Just to add to my original suggestion:
While precise values for FatMax, CHO/Fat oxidation, or metabolic crossover points do require lab-based metabolic testing, there is increasing scientific support and practical application for estimating these values using field data — especially from power meters, heart rate monitors, and established physiological models.
Here are some relevant research insights and modeling approaches:
Scientific Background & Modeling Examples
1. Estimating FatMax and Fuel Usage from HR and Power Data
Several models and studies suggest it’s possible to estimate fat vs. CHO utilization using HR, power output, and zone-based intensity.
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Maunder et al. (2018)
“Fat oxidation during exercise: determinants and constraints”
➤ Shows that FatMax typically occurs between 45–65% of VO₂max — which can be inferred from threshold models or zone estimates.
Frontiers in Physiology -
Venables et al. (2005)
“Determinants of fat oxidation during exercise in healthy men and women”
➤ Demonstrates that fat oxidation rates are largely predictable from VO₂max and exercise intensity.
PubMed
2. Fuel Estimation from Power Data Alone
Some tools (e.g., Xert, GoldenCheetah, WKO) use power-duration relationships to estimate substrate usage:
- Caloric burn = Power × Gross Efficiency (~22–25%)
- Then, based on intensity relative to threshold, %CHO vs. %Fat can be estimated (e.g., higher CHO above LT1/LT2)
3. VO₂max / VLamax Based Models (e.g. INSCYD, Aerotune, Sentiero)
These platforms estimate substrate utilization via field testing:
- VLamax (glycolytic rate) + VO₂max are used to model the balance between aerobic (fat-dominant) and anaerobic (CHO-heavy) energy systems.
- Metrics like FatMax, CHO burn (g/h), Fat burn (g/h) across wattage are derived using athlete profiles and power data — no lab required.
Example from INSCYD:
Understanding VLamax & FatMax
Conclusion
So while individual accuracy will always benefit from lab diagnostics, there is a strong case for implementing data-driven estimates of metabolic profiles in platforms like Intervals.icu — especially considering how much high-quality power and HR data is already available.
This could enable users to:
- Identify their estimated FatMax zone
- Visualize substrate usage across intensities
- Tailor training and fueling strategies accordingly
Would love to hear if others are interested in this too, or have worked on similar implementations!
P.S. Since CHO Used is already integrated into Intervals.icu, this feature could potentially build on existing data structures — making it even more feasible to add fuel utilization curves and FatMax estimations. (Carb utilisation and ingestion on activities)