VO2 Max Was Associated With Endurance Performance Proxies

VO2 maxThe maximum amount of oxygen the body can use during hard exercise. It is often used as a marker of aerobic fitness, but it does not fully predict endurance performance. gets plenty of attention from runners, watches, coaches, and the occasional spreadsheet goblin. This study helps place it in context by comparing VO2 max, exercise economyThe rate of energy expenditure (measured in kiloJoules [KJ], kilocalories [kcal] or oxygen consumption [VO2]) per kilogram body mass (kg) per unit of distance, i.e. per 1 kilometre travelled. A runner with a lower energy cost per kilometre has a higher economy than a runner with a higher energy cost., and threshold-related markers in a large group of runners and cyclists.

Reference: Mougin et al. 35 Years of Joyner’s Endurance Performance Model: Assessing the Contribution of Physiological Determinants of Performance Proxies in 888 Individuals from Recreational to World Class. Sports Medicine (2026) DOI: 10.1007/s40279-026-02439-y.

Study snapshot

A quick, practical summary for runners and coaches.

Quick answer

This retrospective observational studyA study where researchers observe exposures and outcomes without assigning people to a treatment or intervention. It can show associations, but it cannot prove cause and effect. examined lab test data from 888 runners and cyclists. VO2 max and exercise economy best explained speed or power at lactate thresholdLactate threshold is the exercise intensity where blood lactate starts to gradually rise faster than your body can clear it, but you can still keep going for a while. It marks the shift from “this is hard but manageable” to “this is going to catch up with me soon” if you hold it. and lactate turnpointLactate turnpoint (LT2) is a higher intensity than lactate threshold (LT1) where blood lactate shoots up rapidly, effort begins to feel hard, and fatigue comes on quickly. It’s the “red zone” where you can only last a short time before you have to slow down, and signals a shift toward more glycolytic (glucose-using) and anaerobic energy use.. The main caution is that the study measured lab-based performance proxies, not race results.

Key takeaways

  • VO2 max was the strongest physiological marker linked with the performance proxies.
  • The study did not prove that improving VO2 max or economy causes faster racing.
  • Runners and coaches should use lab data as useful clues, not crystal balls.

How confident should we be?

Evidence confidence: Moderate

The study used a large dataset and direct laboratory measurements. Confidence is limited because it was observational, mostly male, and focused on lab-based proxies rather than real races.

Bottom line

For runners, this study reinforces the useful but slightly boring truth: aerobic fitness matters, running economyThe rate of energy expenditure (measured in kiloJoules [KJ], kilocalories [kcal] or oxygen consumption [VO2]) per kilogram body mass (kg) per unit of distance, i.e. per 1 kilometre travelled. A runner with a lower energy cost per kilometre has a higher economy than a runner with a higher energy cost. matters, and no single number tells the whole story. Coaches should profile the athlete, not worship the spreadsheet.

Read the deep dive below for a practical interpretation, actionable decisions, my thoughts, my Rating of Perceived scientific Enjoyment, the study details, full results, strengths, and limitations.

Running science research reviews for endurance runners

The deep dive

The details behind the headline result, including the practical meaning, full findings, limitations, and my interpretation.

Practical meaning

What does this research mean for runners and coaches?

This study is most useful as a reminder that endurance performance is built from several moving parts. VO2 max describes the size of the aerobic engine. Exercise economy describes how much oxygen it costs to run or ride at a given speed or power. Lactate threshold and lactate turnpoint describe important intensity markers during exercise.

The study did not test a training programme. So, any training advice is interpretation, not a direct study finding.

For runners

For marathon runners, the findings are relevant because Joyner’s original model was built around marathon performance. But this study did not measure marathon performance, so the link is indirect.

For trail runners and ultra runners, the findings are useful but even more indirect. Trail and ultra performance also depends on terrain, climbing, descending, muscle damage, fatigue resistance, pacing, fuelling, and not making heroic decisions 7 hours into a race when your hallucinating in the pain cave.

The practical message is simple: do not obsess over 1 number. A runner with a high VO2 max can still be limited by poor economy or weak durabilityDurability is a measure of how “durable” your physiological performance metrics are during prolonged fatigue-inducing exercise. I.e., can you still produce your maximal power at the end of a hard 2 hour run?. A runner with excellent economy may still need more aerobic capacity. The stopwatch, the training log, and the body all get a vote.

For coaches

For coaches, the study supports multi-marker profiling. VO2 max, running economy, threshold speed, heart rate responses, race performance, training load, and athlete history can all help explain what a runner may need next.

The study also suggests that threshold expressed as a percentage of VO2 max may not separate mixed groups of athletes as strongly as VO2 max and economy. That does not make threshold useless. It may still matter for tracking an individual runner over time, especially when tests are repeated under similar conditions.

Practical decision

Should runners change anything?

Maybe. Use the findings as a nudge to look beyond 1 lab number or smartwatch estimate. The study supports broad athlete profiling, but it does not tell runners to change 1 specific training session, workout type, or racing strategy.

Consider this if

  • You only track VO2 max and ignore pace, economy, threshold, training history, and race results.
  • You coach runners and want a broader way to profile endurance capacity.
  • You have access to good lab testing and can repeat it under similar conditions.

Do not overreact if

  • Your watch VO2 max estimate changes by 1 point.
  • Your threshold pace does not improve every few weeks.
  • You race trail or ultra events where lab markers explain only part of the story.

A sensible next step

Use the study as a nudge to build the full endurance toolkit. Develop aerobic fitness. Improve economy through consistent, appropriate training. Track real-world performance. And, if you use lab testing, interpret the results in context rather than treating them like prophecy carved into a carbon-plated shoe.

TIP: Never make any major changes to your training or lifestyle habits based on the findings of one study, especially if the study is small or provides low-quality evidence. Check whether other trials confirm the findings. If there is a meta-analysisA study that statistically combines results from multiple studies to estimate the overall effect of an exposure or intervention. on the topic, look at the effect sizeA number that describes the size of a difference or relationship. It helps show whether a result is likely to matter in practice., the variability between studies, and the quality of evidenceA judgement about how much confidence we can place in the findings, based on study design, bias, consistency, precision, and relevance..

Expert interpretation

My thoughts

Running science from Thomas Solomon at Veohtu

This is a useful study for runners and coaches because it puts a large dataset behind a familiar coaching idea: endurance performance is not controlled by 1 magic variable.

VO2 max was the headline act. Exercise economy was the support band that rocked! Fractional utilisationFractional utilisation is the percentage of your VO2max that you can sustain at a given effort, often measured at the lactate threshold. It describes how much of your engine’s max power you can use steadily without fatigue. of VO2 max at threshold still mattered, but it contributed less when the researchers compared a broad mix of athletes.

The study should not radically change a training plan. It does not show that more VO2 max intervals will automatically make you faster. It does not show that improving running economy will guarantee a better marathon. It shows that these physiological markers help explain lab-based performance differences.

The practical value is in profiling. If 2 runners have similar race times but different physiology, they may need different training nudges. One might appear more limited by aerobic capacity. Another might need closer attention to economy, mechanics, durability, or training consistency. So, should we test more runners in labs? Maybe. But only if the results help the runner train better, not just give everyone another number to fret about over coffee or boast about on Instagram.

My Rating of Perceived scientific Enjoyment

owlRPsE: 7/10

I experienced moderate scientific enjoyment because the study used a large lab-based dataset, clear physiological measures, and asked a useful question for runners and coaches. The fun was slightly dampened by the retrospective observational design, mostly male sample, and use of performance proxies rather than actual race outcomes.

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Read on for further details about the paper.

Research question

What did the researchers ask?

The authors aimed to test how well the classic Joyner endurance performance model held up in a large group of runners and cyclists.

In plain English, they wanted to know how much VO2 max, exercise economy, and the percentage of VO2 max used at lactate threshold or lactate turnpoint contributed to lab-based markers of endurance performance.

Study design

What type of study was this?

This study was a retrospective observational study.

The researchers looked back at existing lab test data and examined associationsA relationship between 2 variables. An association does not prove that 1 variable caused the other. between physiological measures and performance proxies. This design can show which variables are linked with better lab-based performance markers, but it cannot prove that changing 1 variable would cause better race performance.

Participants

Who took part?

The study included 888 participants. There were 495 runners, including 105 female runners, and 393 cyclists, including 42 female cyclists.

The participants ranged from recreational to elite or world-class level, based on the authors’ classification framework. The participants were grouped from Tier 1 to Tier 5 using the McKay participant classification framework.

The authors did not clearly report the participants’ ages, weekly training, main race distance, or main endurance event.

Methods

What did the researchers do?

Who? 888 runners and cyclists
What? Laboratory exercise testing
How long? 1 lab visit

The participants completed laboratory-based incremental exercise tests during 1 lab visit. The runners tested on a treadmill. The cyclists tested either on their own bike mounted on a Wahoo Kickr smart trainer or on an SRM cycle ergometer.

The running test used 4-minute stages, with 30 seconds of rest between stages for earlobe blood sampling. After 15 to 20 minutes of rest, the runners completed another test to task failure to estimate VO2 max.

The cycling test also used 4-minute stages. Blood was sampled near the end of each stage without stopping exercise. After 15 to 20 minutes of rest, the cyclists completed a ramp test to task failure to estimate VO2 max.

The researchers measured oxygen use, breathing responses, blood lactate, heart rate, treadmill speed, and cycling power. They used these data to estimate VO2 max, exercise economy, lactate threshold, lactate turnpoint, and the percentage of VO2 max used at those thresholds.

They then used statistical models to test which physiological markers best explained speed or power at lactate threshold and lactate turnpoint.

Main findings

What did the study find?

VO2 max Strongest marker
Exercise economy Meaningful contribution
Fractional use of VO2 max Smaller contribution

VO2 max was the strongest marker. It contributed about 65% to 76% of the explained variation in the models. In runner language, the size of the aerobic engine still mattered. The force is fairly strong with this one.

Exercise economy also mattered. Running economy contributed about 20% to 22% of the explained variation in running speed at lactate threshold and lactate turnpoint. Cycling economy contributed about 21% to 24% of the explained variation in cycling power at lactate threshold and lactate turnpoint.

The percentage of VO2 max used at lactate threshold or lactate turnpoint contributed much less. It explained about 4% to 6% in runners and about 6% to 11% in cyclists. This does not mean threshold is useless. It means that, in this mixed group of athletes, it did not separate people as strongly as VO2 max and economy.

The full models explained almost all the variation in the lab-based performance proxies, with cross-validated r-valuesPearson’s r-value represents the correlation coefficient, which is a statistic that measures the strength and direction of a linear relationship between two variables, ranging from -1 to +1. An r value close to +1 indicates a strong positive correlation, close to -1 a strong negative correlation, and around 0 no linear relationship. ranging from 0.94 to 0.99. The prediction errors were small: about 0.3 to 0.4 km per hour for running speed and about 5 to 11 watts for cycling power. That is impressive for lab-based proxies, but it is not the same as predicting race-day performance.

The authors concluded that VO2 max and exercise economy collectively explained most of the speed or power at lactate threshold and lactate turnpoint, while fractional useFractional utilisation is the percentage of your VO2max that you can sustain at a given effort, often measured at the lactate threshold. It describes how much of your engine’s max power you can use steadily without fatigue. of VO2 max had limited value for separating performance in a mixed group of athletes.

What helps my confidence in the findings?

The strengths

  • The study included a large sample for a sports physiology study.
  • The study included both runners and cyclists.
  • The participants ranged from recreational to elite or world-class level.
  • The researchers used direct laboratory measurements rather than smartwatch estimates.
  • The researchers used cross-validation to test how well the models generalised within the dataset.

What limits my confidence in the findings?

The limitations

  • The study was observational, so it cannot prove cause and effect.
  • The study measured lab-based performance proxies, not real race results.
  • The sample was mostly male, especially in the cycling group.
  • The article did not clearly report training history, weekly training volume, or main endurance event.
  • The lab tests may not capture all the things that matter on race day: tactics, pacing, environment, durability, event specificity, or training background.

Funding and conflicts

Who funded the study?

No direct funding was received for the study. One author, Loïs Mougin, was funded by a Vice-Chancellor’s Fellowship at Loughborough University.

One of the authors, Andy Jones, reported that he is an Editorial Board member of Sports Medicine (the journal in which the paper was published), but that he was not involved in peer reviewer selection or editorial decisions for the manuscript. This does not automatically weaken the findings, but it is worth noting because editorial roles can create perceived conflicts. The journal appears to have managed this by keeping the author out of the editorial decision process. The other authors reported no conflicts of interestSituations where financial, professional, or personal interests could influence, or appear to influence, research decisions or interpretation. directly relevant to the article.

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