Welcome to the architect of Resilience podcast, where we explore the secrets of overcoming life's challenges and unlocking unstoppable strength through. Deep personal conversations and expert insights. Welcome to an entirely new series on the Architect of Resilience podcast. I say series, yeah, I think it's probably going to end up being a six part series, but who knows how long it will go. I'll be doing this series that with the primary individual,
Alex Miller. So Alex was head of coaching development at Kabuki. Strength was so for a number of years. So he was the man behind the scenes that was doing the research, developing the methodologies and training and educating all of the coaches and developing a lot of the curriculum that was being done in the
seminars that we were doing. And yeah, when I say next generation training principles, that is really what we're going to be talking about here, doing a much deeper dive than how people think about training. And I think where the future of training is going
you may be asking yourself, well Chris, I thought KMS was you. Well yes, the fundamental principles that we started with, you know, those priorities and principles is something that I, it was an output of me learning from a lot of industry leaders. But Alex is the one that has really taken this to the next level. He's been ha, that's been his sole job over these years, has been diving into these that I haven't had the depth
to and taking those principles to a whole other level. And so I'm really excited to share with you his thought process and approach on this. So welcome, welcome to the show. My very long intro. Alexe. Hey man, how you doing? Yeah I'm doing good this morning. Yourself? Where you at right now? Today? Right now I'm in Kuala Lumpur in Malaysia. So it's nice, it's hot, it's like 94, 95 today and three hour thunderstorms. So yeah, wonderful. Living a dream. 3 hours thunderstorms.
Beautiful. We're actually, it's uh, late September, right. And I think we're gonna be in the nineties or even mid nineties up here in Oregon this week. So. Yeah. Nice. Nice. Yeah, I don't know about nice. I'm not one for that, that, that temperature range, I like the seventies. It's like, it's okay. It's almost. Your seventies are great, you know? Yeah, yeah. For real? For real. Cool. So let's start talking. I think a great intro is diving into adaptive modulators.
So within this it's really thinking about the stress index. And with traditional training we've got an overlaid phase where we're building a level of accumulation of fatigue. And within that if we have rest, you know, we're going to have some overreaching phases or see what our peak potential is. But during the course of that you really don't know what your level of fitness is like, your potential for chronic overuse injuries, things of that nature.
They're, they're acute, they may be, you know, precipitated during the course of this, but it's, yeah, it's a bit of an unknown. So really understanding the stimulus recovery adaptation curve when it relates to the training, I think your principles have us like looking at more of a cellular level. Is that correct? Yeah. So we're using information or predictions about what's happening at cellular level to inform gross decisions, lasers, it's just ways we can introduce different stimuli.
Okay. And when we talk about stress index, we're looking at a way to get a handle on what the, like the internal loading is going to be on the body rather than like traditional methods where we say okay, well I'm going to do x amount of volume in x amount of time and that's going to just be how I'm going to judge it. Whereas we know like there's a big difference between doing like 10,000 pounds of volume in sets of
ten and 10,000 pounds of volume in singles. So one is gonna have a much bigger effect on different aspects of the body. Right. So we need to find a way to make a prediction. Cause it's not exact, right. But it's a prediction of stress not based upon loading in a normal sense. We're trying to look at what's happening inside the body. So that's kind of where
this idea of stress index is coming from. Now to give credit where it's due, this is from Robert Frederick, who then took the research on lactic acid and ammonia buildup within muscles over certain rep ranges. And obviously Mike T has done a bit of work around this kind of stuff on certain level. So we're talking a bit of signal transduction theory, right? Yeah. So when we think of signal transduction theory, it's a way that we can understand a little bit better what's happened to the
muscles or what's happening to the body. So we say we have like a signal, that signal goes in from whatever reception mechanism. We can then cause it cascade and changes things along the way. And then we get an output at the end of it. We don't really know what's happening. This is kind of a fundamental thing about this whole process is that, like, as a profession, coaches are just guessing. And this is one of these huge problems that we don't know what's
happening time to time. Like, we're in a situation where like, I can give you a free sets of free squats. That's 0.4 meters a second to do. And one day that's gonna have like, x as a result, uni, and another day it's gonna y as a result, and we just don't know what's gonna be right. So our job, like, using these, these biological pathways, there's an idea of creating. I try not to be too vague, but because we don't know what's going on, we can't be too precise. So we just need to create
principle ways are going to tell us what's going on. That makes sense. So when you talk about like, masking of fatigue with us. But I might have to go back on this one a little bit. By the way, mate, I'm gonna put my hand up there. So let's step back because, you know, I'm familiar with signal transduction theory from the peptide sense. You know, like, what's happening from, you know, a cellular perspective, upregulating receptors. But for our audience, can you walk through, you know, the basis
of signal drain deduction theory? Yeah. So signal transaction theory is just a way to describe what happens within the body when we get a signal from outside. Okay, so we can say, okay, we get a signal, which is a tension on the muscle could, like the, the body doesn't know we're lifting weights. It just knows there's tension. Okay? So that tension then might activate some sort of hormone receptor. Hormone production. Because the hormone receptor causes a reaction within a cell, and that might
be the increase of muscle protein synthesis. It might be down regulation of inflammatory hormones. It could even be something like really crazy, like the idea of controlling diabetes with lifting. So we get that t glute activation, right? So if you put tension on a cell, it causes t group translocation, which opens up pathways to taking glucose without necessarily part of insulin, which is a complete aside. But like, yep, it's a super powerful
way of changing the body. So basically you can think of signal theory as a complicated way of saying you have one input. It goes through a number of steps and that gives us a reliable output. Yeah. And, you know, you're not going to use that in, you know, understanding. Like, how am I applying a set and rep scheme? But understanding those mechanisms, I think helps understand why we have some variability in those outcomes because the body may be at
different states, the nervous system, all these factors. But you know, bearing all that in mind, how would you put this into place as it relates to collecting metrics and you know, as far as subjective objective and putting some of this to use in training. So we use civil transactions theory as a basis to understand it like you say. So we want to know that just because I'm applying a certain stimulus doesn't mean I'm
getting some output in terms of collecting metrics. This is kind of where the, the rail is lifted as such because even though we can't predict the outcomes we can still deal with the data that comes out of this. So if we're looking at something like stress index that we get a prediction of internal stress or internal load which can be related back to this transduction theory because if a internal stress is too high we get different
outcome, right? If we, if we get to the point where we start getting down regulation of positive adaptation, okay, so we start getting into neutral, even negative adaptation, that's still a response to training stimulus. Okay. Just because it's not positive doesn't mean it's not a response, right? Yeah, yeah. It's same as if we're changing manufacturing process and we create a bottleneck. We changed it but it's still
a negative response. Not the response what we want, but it's still a response to what we're doing. So we can have, so when we look at this and we say okay, internal stress is too high, we can still take it into account. So when we're looking at say training volume, in which case we'll take this as a stress index score, we can have a sweet spot to hit and we want to take that
over multiple sessions. So it's okay to go. Let's say we have a stress index of like 15 to 17 and that's kind of where we want to sit over like three or four days, you might bump up to 20 and that's just a really hard session but then we want to see it come back down underneath it. So it's a way of modulating your training without having to rely on how many pounds you lifted. So looking, yeah, we're looking at all those factors via intensity, volume, everything.
And as far as what kind of stress or stress index we are then applying on the system, let's dive into the world of optimizing your overall health. With pushing my physical limits. I encountered significant reductions in my health and I reached out to Merrick Health as the premier telehealth service. I loved their personalized health coaching. From the comfort of my home, they empower you with the choice of
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like sets, reps, load. Okay, we're looking at a. So the research on this comes from accumulation of lactic acid and accumulation of ammonia. Okay. Which is hard to do in a gym setting unless you have some pretty specialized kids. But you take into account effort, which is a really cool thing because I know, and I've said this a bunch of times, if I watch you do a set of deadlifts, your slowest deadlift I've seen is like 0.2 meters a second, which is really
slow. My slowest deadlift is about 0.3 meters a second. So you are grinding out deadlifts much more than I am. So even if we have the same relative loading, you're putting much more stress on your body, because I'm just not able to grind like you are. Right. And so that doesn't. So even though if we took volume into account, let's just say we live to the same, I would still have
less stress because I'm just not trying as hard as you are. And that's not taking into account with traditional loading schemes, which is a huge error. So it's okay saying, okay, I'm going to have three times three at 0.3 meters a second and somewhere like that shouldn't be analogous to us arpe, just say. But if you're not taking your RPE into account, you just kind of prescribe me and see what happens. It just doesn't mean anything.
So that's why the stress index, next level of monitoring training, because we take subjective stuff into account as well. Yeah, and what's, you know, this conversation sounds very ethereal, but when we look at what's making the change is the ability to not just add the subjective nature of what we can capture now, but the easy accessibility that we're
gaining for those objective metrics. Like we're talking velocity in this, we really pioneered the use of using velocity as a tap in to understand where your readiness is and actionable versus using, let's say, well, in the past, there was really, you know, prior to the tools that have developed over the last ten or 15 years, there really wasn't any objective ways to really understand your readiness today and what you can do. And that's really a leading
indicator. Right. That allows us to adjust our training right now versus like an HRV data that's not tied to a specific plan and it's just telling you, hey, rest or hey, push it harder. But doesn't know whether we're actually trying to accumulate said fatigue, you know, in the system.
Because if we followed that, you know, up to. I'm on a bit of a tangent, but if we follow that peer data that's put out in like any of the wellness apps around HRV, and let's say I'm peaking for a marathon, if I'm sitting there managing my HRV on a daily basis and I'm doing it as best as possible, I'm going to get to that marathon and I'm going to perform very poorly compared to what I'd done if I had accumulated fatigue and had a supercompensary effect of that. Because there's phases
like, we have to tie those two together. I know I'm on a bit of a tangent here, but I think the interesting thing is we can have these conversations because these tools are becoming readily available and some of them may not be quite as easily as accessible as right now, but understanding the better way that we can both tie subjective measures with these objective and then better understand
the stress index. Right. And so I think that might be a good segue, maybe not a segue, but time to transition into the k value as it relates to these adaptive modulators in the stress index. Yeah.
Okay. So I'm going to quickly touch on one other thing that does tie into the k value because if I don't touch on this, it's going to seem kind of dichotomous when we think about advantages to stress index system is that we don't have to rely on outdated methods of just, hey, my next block of training has to be this many pounds of volume more because there's an idea that over time your pounds per session or pounds per week or whatever kind of tradition when you do it on has to
go up. Okay. And that's not untrue, but it doesn't have to be forced upwards as a result of like, programming. It's similar to like, hey, have you got stronger and that makes you lift faster or your lifts got faster because you're stronger, right. It's the same idea that you will see a natural progression of pounds per session going up.
But that's not the goal. The goal is to maintain a level of stress that disrupts homeostasis to the extent that you get the highest rate of adaptation or the highest rate positive adaptation, that's kind of the key. So when we talk about K values, we're really talking about. We talk about it in the context of load lifted, just because that's the easiest to understand. But we could equally talk about it in the context of stress index. So we could say,
let's explain what K value is first. K value is essentially the average load you lifted over a block of training to get the said result. Okay, that's good. What is? And then we say, okay, well, if this is. That is. This is the result we got from doing this, then we might have to add an amount to get better results. So this is of the work of Travis mash. So, yeah, we used a lot of. This in my grand goals. Like, when I talked about. Yeah, so, like, we knew exactly what load
that we needed to have, or I needed to have. And this was with Brandon send managing it. But here's that. Here's the, here's the load that I had to be able to sustain in a weekly period to get this goal based on knowing what I've done prior. And so that's literally how we manage that over that period of time. I was like, okay, deadlifts. I'm going to have to accomplish this, you know, x amount of volume and intensity over the course of a week. So I'm gonna have
to add an extra day. I'm gonna have to get there. And I wasn't able to do that prior. So, like, how did we build into doing that? Starting with, you know, doing it as block poles, then building that, like, getting to building up that potential so that I could handle that average load of training. And boom, like, there it is. The output was. Now, once I was handling that average load, being able to accomplish those
lifts. What just really cool about that and that specific example is that, and we haven't spoken about this, like, previously, because it's just a bit too high resolution. Stress index should have a range of motion component as well that we see. And intuitively, this is right, like, we should see less
stress from partial movements. So this kind of current kind of trend towards lengthening partials in training kind of has a. A beneficiary effect that we can say, okay, well, I'm guessing a maybe an equivalency of hypertrophic gain from doing partial range of motion over full range of motion, but I'm accumulating less stress doing it. Or, listen, less internal
loading doing it, which is a super valuable thing. So on your school stuff, so on your community today, I mentioned, like, hey, one of the benefits of single leg work. So we said about single leg rdls, and that's is this. A reduced axial loading stress index is much, much lower if we can reduce this century. So we split stress index up into central and peripheral. Okay. If we take away that
high axial loading, we reduce the stress in the body doing it that way. Just like a really interesting aside that you were kind of doing this shit before you knew you were doing this shit. Exactly. Yeah. Which is cool, right? Well, and we see that with a lot of high level performers where after the fact, we look at and go, oh, this made a lot of sense. Right? Yeah, yeah. And that. That's why it works. Right. It's. It's kind of. It's not survivorship bias. It's like a. It's a being right bias.
Yep. Well, and just because the axial loading was going to be so high in the squat program, you know, that was very intentful, like, earlier in the year, to do some of those heavier or mixed? Heavier. Like, I needed to build my. My upper back strength, my lats and all that to. To be able to stabilize that. Well, I wasn't going to be able to sustain that type of loading doing heavy bent over rows when I was in this phase of doing the heavy squats. That would just be cumulative. Too much
stress. Right. So. So we built to towards that. It was a lot of heavy transformer bar work with, uh, you know, with a big bending moment at the t spine, it was bent over rows, and then we would start pairing that down, and people would think that during that squat phase, I was doing a lot of, you know, other stuff, maybe to. To maintain those strengths, but I. I had to pair those completely out so that all of my stress could be pushed into that one singular thing at that
point. Yeah. Yeah. And that opens up a ton of, like, questions about skills versus, um, physiological adaptation. Like, what's a skill adaptation, what's a physiological adaptation, and how do we sequence those? Which is like a. A super interesting topic, but really comes under a periodization heading that we don't. Probably don't have the time to get into today. Yeah. But at the end of the day, like, if you are managing someone's training, you have the capability of summing all of
this and seeing what their stress index is. If you've got those indexed by that, hey, this movement is a 0.8. This one's a 0.9. Whatever, like, grading mechanism that you use. And then you could see the average training load over a period of time or. Yeah. And see times that by 100 and divide it by the proposed total. Right. Yeah. So this is really getting what K value is. So I'm glad you brought us
back because I would go off and talk about programming for a long time. So K value is the average intensity as a percent times 100 over what you want to do. Okay. So you'd, so you'd have, okay, well, my goal is to reach an 800 pound deadlift. Right? So my deadlift was 793 is my best deadlift. Incredibly frustrating because I was very close to 800, so I could take in the load I was doing that time, which was actually really quite low. It was maybe 1200 kilos a week. 1200? Yeah, it's really,
really low, but very, very high intensity. And I could have gone, okay, well, divide times 1200 by by times 1200. So let me. It's. Right. So I guess is the average intensity, which in my case was about 0.3 meters a second. I did three reps. I never told you this much training when I did this. I just deadlifted for a year. That's all I did. I did three reps, 2323 times a week, like three singles. I went from like a 600 deadlift to a 793 deadlift.
Nice. Yeah, yeah. Anyway, that's, by the way. And I could have just gone, okay, well, it might be that I just need to add a rep. Maybe that is adding the extra rep would have done it. But we don't know until we work for that equation. Yep. And it's not a complicated equation, but you've got to have a data. Yeah, you've got to have your data. This is like the next generation thing, that if we're not making educated guesses, we're making really shit
guesses. But the idea that you can be 95% sure that you're getting something right, okay. And then you make the next assumption. If you're 95% sure you're making that right, great. Do that a few more times. You down to like a coin toss. Like 95% right is only good, like once or twice. And then the only chance of every single step being right is pretty, pretty damn
low. So to me, I'm sitting here thinking, well, wouldn't that be fantastic if, you know, people developing these programming apps were actually building this stuff into it because no one is doing that because it seems like you could really input a really great machine learning model into, you know, having this information to really output some really individualized training based on how someone is actually training.
Yeah, yeah, that's funny. A funny thought there. So, yeah, if you, if you can automate this stuff, you, you open up a ton of ease of programming because you know what you need to do. So, um, are we going to talk about this or this is where we talk about stuff. We're working on something
like this, right? So. Yeah. Yeah, but that's when I say, like, the data analysis, like it's all there and the equations are not that complicated if you, but, you know, modeling all this together, and there's obviously a lot more that goes into training than just the stress index here. But, like, you know, when you talk about it and if you don't have the tools, like, that's a lot of work to pull all that information together to manage it. But it's, at the end of the day, it's all
right there. Yeah, well, it's not, it's not. It's even better than that because these work so synergistically. Like, you, you end up getting better ideas of what's the right thing to do. Okay. Like, you, you already know how much stress you're gonna handle in a given time frame. Okay. And that's your stress index. How well are you gonna respond? Is it gonna be positive and negative adaptation planning on stress index or internal
loading? You already know what the total amount of work you have to do is in a set time as your k value. Yep. And then there's only a few ways those two things combined. So you have a really good guidance on kind of what you want to do. And like, that gives you a ton of predictive value that you can kind of know where you're going to be, which is unknown. Like, yeah, we can. So I'm a big, big fan of Antoni Bond, Chuck. Okay. And Antoni
Bondichuk talks about natural peaking versus force peaking. What we traditionally see in powerlifting is force peaking, where we incur so much fatigue going into a competition that a reduction in load reveals the peak. Okay. That's that idea of, I don't think super compensation is the right word, even though
that is what's happening. What's really happening is you're guessing an increase in coordination, you're getting an increase in beneficial hormonal status rather than just being fucked up from doing too much volume so we're not really getting a bounce back from doing the work. We're getting an increase in performance because of having lack of fatigue. So that's where that traditional peak comes from.
Bondshop proposes an idea of this developmental development of sports form, which is where you just naturally get better and better at something until you stop disrupting homeostasis with that same result or same stimulus. And that's what we're talking about. We're saying, okay, if I predict the amount of work, predict accurately the amount of work that I've got to do and how much longer it's going to take me to get somewhere, you can just naturally peak for something without having to
incur over fatigue. I'm not saying you shouldn't. You should definitely train hard and there should definitely be times where you're kind of working hard and you feel shit. But that's away from comp. It's a way that we can force it. We know that we can force it, but it may not be as beneficial as a natural. I mean, yeah, like name like a power lifter
who hasn't got hurt from overdoing it. Yeah, that's what we opened this talking about, right, was, yeah, like you are in a fatigue state and you don't know exactly where everything's at, and you're right in that edge. And that's when those injuries, that's when those injuries happen. And that's why I've developed a lot of the peptide protocols and stuff that I have to, to then go in and enhance in a short period of time recovery adaptations to get them
back on phase. Well, let's look at this thing, Chris. If we know that a set stress index is going to cause the most optimal rate of positive adaptation, and we kind of know the amount, what we got to do to get somewhere, we can split that evenly over a block of training. Okay. We could in some cases, if you have the right athletes with the right mental kind of predisposition, because it does matter, like personality
matters in training. It just is like if you have someone who can do the same work for 20 weeks, the work the stars to 20 weeks out is going to be way harder than the work at the end of 20 weeks. You're going to have less fatigue at the end of 20 weeks, but still have a positive adaptation because we know the stress is actually going to be right. So you actually go into competition with much less fatigue than you may have, which is huge. That's like a way to make competitions way more
fun because you don't walk in and feeling like shit. I remember some competitions early in my days where I was just absolutely wrecked. I mean, it took me months to recover from a few of those. And the deadlift and squat. People always ask me how hard was that? And I'm like, that's freaking easy. Did you look at my training leading up to it? Like, you know, it's crazy. Do it. A thousand pounds per triple was easy like that. That didn't wreck me at all, which is nuts. Yeah. Let's talk back.
We were talking RPE, you know, subjective measures. So we got RPE, we got reps in reserve. How do you integrate that into this calculation for k value? Oh, that's a really good question. So we take an average RP over the block for the most part because you should be collecting that data like you as a, if you're a coach or if you're serious by your own training, you should be recording all the, all the values that you have a useful.
Okay. And if you want to look at stress loading, then RP is a huge, huge part of that. So I would just take it over the block for that one because we're getting a lot of the other values just from the stress index. So we can include autoregulation within that. You can kind of look at subjective feedback levels that would introduce a daily kind of wavering. But I just don't, it's not as important. I don't think like, yeah, you can update daily maxes based
upon that, which is kind of cool, right? Because you can say, hey, I need, if you're going to use a percentage system, 98% over x amount of time times 100, that's your k value. Or you can say, I'm going to take it at a set speed or set RPE because it's all the same at the end of the day. But it doesn't. Actual weight, I know this is controversial. The actual weight on the bar is not important. It is the stimulus you're guessing from doing the work, which
is important. The end result is the big deal. Okay, so if I was, we're talking about going back to your training back in the day when you were really beat up, you were not lifting near your potential, but then you would go set a world record, which is, you know, quite good. Yeah. So the actual loading apart wasn't a big deal. Yes. Yeah. I just want to go back to my first principles here for a minute. If we think about this using the k value, what is that going to do?
That is going to allow us to really tailor something that is individually responsive to both the training environment and that person's individual physiological responses to training as well as all the other stresses that they may be having in their life. Right. We are going to be able to create a very precise, adaptive
training, individualized for the person. At the same time, we talk about, you know, we talked about lifters getting injuries while they're developing into these, you know, peaking routines, the force peaking, but monitoring the stress individually allows us to avoid that, that overtraining. You know, we're not pushing into at any point an area where we're going to create these chronic overuse conditions.
I mean, there's always still some level of, you know, things like that that happen in life, but we can substantially mitigate that from a training perspective because, like, how many people are pulling out of, you know, a competitive event because they pushed it too hard, you know, in the times leading up to that or, you know, getting back into training, starting training, you want to get into training. This is a period that's really hard to actually know where someone is at
with their physiological preparedness. If they've either taken time off from lifting or they're getting into lifting in that period where they're adapting to that training response. And those, again, are proven to be the most opportunity time for get an injury. Whenever we've had a reduction in our average or chronic load over a period of time. As we come out of that, you know, are. We're not able to sustain. We, we ramp it up. You know, even getting back to normal can be too much
of a ramp or someone's trying to make up for lost time. I speak to that a lot. So, you know, it's. It's really the best way to optimize your, your training. I mean, the hands down using this is going to help you, you know, achieve your peak level of fitness and your peak level of readiness if you're able to, you know, incorporate this into your training. I mean, yeah. Fairly accurate summary of. Yeah, I think you actually
going back to first principles. I mean, you here on something that we, we kind of miss and kind of glanced over, which is if you're using k value as a progression system, then you're already including overload in it because that overload, like, you, you have to do more to. To reach that higher number, right. Yeah. And I. It might not be more weight, it might
be more density, but you might be doing something different. You might. You might get a better result from doing the same work in less time. Yeah. Right. So we can play around with any of these seamless things. So the first principles are you creating dynamic overload? Which is just a way of saying, are you creating a positive stress response? Yeah, I mean, that's the. Yeah, there's no reason not to use it. Like, the math isn't hard. Your phone can do it. I mean, it would be a
lot of work. You must be off getting an app to do it. But, you know, you could make a spreadsheet. It'd just be a lot of work. It would be a lot of work. Now, we have done that and that's actually, you know, how manage athletes. But there's still a lot of, like, manual input of some of these, you know, objective measures that you've got to capture, make sure that they're captured input. And, yeah, it's, it is a lot of work. But the end, it's actually very, I
wouldn't say fairly simple. The algorithms are fairly complex, but the math is fairly simple. Yeah. Like, it's not even the input thing, which is the issue, is that. And this isn't like a dig anyone, it's that people are unreliable. Like, some days, like, we touched for your nine out of ten and my nine out of ten, very, very different. So we'll, we'll be like, trying to
put stuff into. Sen used to joke, his joke. His joke was I would do like a five rep max and, you know, barely finish the last one, be bleeding out my nose and staggering. And he'd be like, what's your rp? And I'd be seven. Yeah, I got three left. I got three reps left in the tank, baby. Yeah, yeah, yeah. Kitty's exactly the same, which is why you guys are great lifters, right? But, like, then if I'm relying on an athlete to input into a spreadsheet,
it doesn't, it kind of messes it up. Whereas if you're on an app, like, I know for a fact the app actually will measure the difference over time to take the Z score rather than a baseline score, which is way more important because then you're looking at, like, is it different from the scores around it? Rather than, is it just a higher low score? That's something coaches do
intuitively. So, yeah, I'll explain quickly. So when I'm looking at a training program, so I'm looking at training log and my athletes come back with like, an effort level of, like, ace out of ten, nine out of ten, nine and a half out of ten. And I'm seeing it and I'm watching the videos and lifting it at 0.5 meters a second, which is,
which is quick. Like, I know that they're wrong, basically, but it's hard for me to do anything about because when I'm looking at the algorithm in the spreadsheet, it's pumping out, hey, don't go heavier. This is a very hard workout. And when it's in the app, the app goes, okay, well, the app isn't confined by emotions or having to pay attention. That stuff all the app cares about. And this is a positive, by the way. It sounds like it's going
to be negative, definitely positive. All you have cares about is, hey, does the feedback from the athlete raise a concern? Is it a concerning change? Is it. So Z scores like the change in standard deviation. So is it making a big enough difference for us to have to revisit it and look at it? So we look at feedback over time and you're saying, hey, this is a seven out of ten, seven out, seven out of ten, no matter what. But one day you're like, oh, this is a nine.
Like, that's a, that's a worry. Okay. Even though everything else is a nine, that your feedback on it is actually, like, concerning because it's such a big difference. So is that difference between numbers, which is important, not the actual number. Right? Yeah, that perception. Now, I think you mentioned something about this is, I think it was, this is what makes great coaches. Maybe that wasn't exactly the line, but this is, people get, there's like an anti data movement
a bit and going, well, what about the art of coaching? And that really is like, I think the art of coaching is that eye to be able to pick up and capture these things that we're not currently doing with data. And also just the fact that there's so much systems out there that collect data for data collections purposes, but they're not providing you action based data. And that's what, here's a subset
that helps us answer a question of which direction we're going. Like, those are the choices that we need to make instead of just compiling data into a spreadsheet so we can try to analyze and reflect backwardly what worked well so we can plan the future, is really, it's just like filing reports in the old days and closing up a drawer and, you know, they're in that drawer, we've
got the data. But you know what, what actually happened, you know, versus a problem coming to someone's desk going, here's a problem. Ah, hey, let's take action. Let's go fix it. That's one step behind. I mean, yeah, I will. Because what you're talking about is basically levels to data analysis. One is, it's like looking at the data, which is rubbish. Like data collection. With data collection sake is mental masturbation.
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just like, I'd go in and people be collecting these. Look, we do SBC, we chart it all. We put it. And I'm like, where's it go? Goes to my manager, and it goes in a file cabinet. I'm like, stop. But we do SPC. It's the right way to do it. Haven't you heard of dimming? And I'm like, what action are you taking? If the dot goes here or these series of dots go here, then we do this x things. Put those x things on the chart, take action, call maintenance,
do whatever it is. Like, that's a value. But filing it and just collecting data is just. It's a waste of everyone's time. Like, it's an administrative. It just doesn't do anything like it. You need to go, what decision? And what action are we driving? Let's get the data to make those decisions. Right? Yeah, you're absolutely right. And, like, coaching has so much to learn from manufacturing, because the reality is that there's much more money in manufacturing than coaching. So they've
developed good systems. So we look at data analysis, collecting data. Let's say collect data. Rubbish. The next step is kind of what you're talking about, which is collecting data and looking back on this and, like, kind of seeing, oh, this happened because of this. Okay, the next step is, is collecting data and, and using that to predict stuff. So what we. And that's really where we want to be. We want to be collecting data to say we think this is going to
happen. Yeah. And not, and that's the predictive, like, we see. And that's what I was going. That's, you know, where I worked with some PhDs and SPC, where it's like, we see this data, something's not bad yet, but that's predictive of this going out. Like, we've got to check and see if the spindle bearings getting
a little loose. We've got to do, like, here's these things so that we can mitigate having downtime or having something go out of, out of spec, instead of being able to say, well, we're 95% within spec. Like, how do we, how do we tighten that and be predictive about issues? And the same thing, potential failure, potential injury. Like, all this data is available
in what's happening. And the same thing is, you know, fine tuning a manufacturing process is same thing as fine tuning a human, you know, humans for performance. And that is where the coach's eye comes in. And that is the ability for a high level coach to be able to see what's going on an athlete, by watching their movement, whether they know it or not, in making the decisions about how to modulate their training appropriately.
And we can take that which is not scalable, and create it in a scalable fashion through using the right algorithms and the right data collection and building that. Well, this is a cool segue. You like this? So this is where we talk about jerk matrix, right? I've been looking forward to talking about jerk, man, the whole thing. Let's skip the long term athletic development for another discussion. I've been looking for this whole thing about talking about jerk
metrics, and this. Is what you're talking about because you're saying, hey, our bearings are going. So we're getting vibration on a system because our bearings are folding up. And that's what jerk measure is. A jerk metric is the amount of change in acceleration. So it's like speed is meters per second. Accelerator meters per second per second. Geometric is meters per second per
second per second. Okay. And what you're seeing in your, in this kind of example, where our bearings are going on a machine and it's going to be a prediction of basically disaster, because if the bearings go, you shit creek, right? If we're looking at measuring a barbell's movement and we start seeing big changes in acceleration.
Big change in acceleration. So you may not see a change in it still maybe 0.3 meters/second and .3 meters/second but we're talking about within that movement, the rate of change of acceleration changes that tells us something very fundamentally about what's happening. Yeah. So. And this is something which. Which robots and an app with camera technology will do better than people. Okay. But, yeah, I can look at a squat and I can go, yeah, you look tired, even though you're not tired. But I can't
reliably do it. Coaches will say they can because humans are pretty good, but not as well as the machine to define it. Okay, so what we're looking for is. Sorry, Karen. Oh, I was. Go ahead. I'm just wanting to interrupt, that's all. Did you interrupt? Why interrupt? Okay, I'm just. Correct me if I'm wrong, but this is going back to signal transduction theory as well as, let's say, some talking about neurocentric approach as well. Our modern day understanding of what is a
trigger point. It's not really a trigger point, but what happens is we've got some overuse or potential damage of a particular area, and we've got release of, you know, some chemicals in that related area, which then restricts the muscle around that nerve tunnel. And that creates basically what we know as a. As a. As a trigger point, but it's not really an adhesion. It's not this knot, you know, type, type thing. But we've got that. And
that's what then starts creating maybe movement restrictions. So on really overview, look at this. Right. But then we have. How does that relate to injury? Right. So that is. This is talking to Doctor Charlie Weingroff. Right. Talking about hypoxic conditions in muscle. And, you know, if we don't clear those in time enough, that's essentially the same process happening. Right. And we've got that acid buildup, and that is basically the highest potentiation
of injury. Well, let's take that a step further. What creates hypoxic. Right. It's an overuse of muscles? Well, it's typically not somebody going into a rhabdo type situation, but it's a breakdown of some small minutiae of firing and an overuse of particular muscles as it relates to that. So if we take that a step further, when do we see that? We would see that with a jerk velocity. We would see
that in that state. So we have an early intervention before we start, as we have a, you know, a muscle start, you know, fatiguing out and not working, then we get the compensation of other muscles kicking in in an overuse standpoint to the point that it impacts the rate of acceleration, which then creates chemical release in the body that affects the receptors, which causes things to happen through, you
know, our sensor signaling. All this leads up into the potentiation of injury issues as well as, you know, maybe some issues relating to the nerve tunnel, which then restricts mobility, which then you've got to do some recovery work later. All of it rolls back into this thing called the jerk metric and our ability to see how smooth the movement is, right? Yeah, that's nailed it. And this is why we need to understand
the single transduction theory. So we understand that when, when blood ph is or muscle ph is lowered, we are decreasing enzymatic activity. We get calcium binding, we get changes in electrical properties of muscles. That's why we have to understand the fundamentals. I want to say fundamentals. I'm talking like there's fundamental particles. That's why we need the smallest building blocks to understand the bigger picture. It just makes sense. Think about it just makes sense.
It does make a lot of sense. If I just keep saying it. When we're taking, let's say we take a camera, we record a movement, we want to be able to say, okay, we have a jerk matrix, which is, again, a change wave acceleration over set amount if the goal of a session is rehab. Okay, so I had an international level athlete, great guy, not powerful thing. He was an overhead sport athlete, and he got stuck here. He couldn't get his arm above this position, which as an overhead sport
athletes is a really big deal. Just getting to slow down and try and go for this position to allow, like, no fatigue to set in, allowed him to get his arms overhead in two or three weeks. It was super quick for someone who had a bad shoulder for years. And it's just understanding. Certain qualities predict change, like understanding that if we can create smoother movements, we're gonna get better movement later on, which is all I say, like. Like,
slow is smooth, smooth is fast. Now, I'm not sure I totally agree with that, but in this situation, like, it. It holds true. Now, we can create a movement which is like constant acceleration under no fatigue. We're in a good place. Yeah, yeah. I think it's absolutely fascinating. And the fact that no one is really utilizing that right now
is really interesting. Now, as you and I know, we've been able to test and view that data because other, you know, other people in the velocity arena are not necessarily have that output or understanding of what's going on and the ability to use that. That data. Yeah. What's contextualizing it as well. Right? Like you said, it is using the data in the right context. Like, just knowing isn't enough. We
have to build upright. Yeah. Now, I have to say, like, you know, me referencing Charlie Weingroff's opinion on, you know, increase in lactic acid being a key driver, and, you know, that correlation between the jerk metric, you know, that's. That's. That's theory. But it seems pretty fundamentally straightforward given we know those mechanisms. And we. We also know now more of the research of understanding of what happens to the nerve and how the
nerves create that. You know, those enzyme releases the calcium binding, these electrical properties that go on and change that, change that tissue around it and change the tissue quality. And that's. That's a word I use quite a bit, uh, for anyone that follows, like my podcast series or some of my deeper stuff, like, tissue quality is absolutely fundamental, like, with being able to manage training. That's one reason that I always got this vaso blitz sitting right back
in here. It's the basal blitz. Pump time. Uh, but, you know, the better we can keep the cellular hydration, the turnover of these, you know, uh, of these components through our tissue, the better, you know, we're going to be like. That's why we start seeing issues like this. You know, injuries develop when you're dehydrated and things of that nature. Right, yeah. I mean, we even missed out. There's tons of stuff we're, like, glossing over, which we should touch on. Um, like, do it. I. Sorry, I
just got so excited. I just ran it. It's the same thing. So you're talking about tissue quality, like, and I'm. Amplification principle, like, great. I really like that application protocol. But would we miss out on the idea? That's the course for another podcast. Yeah, we're missing out the idea that injury causes a decrease in blood flow. Right. So if you go, you know, thermometers, you get, like, a targeted
thermometer. Use them on building sites to find cables. If you get a really sensitive one and you shine on a good joint and a bad joint, you'll see the temperature of the injured joint is lower. Okay, so could we have a decrease in vascular proliferation around a joint? Okay. Decrease in vascular proliferation causes a decrease in oxygen delivery. Okay. So we then have this knock on effect that we're gonna have an increase in metabolic by products that are associated with deoxygenation.
Okay. Deoxygenation, increase in enlarged acid, increase in those byproducts that come from anaerobic respiration. And then we get, again, a decrease in stability or smoothness or an increase in joint metrics, depending how you want to define that. And then we lead into higher likelihood of re injury. So we have this kind of downward spiral of injury risk from getting injured the first time. We know the biggest predictor of future injury is previous injury, which is really
shit. But that's what it is. If you hurt your knee once, you're probably going to hurt it again. Unless you can addresses and then you can like say, okay, this is the information I have. I know if I do over x amount of volume or if I know if my speed decreases under 0.4 meters a second, I had a great athlete, a super strong guy, like 800 pound squat kind of strong guy. Every time his squat went under 0.4 meters a second, within two weeks he'd gain just
like clockwork. It was amazing. Amazing. Not the right word, but like, yeah, we can measure it. Yeah. Like I have a wonderful graphic which like, just says, hey, this is when we. This is when we realized. And then you just see a squat, like shoot back upwards because we just never went under that threshold again.
Okay. Like super, super interesting. So if you know the measurements you're taking and you know how to apply them, or you have like a system or an algorithm which does it for you, you don't have to worry about that shit because it's taken count of. Any. Any last thoughts on jerk metric or, you know, adaptive modulation stress index we want to leave this with. Yeah, actually on jerk metric. Don't think it would just as a weightlifting thing if you're doing cardio as well.
Pretty important, especially for running because changing acceleration of kind of phases of running is a big deal. No, that's probably it. I think it's probably the future. I think if people aren't starting to take into account internal loading in some form, they're probably not going to be getting the best out of their athletes without themselves. Yep. Yep. I think that's a good place to leave it for today. And I hope,
hope we haven't bored everybody too much. But I think that, you know, those that want to join us for this continuing conversation will have an understanding of what I believe is the next generation of, you know, training principles. And so really excited that anybody that wants to check out further information as far as movement quality, videos, articles by Alex, myself. I recently just dropped my 300 page deadlift manual. Thousand pound deadlift.
Got just a massive amount of stuff on the community. You can check that out@chrisduffin.com. and it'll give you that's absolutely free for anyone to come and join and check out a base level of, you know, videos and articles and community, all for free with like minded folks, really incredible contributors as well with Alex as well as Anthony, who is doing the Peptide series, peptide and supplementation and off use medication series. So, yeah, some
really deep dives on stuff. So I know it's not for everyone, but let's keep learning. Let's keep growing together. Thank you all.