Feature Request: Metabolic Profile – FatMax and Fuel Utilization

Feature Requests
1

Hi everyone,

I’d like to suggest a new feature that could be a valuable addition to intervals.icu: the integration of a metabolic profile, similar to what is shown on platforms like Sentiero (see attached screenshot).

The key idea is to visualize FatMax – the point of highest fat oxidation – and show how much fat vs. carbohydrate (CHO) is being burned across different intensities or power outputs. This type of data can be incredibly helpful for endurance athletes looking to fine-tune their training and nutrition strategies.

Specifically, it would be great to include:

  1. FatMax: The power output (in watts) where fat oxidation is maximal.
  2. Fuel utilization curves: Graphs showing CHO and fat usage (e.g., in g/h or kcal/h) depending on wattage or intensity.
  3. Training zones: Clearly indicate zones (e.g., Z2, Tempo, Threshold) and how fuel usage shifts across them.

This is currently well visualized in Sentiero’s metabolic chart (see screenshot), and having something similar natively in intervals.icu would be a major upgrade for those of us who monitor not just performance, but also energy system efficiency.

Thanks for considering this feature – I’d love to hear what others think!

Best regards,
Andreas

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P.S. Of course, truly accurate values can only be obtained through proper metabolic testing (e.g., lab diagnostics). However, with the wealth of data available it might be possible to get reasonably close — or at least provide useful estimates to guide training.

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2

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:

:microscope: 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.

  • 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.
    :link: 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.
    :link: 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:
:link: Understanding VLamax & FatMax

:bulb: 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)

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What would make this integration even more interesting – compared to other tools where you enter or calculate your metabolic profile once – is that here, thanks to the available data, you’d always have the current values available dynamically.

You could track the development of your metabolic profile over time, e.g. how the FatMax zone shifts, etc.

To my knowledge, no platform currently offers this kind of longitudinal insight – at least I haven’t seen it anywhere. Would be a unique feature!

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Great idea! I’ve tried to do this for myself in python using known values to calibrate. I think it’s hard without a large body of data to estimate a VLaMax value from a sprint (unless you can lactate test) but with all the users available I wonder if it indeed could be a unique feature

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Excellent idea. I’ve posted about such a feature before, see this topic: LT1/LT2 and VT1/VT2 - #9 by Ion-Lee_Kuiper

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