Barbell Shrugged - The Practical Applications and Limitations of Research and Strength Training w/ Greg Nuckols, Anders Varner, Doug Larson, and Dan Garner #700

Episode Date: June 21, 2023

Greg has over a decade of experience under the bar and a M.A. in exercise and sports science. He’s held three all-time world records in powerlifting in the 220lb and 242lb classes. He’s trained hundreds of athletes and regular folks, both online and in-person. He’s written for many of the major magazines and websites in the fitness industry, including Men’s Health, Men’s Fitness, Muscle & Fitness, Bodybuilding.com, T-Nation, and Schwarzenegger.com. Furthermore, he’s had the opportunity to work with and learn from numerous record holders, champion athletes, and collegiate and professional strength and conditioning coaches   Greg Nuckols on Instagram  Anders Varner on Instagram Doug Larson on Instagram Travis Mash on Instagram Dan Garner on Instagram

Shark Family, this week on Barbell Shrugged, we're hanging out with Greg Knuckles. If you have not seen Greg Knuckles before, that name is new to you. You've got to go watch how insanely strong this human being is. On top of that, Travis Mash found him in a gym one day and said, you'd be really good at powerlifting and became really, really good at powerlifting, which is very cool. It's kind of tied to Barbell Shrug tied to the Barbell Shrug family here. Today on Barbell Shrug, we're going to be talking about the practical applications as well as limitations of research, how you can kind of structure your training around the science

and all the studies that you see that come out, wondering how to implement all of it, as well as some of the limitations of where we're at in research these days. And as always, friends, you can make sure you get over to rapidhealthreport.com. That is where Dr. Andy Galpin and Dan Garner are doing a lab lifestyle and performance analysis that everybody inside Rapid Health Optimization gets. That's over at rapidhealthreport.com. Friends, let's get into the show.

Welcome to Barbell Shrug. I'm Anders Warner, Doug Larson, Dan Garner, Greg Knuckles. This is so fantastic. I can't believe Travis Mash is in here today. In 2007, you just said that he got you transitioned from like playing real sports to playing sports where you stand still and lift a lot of weights. He did. Yeah. Yeah. I was, uh, I was training to improve my, my vertical for basketball, uh, played basketball, football, baseball growing up. Um, and yeah. And, uh, so I was working with someone at a gym that he was also training at and, um, you know, trying to get my vertical up for basketball, went off to football camp, got the type of concussion that doctors say like, hey,

if you take another shot like that, there will definitely be like very serious long term repercussions. Like you can't do squirts where there's any risk of head trauma anymore. So I said, OK, that's that's unfortunate. I don't like that. But I still had a few sessions left on the training package. So, you know, kept going to the gym, kind of let people know what was going on. And Travis pulled me aside and he said, hey, I've seen you lifting weights. I think you're better at lifting weights than you are real sports. So this seems like a setback now, but let me introduce you to this sport called powerlifting. I think you're really going to take to it.

That ended up being a very good thing. Travis has a way of getting people to lift monstrous weights. It's good to know that you're part of his lineage there. Really, in today's show, I want of dig into the intersection of the practical side of how we use science, where science can kind of go wrong, and really dig into some of the pieces. You run a company called Stronger by Science. You guys post tons of research. If anybody is not following Stronger by Science or Greg on Instagram. Highly recommend getting over there. It's really cool little snapshots on how this,

how like the science plays into really the practical side of what we do in the gym on a daily basis. And the first thing that kind of pops into my brain that you guys posted the other day is about slow digesting proteins. And do we actually need them before we go to bed to continue

not going catabolic at night. Because that's like the old theory that if we don't eat or don't drink casein protein before we go to bed, that we're all going to shrivel up and lose all of our muscle as soon as our head hits the pillow. It's just not true, is it? No, I have, I don't know, I have, I think, a healthy bit of skepticism for a lot of not the protein research itself, but the sorts of inferences people often draw from protein research. So a pretty good chunk of it is just based on like relatively short-term studies that measure um either muscle protein synthesis or even more basic just like rates of amino acid appearance and clearance from the blood like that's the the fast versus slow digesting stuff and then they use those sort of like acute proxy measures to make

assumptions about like each step up the chain. Like, Hey, if, if something causes a more prolonged elevation in blood amino acids, that will lead to a more prolonged and therefore cumulatively larger increase in muscle protein synthesis. If you get a larger increase in muscle protein synthesis, that will necessarily mean more, more muscle hypertrophy over time. Like that's, that's sort of like the, the logical chain there. Um, but I, I think that, I mean, I, I don't just think this, um, like that, that does often break down in a lot of like discrete circumstances. So with, uh, with like fast versus slow digesting protein, I recently wrote about a study from Tromelin and colleagues where they were. So there's like this this common like relatively common thing people do where they're afraid they're going to go catabolic overnight. And so they say, hey, I'm going to consume some protein right before bed. And since it's going to be a long time before

the next time I consume protein, it should be a relatively slow digesting protein that's going to lead to, you know, superior overall protein balance over the course of the night. And there have been studies that compared like casein to a placebo that found, Hey, you know, protein is better than nothing when it comes to muscle synthesis. And it's like, okay, like that's, that's pretty intuitive. Um, but casein, which is relative to way of slower digesting protein in a broad sense, way in case you are both relatively fast digesting proteins, but compared to way casein is relatively slow digesting. So this was the first study to actually compare casein against a faster

digesting protein to see, Hey, we'll, we'll a slower digesting protein actually have a larger effect on overnight muscle protein synthesis than a faster digesting protein actually have a larger effect on overnight muscle protein synthesis than a faster digesting protein. And, uh, long story short, it didn't there, there was no difference. Um, but then kind of like the next step down the road is that if you, if you look at that, like your initial assumption would be, well, you know, whey isn't better than casein, but both are better than placebo in terms of overall muscle protein synthesis overnight. Therefore, it doesn't really matter what protein I use before bed, but I should still use a protein before bed. Like that will help me grow more muscle over time. But honestly, like I'm, I'm

relatively skeptical of that interpretation as well just because like that kind of like a to b uh assumption of logic like if if i see a larger like cumulative acute muscle protein synthesis response that will necessarily lead to more hypertrophy there there are a lot of instances where that just breaks down. So, uh, a few years back, I think maybe 2014 or so, uh, there was a study, uh, comparing the muscle protein synthesis effects of, uh, cooked versus uncooked egg whites and egg whites are a food where when the protein is uncooked, it's quite a bit harder to digest. Yeah, Rocky lied to us.

Do what? Rocky lied to us. Well, he didn't. So that's where I'm going with this. So yeah, egg white protein has lower digestibility if it's uncooked. And this study, you know, took that a step further and said, hey, you know, we see it has lower digestibility. Therefore, we expect it to cause less muscle protein synthesis when when people consume it raw versus cooked.

So they did that study and that's what they found. More muscle protein synthesis with with cooked egg whites rather than raw. Pretty intuitive. But then that leads to the assumption that therefore cooked eggs should cause more growth over time than raw eggs. And then more like so the previous study, like I said, I think it was from like 2014, 2015. More recently, like within the last year or so, they finally did a longitudinal study looking to see like, Hey, if we feed people raw versus cooked eggs over time, do we actually see differences in muscle growth? And they didn't. Um, and that's, that's like relatively common, like a, a similar thing has happened with like animal versus plant protein. So in, in terms of like any kind of like protein scoring system, uh, animal proteins typically do quite a bit better than plant proteins. Like they're in terms of any kind of protein scoring system, animal proteins typically do quite a bit better than plant proteins in terms of completeness of the amino acid profile, digestibility,

et cetera. And acute studies show animal proteins tend to cause more acute muscle protein synthesis than plant-based proteins. And so that led a lot of people to assume for a long time, necessarily, you'll grow more muscle if you have an animal protein-based diet than plant protein, or just higher protein quality versus lower protein quality more generally.

But again, within the last three or four years, they started doing studies on this. And to my knowledge, every study that's used, like a sufficient total protein dose, like that lifters would typically go for. So like 1.6 grams per kilo or above, none of them have found any differences in muscle growth between higher and lower quality proteins. So like there, there is like the, the like the acute protein literature does typically just look at protein synthesis. And then people just assume there's kind of like a one to one correspondence between those acute measures of muscle protein synthesis and growth over time. But that is an assumption that breaks down pretty frequently.

Shark Family, I want to take a quick break. If you are enjoying today's conversation, I want to invite you to come over to rapidhealthreport.com. When you get to rapidhealthreport.com, you will see an area for you to opt in, in which you can see Dan Garner read through my lab work. Now, you know that we've been working at Rapid Health Optimization on programs for optimizing health. Now, what does that actually mean? It means in three parts, we're going to be doing a ton of deep dive into your labs. That means the inside out approach. So we're not going to be guessing your macros. We're not going to be guessing the total calories that you need. We're actually going to be doing all the work to uncover everything that you have going on inside you. Nutrition, supplementation, sleep. Then we're

going to go through and analyze your lifestyle. Dr. Andy Galpin is going to build out a lifestyle protocol based on the severity of your concerns. And then we're going to also build out all the programs that go into that based on the most severe things first. This truly is a world-class program, and we invite you to see step one of this process by going over to rapidhealthreport.com. You can see Dan reading my labs, the nutrition and supplementation that he has recommended that has radically shifted the way that I sleep, the energy that I have during the day, my total testosterone level, and just my ability to trust and have confidence in my health going forward. I really, really hope that you're able to go over to rapidhealthreport.com, watch the video of my labs, and see what is possible. And if it is something that you are

interested in, please schedule a call with me on that page. Once again, it's rapidhealthreport.com, and let's get back to the show. Like in the case of the whey versus casein overnight study, in terms of acute effects, both better than placebo, but casein not better than whey. Over time, I mean, like if you wanted to take a better safe than sorry approach, I'm sure it doesn't cost you any muscle growth to consume a protein bolus before bed. But in terms of extrapolating those acute findings, like I'm relatively skeptical that if you match for total protein intake, that the pre-sleep protein would have a beneficial effect versus just consuming it any other time of the day. I remember the first time I heard about this was Hillary Swank when she was training for

million dollar baby or something like that. And for like five straight months, she woke up every night twice to chug a protein shake. Yeah. It was like part of the story of her going along into like training for the movie. Oh, I used to do that actually. So this is a, this is not a story that's going to cover me in glory, but, um, so I, I didn't, I like, I'm a very deep sleeper. I don't hear alarms at the best of times. So like, if I set an alarm for like 2 AM to drink protein in the middle of the night, like no way in hell I'd hear it. Like it would just wake the rest of the house up. Um, but you know,

I wake up to pee in the middle of the night sometimes. So when I used to be more concerned about this stuff, I would just like mix up a protein shake and just keep it kind of in the bathroom near the toilet. So if I woke up in the middle of the night, I'd have that protein shake to down and you know, go back to bed. And that typically worked. But I didn't wake up to pee in the middle of the night every night. And one time, there was about a two week gap between when I mixed up a protein shake and the next time I woke up to pee in the middle of the night. And you know, I'm not really thinking I'm groggy. I wake up, I go to the bathroom. I go ahead and pee. I like go over to the counter, grab my protein shake, just pop it open, start chugging. It had gone so fucking rancid. It was disgusting. And like, like, just, I just immediately went to the toilet and started

like projectile vomiting. Like it was, I mean, you know how, you know how like protein shaker bottles smell if you don't wash them out and you leave them in your car. Like it was, it was that, but I got like a big gulp of it before I noticed. And my God, one of the grossest things I've ever experienced. But I wasn't even smart enough to say, hey, maybe this thing isn't for me. The only tweak I made is I just started like keeping like just dry powder in the bottle and then mixing it up at game time. But I haven't liked whey protein ever since that day like i like when i drink protein shakes now like i i opt for casein like i you know you know how like if you uh if you drink too like if you drink way too much bourbon and get sick off of it and throw up like

you can still drink alcohol but like bourbon kind of makes you feel a little queasy or whatever. It's it's like that for me with whey protein now, like it makes me a little bit sick just to smell it from from that experience. Yeah, first of all, I totally support this behavior of having protein shakes in the middle of the night. I if I could only tilt the camera that way and show you how much of a bro I am with how many whey protein bottles are over there right now. I've got so many of them. I remember Jay Cutler, he used to get up in the middle of the night and but he did it just purely for calories because he could not eat at all throughout the day and he was sustaining 300 pounds of mass. So I remember hearing about that and doing something similar. I used to actually in high school, I would work out and I would always forget my shaker cup in my gym bag.

And then my mom would do the dishes. And I would always forget and I would hear because I would leave it in there Friday and she would get it like Monday or something like that. Yeah, I would just wait for that. And it would would be amazing but um dude i was real excited for you to come on here um reader of strength theory reader of mass i got the omar isa book that you collaborated him i get the art and science man uh been following your work a long time so i really appreciate what you've done for the industry you put out some absolutely killer content and um uh the what you're talking about with the whey and casein, with the eggs being raw versus cooked, with these situations of 1.6 grams,

high quality, low quality, protein and digestible amino acid scores, all these different things that vary what would determine the quality of research. But sometimes a high quality research study still isn't always applicable into the real world. So that's like something I've always really liked about your content is that you were strong. You did look at research with the intention of what is what's going to happen with this with actual hypertrophy?

Does periodization actually matter? Does the casein actually you were always asking questions that translated into the real world. So I think like your explanation of what you were talking about previously, actually begs a bigger question of extrapolating information from research, because right now, not even right now, the evidence based wave has been around here for like 10 plus years or so it's like really spearheaded, at least in my experience, really spearheaded by Aragon back in 2008, starting that research review. He's one of the first guys that I could remember really, really, I guess, Lyle McDonald before then with the keto book and a lot of things. There was a few guys really spearheading the evidence-based wave that is massively prevalent

today. But I think the evidence-based wave has become popular to a point where if someone cites a study, people kind of just believe it, rather than understanding the true extrapolation and applicability of that study. So I would love for you to talk about and just kind of shed some light on your opinion towards what are the limitations of research? And what should people be looking for instead of just blindly thinking, hey, that guy provided a reference? It's probably true. Yeah, so there are several. Yeah, where to start, honestly. So one thing that I'm not totally sure what to do with, but that I think is, uh, is definitely not considered frequently enough is just like the timescales that research takes place on. Um, because typically if, if you're training yourself or you're training clients, like you're, you have an eye towards,

you know, what am I going to do for the next year, five years, 10 years to maximize my progress over time. But the time course of most resistance training studies is between like six to 12 weeks, like longitudinal studies. You'll, you'll occasionally see one that's 16, but that's kind of the, the upper extent. Um, like, you know, I I'm know, I'm aware of a couple that went like six months or like maybe up to a year, but they're extremely few and far between. is can like to what extent can you extrapolate the findings of relatively short-term studies to long-term training that you do um and also just like how how should you talk about those results because like you know we we like to say that the study found that um you know xyz leads to larger gains than whatever it's being compared against. But I don't actually know if that's the right way to think about it. The study did find that, you know,

thing A led to faster gains than thing B, like larger total gains during the time period being observed. And it very well could be the case that you can just extrapolate that out that, hey, if something's better over 12 weeks, it's going to be better over 12 years as well. But I don't know how confidently you can make that extrapolation. And I don't think people give enough thought to that. Um, and another thing that I think a lot of high level coaches point out and rightly so is that you're necessarily constrained by the population that is available to study. And I mean, most, most studies just use undergrads that are participating in a research project for extra credit, uh, or 50 50 bucks which goes a long ways in college yeah what would i have done it many times i've been very excited about the hundred dollars

i got yeah yeah like what what would i do for 50 bucks right now not a ton what would i have done for 50 bucks when i was 20 holy shit like if if you if you've got like a hit list i can tell you i'd be willing to cross off like one or two of those names for you. Like, well, see, 50 bucks a lot. I was doing it for what is a blood platelets or whatever. They run you through. That was like a job twice a week. Oh, yeah. Like 50 bucks. So, you know, a lot of untrained undergraduate students or, you know, even if they can get like a trained population from the community surrounding the college, it's necessarily a group of folks that are fine with scientists dictating how they're going to train for the next three months.

And who does that group not include? Usually high level athletes, you know? And so there's always questions of generalizability. You know, like how well do studies on untrained people generalize to people who have some training experience? And how well do studies on people with some training experience generalized to like higher level athletes. Um, and, you know, depending on the topic, sometimes quite well, sometimes quite poorly. Um, but you know, that, that's another factor where like, it's hard to say, and it's, it's a relatively large limitation because you can use you can use research on population A to inform practice on population B. But you probably need to approach that translation with, you know, a bit more intellectual humility than if it's a study in population A and you primarily train population A. In that case, like results should translate to the population you're training pretty

well because it's a study on the same population. But as you get further and further away, like as there's a bigger and bigger gap between the population the study was done on and the population that you are a part of or the people you train are a part of. Yeah, the confidence you can have when translating research into practice necessarily needs to go down. But I really think the, I think the biggest drawback is just kind of based on the way that most scientific studies are structured and what they are specifically trying to find. So most research, like most longitudinal training research takes the form of a parallel group randomized control trial, where you want to test like two different approaches to training, you maybe recruit 30, 40 participants, you use some sort of randomization approach to randomly allocate them to two groups.

And, you know, group one does one training program, group two does the other could be higher versus lower volume, high load versus low load, higher versus lower frequency, whatever. Like, you know, but that's that's the basic design. And then you put those two groups through those two training programs. You collect results at the end and you test to see, hey, on average, did group A get better results than group B? Did group B get better results than group A? Or were they similar enough results that we can't confidently say that one or the other was better? And that is an excellent way to go about figuring out what works on average,

kind of what works pretty well for most people most of the time. But, you know, I'm not most people, like I'm me. You know, all of us are individuals, and we don't all necessarily have like the exact same characteristics as, you know, the average of the broader human population. And I think that that, I think that that goes underappreciated, like the, the, the types of inter-individual variability you see and the extent of it. Cause I think, I think a lot of people, when they, when they think through this stuff, kind of their baseline assumption is that there is some level of inter-individual variability that, you know, some people respond to training better, some people respond to training worse. But, you know, maybe the people who respond well get gains that are

like 50% better than average, and people who respond poorly get gains that are 50% worse than average. And that's kind of the range of responses you see. When in reality, it's like three, four times bigger than that. One of the studies I think that illustrates this quite well is a study by Hubal and colleagues from 2005, where they took like 585 untrained participants and put them through just three months of arm training. That's all they did. That's what Dan does too right now. Not a damn thing wrong with that.

That's the program, baby. But yeah, so the average, and that study was like specifically trying to characterize kind of the spread of responses, which, you know, you're not going to get a great view of the extent of variation in training responses if you have a study on 20 people. You know, like what are your odds of finding like a truly high responder or a truly low responder if it's only like a dozen two dozen folks so sample size of 585 put them all through the exact same training program and on average bicep size increased by like 20 like 19 20 but there were a handful of people who like untrained folks their biceps actually got slightly smaller over three months of training. And there were quite a few people who saw gains that were, you know, 5%, 10%, like,

like well below average. And then the highest responses seen in the study were folks whose biceps cross sectional area increased by in excess of 50%. So that's like two and a half times better than average. So yeah, like the range of responses we see to training is huge. And then I think what's even more underappreciated is the variability and the type of training people respond well to. And this is where I think there are just kind of like structural weaknesses in translating science to practice just based on how most studies are done, because they are primarily using parallel group designs. And you're primarily doing statistical tests to determine what produces better results on average, which isn't all that informative about what produces better results for individuals.

So there are studies that are more well-equipped for that. So there are two different types of designs you could use. You can either use a crossover design where, say, over six months, you're going to have two groups of people. You have two training programs. And one of your groups does program A for three months and then program B for three months. The other group does program B for three months, then program A for three months.

So you just kind of like flip the orders. And so you can look at the individual responses to each program. And you can also see whether there's any sort of like sequencing effect, like, you know, is program A maybe more effective when it comes after program B than if you do it first. So there are a lot of advantages to crossover designs. The drawback is they take twice as long to do. And so they're harder to recruit for, and they take more effort from the researchers, but very good study design to look at, you know, not just whether thing A or thing B does better on average,

but kind of like what percentage of people do considerably better with thing A than thing B and vice versa. And then the other design that's becoming more popular that I really like is a within subject unilateral design, where instead of parallel groups, where you split individuals up, and you have 12 people over here and 12 people over here, you instead split limbs up. So it might just be like a lower body study, you're looking at quad growth. And you have like, you know, like, like one leg might do high volume training, and one leg might do high volume training and one leg might do lower volume training. So each individual can serve as their own control. You, you randomize like leg allocation. So you want like about half of people, like you want about half of people to have their dominant legs

doing program A and about half to have their dominant legs doing program B or whatever. But yeah, like functionally you can have each individual serve as their own control, which is really, really good just for controlling all extraneous variables that could contribute to variability and training response. So, you know, when when I bring up the hue ball study, people are like, Oh, well, what if what if some of those folks were like crash dieting? Or what if some of them were not sleeping enough? That may be why some of these folks didn't see much of any hypertrophy. Yeah. If you're interested in kind of like different responses to different training programs, like, Hey, my left leg isn't sleeping more than my right leg. Like my left leg isn't eating better than my right leg. So within each individual, you can control all of those like outside factors that influence

training responses. And so that is, again, like a very good training design that still lets you see what produces better results on average. But then you can also see within each individual, you know, how many people got pretty similar results to both programs with each leg or each arm how many people got like way way better results with program a how many people got way way better results with program b um and so like they're like just some just some examples to throw out here of the sorts of like intra-individual differences that that we see in studies like that so um there was a paper by uh what was the researcher's name uh carniero uh and colleagues that i think was epubbed last year and was just published maybe like last month or so, like pretty recent.

But it used a crossover design. And so it was testing like high load training versus low load training. So I think one group was training with like eight to 12 rep max loads. The other group was training with, I think it was something very random, like 27 to 31 rep max loads or whatever. Basically just like adjusting training loads so that you hit failure kind of within those rep ranges. And so, yeah, like half of the subjects did three weeks of low load training first, followed by three weeks of, or three months of low load training, followed by three months of high load training. Half of them did three months of high load training, followed by three weeks of, or three months of low load training, followed by three months of high load training. Half of them did three months of high load training, followed by three months of low load training. And in aggregate, what they found comported well with the

rest of the research. So on average, you saw pretty similar responses to high load and low load training. That's what we see in basically all of the research on the topic. But then, like, I think the thing that people want to do is look at those studies and say, hey, on average, we see high load and low load training produce similar hypertrophy. So it ultimately doesn't matter. You know, if you prefer training heavier, you can train heavier. If you prefer training lighter, you can train heavier. If you prefer training lighter, you can train lighter. Both of those things will produce similar amounts of muscle growth for you. And if you left off the for you, you would be right. But when you include the for you, you become wrong. Because that is something that is generalizably true on average,

but not so much for the individual. So there were there were 24 total subjects in this study, and they were basically split up one third, one third, one third, where a third of them got pretty similar results to both both training loads, like the like lower body hypertrophy they saw was, uh, like similar enough within kind of the margin for error for the, for the measurement being used. But then a third of them, uh, saw like considerably better growth with higher load training. And about a third of them saw considerably better growth with lower load training. And just as like two extreme examples, um, there was one individual who, uh, like, so they were measuring lean soft tissue mass. So one saw like, uh, like

12, 13% increase in lean soft tissue mass, uh, of their legs following high load training and only like a two, 3% increase with low load training. And then conversely, there was one individual that saw like an 11% increase with low load training and like a 5% decrease with high load training. So if you told either of those people, hey, it doesn't matter which one you do, you would just be dead wrong. That actually brings up something I'm super interested in your opinion on and sure everybody on here has been lifting for multiple decades at this point and every time i see like the new latest greatest thing like oh yeah i feel like i did that when i was like 19

yeah like i feel like we spend a lot of time testing these things to find out what is the best way to do it when the best way to do it is like do that thing for eight to 12 weeks and then go do the next best thing for eight to 12 weeks and then accumulate all of that for you know 20 30 40 the rest of your life years um where do you i mean or just do that for five to 10 years, try a bunch of different stuff. And over that period of time, like you'll, you'll figure out what works pretty well for you. Exactly. And, and I wonder, um, do, do coaches and trainers get kind of like way too lost in these tiny little minutiae, um, differences that don't actually need to be thought about at all until there is a very specific goal

where you should dedicate three, four months to your bench press, if that's the goal. But because in the science community, do you feel like we, specifically when it comes to just building muscle or building strength, we've gotten to the point where we kind of figured it out, but people still have to do studies.

Like it just still has to be created or is there something that is there some magic bullet that we don't know about that we're trying to to uncover because at this point every time i hear the new thing i'm like yeah wait did we just run a study on drop sets versus super sets like versus giant sets like that we should be doing all of them. So here's, so yeah, I think that there is like, still a frontier to explore in the research and a pretty big one at that. So just kind of like continuing on with what I was saying, like the parallel group designs have you know a lot of strengths but also a major flaw which is they really only tell you about averages the um

like like a mere crossover study or a mere within subject unilateral design study that tells you more about kind of it like inter-individual variability in terms of not just total responses, but like the types of training different individuals respond well to and what the spread of responses there is. But there's still a very big drawback there, which is that, you know, it's nice to know that, hey, some people will do way better with heavier loads. Some people will do way better with lighter loads. But the study itself doesn't really give any guidance for will I do better with lower loads or will I do better with higher loads? You still have to experiment for yourself and just try a bunch of shit out. And there's a lot of shit you could do. It will be a very long troubleshooting process to figure out what type of training you respond particularly well to. So I think kind of the next frontier is to

take things a step further, going with crossover or within subject unilateral designs, but then also collecting more just kind of like baseline variables to kind of like characterize the individual and then see if you can find variables that are predictive of responding better to one thing or the other. So, you know, I don't think this is the case, but like, let's just say that for whatever reason, people with relatively long legs respond well to low load training and people with relatively short legs respond well to low load training and people with relatively short legs respond well to high load training. Like, I don't think that's the case, but like just using that for this example.

So if there was a study that like measured leg length and also used it within subject unilateral design and they found like, hey, there's a pretty strong association between leg length and superiority of one of these programs versus the other on an individual level. Then then we'd say, hey, not not only do we know the different responses that people might see. We also now have kind of a screening tool to know where to start. We're like if someone walked through your gym and they just had they look like a daddy long legs. They had the spindliest legs you ever saw you'd say hey you know i think we're gonna start you on 20s you may not like it but we think

that's what you'll respond well to and like the the leg length thing like that's that's a bad example like you might want to i mean things like age like maybe fiber types uh like i don't i don't know like any number of fucking things, but just so I've been taking notes here as you've been going on. And as far as like extrapolating research to the real world, you're talking about time of the study population of the study, the actual outcome measured the response variance between people, the statistical average of the outcome, what the outcome itself was measured, the design of the study, the individual screening component to it. Like there's so much of this that I think a lot of people don't know about because as far

as the time of the study, well, if it was an untrained population, which is normally doing it in a six week hypertrophy program, like anything's really going to work for someone who's untrained and anything's really going to work in an acutely in an acute measure anyway. So like you can get brought down these situations that aren't necessarily applicable to the people who are most interested in reading this, which is like a lot of listeners right now who will probably just jump to conclusions told you that low load was better. I told you that high load is better. And that always kind of comes around. So like, I'm a guy who is a real big fan of like old school bodybuilding, like, like Arnold and Dave Draper and like these kinds of dudes and Vince Gironda, a lot of these guys.

So I know we have to wrap this up real quick. So, but like, I've been dying to ask you here with the limitations that research has, what would be your opinion on how to filter what an old meathead has to tell you? Oh, man. You know, I think that that's, I think that that's a very good question, honestly. So I think that it's,

so one, like I, I probably wouldn't put excessive weight on the kind of opinions of, of any one person. Um, but you know, if, if you're seeing a lot of, a lot of old meatheads saying a lot of similar stuff, um, I think that that is probably like, if you're looking at it through a scientific lens, I think that that is probably pretty useful for generating like a tacit null hypothesis, or, or if someone likes Bayesian statistics more for setting your priors, uh, similar concept, but like in, in like formal science, you have, um, like you're, you're basically testing, uh, what's, what's called the null hypothesis. And the null hypothesis is that like the, the two things we're testing, they don't differ.

Uh, and so you're, you're looking for, um, like you're, you're trying to find sufficient evidence, like sufficient statistical evidence that the two things do, in fact, differ. And if you find that evidence, you reject the null, and you, you know, say, hey, seems like this one thing is better than the other, rather than the null, which is these two things are similar. But I think kind of in the real world, it doesn't always make sense to use that null where, you know, there, there's a, you know, there, there's any millions of numbers of things you could do. And I don't think it's particularly helpful that like, like to take the approach that

if we don't have a direct study on any of these things, We have no idea which one of these options will be better for a particular context, a particular individual, and we have to assume that they are all equivalent. That's the kind of formal scientific way of thinking about it, but I don't think that works in practice. So I think that if there's a topic where there's not much direct research to fall back on, which, you know, when, when it comes to, you know, stuff that like 60 year old bodybuilders are saying who've been training for 40 years, how many people like that are in the research? Like fucking none, you know, like that's, that's, that's a population that's completely unstudied. So I think that the, the sorts of things that

folks who've been in the game for a long time say, I don't think that we should necessarily, you know, say, Hey, we, we can be a hundred percent confident that all of this stuff they're saying is correct. But I think that those commonalities you see are good for generating like useful null hypotheses, like, in the absence of other evidence, it is probably not bad to assume that what they're saying is more right than wrong. And another thing just about like advice from elite athletes in the first place, I think a common rebuttal people will have is like, oh, well, the elite of the elite, they just had the genetics for it. Like, I think that that's true to a large extent. But I also think that like elite athletes can still do stuff that's bad. You know, like they there might be a pretty finite extent to which optimal training will

really improve their results. But there is also a pretty large area where like shitty enough training could or shitty enough lifestyle, nutrition, whatever could like meaningfully diminish their results. And so, you know, the recommendations they give and like the advice they give and the insights they have, you can't necessarily be confident that all of it is necessarily the best thing and will necessarily improve the progress you see. But there is a pretty good chance that none of it is such bad advice that it's going to like really compromise the gains you see. Because if it was, they probably wouldn't have been as good

as they were, you know? Yeah. So that's kind of how I look't know. people who have been where you want to be and have done what you want to do. Like, I think that it's not a terrible assumption to assume that they're more right than wrong. Yeah. Where can people find you, sir? StrongerbyScience.com. If you're looking for a great nutrition app, check out Macro F in the play store and app store uh if you're listening to this you like audio content stronger by science podcast is is out there in the ether uh anywhere fine podcasts can be found and uh that's about it if you want to follow me on social media don't uh just follow the struck by science instagram account i never post on my personal account anymore there you go and he hates

the dms so i do i do hate the dms i really do i really do dan garner at dan garner nutrition on instagram there it is douglas e larson where are you that's it uh greg great having you on the show man been a long time coming i've wanted to have you on here for many years so appreciate you coming out thanks for having me it was a pleasure and an absolute blast yeah you bet uh on my instagram douglas e larson i'm anders barner at anders barner and we are barbell shrug to barbell underscore shrug make sure you get over to rapidhealthreport.com that is where dan Garner and Dr. Andy Galpin are giving a lab lifestyle and performance analysis, which everybody inside Rapid Health Optimization will be receiving.

You can check that out for free over at rapidhealthreport.com. Friends, see you guys next week.