blood lactate range — below LT2, not above it. Marius Bakken ran over five thousand self-tests to validate and refine the system. The Ingebrigtsen brothers and triathlon Olympic champion Kristian Blummenfelt both train within this framework. A 2023 systematic review of the method concluded that lactate-guided threshold training is one of the best-supported training models in elite endurance performance.

The catch: LT2 is a physiological value that requires a blood draw to measure accurately. It corresponds roughly to 4 mmol/L, but it varies between individuals and shifts with training state. “Breathing hard but still able to talk” — that kind of perceptual description can be far off from your actual LT2.

Looking back at those T-pace sessions where my form broke down in the final reps, the more likely explanation is that I was running above LT2, not at it. That feeling of each rep getting slower, the last one held together by will alone — that wasn’t effective threshold stimulus. That was burning myself out at an intensity that was just too high.

I eventually moved away from T-pace workouts and shifted toward sustained tempo running — marathon-to-half-marathon pace, feeling good at the end, able to keep training. Lactate moving, not rapidly accumulating. Giving the body continuous opportunity to oxidize what it produces, letting the whole metabolic network slowly upgrade. Form hasn’t broken down since.

Lactate Is Not Wastewater

Lactate is not a waste product. It’s one of your body’s highest-priority, most efficient fuels — and the core indicator around which modern endurance training logic is built.

You don’t need to avoid lactate, train your body to “tolerate” it, or grind through high-lactate environments to “build clearance.” What you need is a stronger engine for oxidizing it — more mitochondria, faster transport channels, higher combustion efficiency. That engine is built by doing sustained, consistent work in the right intensity zone. Not by running yourself into the ground.

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References

1. Gladden, L. B. (2004). Lactate metabolism: a new paradigm for the third millennium. Journal of Physiology, 558(1), 5–30. ↗
2. Brooks, G. A. (2018). The science and translation of lactate shuttle theory. Cell Metabolism, 27(4), 757–785. ↗
3. Westerblad, H., Allen, D. G., & Lännergren, J. (2002). Muscle fatigue: lactic acid or inorganic phosphate the major cause? News in Physiological Sciences, 17, 17–21.
4. Posterino, G. S., Fryer, M. W., & Lamb, G. D. (2001). Direct effects of acidosis on sarcoplasmic reticulum function in mechanically skinned skeletal muscle fibres. Journal of Physiology, 534(3), 863–884.
5. Bakken, M. (2022). The Norwegian Model. mariusbakken.com
6. Casado, A., Foster, C., Bakken, M., & Tjelta, L. I. (2023). Does lactate-guided threshold training within a high-volume low-intensity approach represent the "next step" in the evolution of distance running training? International Journal of Environmental Research and Public Health, 20(5), 3782.

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What This Blog Covers

Wed, 10 Jun 2026 00:00:00 GMT

A map of everything covered on The Right Pace. Each topic links to core concepts and published articles — find your starting point.

Weekly Training Notes

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🔬 Exercise Physiology

The underlying mechanisms of endurance sport. Understanding these is what lets you evaluate whether a training method actually makes sense.

From Metrics to Intuition: Deconstructing Five Intensity Indicators and Your Physiological Map Jul 15, 2026
Your Achilles Tendon Isn't Just a Spring Jul 7, 2026
Three Heart Rate Zone Methods: HRmax, HRR, and LTHR Jun 25, 2026
Running Form Is an Outcome: How Movement Is Generated Jun 23, 2026
Running Economy: It's Deeper Than Your Form Jun 22, 2026
Train with a Purpose Jun 16, 2026
Zone 2 Training: Why All the Talk Jun 15, 2026
LT1 / LT2 and Training Zone Models: The Map Is Not the Terrain Jun 12, 2026
The Mechanism That Actually Makes Training Work Jun 12, 2026
Your Muscles Never Run on Just One Engine Jun 11, 2026
Lactate Is Not Your Enemy Jun 10, 2026

📈 Training Science

Coaching philosophies and how elite athletes structure their seasons — standing on the shoulders of giants to shape your own training.

Is Training a Science, or a Craft? Jun 30, 2026
The Mechanism That Actually Makes Training Work Jun 12, 2026

🦶 Movement Control

Running is more than going faster — neuromuscular coordination, gait efficiency, and the mechanics behind movement patterns.

Running Form Is an Outcome: How Movement Is Generated Jun 23, 2026

⚡ Running Economy

Same VO₂max, running economy decides who's faster. From biomechanics to energy efficiency.

Running Economy: It's Deeper Than Your Form Jun 22, 2026

📚 Book Notes

Books, papers, podcasts, interviews — different sources, same standard: distilled perspectives after genuine digestion, not summaries.

Zone 2 Training: Why All the Talk Jun 15, 2026

🏁 Race Reports & Race Strategy

From build-up to race day — training block planning, pacing strategy, post-race analysis.

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Welcome to The Right Pace

Tue, 02 Jun 2026 00:00:00 GMT

Why “The Right Pace”?

The cost of accessing endurance sports information is now effectively zero. YouTube, Instagram, podcasts, training communities — more content than anyone could ever consume. But the democratization of information is also the democratization of noise.

When anyone can publish training advice, the burden of quality control falls entirely on the reader. Exercise science has a steep learning curve, and without sufficient background knowledge it’s genuinely hard to distinguish sound training principles from ideas that circulate widely yet have long been contradicted by the research. What makes this harder is that misleading advice tends to carry the most compelling narrative — intuitive, memorable, easy to act on, yet glossing over the places where the physiology actually gets complicated. You’re not failing to put in the effort. You’re navigating with a beautifully drawn map that points the wrong way.

The Right Pace starts from that problem. I’ve spent a lot of time reading books and research — not to accumulate information, but to find the ideas that actually hold up. Every article here is built on primary sources: books, studies, papers worked through directly, not summaries or second-hand takes.

Running is about finding your right pace. What I’m trying to do is help you find the right path.

The Right Pace & The Right Path.

What’s Here?

This blog focuses on reading notes and learning from the science of endurance sports:

Book notes: Key takeaways from endurance and coaching books
Training theory: Heart rate zones, periodization, running mechanics
Exercise physiology: Lactate threshold, VO2max, aerobic base
Race reports: Marathon and ultramarathon experiences

Most content is in Traditional Chinese, with some posts in English.

If you’re curious about the science behind running performance, you might find something useful here.