Jem Arnold Profile picture
Oct 29, 2022 17 tweets 12 min read Read on X
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/
3/15
Barstow has a comprehensive overview of technically and biologically important aspects of NIRS, with recommendations for standardised methodology and nomenclature. This is a must read IMO for starting to understand the nuances of NIRS interpretation
pubmed.ncbi.nlm.nih.gov/30844336/
4/15
@JanBoone2906 et al at Ghent University have published definitive experiments describing NIRS response profiles during incremental ramp testing. Start here to visualise how NIRS responds in an intensity-dependent manner
pubmed.ncbi.nlm.nih.gov/27613650/
5/15
@MuriasLab et al at U of Calgary have also exhaustively investigated NIRS responses during exercise, characterising the deoxygenation breakpoint / HHb-plateau along with other threshold demarcations
pubmed.ncbi.nlm.nih.gov/25606817/
6/15
There is a wonderfully productive ongoing debate on the association between NIRS and other physiological breakpoints
pubmed.ncbi.nlm.nih.gov/29975303/

pubmed.ncbi.nlm.nih.gov/30134366/
7/15
This debate has helped reveal operational limitations of how we use and think about 'thresholds'. To me, a critical aspect is the uncertainty inherent to any breakpoint detection method. Elegantly demonstrated by @KevinCaen @JanBoone2906 et al
pubmed.ncbi.nlm.nih.gov/35435465/
8/15
Another exciting application of NIRS is for non-invasive measurement of mitochondrial function, introduced by Ryan, @InfraredRx, @harrybrossiter et al. This method is now widely used across clinical and sport applications
pubmed.ncbi.nlm.nih.gov/28684592/

pubmed.ncbi.nlm.nih.gov/22582211/
9/15
It's important to consider what *isn't* illuminated by NIRS. NIRS is hyper-local. There are critical spatial and (bio)mechanical effects to consider, such as deep vs superficial muscle heterogeneities
pubmed.ncbi.nlm.nih.gov/26404619/

pubmed.ncbi.nlm.nih.gov/28912130/
10/15
Vastus lateralis is the primary locomotor muscle studied in cycling. Rectus femoris in running. There are important modality- and quadricep head-specific recruitment & deoxygenation patterns
pubmed.ncbi.nlm.nih.gov/28970805/

pubmed.ncbi.nlm.nih.gov/29722622/
11/15
Consider NIRS responses of non-locomotor muscles during exercise such as bicep or deltoid, and what this might reveal in terms of systemic metabolic priorities
pubmed.ncbi.nlm.nih.gov/20204819/

pubmed.ncbi.nlm.nih.gov/35431982/
👆my colleague's paper
12/15
Adipose tissue thickness has large effects on NIRS signals. Especially consider differences in male & female subcutaneous fat distribution on quadriceps when interpreting locomotor NIRS signals
pubmed.ncbi.nlm.nih.gov/28151429/

pubmed.ncbi.nlm.nih.gov/28935822/

also
13/15
Consider how NIRS measurements at the microvasculature (capillaries) are *expected* to differ from classical experiments that measure at larger conduit vessels (e.g. femoral or brachial aa. & vv.)
pubmed.ncbi.nlm.nih.gov/32940560/
14/15
We have to consider *mechanical effects* like isometric vs rhythmic (e.g. cadence) contractions, which change recruitment patterns and spatial distribution of tissue & fluid volumes into and out of the illuminated area
pubmed.ncbi.nlm.nih.gov/27126859/

pubmed.ncbi.nlm.nih.gov/15557016/
15/15
There are so many more applications & nuances to NIRS

Start with the reviews at the top of this thread and come back later for the more nuanced experiments toward the bottom

Dig through the citation trains of these papers to discover what else you might be interested in!
16/15
Or listen / watch me try a few times recently to articulate a story about oxygenation response profiles during incremental exercise testing, trying to keep all of these nuances straight 🥴

scientifictriathlon.com/tts358/

Another very recent application from Pilotto, @AdamiAlessandra, @harrybrossiter, @PorcelliSimone et al is the evaluation of O2 diffusion capacity

This is quite exciting to understand muscle O2 delivery & uptake kinetics after higher intensity exercise

pubmed.ncbi.nlm.nih.gov/35930524/

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More from @jem_arnold

Jul 5
A new review of flow limitations in the iliac arteries (FLIA) / endofibrosis 🩸🩸 in female athletes 🚴‍♀️ 🏃‍♀️

A quick summary and context 📚🧵👇 Image
FLIA is a condition affecting endurance athletes where blood flow is limited by compression & kinking in the iliac arteries during exercise. The athlete is limited by gradually worsening pain & claudication in one or both legs

More background info here:
FLIA patients in the early literature were exclusively male. ~25% of recent cases are females. This increase is probably related to participation rates in elite sport, awareness, and resources to detect FLIA in female athletes Image
Read 11 tweets
Jan 21
🩸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 Image
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/ Image
We don’t actually care about the deflection point in a lactate curve on its own

True exponential curves don't have deflections. it's like finding the corner of a circle

There are lots of corners depending on our operational definitions 🫣

3/
DOI: 10.1371/journal.pone.0199794 Image
Read 17 tweets
Dec 12, 2023
@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/
Image
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

The second thread was me trying to figure out how to talk about and visualise uncertainty in estimating group-level outcomes (confidence intervals) vs predicting individual outcomes (prediction intervals)
Read 5 tweets
Oct 26, 2023
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
Image
iiiiiinteresting. That pesky dissociation between improvements to V̇O₂max and TT performance outcomes? 🤔🤔 Image
hmm. Thesis concludes non-sig relationship, non-meaningful R² btwn %VO2max & ⬆️VO2max based on Δ% pre-post

Conference abstract reports significant and very meaningful R² relationship between same, with ⬆️VO2max in Δml/min/kg
Image
Image
Read 4 tweets
Sep 29, 2023
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 Image
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/ Image
*Confidence intervals* (CI) represent a range around an observed estimate that we can expect the 'true' pop mean to fall within based on the variation in the sample

Small sample size (here n=20) produces wide 95% CI

We have low confidence where the 'real' pop mean is
3/ Image
Read 15 tweets
Sep 22, 2023
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/ Image
14 studies with 299 total subjects (F n=51)

Including well known papers from @StephenSeiler @PaulBLaursen @BlueSpotScience @JohnAHawley and others

Training interventions were 2-12 weeks duration

The largest sample size for any group was only 16... (foreshadowing!)
3/
Image
Image
Read 14 tweets

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