Precise tracking and the label error claim

Philip Pape 0:00

If you weigh your food to the gram and log every bite and the scale still doesn't do what the math says it should, this episode is for you. The numbers in your food log carry far more error than you might think. And most of it isn't on the label, it's in the log itself. Today you're gonna learn what the 20% rule on food labels really says, where the biggest error in your tracking comes from, and how to track macros so that error stops mattering. Welcome to Wits & Weights, the show that puts a popular piece of fitness advice under the microscope, finds the hidden reason it doesn't work, and gives you the deceptively simple fix that does. I'm your host, certified nutrition coach Philip Pape. And this one is for anyone who tracks or used to track or quit tracking because it started to feel like a chore or tedious. And if you're over 40 like me, you're probably working with a smaller calorie budget than you had when you were, say, 25 years old, which means that the same mistakes, mistakes you might make in tracking, and we're gonna get to that. Hold off, don't get mad at me. All right. We all make them and you'll understand why this is super important for today's episode, why it's a bigger share of the total now that your calories might be a little tighter. Now, the reason your calories are tighter is is a whole separate discussion having to do with metabolism and lifting weights and muscle mass loss and all of that. We're not gonna get into that today. I want to help you become a better tracker to enjoy it and to have it be useful and helpful to you. So the advice you've probably heard, maybe from me at some point, to be honest, is weigh all your food, log it, be precise. The math's gonna work out because you are tracking, it's going to help you make better decisions. And then you learn things like wait a minute, food labels can be off by 20%. Can I not even trust it? Isn't that gonna screw up my tracking? Why bother? What's the point then? Okay. And that is a fair question that I've seen a lot. And today I'm gonna evaluate those claims, any claim around precision of tracking and labels being wrong because there is right and wrong in all of this stuff. And what I hate to see is when a social media person's like, well, you know, we don't do tracking in our program because labels are so off that it that it doesn't help. No, you're making a conclusion, a leap of logic from a premise. It may even be a false premise, but even if it's not, there's a whole chain of illogical conclusions that are going on there to get to that point. And I don't want someone to come to me and say, Oh, I tried tracking and didn't work. Or someone said, you know, the labels are have a lot of error. So why would I track? We're gonna deal with that today. Now, if you heard episode 510, go back in the feed. It's just a few episodes ago. Jenny talked about a tracking lesson that took her many months to understand that is gonna tie today's episode. So go check that out if you haven't heard it. And then the next episode 5-11, you know, I kept telling you to hey, watch your trend weight, watch your waist, watch different data points instead of just one number like scale weight, and it's connected to proper tracking. So this episode today is the why behind both of those. All right. So they all tie together nicely. So if you haven't heard episode 5, 10, and 11, maybe check those out first and then come here. But honestly, you can do it in any order. Stick around to the end because I'm gonna share one change to when you log your food. Not what you log, but when you log, that is connected to an increase from 56% to 89% of people reaching meaningful weight loss in a 16-week program. And it won't take you any extra time. Hmm. Okay,

The FDA 20% label rule and rounding

Philip Pape 3:45

first, I'm gonna go through what the FDA rule on labels says. So you understand what companies are held accountable to when they make the nutrition label on the package. It's often different than what I hear a lot of people saying. A lot of people misunderstand that. We're gonna clear that up. And also what happened when researchers bought food and tested it in a lab. What do they actually see when they test these things? Then we'll look at where the bigger error in your tracking actually comes from, because you're gonna find that the label stuff isn't as much of a concern. We're gonna get a little uncomfortable today. We're gonna face our own demons, and it's gonna help you out at the end of it. And then I'll give you uh how to track macros so that the error essentially cancels itself out. Ooh, nice, right? Sounds like math, but don't worry, it's not math. It's just simple tactics, simple strategies. And then also a handful of foods where being more precise is probably worth your time because you don't have to be that precise with all types of foods. We're gonna get into all of this today. All right, so let's start with labels. 20% error. This this gets repeated constantly and almost always a little bit wrong. The rule is in the regulations for the FDA on how companies label their food packages. And it splits the nutrients into different groups for calories, total fat, saturated fat, sugars, and sodium. A food product is out of compliance, that's the term, if it has more than 120% of what the label says. So it can essentially go up to 20% over in reality versus what the label says. If it's under what the label says, the the FDA actually calls that a reasonable deficiency and doesn't care. So it's kind of asymmetric, right? In one direction for, again, calories, total fat, saturated fat, sugars, sodium. For protein, carbs, and fiber that occur naturally in a food, it goes the other direction. I'm gonna come back to protein later because it does matter for one specific group of food. So protein, carbs, and fiber, it can't be under 80% of what the label says. So when you hear labels can be off by 20% either way, yes and no. It's not quite right, right? It depends. Calories have a ceiling with no floor, which means the slack only runs in one direction for each nutrient. That's where it gets really confusing. Now, you don't have to know all this. I'm just kind of educating you. I educated myself through the research. It's interesting. There are two more details, though, that people leave out. The first one is that the FDA tests a composite. A composite is 12 packages pulled from 12 different shipping cases and blended together. So the rule is really related to the average of this batch of packages, not just one package. And like an individual bag of chips can be more than 20% off, and the product would still pass. But of course, it, you know, it averages out. That's kind of the point. And then the second detail is the 20% is a legal limit. Okay, it's not a measurement. It tells you how wrong a label is allowed to be, not how wrong labels usually are. And those are two totally different things, aren't they? Then there's the rounding error, which I I find kind of fun, you know, as an engineer. I'm sorry. Calories above 50, round of the nearest 10. Anything under five calories can be listed as zero. Okay. Your your olive, your spray olive oil, be careful. It's not, if you use a lot of that, it's not zero calories. And then fat under half a gram can be listed as zero. So again, that is why cooking spray can say zero calories when it's oil in a can, which is crazy. It's crazy. You know, one quick spray, fine. Doesn't really matter. But if you're like, you know, really spraying that pan or using it a lot, those add up to something. You know, you're putting it on your food, on your salad, whatever. You know, and the same can apply to really anything where they're rounding it down. Heck, even diet soda. Have you ever seen like on a large amount of it, it could actually be a couple calories? I mean, it's it's negligible, but they're rounding down.

Protein that's easy to log consistently

Philip Pape 7:54

Now, before I get to what happened when researchers tested it in a lab, I definitely want to tell you about today's sponsor, True Nutrition. They make protein powder that you build yourself. You pick the protein, the flavor, the sweetener, all of it. And since today's episode is about labels, I'm gonna say this right up front. They had no input on anything I'm telling you in this episode. I use them directly. I love their product. My version of protein from them is whey, egg, and casein, kind of like a three-in-one. I use cookies and cream sweetened with stevia. I'm a man of habit, a creature of habit. I add a little psyllium husk for some more fiber, a couple of B vitamins, mainly for the B12. Takes a couple minutes online to set up. You pick the base, they have over 20. You pick your flavor, how strong you want it, any boosts, and then you can save it and reuse it in the future. And that's what I like for tracking, honestly. When I have to eat a lot of protein, I can't get it all from food. My target is 180 grams a day, which is a little bit challenging to get on food alone, especially on days where I'm really busy. And the protein shake is consistent in my food log. It's the same every day, same mix, same amount of scoops. It's one less thing I have to worry about. It hits my protein and I can track it. It's really, really easy. That is true nutrition.com/slash wits and weights. You'll get 20% off your first blend with code WITSANDWEIGHTS. Everything is third-party lab tested, first party verified. They make it in their own facility and they ship everywhere now, over 150 countries. I love the flavor personally. When you, you know, people ask that, how does it taste? It's smooth, it doesn't have a chalky aftertaste. I use it almost every day, and I love True Nutrition personally, and I think you will as well. To support the show and get 20% off your first order, go to truenutrition.com/witsandweights. The link is in the show notes. That's truenutrition.com/witsandweights. Use code WITSANDWEIGHTS. All

Lab tests on label accuracy

Philip Pape 9:49

right, so how wrong are these labels in reality? Well, they can test it. Okay. They've tested it. Uh Susan Roberts group at Tufts University, who do work on energy intake, they bought some food and they put it in a lab. And in a 2010 study, they bought 10 frozen supermarket meals and 29 restaurant items that were all sold as low calorie or lower calorie choices. The frozen meals averaged 8% more calories than their labels. The restaurant items averaged 18% more calories than their labels. Um, and a few of them had, guess what, close to double. So that's 100% more calories than was stated. This is the restaurant items. So a lot of people cite this study when they say, look, the labels are lying, but it was a small study. They picked the, you know, the dieting items on purpose for that was what they wanted to look at. But the same group a year, or not a year later, yeah, a year later, 2011, they used a bigger sample size. They got 269 items from 42 restaurants in three states chosen at random. What did they find? On average, the stated calories were almost exactly right. They were only off by about 10 calories a portion. That's negligible. But the spread, the variance was actually pretty wide. About one in five items had at least 100 calories more than the menu said, and only 7% were within 10 calories either way. So the average worked out, but the variance was a large variance, which kind of makes sense intuitively, right? Unless you're, you know, I guess using, well, no, they're even if you had a robotic factory. Okay, again, these are restaurants, so you're not doing that. So forget what I was gonna say. It's human beings putting this stuff together. Now, for the packaged snacks, a slightly later study in 2013 from the National Institutes of Health, the NIH, they tested 24 popular snack foods and found that they average about seven calories per serving over the label, which is basically nothing. So those are actually pretty close too. So if we put this together, packaged foods from national brands are pretty close on average, off and off by single digits. Restaurant food is right on average, you know, is correct on average, but unpredictable from item to item. And the occasional dish is way off. And one caveat here I have to note is that those restaurant studies were done before the 2018 menu labeling rule, right? When you started to see menus have the calories all over the place. And I couldn't find a newer study that measured restaurant food since then. So take it with a grain of salt, pun intended, I guess. So the label is probably not quote unquote lying to you as much as you know the headlines and social media would like to say. It's a little fuzzy, of course. For calories, the fuzz mostly runs in one direction. But that leads me to where the real error comes from

Human logging error and doubly labeled water

Philip Pape 12:49

then. Because if if the labels are kind of canceling each other out, then it's not really that as big of a concern except for here or there. I'm gonna say this as nicely as I can. The biggest error is us, us human beings doing the logging. And this is myself included, and it's how we log. I'm not talking about gaslighting and dismissing people and saying, oh, you're not even logging, or you don't have no idea how to log. Not that at all. This is legitimate stuff we have to understand about us. The best way to check what people really eat and measure their metabolism is a gold standard method called doubly labeled water. You drink water with tagged hydrogen and oxygen, and then over a couple weeks, researchers track how fast those tags leave your body through excretion, right? And that tells them how many calories you burned. If your weight stays the same, then what you burned is what you ate. So it's effectively like a lie detector for all the claims about food logs and metabolism. Um, in fact, it's it's it's really detected one of the biggest lies of all in that we think our metabolism declines with age when it does not. In the open study, O-P-E-N, all caps, if you want to look it up, 484 adults in Maryland did exactly that. When they reported what they ate over 24 hours, men came in 11% under what they ate, and women came in 17% under what they ate. In other words, they reported 11 and 17% less than they actually ate. So they ate more than they thought they ate. About one in five people were clear underreporters, while barely one in a hundred over-reported. So the error almost exclusively runs in one direction. We estimate less, sometimes far less, than what we actually eat. Now there's the study I bring up a lot by Lichtman in 1992 in the New England Journal of Medicine, people with obesity who call themselves diet resistant or weight loss resistant, saying things like, I eat almost nothing and I can't lose weight. They reported eating about a thousand calories a day, but the doubly labeled water said they were eating 2100 calories a day. So they underreported by 47%. Now, this is people thinking they're eating a thousand calories, which if I eat a thousand calories, I'm gonna be hangry all the time. I'm gonna be, I'm gonna feel like I'm starving. And I'm guessing these are, you know, people with obesity eating a thousand calories, it might feel even worse. So they underreported half by half, which is insane. Now, their metabolism was normal, nothing was broken. They weren't, I don't think they were lying, but their logs were missing some of their food. Uh, you know, it's gotta be that. The logs are missing half the food they were actually eating because of what they were reporting. Either, you know, items of food, quantities of food, or the way it was logged missed on the calories of food, right? It's one of those. Now, those are what we call recall studies. If you weigh your food and log it as you go, you're probably doing a lot better than a 24-hour recall study. Okay, they brought these people back to recall from memory what they ate. Okay, but it's important to understand that because it means we are prone to a certain pattern. And this pattern shows up everywhere when it comes to humans and reporting. A recent analysis of national survey data found under-reporting was more common in people on low calorie diets, and that reported intake lined up worst for people on low carb diets. So, in other words, the people trying hardest to eat less tend to undercount the most. And I think that's just human. I think that's just human. You you've got the licks and bites, right? The the bite, biting or eating your kids' food, finger foods, a handful of almonds, oil in the pan that we talked about early, cream in your second coffee. Maybe you always log it as one coffee, but you're really drinking two or a third from the pot. You know, all of that stuff. And there's a lot of it where it doesn't feel like you're eating something, maybe it doesn't get logged. Or you eat some vegetables and they happen to have some oil and you don't log it, et cetera, et cetera. Now, what about when you're doing this in an app? People worry that the food database in a logging app is garbage because anyone can add an entry. Like MyFitnessPal is prone to that. But when researchers have entered the same records in MyFitnessPal and compare them to research grade databases, actually it's not too bad. Total calories came out within a few percent. Obviously, you have to pick the right foods. And an app like MyFitnessPal, I'm not a huge fan of just because they have so many extra things that you can pick the wrong things of, but they're getting better at that. Macrofactor obviously has a validated food database as well. So they're all kind of catching up. And I don't think that's really the main problem for calories. I don't think the database is the main problem. Now, protein's a different story. I'm gonna get to that. But let's back up and say, okay, we've got three sources of error. We've got labels from food that are off by a little bit, not as much as we like to claim on average, on average. So some are gonna be high, some are gonna be low. That's always been my argument for many years is that I don't even care about that because some are high, some are low. It averages out. We have app databases, which are not as far off as we might think. And we have your own logging that can be off by something like 10 to 20% or more, sometimes way more. And so, guess what? The the labels and the app database is not where the thing to focus is, which is great because we can't control those, can we? That's actually a good thing. You see how I like to reframe. That's a good thing. So if you've been stressed about those things or thinking that it's affecting your logging, don't worry about it. What you need to worry about is that spoonful of peanut butter. Which, depending on how you log it, if you logged it as a tablespoon instead of grams and it's twice as many grams as you thought, boom. That is that times 20 throughout your week could be the issue. Now, for some foods, the label error goes the other way, which is kind of cool. It's a nice little bonus. USDA researchers ran a controlled feeding study with 18 adults who ate almonds and they measured how much energy the body absorbed. A one-ounce serving of whole almonds gave people an intake of 129 calories, while the label says around 160 calories, and the standard calculation says close to 170 calories. Chopped and roasted almonds gave a bit more calories, and nut butter is closest to the label because it's already fully processed and you're absorbing most of it. So interestingly, for more, the more whole food and the least processed it is, this is not going to be a surprise, but it's helpful to remember for something like whole nuts, the label might actually overstate what you end up absorbing. And we see that when you compare whole foods to processed foods, where, huh, maybe I'm simply absorbing less of the calories in the whole foods, and that makes a big difference too. The more and more whole foods I eat, even though I'm tracking the same way, somehow I'm actually, not somehow, I'm actually taking in fewer calories than the next guy or gal who's tracking maybe eating the exact same calories on the label, but it's actually absorbing more than you are. So this is where I want the picture in your head to change. If this isn't blowing your mind already and making you rethink this, every number in your food log is an estimate. Of course. Some are high, some are low. The biggest error is any food that you just are not logging at all, okay? By definition, that makes sense, right? That sounds hopeless until you realize what matters is not how precise they are, just how consistent they are, which is a much easier problem to solve. It's actually an engineering problem. It's very easy to solve. So I'm really excited about that. And I have been for the last few years as I've coached clients through this process and they realize all I have to do is be consistent. Sometimes I don't even have to log things. Like I take fish oil every day. I don't log it. Why? Because I consistently take it every day. It's the same calories. Mathematically, it's not going to affect me one iota because what matters is the change over time, your metabolism versus your intake and how it changes over time. I don't truly, truly care the precise amount I'm eating. I just care how much I'm eating relative to last week and this week and my deficit, okay? Or my metabolism. Now, at the very end of this episode, I'm gonna share that one change to when you log. But before

Calibration, bias, and noise in your log

Philip Pape 20:40

we get there, I need to explain an engineering idea called calibration. Very simple. You guys know what I'm talking about. Let's say your bathroom scale reads three pounds heavy every single time. Does that mean it's useless? Not at all. Because if it says that you're down two pounds over the last month, even though it reads three pounds heavy, you're down two pounds. A consistent error, what we call bias, doesn't hide change. It doesn't hide change over time. What hides change over time is noise and drift. That is readings that bounce around or that slowly creep over time. Like if that three pounds heavy on the scale became four pounds heavy, became five pounds heavy, et cetera, then it would be less useful, right? And then noise also hides change, things going up and down a lot. Your food log works the same way. Let's say you consistently log 300 calories less than you eat because you forget the oil and you eyeball the peanut butter, but you're pretty much eating that regularly. Well, if you log 1800 and your weight holds steady for a few weeks, then 1800 logged calories is your maintenance based on what you logged. It doesn't matter that your real maintenance might be 2100 because you're simply forgetting to log 300 calories every day. It will effectively appear like 1800. Now, if you want to lose fat, you You're gonna drop the logged number from there, meaning you need to go in a deficit relative to that number, what your perceived or effective metabolism is, not what your true in reality metabolism is that you may not even know because of those 300 calories you're not logging. I hope that makes sense. In other words, the the 300 calorie error comes along for the ride and gets canceled out. Your weight trend is what's telling you what's happening, if it's working. So this is just energy balance math. All right. The great Kevin Hall, the NIH researcher on energy balance, he published a rule of thumb in The Lancet in 2011, where every 10 calories a day you change your intake for good, eventually moves your body weight by about a pound. So small, consistent changes do add up. They're just slow. And your trend weight tells you which direction you're heading before the actual math works out. And that's because of all the noise. You see how this comes full circle? In other words, that's why we care about the trend and we don't care about the sh the noise and why we need time and patience.

How to track macros with 3 rules

Philip Pape 23:00

So here's how to track macros so that the error stops mattering. I'm gonna keep it to three things, okay? Number one, log the same way every day. Ah, okay. Now now you see why it's important to log every day, but also log the same way. Use the same app, use the same entries for the staple foods that you always eat. You could just copy, paste, copy, paste, copy, paste. Your oatmeal is always the same entry and not like looking it up manually each time and picking a slightly different oatmeal. Okay, even if you're not sure what cut of meat to pick, whatever you picked, if you eat that thing every time you eat it, you pick that thing. That keeps your error consistent instead of random. Okay? Very important. Log the same way every day. Number two, weigh yourself often and use the trend. Daily weigh, daily weights. So if you weigh yourself every day, it's gonna bounce around a lot. This is the noise we talked about, the variance because of water, salt, carbs, inflammation. If you're a woman who's menstruating, this is your cycle. Okay. The if, but if you measure it every day, despite that, okay, you measure it every day, you'll have a smooth uh trend over time. Every, you know, I'll say three weeks is a good amount of time to take the average. Some people will use a weekly, but I think that's too quick to it. There's still too much noise after one week. You need to look at two to three weeks, ideally, three weeks to compare over time. And that's gonna tell you are you really gaining or losing body fat or holding maintenance from which to adjust your target calories? It's what an app like Macrofactor uses, in fact, to give you those target calories. And if you don't know what Macrofactor is, go check it out, download the app, use my code WITSNWAITS, all one word, and you'll get a free two-week trial. I had to throw that in, guys, because you know how much I love the app. Number three, put your precision where the biggest payoff is for being precise. So, what do I mean by that? Well, if you're using a food log, which you should or a food scale at home, which you should when you're at home, why not? Takes a few seconds, then you're gonna weigh to the gram, and it's totally worth it for things that have a lot of calories. So, calorie-dense foods, oils, butter, nut butters, nut, cheese, dressing seeds, as well as slightly less dense foods that have a lot of calories, like meat, you know, lean meats and various proteins and such. A tablespoon of oil is so calorie dense. It's 120 calories. So it's worth weighing that because I'll tell you, just like three or four, three going from three grams to five grams, it's like, whoa, there's an extra, you know, 20 calories or whatever. It's it's insane. And again, so doing it for those and doing it for your main proteins, I think, are important. For protein that's naturally in a food, remember that the FDA rule is inverted. The product has to contain at least 80% of what the label says. More than that is fine. So a compliant label can actually overstate protein by up to 20% on the 12-package average. In the two app database studies, I think I mentioned those, the app's red protein are about 80% or sorry, about 8% low compared with the research databases. Right. And the errors point in different directions. So on any given day, you can't necessarily count on them canceling out. So if you're aiming for around, let's say, the minimum 0.7 grams per pound, a lot of you aim for more than that, being off by 10 to 15% on your three or four biggest protein foods can definitely add up to real grams. So for those foods, use the actual label or verified entry and weigh them. That's all, that's all. Just weigh them. I would just weigh everything. My general rule is if you're gonna have something complicated with a lot of ingredients, pick a common food item that represents the whole thing, or use the AI feature, take a picture and throw in what the ingredients are, and then just go by the grams. It's gonna be pretty close. Now, what about protein powder? The most recent independent US testing I found from Consumer Lab this April 2026 found that every powder they tested had its listed protein, so the protein it said it had, and the products that failed the testing were flagged for things like cholesterol and sodium. So if a protein blend lists free amino acids, glycine, taurine, or creatine, that would be your cue to look closer. I mean, you basically want a well-respected brand of a protein powder that just contains all the amino acids. Popular brands would be like Optimum Nutrition, for example, or Legion, or the one I talk about all the time that I use, which is True Nutrition, which True Nutrition is awesome because you can create your own, you know, and you know what's in there.

Restaurant meals and fiber on labels

Philip Pape 27:33

Uh, restaurant meals, this is the tricky one. Okay, this is where the label is probably the least relied. If the if there is a label at all, right? A lot of restaurants, just mom and pop restaurants, aren't going to have any of this stuff listed and depends on where you live, but don't sweat the precision. I want you to log your best guess and be conservative, meaning log more calories than you think rather than less. I always put something like 25% more, okay? Maybe 50%, if you know they go really heavy on oils and butter and things like that. And you can tell by looking, tasting, things like that, but you can't always see all the fat. Now, I mentioned earlier, you know, if your metabolism is a little bit on the lower end or if you're in a deficit, something like that, a logging error will have a bigger impact in absolute terms if your calories are tighter, right? So I think chasing grams on everything is maybe the wrong place to place your attention. But calibrating against your trend, like we talked about earlier, and then making sure the main calorie-containing stuff is all weighed and accounted for, and then you're conservative with restaurants, those will go a long way. And then there's another little thing about fiber and net carbs and all that. I say don't worry about any of that, just log what it says. The more fiber you have, the fewer calories per gram you're gonna have, and it's just gonna benefit you, but you're still gonna log what the label says. And the labels will already account for that in the calories. Meaning if you have a gram of fiber, it won't count as a full four calories like it would for other carbs. It probably counts as like anywhere from zero to like two and a half, but that's already reflected in the label. So don't try to do the math and say, oh, the math doesn't work out. That's why. The more fiber it has, the more it's not gonna work out. It's just gonna have fewer calories, which is not a bad thing if you're trying to limit calories. So to recap, the 20% label rule, don't worry about it. It cancels out. Consistency handles error the best. Okay. And you need to be precise on certain types of food that are more calorie dense. All right, before we wrap up, I promised you that one change to when you log your food that might give you more success. Before I do, if you're wondering what I use to do all this calibration, I briefly mentioned it. The app is called MacroFactor. It is not my app, it's from Stronger by Science, but I use it. So does pretty much everybody in our coaching programs. I'm an affiliate. So if you want to support the uh podcast, I get a small, very small cut if you sign up using my code WITS and weights, all one word. Download MacroFactor from your favorite app store. What I like about the app is it takes your logged calorie intake and your trend weight. This is why it helps to do it every day. And it works backward to give you an expenditure number, a metabolism number that is updating in real time. And then every week you check in, quote unquote, you check in with the app and it gives you an updated target for your calories and macros based on your goal. So if you're trying to lose weight at a certain rate, you're trying to maintain, you're trying to gain weight at a certain rate. Okay. And it has a good database, it's super fast, all of that. So go to MacroFactor in your app store, use the code WITNWAITS all one word. I will throw a link in the show notes if you want to learn more about it.

Bonus: the best time to log food

Philip Pape 30:35

So if you track your food, when you log might matter more than how carefully you log. What do I mean? In a 2026 study of 449 adults in a 16-week weight loss program, researchers looked at when people log during the day. About 89% of the people who logged two or more times a day reached clinically meaningful weight loss, compared with only 56% of people who caught up on their logging after the fact. Logging right after you eat takes the same time as logging at night, but it's at night. You're reconstructing your day from memory, you're doing the recall thing. All the bites and the splashes and the licks go missing. Food goes missing, food just disappears from your memory. Even the once-a-day loggers hit 71%. So you don't have to be perfect. Although it's much easier to just build a habit of when I eat, I log. When I eat, I log. When I eat, I log. Very low stress, very quick. You get used to it, copy paste, becomes a very simple habit to ingrain in your daily routine. All right, until next time, keep using your wits, lifting those weights. And remember that your food log doesn't have to be perfectly accurate to work for you. It has to be consistent, and your weight trend will tell you the rest. I'm Philip Pape, and I'll talk to you next time here on the Wits & Weights podcast.