Compare thresholds transparently

Lactate threshold method comparison: why LT1 and LT2 can differ

Threshold methods do not answer exactly the same question. Their differences are not an error message; they show how clearly your data describes a threshold.

Why methods differ

Models use different assumptions: fixed lactate values, curve geometry or changes in slope. The same curve can therefore produce different LT1 or LT2 values.

  • Dmax uses curve geometry
  • Mader uses physiological model assumptions
  • Log-log and Delta inspect other curve signals

Interpret spread correctly

When methods converge, interpretation is often more robust. Wide spread is a prompt to inspect curve, stages and protocol, not an invitation to choose the preferred value.

  • Do not treat one value as absolute truth
  • Check data quality before training decisions
  • Keep methodology consistent across retests

For training and repeatability

Choose a traceable method for your own trend and keep it consistent. Comparing methods is more useful for Zone 2 or threshold decisions than an unexplained cutoff.

  • Use LT1 and LT2 for different purposes
  • Training guidance is not a medical diagnosis
  • Compare your own data directly in the analysis

Useful next steps for your test

Analyze your own lactate values

Enter pace or power and your measured lactate values. Get your curve, LT1, LT2, VLamax and training zones immediately; save and compare two of your own tests for free.

Why you can trust the analysis

LactateThreshold does not turn measurements into false precision. The app shows the curve, method comparison, plausible thresholds and the limits of your test protocol transparently.

Method comparison instead of a single black-box value
Clear separation of LT1, LT2, VLamax and training zones
Limits of self-testing are stated openly
Training guidance, not a medical diagnosis

Frequently asked questions

Which method is best?

No method is automatically best for every protocol and curve. Transparency and repeatability matter more than a supposedly perfect single number.

Should I switch methods for every test?

No. To assess a trend, keep the method and protocol similar across comparable tests.

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