View all show notes and timestamps on the KoopCast website.
Episode overview:
Jeff Rothschild is a registered dietician, PhD, and research associate at Sports Performance Research Institute New Zealand.
Ever been curious about the science behind fueling your athletic performance? Look no further, as we are joined by Jeff Rothschild, a registered dietitian and researcher, who is here to break down the complex concept of carbohydrate periodization for trail and ultra runners. We dissect the misunderstood principle of "fueling for the work required" and dive into how an athlete can manipulate their carbohydrate intake during periods of high intensity or high training loads. Jeff's innovative research on this topic unveils an index for quantifying carbohydrate periodization - a game-changer for athletes aiming to enhance their performance through optimized nutrition.
Episode highlights:
(17:32) Calculating periodization: any metric of training load, daily carbohydrate intake in g/kg, correlation coefficient, reasons for including range and monotony
(36:24) How to track your carbohydrate periodization: track your diet, consistent measures of training load, TSS or CR100, TSS is best, use the spreadsheet in the description, help from Jeff’s app
(43:38) Use cases for carb periodization: predicted TSS for your upcoming workout, adjusting carbohydrate intake based on each day, you can consume more after the workout if the true TSS was higher than expected, examples
Additional resources:
The quantification of daily carbohydrate periodization among endurance athletes during 12 weeks of self-selected training: presentation of a novel Carbohydrate Periodization Index
Jeff’s app-https://rothschild.shinyapps.io/carb-index-app/
SUBSCRIBE to Research Essentials for Ultrarunning
Buy Training Essentials for Ultrarunning on Amazon or Audible.
Information on coaching-https://www.trainright.com
Koop’s Social Media: Twitter/Instagram- @jasonkoop
View all show notes and timestamps on the KoopCast website.
Episode overview:
Jeff Rothschild is a registered dietician, PhD, and research associate at Sports Performance Research Institute New Zealand.
Ever been curious about the science behind fueling your athletic performance? Look no further, as we are joined by Jeff Rothschild, a registered dietitian and researcher, who is here to break down the complex concept of carbohydrate periodization for trail and ultra runners. We dissect the misunderstood principle of "fueling for the work required" and dive into how an athlete can manipulate their carbohydrate intake during periods of high intensity or high training loads. Jeff's innovative research on this topic unveils an index for quantifying carbohydrate periodization - a game-changer for athletes aiming to enhance their performance through optimized nutrition.
Episode highlights:
(17:32) Calculating periodization: any metric of training load, daily carbohydrate intake in g/kg, correlation coefficient, reasons for including range and monotony
(36:24) How to track your carbohydrate periodization: track your diet, consistent measures of training load, TSS or CR100, TSS is best, use the spreadsheet in the description, help from Jeff’s app
(43:38) Use cases for carb periodization: predicted TSS for your upcoming workout, adjusting carbohydrate intake based on each day, you can consume more after the workout if the true TSS was higher than expected, examples
Additional resources:
The quantification of daily carbohydrate periodization among endurance athletes during 12 weeks of self-selected training: presentation of a novel Carbohydrate Periodization Index
Jeff’s app-https://rothschild.shinyapps.io/carb-index-app/
SUBSCRIBE to Research Essentials for Ultrarunning
Buy Training Essentials for Ultrarunning on Amazon or Audible.
Information on coaching-https://www.trainright.com
Koop’s Social Media: Twitter/Instagram- @jasonkoop
Trail and Ultra Runners. What is going on? What's happening? Welcome to another episode of the Coupecast. As always, I am your humble host, Coach Jason Coupe.
Speaker 1:In this episode of the podcast, it's about a topic that many of you have heard about, and that is periodizing your nutrition and specifically periodizing your carbohydrate intake. This means that during periods of high intensity or higher training loads, you would increase your carbohydrate intake to fuel for the work required, which is another phrase that I think many of you will be familiar with. However, like any other well-intended piece of practice, there exists a large gap between the philosophy of periodizing carbohydrate intake and actually doing it. Questions like how much should you actually increase carbohydrate for the increase in workload, and are the athletes actually doing this in the first place, still a bound, even though we know a lot about this topic. So enter into the mix today's guest, jeff Rothschild, who is a registered dietitian, phd and a research associate at Sports Performance Research Institute in New Zealand. He recently authored a paper that caught my eye and that proposes a novel index for quantifying carbohydrate periodization, and then looked at a group of athletes to see if, in fact, they were actually doing it. That is the subject for today's podcast and with that as a backdrop, I am getting right out of the way.
Speaker 1:Here is my conversation with Jeff Rothschild all about carbohydrate periodization. Jeff, we'll get into it, man, I appreciate it. Thanks for coming on the podcast today. I have a feeling this is going to be an interesting and really practical one, because we're going to kind of demystify something that a lot of people will, on the surface, recognize as something that they should be doing, but maybe you don't know how to implement. And I think that one of the pieces of research that we're going to kind of focus on that you're the author on it kind of took an initial stab of how we can actually see if athletes are in fact taking the advice that they have been told to take. So, once again, I appreciate your initial step in trying to demystify and try to bring more of a pragmatic approach to what we're going to talk about today, which is carbohydrate periodization.
Speaker 2:Cool. Yeah, well, I appreciate the invitation and, yeah, I'm happy to chat.
Speaker 1:So let's kind of like walk back to the beginning, right? I think that when we started to recognize that carbohydrate is an important component of particularly an endurance athletes dietary makeup, one of the things that came about from that is okay, first off, how much carbohydrate do we need? Right, and we started to get a fix on that. And then we started to get a fix on well, do we need different percentage of our diet coming from carbohydrate during different times of year or during different types of training? And then the answer to that question is, like you should actually kind of like make sense, but let's put it through more, a little bit more of a fine tooth comb.
Speaker 1:And then came this notion of fueling for the work required, which a lot of listeners and athletes will be familiar with, to which I've always had a love hate relationship with, because I appreciate the strategy. But if I'm going to actually implement this with an athlete and I know a number of nutritionists that I work with kind of feel the same way if we're going to actually implement this with an athlete it becomes hard from a number of different standpoints. First off, where the guidelines might, we always want guidelines to go with, but the second piece is how much periodization in quotes should actually happen, and that's kind of what you tried to tackle in some of this initial research, and so I want to get kind of kind of a little bit more of a background before we get into the paper itself. Like what kind of is that a correct interpretation of what you guys were initially trying to get a better fix on?
Speaker 2:Yeah, yeah, no, absolutely. I think that that was a good intro. I think you know so. Just, I guess my background is both as a practitioner and researcher. But as from the practitioner side, it's like, yeah, this concept of fueling for the work required, it's genius. That as a phrase makes a lot of, it resonates with people. People get it, athletes get it right away. You know, you don't have to know anything else if you just heard that phrase. So, yeah, that makes it make sense.
Speaker 2:Eat more on on harder days or longer days, and eat less on shorter days or easier days, but then the and it's like okay, and and it's getting so much traction and I think it's a wonderful thing, but how much more like that, that is like still like a big question is like okay, well, for some, to some athletes that might mean, oh yeah, I'm going to add a banana today, and other athletes that might mean like I'm going to add like four plates of pasta today, you know, and they both could be right or wrong, but it's like there's just no. I feel like the conversation, for a number of reasons, has often just stopped there at this fuel for the work required, and so, as you kind of, you just just try to try to help navigate, like, okay, what does it mean, how much? And as a starting place, I wanted to look at what are athletes actually doing to get a sense of, like you know, most athletes, let's say most endurance athletes I think I've heard this phrase and might even acknowledge that, okay, yeah, I try to kind of adjust my intake for the training. But one, are they doing that? And again, by how much? And to your earlier point, yeah, I mean some people, I guess, if we think maybe just have a course overview of it used to be just, let's say, a static carbon amount that someone would be recommended, and then it might be, like I said, time of year, what season are you in, and maybe kind of adjusting it that way.
Speaker 2:But it doesn't even make sense. I mean, if you're training, even in a earlier base phase of training, you're still going to be doing different workouts on different days, so it wouldn't make any sense to eat the same amount across the week. You know so and I think I think I don't think anyone really argued with that. So then it's like, okay, well, you can change by phase, you can kind of lean towards lower carbon, a base phase or something. But then again, if your workouts are changing each day, it makes sense to change it, what you're eating each day, based on what you just done and what you've got coming up. And so then that gets us into this kind of more meal by meal or kind of day by day periodization.
Speaker 1:In the way that I look at it is just through the lens of prescription, right? Most athletes will be more comfortable with thinking about prescription in terms of the volume that they do, either by miles or by time or however you want to. However, you want to prescribe volume and they want to know. Okay, how much volume? How much should I increase from this week to next week? This is the most common question that I get of all time how long should my longest run be in advance of an ultra marathon and when I? When I take that part of it?
Speaker 1:And fundamentally, one of the things that we're doing as coaches is prescribing things for athletes in one of the prescriptions that we are typically using is volume for the running prescription. If we take that analog over into the nutrition space, we can also prescribe macronutrient composition, the change in macronutrient composition, the total amount of calories, the timing of some of those calories and all of the little different elements within that. Thank you, most athletes will think a whole lot about the former how long should my longest long run be? And maybe put 50% effort and this is where you can put your practitioner hat on. You can dispute the percentages that I'm putting on this, but maybe put 50% as much effort into the nutrition side of it as they're putting into the actual running side of it. And I think part of that is kind of the ambiguity within what should actually be prescribed. Is it 50% carbohydrate, is 60% carbohydrate and things like that.
Speaker 2:Yeah, 100%, and there's ambiguity. It's very clear run X miles or at X pace or whatever. They don't know. There's nothing to tangibly grab. How much did they increase on their long training days? I mean, there's nobody knows. And so that's who?
Speaker 2:Yeah, this research was kind of a step towards figuring that out and also just to I think people kind of get it at this point but like but simply, if someone needs, let's say, three to on average 21,000 calories a week, 3,000 calories a day, but their training is different each day, so probably on some days they need 4,000 or 4,500 and some days they might only need 2,500.
Speaker 2:And so it wouldn't make sense.
Speaker 2:I mean, you could be in a weekly balance by having 3,000 every day, but it doesn't make any sense to eat 3,000 every day. It's similar to this within day energy balance concept. It's basically a bit of a sidestep, but there's some good, great research that I think is just fascinating, that within day energy balance if it's kind of how many hours a day you're in a really big deficit or surplus, if you are in a calorie balance at the end of the day but you've ate nothing in all day and you eat all of your calories at dinner. That's not ideal. You wanna kind of make it a space appropriately. So if we just zoom out one step to that the weekly level, same thing you could be in a perfect balance at the end of the week. But if you've essentially ate all of your calories the same every day, you're gonna be in deficits and surpluses on these different days. So the idea in my mind is to match that a little bit better and that's gonna kind of support where all things related to training and recovery.
Speaker 1:Yeah, and going back to the training analogy, it's in if we wanna use volume as that analog. It's the exact same thing. You can peanut butter, spread your volume out throughout the week. You have 10 hours of a week per. You have 10 hours per week of training that you can do, and you can do that in two hour chunks times five days. Or you can do a three hour run and a one hour run and a three hour run and a one hour run and a two hour run and it comes out to the exact same weekly volume, but you've distributed that volume in a much different way. That is going to have a slightly different outcome as compared to doing it another way. Yeah, okay, so your research.
Speaker 1:I'm gonna go through the title here. I know some of these titles seem kind of pedantic. That's no disrespect to you. That's just the way it works in academia. It's the quantification of daily carbohydrate puritization among endurance athletes during a 12 week self-selected training. Presentation of a novel carbohydrate puritization index. We're gonna focus on the carbohydrate puritization index in just a second, because that, once the title alludes to, that's the novel piece of it and I think that that's extremely. It's obviously unique in the whole space, but I think that it that's the groundwork that I wanna kind of come from. But can you describe the setup in general, like who are the athletes that you are tracking, how are you tracking them and what did their training look like?
Speaker 2:Yeah, yeah. So this came from one of my PhD studies. Which was it initially? Was it focused on this? It was more well. Basically I started to realize I couldn't kind of believe how much some people track, like diet tracking studies are tough to do. People don't want to, don't track that. But then I started realizing there's a small subset of endurance athletes that just track everything they're into it.
Speaker 1:They're into it. They're into it and even diet.
Speaker 2:So when I found one guy that was just like yeah, I track my diet just because I like doing it, and then of course he tracks the training and of course tracks HRV and basically all these kind of ducks line up and I thought I started looking at his data. I kind of joined it all together and kind of that's really interesting. And then, as we were, I was doing a different study as part of my PhD, a training study that all abruptly got paused because of COVID and I'm here in New Zealand, so we just basically it just we had some major shifts abruptly in 2021. So while that was on pause, I started kind of playing with this data and some other stuff and I just got me thinking like if I can capture this remotely and there's one guy that endurance athlete training, you know, 12 to 15 hours a week, that kind of eight, 10 to 15 hours a week. There must be more and I can do this remotely.
Speaker 2:So basically led to a study where I looked for people that were already tracking all this stuff. I'm not gonna ask someone to track the diet for a long period of time, that's just. This is not gonna happen, but there's so many there's enough people around the world in the endurance space that were already doing that but I thought, hmm, so basically I looked for people that were regularly tracking their diet, regularly tracking their sleep, like with an order ring, their HRV, you know, if you the training, all that stuff. And then so I ended up having about 50 people complete a 12 week study of essentially tracking their diet, sleep, training, hrv and some subjective things every day for about 12 weeks and that with the initial kind of the primary goal of looking at recovery. Can we kind of predict and understand what's contributing to their perceived recovery status? So is it? Is it simply training load? Is there a mix between kind of diet and training load or other things? You know? Just trying to see what we can find out in this kind of unique type of dataset.
Speaker 1:So a big group, right, 55 people. I think you mentioned about 50 people. I'm reading the paper right now. So 55 people.
Speaker 2:Many countries represented 10 countries and from also a variety of endurance sports triathlon, running, cycling and rowing Exactly yeah, well, yeah, one guy trained rowing, but most of it is mostly a mix of kind of that runner, cyclist, triathlete, and I know there's probably some differences in there, but you know, like each person was kind of it was really so there was about over 4,000 days of tracking altogether, and so there's kind of different ways to analyze.
Speaker 2:You know everyone together or kind of for each person, kind of look at their 12 weeks of and then kind of make inferences based on that. So, yeah, so there was a really big unique dataset that, yeah, it basically allowed me to go in a lot of different directions, one of which got me thinking, hmm, if I've got these training load measures and we've got their diet, and so, you know, this concept of feeling for the required is always something I've been familiar with for quite some time and thought about a lot, and so it got me thinking, you know, well, let's see what people are doing. And then if we thought, if we can see what people are doing, well, are some people doing it better, or is there better, or can we even just quantify it in any way, you know? And so that's kind of what led to that little arm of research.
Speaker 1:The subject pool is actually kind of interesting because, like you had mentioned, there are people that are into it, right, there are people that are already tracking everything. You're not forming a pool first and then saying, okay, we want you to track everything, there are already tracking things, and then you're bringing them into this study, which it's interesting from a couple of different standpoints. I mean, obviously there's a little bit of selection bias or self-selection bias in that pool, but one of the things that actually came out was the mean carbohydrate range that these people were actually. These people were actually undertaking. I mean, you really did have the whole spectrum from the very prototypical low carb people to the medium carbohydrate people to the high carbohydrate people.
Speaker 2:Yeah, yeah, it was a really cool range in that regard. Yeah, I'm right in front of you, but yeah, like from quite low carb to fairly high carb, yeah, yeah, 1.2 grams per kilogram to seven grams per kilogram.
Speaker 1:I mean that's a big range and I would say that 1.2 grams per kilogram is a prototypical low carb, lower carbohydrate athlete maybe not a keto athlete very strictly, but a lower carbohydrate athlete certainly.
Speaker 2:Yeah, and that was where the average is for, like, the average values for someone's 12 weeks and then I believe, the highest single day was like 16 and 17 grams per kilogram. Yeah, 17, which?
Speaker 1:I mean once again. That's like two to France level, you know that type of grams per kilogram that you're looking at, or something else really high level, where it's almost exclusively carbohydrate, you know you can do the math on that.
Speaker 1:That's a lot of carbs, yeah, okay. So the novel piece of this is this periodization index, and I don't know if this is gonna be the vocabulary that we end up using later, and you can certainly kind of like OPI on that, since this is new. But why don't you explain to the listeners how you're actually trying to quantify the degree of and I'm having a hard time describing this as well the degree of periodization that they were deploying within their, within the carbohydrate intake?
Speaker 2:Yeah, yeah, and actually I've kind of it's gone through a few iterations that what you're referring to is a preprint. It's kind of it's, I guess, just also a bit of a side on this. The publication process is, you know, it's an interesting one, especially with novel things. I have had, you know so this is from my PhD I've published seven studies for my PhD. This one, I truly believe, is the most impactful we'll have. The is the most unique novel. You know, it's the biggest contribution, I believe, out of my PhD. But I've had seven other papers published and this one has yet to be published. It's gone through some iterations and I can talk about some and I'm actually gonna currently do it some other research to kind of support this, some of the assumptions made which we'll come back to in this kind of index which I've.
Speaker 2:I think the carbohydrate training index is kind of the current iteration which makes more sense because periodization is a bit of a loaded word, sure, well, actually, just to go there for a minute. So this concept of the fueling for the work required is, you know, as I interpret it is eat more on, you know, harder, longer days and eat less on easier, shorter days. Then there's also this kind of component of strategic withholding of carbohydrate, the doing low glycogen sessions, that, even though that is kind of that, is considered kind of carbohydrate periodization. To me that is not fueling for the work required. So if you go into a hard work, a workout, under fueled, then you're not, almost by definition, fueling for the work required. You can, you know, do a low glycogen session to try to get some specific adaptations, but it is a distinction. So the term carbohydrate periodization kind of, whether explicitly or implicitly, refers to that kind of approach as well, which is that is not being quantified. It's this is attempting to quantify per day, how are you adjusting your intake, carbohydrate intake, with your training, and so then I guess that's a good segue into what it actually is.
Speaker 2:So there's kind of three things that would get quantified. Well, four, you need your training load, and that's one of the things that I'm currently researching is actually, and what I feel pretty confident in saying right now is almost any measure of training load will be fine to use. So it could be TSS, it could be total worked on like on the bike, it could be session RP duration, that kind of that calculation. So I feel pretty confident that any consistent measure of training load can be used in this. And again there's those research that, yeah, we'll be able to share soon.
Speaker 2:So with that said, you have your training load and then you have your daily carbohydrate intake, and I think in grams per kilogram is the way to do it, because then you can kind of compare better across people. So for each day so let's say you recorded that for week, two weeks, three weeks, four weeks, I don't remember you'd have some number of days where you have a point for your training load and a point for your carbohydrate intake. When you plot those you'll see kind of a I generally or ideally, kind of a slanted correlated line. So for people that don't know, like a correlation we can quantify a correlation Like as something increases, how much, how tight is the increase between two things, and so we get that value, that's the R value, and so it'd be much easier to kind of see with visuals. But you can imagine kind of a bunch of points that are kind of moving in the same upward diagonal direction.
Speaker 1:So that is In a linear fashion. So as you yeah, generally in a linear way.
Speaker 2:So as one increases, the other increases Exactly proportionally. So yeah.
Speaker 2:Yeah, yeah. And then we, so we can quantify how much, how tight that relationship is. It would be. If it was a perfect straight line, it would be one, and if it was zero relationship it would be zero. So somewhere between zero and one kind of gives us a you know kind of a percentage of how much that's related. So we take that value, but that's not alone, is not enough.
Speaker 2:See, a lot of people might want to stop there and I know it was the first thought I had. But then you know the person I talked about before like, let's say, I had a banana or you know, on big days and I don't have a banana on the other days, like there's, there might be a really good relationship between your carbs and your training, but you might go from like, or training, you know a training volume, or let's say, five miles per day to seven miles per day, and so as you increase that, like it's not going very far. You know what I mean. So there could be a great relationship, but you might only go like, yeah, between 45 minutes run and 90 minute run and that's it. There's no like really long things or anything like that. So even though there could be a tight correlation. There's not much difference between days or between your intake. I should say so that I was like, okay, so if we've got we can have a. The correlation is important.
Speaker 2:But then the range, so that would be defined as the highest day and the lowest day, the difference between the highest day and your lowest day. So how far along the spectrum are you going really high in your high days and low on your low days, or are you kind of always in the middle? So then we can take that correlation value. So let's say it's 0.7. And let's say your range is five grams per kilogram. So you go from two grams per kilogram on your low days to seven on your high days. So there's five times 0.7. That gets us to 3.5. And it could also stop there.
Speaker 2:But then it's like well, how often does someone do that? Are they always in like a small range? But once in a while they got up to the high or low range. That wouldn't be the same as if someone is really kind of going high on Mondays and Wednesdays and really low on Thursdays and Fridays, and so they have a lot of movement in their daily diet. So to account for that there's something called monotony, which is basically it's the average intake divided by the standard deviation, so it sounds a little bit complicated. I did make an app for people to play with this and kind of put in their own numbers which you could pop in the show notes. Have you seen that, by the way?
Speaker 1:Uh-uh, I actually haven't seen that app.
Speaker 2:Oh, okay, cool.
Speaker 1:I'll send that to you.
Speaker 2:I'd love to take a little bit. So people can go on. Basically, all they need to put in is their training load and their diet, their number of grams of carbohydrate in a day, and it'll kind of calculate all these things and give you this index, plot the things for you. So, anyway, the formula then would be that correlation value times the range divided by the monotony is the term for the relationship between your average intake and how much it varies the mean and the standard deviation.
Speaker 1:And what you're ultimately getting at is a score. Right, you have these three variables, you're multiplying to and then dividing by the other one, and ultimately you're coming up with a score that is let me see if I can kind of colloquialize this just a little bit. So correct me if this interpretation is missing some detail. You're having a score with how much the carbohydrate intake varies according to how much the load varies as well.
Speaker 2:But yeah, I see it as it's a single score to know how much someone is matching their intake for their training, how closely they're matching it, like how tightly, by how much, how high and how low they go and how often. That's how close they're doing it, how far they're doing it and how often they're doing it.
Speaker 1:Okay, perfect, that's a much better explanation, and so the scale on this runs from zero to seven or eight is what I'm seeing in the paper, right, potentially you can go even higher, just depending upon the math.
Speaker 2:Realistically, yeah, yeah about there. I mean there's technically no upper limit, because, but realistically, yeah, that's about the high range.
Speaker 1:And so your initial kind of take at this proposed a framework for how we can categorize each score and what that means in terms of how they're give me the I'm going to use the preprint vocabulary because that's what I'm looking at right now how they're essentially periodizing their carbohydrates. You want to go over that piece as well, just so we get everybody oriented on what this new mathematical calculation means and then how we could actually potentially apply it for the people that are going to go and get the aft and the show notes.
Speaker 2:Yeah, yeah. So basically, if you're you know you need a couple of weeks probably at least of data to do this, let's say you've done it and you get your score. And basically we have these delineation points of less than one, between one and two, or above two, or above three and a half, for basically, the higher the is more. Now, interesting things, I don't know that more is better.
Speaker 2:Right, that's a good clarification and this was one of the pushbacks in the publishing process. Like these reviewers are saying, how do we know like they want me to sell them on why people need to do periodized carbohydrate? My feeling and my job isn't here to sell periodized carbohydrate intake. That has already been done. It's in the. You know the kind of official recommendations. The best recommendation we have are to adjust your intake based on your training. This, a periodized carbohydrate approach, is the kind of the consensus right now. I don't feel like this paper needs to defend that why people need to do that, or that it's going to have better training adaptations. It may. It may well do that, but for me it's. We can't even study carbohydrate periodization until we know how much periodization someone is doing.
Speaker 1:Yeah, because the subject is to quantify something. The periodization degree is subjective. Yeah, unless you quantify it. Well, that's right.
Speaker 2:Exactly. So we have no idea. If you know, if, if this periodization has an effect, because we can't set up a study, because we can't have two groups with different amounts of periodization, I mean kind of can have someone flat and and periodizing, but like to compare across studies, or to you know again yet to see if you're a practitioner or a coach, and then you can, let's say, take someone's two or three weeks, so they're diet and training, and you can say, hmm, looks like you're actually doing pretty good with this matching pretty well on the big days and the little days, and you know, and all as well. Or maybe you know it's not too bad, but we can kind of tighten up. So sometimes on those on an easy day, you probably you might you tend to be overeating a bit, and on the lower day, on the harder days, you might be under eating, which that might actually be reverse. So someone might under eat on a hard day and then the next day they have an off day or an easy day and then they end up overeating, which you know goes back to what I said before at the end of the week they're coming out even, but is that as the best they can get out of their body.
Speaker 2:Maybe, maybe not. So this is a tool to say oh, you know what, on these days, you're kind of, when your TSS goes over 200, you're really falling short, whereas and then you, you have a TSS of 20, but you're like way over shooting. Again, that's just because your body is kind of adjusting or kind of adapting. And now with this, you could say okay, when you have a TSS of 100, here's our goal, like based on your typical intake. We can plot this and see the kind of the trend line. Say, when you have a TSS of 100, you might, you know, your carb goals should be around 350. And then you know, and it's scaled out accordingly.
Speaker 1:So for the listeners that are unfamiliar with TSS score, essentially it's just no, it's okay, I can go over. I'm used to doing this because we use training peaks. It essentially is a scoring system to rank how much training stress an individual workout has, and it can take a workout like an endurance run or an interval run and it tries to kind of alchemize all of these different permutations of intensity and volume into a single unified score. And without getting too pedantic about all the flaws and kind of like issues with that, it's just a way of standardizing how stressful the workout is and a score of 100 is equivalent to running for 60 minutes as absolutely as hard as you can, or for two and a half hours at about an endurance intensity. Both of those workouts are going to produce a training stress score of 100. So when we're normalizing this to a training stress score of 100, just realize that that's about the equivalent that we actually see in the real world.
Speaker 2:Yes, that was a great explanation and I'll say and this is from some of the research I'm doing right now and I don't think I've told anyone this, really, but you know, people bag on TSS.
Speaker 1:I'm one of them, it's okay.
Speaker 2:Well, I'll tell you I'll bag on it a little bit. I think it's the best thing to adjust your carbohydrate intake on and I'm going to have a really cool paper on this but basically, like you just said, the amount you'll burn in an hour super hard of carbohydrate is probably about the same as you would burn in that two and a half hour Easy one.
Speaker 1:I could get on board with that. 100%, yeah, yeah.
Speaker 2:And so in this context, if you wanted to, if someone were looking for some way to base, to say, okay, what can I pinpoint? My carbon take on. Yes, this is a really good way to go. Now, I think the carbon-reaction RP time duration is fine, I think any of them are kind of fine, but TSS, you know, is we'll really do the job.
Speaker 1:I could 100% get on board with that. Okay, let's move back to the paper here. So we've got this scoring system. Higher means more peerization but doesn't necessarily mean better. That's a question to be answered for the future.
Speaker 1:There were some interesting data that could be pulled out of this, based on kind of the type of athlete, and I'm going to use two different types that we can talk about separately. One of them was the competitive level and the other one was within each group's kind of habitual diet. I mentioned earlier that you got a lot of low-carb people in on the study and they tend to be they don't don't everybody send me hate mail for the statement, but they tend to be some. They tend to be a group that just focuses on their diet more because it's because it's necessarily more restrictive and they kind of have to. So it makes all the sense in the world to me that you have that. You were able to find people that already are tracking their diet, that are more from that kind of like cohort of athletes.
Speaker 1:We'll talk about that second. We'll talk about the competitive level first. What can we say about? First off, you can kind of categorize them into how the paper categorized the amateur, the high-level amateur and the elite non-professional athletes, and did you notice any patterns within those groups?
Speaker 2:Yeah, so that was. That was one of the, the kind of the sub kind of things of interest. That that was like I guess secondary aims is like figure out, okay, we have this, this index, overall, how are the athletes doing it? And then is there a difference between these competitive levels. So, like I said, yeah, we, if you look at we, I classified them to amateur, high-level, amateur, elite, non-professional.
Speaker 2:So that would be like amateur would be people that like just train but you know, don't race, or if they race they don't expect to be on a podium. And there's people that kind of race and kind of like expect podiums and and are you know, I forget the verbage I use but and then the elite non-professional, the people qualifying for a Kona, you know, the 70.3 world championships, that that kind of. You know, at an age group level, there was a couple pro pros in there, but generally and I lumped them in with the elite non-professionals because there's only a couple, but basically it's the people that are qualifying for the international world championships and things like that. So there was a difference with where the, the higher level athletes did show a higher degree of periodization, and then there was there was no difference between sexes and no difference between habitual diets.
Speaker 1:And can you say, and can you say anything? Hold on before we, before we leave the differences on the competitive level of the athletes. Can you say anything into how their total training load affected that? Because it would be. It'd be very easy for the users to extrapolate. Well, the high-level athletes are just training more, so they have more opportunity to periodize their carbohydrate more, so it's just a natural effect of that, of that cohort, can you say? Can you say anything to that effect?
Speaker 1:But yeah, no, the training load wasn't that different between the levels believe it or not, which is interesting, right, I mean, I mean, once again, this is, you know, one study, one selection. But you would think that the elite, non-professional group, the higher level athletes, just naturally have a higher training load than any of the other groups. But what you found, at least in this group, that their training loads weren't all that different.
Speaker 2:Yeah, that's right. I mean you had a few, you know, because there's a few of the amateurs that were training, you know, 12 to 15 hours a week. And then you had I think it was to do with the time of year the elites were, some were lower in that, you know, seven to 12, but probably because you know it's just like the right part of the season or the wrong part of the season to be measuring that. So, yeah, the range now that I've seen it in, the elites were kind of like, I think like seven or eight to like 22 hours a week on average, and so that big range makes it harder. Like I mean, generally, if you look at all of them there's, it looks like the higher level people aren't training more but like statistically there's just not a difference. But that's yeah, because of you know just the amount of people in each group and kind of the few. You have a few kind of high ones and the amateurs and a few low ones in the elites.
Speaker 1:Okay, so you already mentioned the next two categories. Let's kind of run through them. Any differences male to female?
Speaker 2:in terms of the index or the training volume Just the index, the index.
Speaker 1:sorry, we're going back there.
Speaker 2:Yeah, I know that was no differences, yeah.
Speaker 1:Finally, what everybody wants to know are low carbohydrate athletes periodizing their carbohydrate more or high carbohydrate athletes periodizing their carbohydrate more, or is there no difference?
Speaker 2:Well, yeah, in terms of the index there was no difference. But what I did see then I also separated out by the effect of duration. What's affecting people's intake? I separated this by high, medium and low carb groups. Are people adjusting based on training load? There is a like the medium and high carb groups are adjusting more on just training load between the daily intake. That's largely based on the duration of exercise. Basically, people that have in the medium and high carb groups they're adjusting based on higher, longer, shorter exercise, whereas the low carbers aren't really doing that. None of them, no one's really adjusting based on intensity.
Speaker 2:This is a little bit separate from the index. This is more about what is driving, what's making someone adjust their intake. That's interesting because, like I said, it's largely driven by duration in the medium and high carbers. But, like we just talked about, you could burn as many carbs in an hour as you could in two and a half hours. So that, I think, is a mistake, is quite the right word, but it's a bit of a blind spot for people. That's where, again, this comes in to say, okay, we need to consider training load as a load measure that considers volume and intensity, and you really, I think are going to be better off adjusting your carbon take based on training load, again, that mix of volume and intensity, as opposed to just looking at your duration of exercise.
Speaker 1:Yeah, and once again this kind of goes back to what periodizing carbohydrate actually means. Right, Are you fueling for the work required? And actually, here I'll give you a phrase. You can include it in the next iteration of it If you think it's clever enough. I actually like to look at it as fueling for the adaptation desired, because you can absolutely manipulate macronutrient and take in order to do that. Now, whether you choose to do that or not, what strategy you choose to do is, it's in the eye of the practitioner or the athlete.
Speaker 1:But you can absolutely use that as a tact to fuel for different types of fuel and try to elicit different types of adaptation based on the fueling and for the audience. That's kind of. That's kind of like lost in this pedantic argument. I'll give you a very simple, like a two-pronged answer for it. You can fuel in a low carbohydrate state that Jeff mentioned earlier in order to try to accentuate some sort of fat oxidation properties If you think that that is a material outcome in the training process, you can absolutely do that and do that in a low carbohydrate state. You can take the opposite approach and fuel in a really high carbohydrate state in order to kind of maximize the power output or the pace that you can achieve for a high intensity session, which would have a slightly different basket of physiological outcomes associated with them. Those would be two different examples, even on the same workout where you're fueling for the very specific adaptations that you are desiring.
Speaker 1:Absolutely. Yeah, I know Well said Okay, so let's try to pull this all together. Right? So you've got this novel index. Try to summarize the paper in just a couple of sentences. Are athletes actually taking this?
Speaker 2:advice of periodizing their carbohydrates or what. In general, the endurance athletes aren't doing a good job of it. And also I should note which I didn't really say there was. No, I didn't tell the athletes that I was going to be measuring this.
Speaker 1:They were really free through this whole study. They're blinded. They're almost blinded.
Speaker 2:Basically, I just said just do whatever you're normally going to do and just we want to track it. I don't care how hard you train, how easy you train and whatever you eat. Like there's no judgment, there's no, there's no, just really in your native environment. That was also a big part of the study. It's like we have lab studies that can be two weeks or three weeks or whatever, but just take a long period of time. You know, long-gauge 12 weeks. Do whatever you normally do and we just want to record it. So that was again at the outset.
Speaker 2:So, with that said, without any kind of specific instruction most people aren't there was a handful of athletes that did a really good job and I actually went back to the guy that kind of had the highest score. I said you know. Afterwards I said you know, explain kind of what I was looking at, and he's like he wasn't purposely measuring it or counting it, he just kind of intuitively was going oh, I eat more on the you know big days and things like that. But he, so he did a good job. But I don't think on average people there's a disconnect between people thinking that or acknowledging that and actually doing it. That's where this can come in handy. So from a. So if I'm an athlete or you know to an athlete listening, if you want to try and do this a little bit better, I think the first thing you could do is start by tracking your diet. It just you know, it's not, that's not for everyone. That can really drive people crazy. So if you really it's not worth the stress, if you really don't like tracking, but if, if you're listening to this far, you probably are curious about it at least, so let's say you tracked your, your carbohydrate intake in particular. That's really all we need for I don't know, call it 10 days or two weeks, and each day that there's a consistent measure of training load.
Speaker 2:Now, if you're using training peaks, tss is a great one to use. If you're not, you can use session RP times duration. So with that, I would suggest, highly suggest something called the CR 100 org scale. It's much better than the 10 point scale that often gets used. And don't use the, the kind of built in 10 point scale and training peaks Kind of a whole nother discussion, but that that built in one is is worse than worse than useless. But if you have a good scale to use and you can get this measure very, you know quite easily.
Speaker 2:Anyway, we you just need some daily total measure of training load. Tss is probably the easiest and best, so if you can just use that, so you you collect that, just write that down. You know an Excel sheet or something. For a week or two weeks you've, each day, next to the training load measure, you've got your total carbohydrate intake, and then you can go over to that spreadsheet that I'll give you the link for and have a look. Just visually you'll see is there a nice kind of relationship or is it kind of just flat, and it'll look like a whole bunch of random points. I've seen the whole range in that study. If you're maybe there's kind of a relationship but you feel like it could be tightened up, well that's a good step.
Speaker 2:So what you can do then is, again, an app will show you how many how to calculate based on your TSS.
Speaker 2:It'll give you a little formula. It says TSS, your carbon take. On that line will be TSS times, I don't know, 0.8 or whatever, plus this number. It's just a basic formula that you can then calculate your daily carb goal based on the TSS and that puts you on that line. That doesn't say if that line is the right line for you, but that's just kind of like around what you've been doing. If you want to tighten up to the line you've already have, that would be a way to do that. It'll also then kind of give you an idea of how far the ranges of those high and low days and how often you're doing it. So again, but, put simply, the first step I would do is get the 10 days or 10, two weeks of tracking and see how closely you are actually lining up with that and maybe, just like I said, tightening up to the line so that your big days are, you know, or let's say, more fueled, or the lower days, or less fueled or whatever, and then you've got to take it from there.
Speaker 1:It was interesting to me, kind of backing up a little bit to the findings that even amongst a what I would call an advanced or a sophisticated user group people that are into it right, they're into tracking stuff, they're tracking things on training peaks, they're tracking their diet Anybody who's tracking their diet is an advanced user.
Speaker 1:That's not an easy thing to do. They're into it that even amongst this user group they're still not varying or undulating or periodizing their carbohydrate to much of a material extent. When you look at them on average, sure we're gonna have people who do it really well, but on average across the whole group they're kind of not doing it very well. And then the solution to that is not dissimilar to anything. It's just like let's just track it first, become more aware of it and then normally a lot of the. If you have reasonable guidelines, which you're trying to come up with, that's the ultimately what this index is gonna be a part of, or some reasonable guidelines. If you have some reasonable guidelines, the tracking plus the reasonable guidelines combined result in you just being more compliant on this area and more effective in this area.
Speaker 2:Yeah, no, absolutely. Do people watch this? Are we on YouTube as well, or is it just a listen? Well, yeah. So here's the app. This is kind of the default, how it comes up.
Speaker 2:You put your body weight in kilograms or pounds and I just have three values in here just to kind of get it going. But you would go over those or we can add in a few more, and so if we look here, so along the left, the body weight, here's the units, and then you'd paste in. If you've recorded for a week or two your intake somewhere, just paste that in your carbon taking your load, and it could be again any training load measure, because it's about the relationship. That's what I think is a real benefit of this too is you can use any load measure because we're looking at really the relationship. So you see these green dots, those are my carbon takes along the bottom and the training load, and here there's a pretty nice relationship.
Speaker 2:Here the correlation value is 0.99, so that's really high, and it's just because I put in these examples manually. Then it shows me the range. So the highest, the lowest day is five grams per kilogram, and then the metatony again is that it's the average versus the standard deviation, which you don't have to worry about, other than it just goes into calculation. So right now this is pretty good. So if I attract four days, this is pretty good. Also, I guess I apologize to anyone just listening to this but go online and discover yourself. But let's say I'm gonna just put in a couple other ones and then all of a sudden I'm gonna have not so good relationships.
Speaker 1:I'm just gonna like so what Jeff is describing here, while you're entering this information in, is the app that he created and there's basically three inputs on it your body weight, your daily carbohydrate intake and the training load that corresponds to that particular day.
Speaker 1:You start entering those daily values day after day after day, and you'll have you know, jeff's study that you guys did 12 weeks right, but after a couple of weeks you've got enough data points to kind of create a strong trend line and then the app automatically calculates the carbohydrate training index, which is what we were just talking about with the point value on. It, tells you how tightly your carbohydrate intake to your training load is correlated, what your carbohydrate range is and what that carbohydrate monotony is. So the three components that actually go into that as well, so describe. You know, initially you had a very nice tight, fitting line with your fictitious data and then you just started entering, you know, not random pieces, but things that didn't quite fit the line, and you can see the carbohydrate training index number go from a green number to a red number.
Speaker 2:Essentially, yeah, so basically here, if this was your intake versus your load? There's essentially no, it's just random. You're just like eating anything on any day and training whatever, and so this isn't, you know, ideal theoretically. At the bottom it says against the determinant of carb intake to match your load shown by the gray line. This is the formula. So in this case, if this was you, we really wouldn't want to match that line because there's really no relationship there. But if I got rid of those extra points and then here now we're back to where we started. Now you can look at this, so you could say your training load measure and again, just in the same units, times this 0.914 plus 144, that'll put you right on the line. So if all of a sudden you have a training load of 150, you can do the math and then it'll show up. You know right where that line is. So it can really give you a goal to keep this. And again, is it the perfect line for you? You might want to, you know, feel free to adjust up or down, but that is a way to kind of yeah, get it kind of a bit, you know, dialed in more tightly.
Speaker 2:There's also a second tab here, though. Just some examples. Top two are from Cyclist or in Grand Tour. They're doing a really good job periodizing. And then the bottom two were two of those study ones One where there's, on the left, zero periodization, you know, it's just like people are eating anything. And then the other one this was one of the better ones, as I alluded to. So does it? The guy's not perfect, but doing quite a good job of kind of increasing as those training load numbers go up.
Speaker 1:And so, theoretically, one of the use cases of this is, if you know the training load prescription, or you know the training stress score that you are going to prescribe or you are going to perform for the day and many cycling coaches use this as their as a component of what they prescribe I want you to go out and do 300 TSS points on this ride, and you can certainly do it from a running perspective, either using training stress score or heart rate training stress, or run training stress there's all these different kind of different permutations of it. Theoretically, one of the use cases of this is now you know what your carbohydrate intake should be Right now. You know exactly how many carbohydrates you should be able to take in, based on, if I know, my TSS or whatever load metric that you want to use. Is this, these are the carbohydrates, or this is the amount of carbohydrates that I should take in for that day?
Speaker 2:That's right, yeah, and you can start with kind of an estimated TSS and then you do a workout, assuming you're in the middle of the day, and then just after that you just kind of adjust if the TSS was different than you expected.
Speaker 1:Yeah, and it's a wonderful output because once again, if you can think about it, I normalized a two and a half hour endurance run to a hundred training stress score points. But then if you took that two and a half hour run and let's say an hour of it was hard, that would increase the training stress score for that particular run and then you would therefore increase your carbohydrate intake to whatever degree that was indicated from this system. That's right exactly. Do you have any athletes that are like actively like using this or kind of like piloting it out?
Speaker 2:Absolutely. You know, with people I don't often worry about the index number per se. I might calculate it at some point, but it's more of. I definitely use this approach of okay, we might start by tracking a week or two, or, if they've already done it, I'll kind of just get a sense of the relationship. They kind of intuitively choose, you know, higher or lower carb, whatever, based on that, and then we can usually ends up being maybe some relationship, but not as tight, and then we just can tighten it up. So I can say here here's you know your daily goals for calorie and carbohydrate based on the train load metric, and so you know it's. Sometimes it takes a little iterating because, like, sometimes the high days might feel too high or the low days might feel too low, but once you, it allows you to kind of very methodically and quant objectively dial it in.
Speaker 1:And I think we should clarify this a little bit and you can take your scientist hat off and put your practitioner hat on or maybe, you know, have a little bit of a blend of both of them for this particular question. So I'd be remiss not to ask because this is going to be a specific ultramarathon question or ultramarathon audience that we would be remiss not to talk about. The variation in total volume that you can see within that sport group from day to day, and you see this a little bit in the cohort that you had, where some days are an hour and then some days are eight hours. They're going. If you have this mathematical equation of I should take in this many carbohydrates, eventually it's going to be so excessive that it's not practical to take in that much food. So what then would be the council for the athletes that have those that do have those big variations are going through a big training block when they look at this number and say I just can't, I just kind of can't eat this much on one single day?
Speaker 2:Yeah, no, I think you have to be sensible about it. I mean, if there's an eight hour day, I mean that I assume in most sports that's not happening all the time. You know I work with also, you know, high level kayakers, and when you think about them, or rowers, I mean they're training a ton but there is still this undulating across days and you know you have to be able to still get there, you know, across a few days span. So, yeah, I mean, if you don't match perfectly, it could still give you an indication, probably what you need to make up for the next day. But again, that's, I assume, the rare occasion.
Speaker 2:You know, the race day, the ultra race, like yeah, then you just got to kind of do the best you can. But if we're talking about, like most training days, yeah, I think it's Pretty, like, most of the time it'll cover most things a six-hour bike ride, I mean, even then you know the TSS will kind of yeah, it'll, it'll kind of drop and I think it think it'll still pretty much adjust accordingly because it's gonna. If you're doing a four-hour ride, well, the hourly TSS is gonna be, you know, lower, so yeah, so basically, on those really long days, I mean, yeah, there's a limit. You got to be a practitioner and be sensible and do the best you can.
Speaker 1:Yeah, sometimes when you get into those six, seven, eight hundred or even thousand TSS points per day, it's they're just so anomalous and they're just so big that you just realize that you can't cover everything Within that one period, yeah, yeah, no, that that's and that's fine and that's just like on.
Speaker 2:So, with even those, if we plotting the days, I mean, yeah, like if you're carb loading before a race course, it's gonna be off the line. Or if you have, like, like you said, an anomalous TSS day, like yeah, it's, that's not gonna like, that's okay, just, but we're talking about, like the bulk of someone's training, you know, is it giving us a sense? Yeah, 100%.
Speaker 1:Well, first off, like congratulations on coming up with this. Like you said, this is novel. There's a piece of it, and it seems like you've been fighting in the academic circles exactly what to call it and what it actually means.
Speaker 1:But I applaud the ingenuity here because it's something that a lot of athletes have started. They've just wanted to know how that to actually quantify it right They've heard this phrase fuel for the work required or, as I'd like to say, fuel for that adaptation desired, but how we actually practical, practically implement that. And I'm on the practitioner side where I actually have to give Prescription to people versus the advice right, actually have to like, literally prescribe it to people. We're always looking at different ways that we can Create guideposts for that type of prescription, and this can be one that can easily be incorporated into the, you know Daily vernacular and guideposts that we actually use. So I really appreciate it. I'm gonna leave links in the show notes to the app that I had no idea, even even when I was doing all this research for For this.
Speaker 2:I could not find it so what that says about my research or from where that, yeah, not widely shared yet, but yeah, no, I'm happy for people to use it as a free app and, yeah, you can enjoy it.
Speaker 1:And yeah, so I'll leave links in the show notes to that as well as to this particular piece of research. But can you tell listeners where can they find a little bit more about the work that you do and and some of the things that you might have coming out of the Seven pieces of research that that was part of your PhD thesis?
Speaker 2:Yeah, yeah, I suppose from the research standpoint, twitter is probably the place that you know it's easiest place to kind of to share research and that stuff. My Twitter is, I think it's.
Speaker 1:Everybody flubs that. It's actually.
Speaker 2:Eat, sleep Fit. Jeff, my website is eatsleepfit, from a kind of a personal you know, nutrition practice stuff, and then there's a separate research website. But if you go through the Twitter that both the links are on there and it's probably you feel free to reach out that way or to follow. That's right. I certainly would always post research and things. There's also another app that, if you look at my pin tweet, there's another app of within day band energy balance, so that lets you calculate kind of your daily how you're doing within a given day, and so that could be helpful as well.
Speaker 1:Very cool. My encourage listeners to check all that out and, once again, thanks for coming on the podcast and Explaining this aspect for us and how we can kind of be just become better at fueling our workouts. Well, my pleasure. Thanks for having me. All right, folks, there you have it, there you go. Much Thanks to Jeff for coming on the podcast today and enlightening us.
Speaker 1:I hope that this is an area that we start to get some better Standards of practice around, because it is an intriguing concept. We do see that there is some efficacy around it. However, we still don't know how to actually fuel for the work required. Each practitioner is taking a little bit of a different spin on it and I think if we can come to some sort of Collective agreement about some norms and practices in this area, it would help out athletes and coaches alike. If you like this podcast, please feel free to share it with your friends and your training partner that your training partners. This is how we get the word about the podcast out. As always, this podcast is not monetized in any way, shape or form with sponsorships, so I'm very much appreciative when you guys share this podcast with your training partners.
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