This is an interesting read:
In 2020 I tried using INSCYD to improve training, purchased 6 tests. After 6 months I wasn’t able to find any value. During 2019 I spent a lot of time investigating Mader’s work and studies, which is why I decided to try it in 2020.
Personally I’ve found the most value from using WKO5 to assist with decision making on training.
As a coach, I can say that the mader’s model works very well, with some considerations: the 2 components of the model are vo2max and Vlamax , so it’s very important that these two values are measured or correctly calculated. Especially the Vlamax (very challenging to calculate).
When these two metric are correctly measured, the model works fine. Everyone who has a lactate meter can self check its accuracy without tons of scientific papers; when “real world” matches “model” there’s nothing to say.
Second step: after the model you have to train and take decisions. This is the big part of the problem:
The model was built with elite athletes in mind (rowers and swimmers primary) that have “unlimited” time to train. The amateur athletes who has 6-10 hours/week can’t use the model at its full power because some adaptations needs very long time. For example, with 6-8 hours/week it’s possibile to increase Vlamax but it’s very difficult to decrease it. So, if you spend your time to decrease it with 6-8 hours/week, probably you’ll not see any changes. But the model give you the nutrition part, and it’s useful for everyone, even using others approach.
I think that for an amateur athletes an “hybrid” approach is the way to go; using Mader to built vo2max intervals and nutrition and other methods like power-duration to look at interventions for the general picture.
I, unfortunately, cannot see any way in which you can introduce any meaningful measure of carb utilisation beyond ~90 minutes. Even then, you are having to make a large amount of assumptions, but the figure calculated might still be of some practical use to coaches/athletes.
There’s a few key reasons why I cannot see it working:
Yes, lots of variables.
I’m still unclear on how accurate post-ride estimation of carb oxidation would be a game changer. We already have good guidelines as the basis for experimenting on what works for each of us.
Here is a recent study Increased exogenous but unaltered endogenous carbohydrate oxidation with combined fructose-maltodextrin ingested at 120 g h−1 versus 90 g h−1 at different ratios | European Journal of Applied Physiology
on ingesting 90g/hr vs 120g/hr carbs (fructose and maltodextrose):
3 hours and look at carb and fat oxidation which stayed about the same:
well trained cyclists with vo2max of 63 +/- 7, average FTP around 340W, and riding 3 hours upper zone2 somewhere in the 70-80% range.
They took ~60g or ~80g of carbs at start, and then 18g or 23g every 15 minutes.
Again, fat and carb oxidation rates stayed about the same.
I’ve done some 6-7 hour rides at .8 IF or greater, without much performance loss near the end. Here is one:
and another one 3 weeks later:
Fueled at 60-90g/hour.
Why do some consider post-ride carb estimation to be a game changer?
Can you share your spreadsheet?
I suppose in theory if you know exactly the amount of carbohydrate oxidised and ingested in a ride/race, you know exactly how much you need to ingest to maintain parity. The reality is very different!
The reality is my point.
And further, day to day, what and when you eat OFF the bike is more important! So if you are riding 2 hours a day, the focus should be on the other 22 hours!
Yep same here still, as reported before. I have an 18 minute commute with “143 grams CHO used” which is more than 10 times the Xert estimate. Probably because of one sprint that resulted in a 208W NP vs 128W average, though even 18 minutes of 208W average surely wouldn’t cost that much CHO?
Has anyone been able to display CHO and CHO in in the activity in the calendar view? If so, could you please share the steps? I gave it a try, but I was unsuccessful. Similarly, I’d like to have a chart that plots CHO over time. I didn’t see a plot available for it.
CC @david
Thanks!
I have added those. I would be possible to do it yourself using custom activity fields but easy for me to do for everyone.
Thanks. Any plans to add auto-import from Garmin FIT file? Or does that exist now and I haven’t enabled it?
If you can figure out what field(s) are used for that I will try add it. I had a look at one of your recent rides on fitfileviewer.com but didn’t see anything obvious.
Thanks, recent rides I’ve been eating before & after.
Posted earlier
Carbs ingested is now filled in from session field number 177 which is calories consumed, divided by 4 to get grams (assuming that is mostly carbs). Will deploy Sat AM.
Working! Thank you 
Unless your doing a recovery or z2 ride you’re not going to ingest as much carbs as you will burn.
Really just a matter of gut tolerance for longer harder rides >90mins
Very nice feature, sadly it doesn’t work well for rides with a lot of freewheeling (which happens all the time in the mountains). For example my today ride has CHO utilisation almost as high as calories burnt (355g CHO and 1587 kcal, meaning 1420kcal from carbs) even though I was below threshold all the time and for the most part significantly below it (which means I should have burnt some fat as well).
Do we have access to the data from the paper the calculations were done on? I am willing to spend time finding a better function if I can get the data. It won’t be difficult as the researchers based it on TSS which increases with time, not with actual work/intensity.
For me, the numbers given by this TSS equation seem too high.
I have done several metabolic cart tests and converted the data to carb and fat consumption for a given range of power.
For example 150 W → 45 g/h ; 200 W → 100g of carbs/h
I will keep beating the same drum: TSS is a metric that increases mainly with time, not actual work. It doesn’t work for measuring training load. It doesn’t work for approximating CHO consumption. It doesn’t work for anything power related. It’s a shame piece of good research was wasted because of this nonsense but here we are.
What would work better is using the power curve to approximate CHO consumption and then integrating over it (taking area under it). If we can get some actual data we could try to fit the curve and get some reasonable estimations.
Hi, I’m sharing my spreadsheet here if you still would like a copy:
Thank you.
