🩸Blood lactate [BLa] does NOT increase exponentially during high intensity exercise 🧑‍🔬

Why do we make this common mistake?

I think because we have focused too much on the lactate test

And forgotten what information that test is trying to give us about real exercise
🧵1/14 We are probably familiar with the🩸BLa curve during an incremental exercise test

As intensity increases 🩸BLa accumulates at a faster rate, approximating an exponential increase

We can estimate a 'threshold' in this curve, but what is this threshold telling us?
2/

@SciTriat and I just chatted about training above or below threshold to improve V̇O₂max and performance, interpreting group-level research to individual-level application, and how much day-to-day variability to expect in common training metrics
scientifictriathlon.com/tts420/
We talked about a few threads I recently posted

The first was about our recent meta analysis on training above vs below the maximal metabolic steady state effects on V̇O₂max and TT performance outcomes with Dr. Michael Rosenblat & @StephenSeiler

https://twitter.com/jem_arnold/status/1705360837211492672

Reading now. Hitting a lot of check marks on my training study wishlist 👏👏 @ingvillodden et al

The plot thickens on interval programming! Very well described methods. Definitely more papers to come from this dataset

https://twitter.com/FredrikMentzoni/status/1717068398545310041

iiiiiinteresting. That pesky dissociation between improvements to V̇O₂max and TT performance outcomes? 🤔🤔

Significant correlation doesnt imply good individual prediction

If you have asked "Why cant my apps predict my response when they have so much data on everyone?"

Because lots of data improves *confidence intervals* but DOESNT improve *prediction intervals*

Lets simulate! 🧵/14 There is always uncertainty in any measurement

We make estimates about training data by sampling from the population of athletes

We observe the sample to estimate the population

I'll simulate some power data representing threshold or TT performance and sample from it
2/

Does training above max metabolic steady state MMSS (CP, FTP, MLSS, LT2) improve V̇O₂peak & time-trial in endurance-trained athletes MORE than training only below MMSS?

Think the answer is obvious? We kinda did too. So we did a meta-analysis! 📚🔖
🧵/13

link.springer.com/article/10.100… We looked at research comparing groups of trained runners, cyclists, or rowers performing:

1) exercise ONLY below MMSS (MICT, heavy, tempo, sweet spot..)

vs

2) exercise below AND above MMSS (HIIT, SIT, severe & extreme domains)

with V̇O₂peak, Wpeak, or TT outcomes
2/

Critical Power (CP) is ⬇️ at 100 vs 60 rpm, and costs ⬆️V̇O2

But preferred cadence is typically 80-100 rpm 🤔

Does training at ⬇️or ⬆️cadence improve CP & V̇O2max more?

I find the physiology fascinating! Here’s some of what I'm learning🚴‍♀️🚴🚴‍♂️
1/🧵
pubmed.ncbi.nlm.nih.gov/25523595/ I recently said CP is ⬆️at ⬇️cadence because blood flow (BF) is ⬆️

That's not quite right

BF is ⬆️ per pedal cycle, but BF and O2 delivery are ⬇️per unit time

There must be another reason why CP is ⬆️at ⬇️cadence 🤷‍♂️
2/

https://twitter.com/jem_arnold/status/1661754764751962112

Critical Oxygenation may estimate critical power with NIRS measured at the quadricep (RF) in a single-session multi-stage protocol

I wanted to test the method with our dataset of mixed fitness competitive F & M cyclists

It doesn't quite work for us. but why?🤔
🧵/14

https://twitter.com/APSPublications/status/1649489471312744461

21 competitive cyclists (cat 4 to national level) perform 5-1 tests with multi NIRS measures, including rectus femoris (RF) as per OP Matthews et al

As in this representative athlete, the RF profile is generally parabolic: ⬆️SmO2 at low workloads, ⬇️SmO2 at high workloads
2/

Throwaway line in a thesis I'm reading got me thinking regarding adaptive effects of intermittent HIIT vs SIT vs continuous HIIT:

Is the *rate* of metabolic disruption important for adaptive signalling, independent from the magnitude of disruption? 🤔 ... The former presumably being greatest at work bout onset, the latter being greatest at end

SIT would presumably induce the greatest onset flux

Intermittent HIIT (eg 30x15s) a high volume of onset events

While the end magnitude may be similar across all (ie iso-effort) ?

Weekend project: simulated data based on observed NIRS recordings during 4x4-min cycling HIIT session in severe (SVR) domain with @MoxyMonitor

End-work SmO2 (last 60-sec mean of each bout) may ⬆️or⬇️ across the session related to caliper skinfold thickness (SF) at VL
1/🧵 ⬇️SmO2 across bouts in LOW SF group (red):

⬇️SF ⇒ more optical signal comes from metabolically active muscle tissue, where ⬆️VO2 during SVR as ⬆️[metabolite] (contributes to ⬆️VO2 slow component)

Hence ⬆️⬆️O2 extraction relative to ⬆️O2 delivery ⇒ ⬇️SmO2 in deeper tissues

Michael Rosenblat recently meta-analysed the effects of HIIT vs SIT for improving ⬆️VO2peak and ⬆️cycling TT performance

HIIT & SIT were similarly effective at improving VO2peak

BUT via different mechanisms
AND with different results to performance

researchgate.net/profile/Michae…
1/🧵

https://twitter.com/TStellingwerff/status/1600898003262648321

HIIT was subgrouped as short (<2min) med (2-4min) and long (>4min)

There were no significant differences between HIIT or SIT on improving VO2peak
2/7

Oh great question. I can't recall any literature specifically on NIRS & multi-muscle placement with bike fit, but I'd love to see that!

A few papers come to mind:
1/6

https://twitter.com/coachjorgem/status/1588600686245134336

Saito et al 2018 performed incremental tests in aero & upright. They found no differences in duration or Wpeak between positions, but observed lower saturation (TOI) in VL & RF in aero. They suggest this could reflect greater musc O2 uptake in aero 2/6
pubmed.ncbi.nlm.nih.gov/30125046/

An overly-long and still non-comprehensive reading list for understanding #NIRS #muscleoxygenation in sport science! 🧵

1/15
Start with this concise opinion piece from Perrey on the emerging promise and future direction of NIRS research & application
pubmed.ncbi.nlm.nih.gov/35321522/ 2/15
Perrey & Ferrari's review on NIRS in the context of sport science gives background and is a great jumping off point for NIRS studies in your particular sport of interest
pubmed.ncbi.nlm.nih.gov/29177977/

The 'Incremental RPE Step Test' is a fun and useful experiment to perform with athletes

Used to cue athletes to pay attention to sensations across intensity spectrum

These are some typical responses I've noticed

Have you used something similar? We often set off too hard and overestimate power at low intensity

MOD domain or 'aerobic' zone is large! Not everything has to be right up at the top of it

'easy' ≠ 'good hard'

Sensations I cue include:
• light legs
• glass cranks
• nose breathing
• loose shoulders

Lactate curves in a 5-1 multi-stage cycling test across workload (W/kg). Modelled with VO2peak (range 44-74 ml/min/kg). ↑ Wpeak ∝ ↑ VO2peak along x-axis.

Nice to see the expected longer steady-state achieved by higher fitness subjects before inflection point 1/2 I think the really interesting trend is BLa plotted on relative workload (% individual Wpeak, as proxy for intensity).

Higher fitness:
Longer steady-state
Nadir at ~60% Wpeak (vs no nadir)
Inflection starts at higher intensity
Higher BLa at Wpeak (higher La- flux?) 2/2