Here: LT1 @ 87% of max and LT2 at 94%. Looks like both have moved up, but the “middle zone” for those elite runners was only between 19-20.5 km/hr. The physiological demands of mass start cycle racing dictates a different power/energy curve, but in a sport where steady state is king, the proof is right here.
That’s not evidence of any closing of the gap? No control group, etc.
I know you like being the grand contrarian, but we don’t need a control group when we know that most of us mere mortals have LT1 around 70-75% of MHR. and that almost none of the athletes in the study had exceptionally high VO2 peak values. One of them in the low 60’s, but all of them had very high fractional utilization for their marathon pace. We only have to look at previous data to know it is different than regular amateurs.
The question isn’t whether LT1 increases with training, it is whether it increases more than LT2, such that they move closer together.
This study does not provide any evidence in support of that belief - in fact, if anything it suggests the opposite, since LT1 and LT2 still differed by 9% of VO2max, comparable to what you would see in less well-trained individuals (depending, of course, on how LT1 and LT2 are defined).
Damn! I love bananas and I always eat a few when I ride. 
Thanks for your reply. Usually I do a long ride at the weekend. I start fasted with black coffee and start eating normally about after an hour. By the end of the third hours, I feel like if I had done a 5hr ride. I recover well from these ride.
I don’t intend to start doing multiple sessions a day with fasted HIIT. You convinced me to read more on the topic and if there is no proven benefit, I might start eating earlier in the ride.
How comparable? I’ve never seen anything even remotely that close. A 9% difference is akin to someone with 250 Watt FTP, but a 220 Watt LT1. Realistically, we’ll probably only see that if their FTP is also a very high percent of VO2 max.
So wouldn’t this be easily shown by the differences in individual power duration curves? The flatter the curve, the higher fractional utilization and thus the 2 points are closer. A steep curve would then have a bigger difference between them.
A ~10% difference is normal, even in untrained subjects.
E.g., LT(1) in untrained persons is usually around 55-65% of VO2max, but they can maintain 65-75% of VO2max for an hour or so.
I don’t know how you would identify the initial increase in lactate from the power duration curve. Indeed, isn’t that the exact problem that people are attempting to solve here, by measuring lactate?
Thats exactly what I do. My belief is that going for an hour or so before starting to eat helps the aerobic system get up and running more easily. If I’m going out on 4/5 hr hard group ride though I make sure I have a big breakfast 2-3 hours before setting off as I can’t get the food down quickly enough on the ride. I do notice how much higher my HR is for a given power output though if I go out soon after having eaten as well as after the coffee and bacon sandwich stop at 2\3rds distance.
cool, something to play with during the next lockdown … so next week
Mmm … my Wahoo Bolt does not record RR for HRV. Damn, can’t run this with my decoupling test. My Suunto 9 does but I only use it for running.
Went for an easy 2.5h run today. These are the RRs
using the transformations in the notbook I end up with this
Intensity level would be correct if 0.75 * alpha1 ~ AeT. However, this run was to variable, up and down. This is really something for erg mode on the trainer. I will use my watch the next time and not the Bolt.
I was doing exercise HRV readings in 2019 and even though there’s no overwhelming confirmation from science about HRV signalling AeT…it appears that it does/can.
There is something to it, unfortunately I only have data on runs.
This week I didn’t feel so well, not sure what it was. This is a 90min run on Thursday. Following a rest day (I always feel sluggish after rest days). Heart rate and pace would indicate around AeT. However, this shows clearly a more demanding effort:
In contrast, a similar run (route, heart rate, pace) from a few days before where I felt good (on that day and the days around):
Furthermore, I’ve started tracking morning HRV again (wanted to give it a try again). The days around the more demanding run would show a fairly low morning rMSSD reading, while the “normal feeling” run had expected rMSSD values.
Definitely something to play with for the next few weeks.
Are just running the python from collab? My Garmin 530 has been logging HRV and I’d like to run some data thru it, but not on Google servers. So I’m thinking of pulling out the code and running it on my computer.
Yes, I just run it in colab. Had to change the code slightly as Suunto exports HRV slightly differently in fit files.
you should be able to get it from here as well and run it locally: GitHub - marcoalt/Colab: Colab notebooks for various projects
Is it possible to run this without any knowledge of computer programming?
Thought I’d post my n=1 recordings.
Perhaps the best one I have is from a cardiac stress test I did on the trainer.
Visually it tells me one thing, numerically another; so…I can only guess at the efficacy.
Inflection point @ 115bpm with DFA/alpha 1 @ 1.18…
…however, DFA/alpha 1 remains steady (at above value) until ~132bpm when it drops 25% to 0.88 (now showing RR graph):
So, do I believe AeT is at the visual inflection point (115HR), or the “mathematical” inflection point (132HR)?
My theory is that 115 is perhaps more my FatMax and 132 my AeT (which is also consistent with my MAF). Insights?
:
:
Also, a post on a different HRV thread which might interest you:
Very interesting. This is my last ramp test results:
And Alpha1:
Looks like ramp test is too short to be anaerobic work reflected in Alpha1?
I have 105 minutes long run which I previously thought it was my AeT and looks like Alpha1 confirms this:
HRV is due to parasympathetic activity. There’s really no reason to expect it to be linked to metabolic responses.
Yeah but it’s fun to run your data and see if there is some correlation despite that. Data often finds interesting and unexpected linkages.
