Stop Letting Tech Do the Work: The Old-School Method That Actually Makes Diet Changes Stick

Hello and welcome back to Chasing Excellence, I am Patrick, which is so very much for tuning

in this week.

Today's episode is a little bit different.

We have done this once or twice before, but we're bringing over two conversations from

my other podcast, The Consistency Project that I do with Miss E.C. Sinkowski.

E.C. is my go-to for all things nutrition, she's an absolute master at cutting through

the noise and making complex science actually makes sense for muggles like me and perhaps

like you.

I'd like to think of it as high-fact low BS.

If you've been listening to this show for a while, you certainly know E.C. well, she's

been on a bunch.

In fact, we did actually do this, brought some shows over into Chasing Excellence in January

with an episode we called an upside down food pyramid and new healthy labels won't save

us.

So if you like this and you didn't listen to that, do go back and then of course check

out The Consistency Project podcast wherever you're listening to this so you can get even

more.

Before I tell you about what's to come and we get into the episode, I've got an announcement,

I've got something I'm excited to tell you about last year or perhaps a little bit over

a year ago, I wrote my first kids book.

It's called The ABCs of Being Happy and Healthy.

You can get it on Amazon and just this week, I published my second kids book.

It's called What's a Win I Won Today and the idea is pulled directly from a conversation

that we had here on the podcast with our friend Mark England of Enlifted.

It was in an episode where we were talking to Mark about the motivations that we often

have behind our fitness, behind our pursuit of health and how sometimes that motivation

is fueled by fear, by want, by shame, by self-judgment and I asked him in that episode,

what's something that we can do for our kids to ensure that they don't grow up with these

fears, with these judgments, with this shame, such that their pursuit of health is done

out of love and excitement and enthusiasm and his advice, which I loved at the time.

I loved it so much that I wrote a kid's book about it, is to have your kids create

what he called a wins journal.

At the end of every night, have them write down one thing that they did well where they

excelled, so they can get into the habit of seeing themselves through the lens of self-appreciation

and self-love.

So I spent the last year working on this book, again it's called What's a Win I Won Today,

it is on Amazon, I will put the link in the show notes.

If you've got kids, if you know somebody who's got kids, I would love for you to get a copy

of this book and of course if you didn't copy of the ABCs of being happy and healthy.

I really believe in starting these conversations with our kids early, the conversations that

Ben and I have on the show all the time, that's what these books are about.

It's about introducing ideas and conversations and vocabulary, so that our kids grow up

knowing what it took us entirely too long to learn.

I thank you for checking out the book, for getting the book, for sharing the book with

your friends and cousins and colleagues, again one more time it's called What's a Win

I Won Today, it is available on Amazon.

Okay, enough about that, today's conversation with the EC Tackle Something, a lot of us have

wondered about, which is, can technology finally fix our nutrition struggles?

So first we're diving into calorie trackers and wearables, those apps and devices promising

to tell us exactly how many calories we're burning and what we should be eating.

In our second conversation we're going to look at the newest promise on the block, you

know it, you love it, you perhaps fear it, AI and AI powered diet tools that are supposed

to make weighing and measuring effortless, spoiler alert, if you know EC like I do, you

know that she's very skeptical of ideas like this and so she's going to show us why both

of these approaches miss the mark in some way and more importantly, why the solution is

actually a lot simpler and probably already in your hands.

Okay, so without further ado, let us get into the first of these two conversations all

about calorie trackers and wearables.

Just want you to know that my Fitbit says I've burned approximately a thousand calories

today and all I've done today is sit here and for about ten minutes I've wrestled

with my children to get them out of bed.

So that was it for me so far.

You purposely said that to spin me up.

We are going to talk about how accurate calorie counters are, wearables, apps, etc.

As it relates to them telling you how many calories you've burned throughout the day.

Cool?

Yeah, let's do it.

Where should we begin other than with the number of calories I've already burned today?

Yeah, I mean if you've been listening to this podcast for a bit and especially if you've

taken the master class now accelerator, you know that I don't use these calculators,

apps, wearables to figure out your Fitbit, to figure out your cool work needs and we've

certainly talked about the process.

I use in other episodes and we'll put them in the show notes, but I'm not sure we've

gone through some of the data and the evidence for my approach.

So we're going to talk a little bit more about that.

I'll just assume you hated technologies.

Right.

I don't need any more data than that.

That's right.

I'd like to go back to 1900.

You know, accurate are these calorie trackers and wearables that tell you how many calories

you're burning each day.

And the short answer is not much, but the better answer here is you don't need them

so stop worrying about them.

We have a better way.

I think that's, I think that's really kind of one of the takeaways I'd love for people

to really let sink in.

It's not just you don't need them.

We have a better way.

And if you can get your mind around the process that we're going to talk about, you're

going to all of a sudden be like, oh my gosh, that's so obvious and intuitive.

Like why didn't I think of that?

And you'll see how all of these apps wearables, et cetera, are just this unnecessary noise

and believe me.

I followed and believed that too for years as I've done all of the things that we hear

about and see about in the media.

But you're going to see how unnecessary all that noise is and that this approach really

is the silver bullet and I'm not exaggerating with that.

Cool.

So let's begin perhaps with some background on even how these calorie counters kind of

come up with the number that they say.

And obviously I was joking, but my Fitbit doesn't tell me a caloric target.

It just says, hey, I think that this is what you've, you've burned today.

Okay.

So we're just typically talking about the ones that say, hey, based on your age, your

weight, what you say about yourself, this is what you should be burning every day.

And here's how much you've gotten so far.

Is that sort of like, it's those, it's that one, two punch that we're really looking

at?

Yeah, I mean, because there's, there's both of these, there's ones that like you have

and we are going to talk about them that are saying, hey, this is how many calories you've

burned.

And there's also ones that exactly, like you've said, you know, I'm this height weight

age and I want to lose 20 pounds, how many calories to eat, right?

Yep.

So I'm going to start with those first ones, these are predictive equations that to determine

how many calories to eat.

Now, there's lots of these different predictive equations.

Two of the well-known ones are the Mifflin St. George equation and the Harris Benedict

equation.

Of course.

Everybody knows these things.

Everybody knows these.

The Mifflin St. George equation is considered to be more accurate, but both basically they

have these male and female formulas.

And then you plug in your height weight age, as you said, and it's going to tell you

your basal metabolic rate.

Now, we will get into exactly what your basal metabolic rate is.

But I'd like to explain kind of how these equations were developed.

They're developed through a process called indirect calorimetry.

Direct calorimetry would be if we were able to measure the heat you produced from your

metabolism.

And so this would be like putting you in a chamber to isolate the heat production from

your body versus just the heat that exists in the environment because through metabolism

when you burn calories, heat is given off.

This is how we maintain a core body temperature.

So basically, I hope you can kind of imagine that measuring your heat production is a pretty

complex and involved process to come up with this chamber to do that and to do people

one at a time, right?

So we've come up with these indirect calorimetry measurements, which I'll tell you are still

rather involved compared to, let's say, going to a website or putting something on your

wrist.

But nevertheless, one of these types of indirect calorimetry is basically to have people

breathe into a mask and kind of a device that's going to determine, okay, what's the rate

of oxygen going in?

What's the rate of carbon dioxide coming out?

That's going to be indicative of your metabolism because of course carbon dioxide is given

off when we burn calories.

And so they measure this gas exchange instead of measuring your heat production in a closed

chamber.

They measure this gas exchange and then they do this for lots of different people and

they come up with a formula that they can kind of use sex, height, weight, age to best

estimate the calories burned.

But lots of different equations have been developed for this calories burn number.

And we found out that really the number of calories you burn will change based on like

what's your percentage of lean versus fat mass or even it can change based on your ethnicity

or race.

Like we've come to learn, okay, this one is better if you're Hispanic or this one is better

if you're obese and on and on, right?

Now I mentioned that many of these equations calculate or the two I mentioned, the Mifflin

St. George, Harris Bennett, they measure your basal or estimate, I'm sorry, your basal

metabolic rate or BMR.

That is the absolute minimum number of calories your body needs for basic life support.

Basically, if you haven't eaten anything, you're lying completely still and you have a

heart rate and your cells are alive.

How many calories you need for that is your basal metabolic rate.

There's also something called your resting metabolic rate, which is close to that but not

exactly.

Like you could have food in your stomach, you could be sitting upright.

Your resting metabolic rate is about 10% higher than your basal metabolic rate.

And I bring this up, this distinction between the two, we're already sort of in the weeds

here.

But I bring this up because technically your resting metabolic rate is more accurate

to use when you're figuring out your total calories burn in the day.

But oftentimes in these equations or even in conversation, people will use BMR, basal

and resting metabolic rate, RMR like interchangeably already potentially introducing some error

because they could be 10% apart from each other.

People while they're close enough and it's like, well, not really, they're not the same

thing, right?

Now, beyond that, you have to remember that this basal metabolic rate, nor resting metabolic

rate, whichever one we end up using, is neither of them are the total number of calories

you burn in the day.

They are the largest fraction of the calories you burn in any single day, assuming you're

not a professional athlete, they're going to account for about 60 to 70% of the total

calories you burn, but it's not the total number of calories.

The total number of calories you burn is called your total daily energy expenditure or

TDE, sometimes just your total energy expenditures, what they'll say.

But again, your basal metabolic rate, nor your RMR, is not the same thing as that.

It's not your total daily energy expenditure, not your total number of calories burn.

I just really want to stress that because people will often be told their BMR depending

on what device or scan or website you go to and it's important to know that that is

just a fraction of your calories.

In fact, I just had the question a weekend or so ago, is someone's like, what about

in-body scans and BMR?

That is about 60 to 70-ish percent of your total calories needs, and that's what many of

these predicted equations are first using to come up with total calories burned.

All right, so the equations estimate this BMR or RMR, and then how do they go from that

to total calories burned?

Yeah, and basically this comes from developing another equation based off of observations

of people that they sample.

They take your RMR, your resting metabolic rate, although it could, and sometimes BMR,

and they basically multiply it by an activity factor.

This is why all these apps and trackers and wearables to figure out how many calories

to target, you have to select how active you are.

They often have these different categories and descriptors, like you're going to say,

I'm sedentary, or I'm moderately active, or I'm very active, and it's on and on, right?

And I remember doing this, you read the descriptors, and you're like, am I very active?

Or moderately active?

You know what I mean?

And you spend a couple of minutes being like, I don't know, I work out five times a

week, and it says three to four, six to seven, and you're just like, oh, go ahead, which

one do I pick?

Eventually, you settle on one, you pick with it, you select that, you put in the other

thing, height, weight, age, et cetera, and then it just spits out a number, like go

eat 2,000 calories or whatever.

Now, how they come up with this number is, again, they measure lots of different people,

but they're using another indirect calorie remedy type of measurement, and we've actually

talked about this one before.

They use something called doubly labeled water.

It's actually a very great way to measure how many calories you burn in any single day.

It's just that it's very expensive to do, very time consuming to do, just like sticking

you in a chamber and measuring your heat, that like you or I are not going to get this.

This is what they do to run experiments, but you know, when we look at like a large-scale

population, it's just infeasible to do this, but doubly labeled water, the water has been

chemically tagged, the hydrogens and the oxygens, and water, like carbon dioxide, is created

in metabolism.

So when we burn these calories, we end up producing some water and it gets lost in our urine.

So after you drink this chemically tagged water, they then also collect your urine for

a period of days.

And so some fancy math to see how much kind of went in versus came out will determine

how many calories you're burning.

And again, it's quite accurate to determine the number of calories you burn in a day.

And this is not just your resting metabolic rate.

This is not just this baseline level of calories to sustain basic function.

It also includes all the calories that you expended to move around.

Let's say all your steps per day, your daily chores, but also in your cross-fit workout

or whatever workout you're doing.

That's how they ultimately come up with, okay, they measure this across lots of different

people.

And then they're able to say, okay, what are the different multipliers that like best

describe, you know, the total number of calories we're finding based on whether or not

they're quote, you know, moderately active, so on and so forth.

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Okay, that seems like a lot of math.

And math is good.

So what's wrong with all this math?

Yeah.

Math is often good.

But you want to think about this process I just described as they're basically figuring

out equations that really reflect the average of the population they sampled.

They took a lot of data points and then figured out what equation best describes all of

them.

And while averages I talk about averages all the time in this podcast because they give

us general trends, general amounts that we always have to remember no one is an average.

And I love height as an example.

These height for so many things, but I think it's so easy to understand.

The average height for men in the U.S. is 5.9.

The average height for women in the U.S. is 5.4.

There are many men listening to this podcast who are not 5.9 and there are many women listening

to this podcast that are not 5.4.

Sure.

Some are that exact height.

But more people than not are different heights than those averages.

And this alone should explain to you why using an app or tracker or wearable will not necessarily

get the number that is truly specific to you.

You are going to get some representative average for, let's say, men who are, quote,

very active.

Like, if you select men who are very active, then you are going to get the representative

average for that bunch.

Or if you select, I'm a female and somewhat active, you're going to get the average for

that bunch.

That average is certainly closer to your caloric burn.

Then let's say any possible number in all of the numbers that exist.

But there is still some margin of error there and this is really that activity multiplier

tends to add in most of the error for people in their total caloric burn estimates.

And there's a few reasons why.

First of all, activity is so variable.

Like what somebody considers intense or moderate is going to be very subjective based on their

current capacity.

If we were to even take the same workout and run it across a bunch of different people,

like go run 30 minutes.

How many calories are burned across, let's say, 20 different people are going to be so

variable.

How heavy are they?

How much muscle do they have?

How fast did they run, right?

And so yes, they try to estimate what that looks like for men versus women and highly

active men versus less active men and so on and so forth.

But again, it's going to be the average output of whatever kind of descriptor bucket you

put yourself in, not necessarily this perfect number.

The other very, very nuanced understanding of calories burned is, we've talked about

this before, but here we go, the number of calories burned any single day is influenced

by the number of calories you eat.

Again, the number of calories burn in any single day is influenced by the number of calories

you eat.

And there's nowhere in these equations that take into account how many calories you're

eating.

And this phenomenon of how many calories you're burning being influenced by how many calories

you're eating is called metabolic adaptation.

And most likely shows up in a varying number of calories burned in what's called meat

or non-exercise activity thermogenesis.

We've done deeper podcasts, dives on metabolic adaptation and meat, but you want to think

about meat is all of the movement you do in a day that's not like your workout.

It's when you stand up and sit down from your desk.

It's when I'm moving my hands while I'm talking right now.

Sometimes people will find themselves tapping their toes while they're working, right?

It also is the movement when you're washing dishes.

So it's all of the non-exercise movement you do.

And it turns out this non-exercise movement is both conscious and non-conscious.

Like yes, you go decide to wash the dishes, but sometimes you decide to go wash the dishes

because you have more energy.

Or I'm sure people have realized at some point like gosh, I'm tapping my toes.

I didn't even think about tapping my toes.

Or there's some days when you go shopping and you decide to park far away from the store

and walk the distance in or you're really stoked to take the stairs instead of the escalator.

Why is that?

Some of that is the non-conscious control where the body is changing.

Your energy expenditure based on how much energy is coming in.

It's kind of trippy to think about, but your body is trying to burn off some of the extra

energy.

And so it will make you do more movement without you necessarily deciding that you're

wanting to go, let's say, get more steps in.

And so it's, I guess the Luffle Holtz paper in the show notes shows that we've got oftentimes

2,000 calorie swing between what people are burning in this non-exercise session movement,

even if they're kind of like the same size person.

I mean, that's huge.

This isn't just about whether or not somebody like decides to get in more steps per day.

Some of it is under the influence of how many calories they're bringing in and that's

going to affect how much extra movement they do besides their steps per day or besides

their, let's say, CrossFit workout.

And so these predictive equations are always going to be missing that component of how

your intake is affecting the number of calories you burn.

So quick little review.

I know that was a lot.

The predictive equations are using generalized equations to figure out kind of this basal

metabolic rate or resting metabolic rate.

That is going to have more or less error depending on kind of which equation you use based

on which population they studied, again, ethnicity, how lean you are, all that stuff.

Then they take that generalized kind of number and they multiply it by generalized activity

levels to figure out your total calories burned.

And again, that's going to have some error in there because what is your actual exercise

capacity relative to the group that you described yourself as moderately active, vigorously

active, whatever.

And then finally, that estimate also does not take into account how much you eat, which

is affecting your total calories burned.

So it's kind of like, okay, we have an estimate.

Now we're going to make another estimate on top of that and we're going to round it out

by making another estimate on top of that.

With the game of telephone, right, it only gets worse as it goes along.

It doesn't get more accurate.

And so yes, there are going to be some people where it does actually work out.

There are some people that are actually five, four listening to this were female or five

nine who are men, but it's not most of the people.

And so this is what's happening with these calorie trackers.

It's not wildly off.

Like there's not a lot of seven foot females listening to this podcast.

However, it's not going to be very true or accurate at the individual level.

What does I assume there's been research on these various equations in the way that they're

built and calculated, et cetera?

What does some of the research say about these equations that then eventually get to an

app or a watch or a wearable?

Yeah, there's a bunch and some of them, a lot of them what I find is sometimes in these

studies when they figure out, okay, this predictive equation is slightly better for this population

as they get really stoked on like, okay, well, now we're going to, now we know that this

is the population for Caucasian, you know, overweight women and they get really drilled

down into these individual ones.

Okay, well, this is the one that we should use for lean athletic men or whatever it is.

I still am like, okay, we've got a better way, but nevertheless, there are plenty of

these studies which find this disparity between the predictive and the actual.

And there was this great study from last year that looked at 10 of these predictive equations.

I mentioned two of them, the famous ones, the St. Michelin St. George and the Harry Spendidic,

but nevertheless, they looked at 10.

They looked at 10 and basically, they found that all predictive models, so all of these

predictive equations underestimated the total calories burned.

And none of them met all of the criteria to be considered accurate.

We've got 10 popular equations, none of them were considered accurate, basically to kind

of summarize the disparity.

People were burning on average per day, 3,300 calories and the equations were predicting

on average 2,400 calories.

That's a pretty big disparity.

And they also found that they were more likely to be inaccurate the more active the person

was, which has been my experience.

I have had some very active crossfitters come to me and they're trying to eat 1,700 calories

today.

It's like, yeah, that's not going to work.

No wonder you can't sustain that.

No way.

It's too low.

It's too low.

And then another study that I wanted to point out kind of because I mentioned that that

exercise component is where we end up where that activity multipliers and where we get

a lot of error introduced.

There is this Germany study in the show notes.

It looked at the accuracy of your Fitbit there on your on your wrist, along with Apple

and Garmin devices kind of for calories burned during movement.

Like yours is saying, you know, 1000 calories so far.

They did acknowledge that these wearable devices were pretty good in step count or heart

rate, but quote, none of the tested devices proved to be accurate in measuring energy

expenditure.

And that's calories burned.

So they basically summarized it by saying that these devices were off by at least, at

least 30%, but oftentimes worse, meaning the watch could say 500 calories, but it could

have been 350, could have been 650, who knows?

Right.

Okay.

So we've got one study looking at 10 equations, none of them are accurate.

That's that's for estimating your total calories burned.

Then we've got this study looking at these different devices saying, how many calories do

you burn so far today or burn in your movement, burn in your activity?

They're 30% off.

You know, and people are wondering why they're like going from device to device and not

getting the right answer.

And again, I've been there.

So I am empathetic to this process.

But basically these devices and equations are giving you estimates.

They are not, they're describing the population on average that they tested.

They are not giving you information that is unique and personalized to you.

Okay.

Okay.

So you've teased us a few times now that there's a better way.

Can we get to what the better way is?

How to do it.

If you guys can wrap your mind around this, it's going to seem so intuitive and so obvious

and believe me when I find it, I was like, oh, yeah, I was like, why, why is there so

much noise around this?

Your weight is your best indicator of what to do with your intake.

If you are maintaining your current weight, you're eating the number of calories to maintain

that current weight.

If you're losing weight, you're eating fewer calories than your current weight needs.

If you're gaining weight, you're eating more calories than your current weight needs.

And so let's phrase this in the way of the most common goal for clients that I work

with.

If you want to lose weight, lean out, lose body fat, however you want to phrase it, it

means that you're eating more calories than what your goal weight needs.

If you want to lose weight, lean out, lose body fat, whatever it is, you're eating more

calories than what your goal weight needs.

To be clear, none of what I just said is you're eating more calories than what your app

says.

So you can skip all of the estimates and the inaccuracies of the wearables and the apps

entirely, they aren't accurate, so we're not going to use them.

This is what you need.

You need one to know what's going on with your weight and two, you need to know what's

going on with your dietary intake in terms of calories or macronutrients.

When I say that you've got this knowledge about your weight and your dietary intake, let's

take the example of let's say you know that your dietary intake is 2,500 calories per day.

And so, okay, I want to lose weight.

That's the most common goal I work with.

We now need to eat less than 2,500 calories per day and that's going to drive weight

loss.

That's it.

It really is that simple and elegant of a solution once you get your mind around it.

Your weight tells you exactly what you need to do with your intake.

Now, we've certainly described this process before.

We're going to put the podcasts and the show notes that kind of go through that a little

bit more.

And again, it's in my accelerator formerly masterclass that will start together in January.

But I do want to highlight for people with this process, you cannot assess your intake

nor your weight on one day.

And that's where we just lost people.

That's why I said, okay, interesting, you see, I like the theory, nah.

You cannot assess your intake nor your weight on one day.

This process to understand what's going on with your intake and what's going on with

your weight is going to take a minimum of 10 days, oftentimes 14 days, to truly figure

it out.

And the app and the wearable and the website or whatever tells them that answer albeit

inaccurate in seconds.

And so of course, it's going to be more enticing.

But we need this length of time for a couple of reasons.

One, you aren't a robot.

Your diet changes based on your activity level.

Sometimes you spent the day in the couch, sometimes you did a really hard long workout.

Your diet also changes just based on quality and quality.

Some days you eat all the things, some days you eat lots of fruits and veggies, right?

This is what real nutrition looks like.

It's not this performative.

I eat the same perfect things every single day.

There is variation there in terms of quantity.

There is variation there in terms of quality and we want to capture that.

We want to capture your real life diet, not a diet that you do in one day.

And then your weight is also something that takes time to assess.

Your weight, especially your fat mass, does not actually change that fast.

So anything that changes fast, like two pounds, between two days, that's water weight.

And so this is why we need that 10 to 14 days, especially in the beginning of the process.

Once you're off in a direction, we might be able to kind of reassess a little bit more

quickly or know a little bit more quickly.

And especially in the beginning of this process, we need to figure out what the heck is going

on.

We need to determine, is this a real weight trend?

Or is this just a bunch of water weight fluctuations, which are routinely one to three

percent of somebody's body weight, one to three percent of somebody's body weight?

I mean, at 150 pounds, we're looking at 1.5 to 4.5 pounds up and down any single day.

And so because fat mass doesn't change that fast, we need to wait long enough to be able

to put a trend together.

And so there we are, at least at 10 days, at least at 14 days.

And so I think that's some of what is challenging for people.

There's certainly this kind of longer period that I'm requiring versus type in five variables

in off you go.

Yep.

Got it.

Okay, so we need to know intake and we need to have a good sense of the trend of our weight

over, like you said, 10 to 14 days.

So these are this better way, as it turns out, is something we've talked about a lot.

So it's not a new way, but it is the better way.

What prevents folks, you know, you just mentioned a little bit, which is the shiny objects

syndrome, which is like, I can just press a button and it tells me.

So there's like, okay, that's sort of table stakes.

We know it's, it's easy to get distracted with things that are easy versus things that

are effective.

But what stops people from being successful if they're like, okay, you see, got it?

All right, I'm ready to do this.

Like where are the, where are the challenges that they are going to likely bump up against?

And how can we help them get past them?

Yeah, I mean, a little bit related to the shiny object thing.

I do think we put a lot of trust in technology.

And obviously there's many cases where we should and it's incredibly useful and all these

wonderful things from it.

But I think there's this sort of, oh, well, you know, this little device has this graph

and it just spits out a number and it's so accurate and it's tracking me every step

of the way.

And it's like, of course, it's going to be more accurate than me.

And I think once you understand how complex biology is and how describing a population

is so hard to do, you start to realize, like, actually, no, like these are best estimates,

but they're never going to perfectly describe you.

So I think we put a lot of faith in this, like, not just shiny object, but numbers and

graphs and technology.

When in reality, we have more, more accurate tools just in our own hands.

I think to, besides just the waiting of the, the 10 days, 14 days to kind of get this

plan together, I do think in our age of, you know, Amazon Prime show up immediately,

we like to know that we're immediately in the right direction of everything.

Like, give me this instant feedback now.

I am on the path.

And I can't tell you that.

I know my plan will be more sustainable and will be more accurate and we'll get you

in the direction that you want.

But I can't tell you today if we have the exact numbers today.

I need to get to day 10 or day 14.

And so I think there's a little bit of that, like, I like the reassurance I like to have

a plan.

And it's interesting that a lot of times we're almost interested in having a plan versus

necessarily knowing it's the right one, which is a little bit disconcerning, but I think

there's a little bit of that there.

I will say the other hiccup that I've seen people have is if they try to do what I've

described out of the gates and let's say they're currently doing macros now and they haven't

been able to sustain it, they haven't been able to see results and they want to jump

to my system and they just want to go away and measure their diet is now.

I find that they have a very difficult time releasing the idea of what they think they're

diet should be.

Like if they've been targeting 1700 calories from whatever coach or app or tracker, when

they go away and measure their diet, they're going to try to fit themselves into 1700

calories because it's the system that they've sort of come out of.

So one of the things I like about the three pillars method, it's certainly a secondary

benefit to my original intent.

But I have people go through the three pillars method process where we don't actually look

at calories and macros to start.

We look at the weights of your fruits and veggies and then we look at the protein and kind

of gets people away from some of those old numbers that they might have been holding

on to or thinking that their diet quote should be.

And it starts to kind of get more of that whole picture view of their diet, not what

their diet quote should be on let's say their perfect day, but instead we start to capture

the range of that their diet is, which I mentioned is so important for this.

And so I do find that sometimes if people are coming in with a number, they'll kind

of make their diet fit that number instead of ultimately like, what is your diet really

look like across the stretch of days to be meaningful?

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So we just heard EC break down why those calorie trackers wearables and predictive equations

are quite as accurate as we've been led to believe they're built on averages and estimates

and they can't account for the nuance of your actual metabolism or how your body adapts

to what you eat, but here's where it gets interesting because now there's a new wave

of technology promising to solve, well I mean it's promising to solve everything AI tools

they can have look at a picture of your food and tell you exactly what's in it apps that

will plan your meals for you systems designed to finally make tracking your diet easy and

automatic as you're about to hear EC has some thoughts on it because it turns out the

same fundamental problems we just turned we just talked about AI doesn't fix them either

in fact in some ways it makes it worse so let's pick it up from there I'm excited

about this episode we're going to talk about with a rise of AI that begs the question

of what we can use it for a lot of people are asking and specifically what we're going

to talk about today is how perhaps it can help our nutrition for the better yes yeah

you sent me five reasons why AI we're not even going to bury this year why five reasons

why AI will not fix your diet so in case you were in case you were really excited

we don't want to stamp that out real quick um so let's go through let's go through each

one of them one by one first one you sent me AI is not accurate in portion or calorie

estimation yeah do you mean like you take a take a picture of it is that kind of what you

mean or is it even our people doing something beyond that yeah yeah I mean before I get

into that but even just how you say we're not going to bury the lead here like sometimes

I was like gosh I'm such a buzzkill and all of this thing as soon as a new idea comes

up I'm like that's dumb anyway let's go into why but yes what I am talking about here

is um right at this stage mainly talking about these apps where you would take a picture

of your food and it's going to tell you how many calories are in it it probably could

also automatically log it for you I know my fitness pal already has something like this

I don't know all of the apps that are I think there's oh gosh I know the name it's

to slip my mind I'll think of it but anyway there's also I'm sure other apps out there

that are already working on like they're going to preplan your day or whatever you've logged

it will start to make suggestions based on how much you have left in the day like all

of those types of features or what I'm sort of talking about now and kind of the status

of AI and I think there certainly is some utility to some of them but yes the the lead here

is and I don't think that these features are actually going to fix your diet for you and as

you kind of already mentioned this idea of taking a picture and logging turns out at least right

now AI is not particularly accurate in portion or calorie estimation and this actually sounds

a lot like the podcast that we did I think it was at the end of the year talking about how

like calorie trackers and apps don't estimate um the number of calories you burned very well and

here we go calorie you know apps and trackers can't estimate the number of calories in your food

very well um but until anyway I guess it's the Ferdolfson 2025 paper in the show notes they

found that chat GPT clawed and Gemini the three big AI platforms that the mean absolute percent

error for weight and calories was off by about 35 percent okay what does that mean

you weigh your apple the apple is actually a hundred grams in reality AI would tell you that

it's anywhere between 65 and 135 grams in weight and that apple that weighs a hundred grams in

reality it's 50 calories and the apple says something between maybe 32 and 67 calories now

on a single apple scale that doesn't weigh very much or have many calories this doesn't sound

that bad but of course as the portion size and as the food has more caloric density grows the more

this error will also grow with it so if you are eating something that is in reality 500 calories

the apps could be telling you something between 325 and 670 calories now we start to have a good

amount of error and that might just be one meal of course not all of the things that you're logging

and what we found in that paper is that it actually tended to the error tended to be on the

under estimation side of things not over estimating it which in that case if you're using it for

a log all of your things of course you're overeating more calories than you think they also found

that the macronutrient breakdown such as protein which we happen to talk about once or twice and

we think that people should be tracking on it was even more off the mean absolute percent error

there was 60 percent not 35 percent so you know as it turns out a 2d picture what's really going

on here a 2d picture is very hard to get portion size and all of the components in it

it's very hard to pick up okay how much is actually there and what are all of the ingredients in it

there's another study by O'Hare 2025 in the show notes it was just looking at chat GPT not the

other two and again it was looking at basically taking pictures and what's the accuracy of it

and similarly they also found the same issues that kind of as the portion size grew so did the size

of the error and it really was off to a significant degree now both of these articles do point out

that it turns out people are wrong too we've talked about that before that we're not very good at

weighing and measuring either and that these margins of error that they found with the AI is somewhat

similar to what they'll often find with people weighing and measuring their food I think my kind of

counter to that or why I'm still of the belief of you know people weighing and measuring their food

as I truly believe it's a learned skill where people can get more accurate with it that the

pictures are not at this time showing that they're getting more accurate so yeah people might be

off by 30 percent to start but over time and learning they can certainly increase their degree of

accuracy so they don't have that same percent error as they're weighing and measuring

it's almost as if the the taking out the pictures is too lazy in the lazy macros right it's

it leans too far towards the lazy and not enough towards the macros yes yes and I actually think

that's almost a great lead into what we're going to say next reason number two less annoyance

and recording food means less awareness of eating behavior yeah I actually was just talking about

this with my accelerator group last night I think one of the things that's really easy for people to

forget about logging is the annoyance of logging is a primary reason why it's so valuable as a tool

like as much as it sounds nice to have this app that would just automatically track everything you

are eating and you you know you don't have to look up these foods in a database one by one a lot

of what weighing and measuring does is that it creates friction in the moment yep to make a different

decision and so the less effort there is in the moment that you're eating the more likely you

are going to eat more than you originally intended or that you want to be in line with your goals

and it's more likely that you would get to the end of the day and something like an AI just

tracking everything and just be surprised at how many calories you consumed and you get to the end

of the day you're like oh wow that's more than I thought I guess the diet starts tomorrow

and you just sort of stay in this infinite loop of no change so instead a tracking app assuming that

you log it when you eat it which is really what I recommend especially when people are learning

this process makes you figure out whether the day is actually going to work out as it's happening

in real time and that you may want a larger serving of chips or ice cream or chocolate but you

realize that the serving you originally wanted is going to leave something too small for later

in the day like you won't have enough calories for dinner so you adjust in the moment and eat less

and even that moment of tracking you just have this pause it doesn't it doesn't always work out

but you typically have a pause like am I eating do I want to eat do I want to bother logging this all

of this stuff you give yourself the opportunity to change the behavior so what seems to be an

annoyance is actually giving yourself a chance to change so we got to flip the script on that a

little bit now I'm sure you know some of the AI enthusiasts would counter that you know you could

take a picture and AI would then tell you okay well this is more calories than you should eat you

should eat half of the portion you know then what you originally planned or something like that

but I just see it going towards a behavior where people will again kind of keep that same

mentality of the diet starts tomorrow because like kind of let's think through the logistics of it

let's say that you were making I don't know a sandwich for lunch if I knew that AI was going to

capture everything I would probably just make the sandwich in the portions that I wanted to plate the

thing then take the picture AI might tell me okay that's 800 calories you should have half of it

at that point the thing is already made it's already done it's already ready to go I would probably

say oh okay I'll make up calories later or again the diet starts tomorrow conversely in our current

system how people are logging it you are logging the bread slices you were logging the turkey you

were logging the mayonnaise and so you see in the moment wow I'm putting together something that is

too large and so again I think the whole process or the order of operations of the current shall we

say old school style is what ultimately is going to change your behavior versus AI just letting

you know okay that could be too much or something like that interesting because the AI is going to

try to make it as easy and painless and frictionless as possible to measure and your argument which

I think is the right one which is the measuring is the point not necessarily the number that comes

out of the other end the the other end of that whatever that tracking process looks like of course

the numbers do matter but it's the it is the measuring of them that matters more than the end

result of that measurement we've we've talked about this I think a few times recently but definitely

over the years you know the body doesn't have an exact number of calories to that needs the

metabolism adjusts up and down and in fact some of these overfeeding studies we can be off by the

number of calories that we eat and our body will adapt and burn them off and so that lets us know

that we don't have to be perfectly accurate yes the numbers matter to some degrees you're saying but

it's not quite exactly as perfect as people think so we have some margin and error in our targets

we want to endeavor to log and we want to endeavor to be accurate and certainly there is going

to be in a volume that's too much but that whole spiel is really trying to get people to like it's

more about consistently getting close to what you're supposed to be eating and that awareness is

what ultimately drives that versus it needs to be such a precise number so AI is really going after

the accuracy of what you're eating when the weighing and measuring under the old school system

really is bringing the awareness to the behavior and that's where the value is

got it number three AI doesn't fix underreport it yeah so one of the primary reasons that we deal

with shall we say an old school weighing in measuring is under-reporting which means like the

log doesn't reflect everything that you eat you know you go out for pizza and you don't really

want to say that you ate four pieces so you record two or you just happen to forget that you had a

cookie between meals and like that and that's just it that under-reporting happens because people

forget or there's some shame about it they forgot maybe they had some snack in between or whatever

it is or they don't want to admit that what they ate having a device that accurately tells you how

many calories are in something does not change the human behavior of logging like you would still

have to be responsible for taking the AI pictures even if it was perfectly accurate of all the

things that you ate so like you would still have to take the picture of the four pieces of pizza

and say log at all you would still have to remember all of the cookies that you have in between meals

or the you know the coffee drink that you got on the way to work or something like that so the

fact that you have a more accurate device does not change your memory to record all of the things

right and maybe there would be some evolution in AI where you'd constantly have you know your phone

recording all your movements at all times to me that starts to be very true and show-ask and I'm

like I don't want that like I don't want a camera on me at all times recording every last thing

your smart glasses yeah like I mean yeah you know there's some things where you're first like oh

that would be easy and then you're like I don't want that like I don't want just a running record of

everything in my life at all times so this is where it's like okay yeah maybe maybe the technology

will get to a point where it is really accurate but the person still has to be willing to record all

the things and remember to record all the things if it really is going to capture what they're eating

so hey I won't fix that reason number four logging the right number of calories doesn't change your

hunger or satiety yeah I mean I think this might be kind of my my main kind of pushback maybe

I should have made this the first one but if counting calories was the real path to success

I think we would have more successful weight loss stories by now or at least successful weight

loss maintenance stories by now and we certainly don't most people gain it back we've discussed that

quite a lot and I think there's lots of different reasons for that one of course is our modern

food environment but another one is the hunger that people experience when they lose weight and

when they're eating in the caloric deficit so you need to rely on food that is actually filling

and satisfying to be able to hit the right number of calories so when people hit the right number

of calories on processed food they are going to be more hungry compared to doing something like

the three pillars method where they fill up on all these fruits and veggies and protein if I thought

that counting calories was the really the ticket to sustainability then I'd have the counting calories

method not the three pillars method we've got these other two pieces in place first because we need

them for the calories to be successful so again AI might get more accurate in the sense of how many

calories are in this lasagna but that doesn't help the person make choices that are actually going to

keep them full to keep those calories in line now of course you could potentially train an app such

that it trains the person in the three pillars method system so they first fill up on these fruits

and veggies and protein but I'm going to tell you that's not what these apps do right now it's

kind of this point and shoot thing saying okay this does have 500 calories in it when in reality the

person needs to be picking things that are truly going to keep them satisfied in the long-term

controlling satiety and so this is the big one it doesn't necessarily prioritize the food that is

going to make the caloric deficit sustainable. Got it. Reason number five the AI will not save your

diet using an app to tell you what to eat means you'll need the app forever. I mean sometimes I

think I should have been born in like the 1920s I'm just like can we just use a post-it note like

what's going on here. I mean I built an app I like apps I love I think there's a huge value

there and I also think the idea of using an app to forever tell me what to eat sounds completely

miserable. And other things I mean there's other things in my app that I think are really used

for like the meal ideas and education the Q&A so it's not just about like logging and all of that

stuff but to me I'm just like with all of this tech stuff I'm always so surprised that people want

this like you want more notifications you want more things to do you want like another device telling

you something at all moments and all times I mean to me in modern busy lives with all these things

that we have to do I want less things to do the less notified please stop notified. You know eating

especially it's something that we have to do multiple times a day for the rest of our life you

want to be checking in with an app and being directed at all moments and all times don't you want

the autonomy and the freedom and the knowledge to not me that I mean I don't know that's just maybe

a personality thing coming through but to me it's like not an end state that I would want in any

sort of imagination but yeah I mean if the app is calculating calories and telling you what to eat

it removes any burden from the person that they are actually going to learn and retain any of it

which then is going to prevent them from being able to step away from using an app the process of

weighing and measuring again is is not to get an accurate log I mean that's there and certainly

we need that to some degree but it's really secondary to the person changing their behaviors around

food and understanding what food choices they need to put together that are going to get them

to be more successful and what food choices they like and what keeps them satisfied as we just

mentioned and learning all of that is what should be happening when you're weighing in measuring when

people just weigh in measure just to get something on the log it really is approaching it in

in a way that I don't think is most beneficial for them you want to actually be like oh wow okay

that's how many calories that is okay wow this is what I have to do to get my day in line and these

are the foods that I like and I kind of know that when I eat these foods that I'm close to my

protein and I'm close to my calories because that's ultimately how you get away from tracking and I

I work with clients all the time that are so sick of tracking and it's like okay well that's

what we want to get to we want to get to a point where we're hitting our nutrition goals and not

tracking so yeah this idea that your app is just going to tell you what to eat is setting yourself up

for always needing an app and always tracking and so far in my experience that's a very small

percentage of people who want to do that and so to get away from tracking what you really need to do

is dig in to learning about food learning about your food choices learning how to put together days

and ultimately setting the habits that's what's going to get you away from that and being able to

achieve the outcomes you want without any tracking at all yeah and it seems like at least in the way

you've presented it here that the solutions quote unquote solutions are all trying to say hey

we're going to take the hard part out of weighing and measuring and I think as you say all of the time

you don't say it with these words but like the hard part is the point yes and to skip over that is to

skip over all of the potential benefits or the potential escape from whatever trap you've been stuck

in for however long you've been stuck in it totally it's just like exercise it's like I want to

look really lean or I want to be really fit it's really hard I mean you know it's really hard you

got to do it every freaking day and you got to push yourself and you got to test yourself you know what I

mean it's the same thing you want to get be really especially in a modern food environment I always

like to give the analogy like if we dropped you off on some remote island in the south Pacific and

the only thing that you had available was like you know fish some type of white fish and some type of

you know coconut fruit coconut okay it's really easy it's really quite easy because you don't have

all these other choices but that's not where we live so in our modern food environment the only way

yeah it's hard it's hard you're gonna have to kind of figure out how to do this without all of that

stuff but that ultimately is going to get you to a place that I think is easier and a place of more

freedom yeah it's it's it's an interesting dichotomy in that we suffer from too much convenience and

we want to be sold the idea that to get out of the downfalls of convenience is here's another

convenient option for you when in fact you have to like double down on the inconvenience and the

friction in order to get out of the trap of the convenience you first got yourself into or that

was is present all around us and I think the AI is it's great in a lot of ways I think in let like

almost every piece of technology its main argument is hey look at how much more convenient we can

make this and to your point nutrition fitness relationships the things that actually matter

those are the places that we actually have to really resist the temptation of choosing the

convenient option because it's almost never the solution that we think it is and that's my real

passion with nutrition and with my accelerator and my masterclass is like I really love empowering

people to have the knowledge to do this and I think again there's this initial just tell me what to

do and to me I'm like you don't want that like you want that today but in six months you don't want

that you want to be free from all of that like I'm like solving for freedom and autonomy and so

we have to go through this period that does feel more inconvenient but I think it ultimately is

is again what you're saying it's like where the benefit is and where we ultimately want to be

all right here's the good news in all of this you don't need the next app the next variable the next

AI tool to improve your nutrition you already have everything you need the ability to make choices

the awareness to track what you're eating and the agency to learn what works for you but here's

the even better news all of this technology all of these tools all of the conflicting advice

turns out it's all just noise and the clearer you get on that the easier it becomes to ignore it

that's why I really value my conversations with EC and really all the work the EC does she cuts

through all the noise and brings you back to what actually matters eat whole foods and the prep

romance the boring basics as we like to say make simple choices more often than you don't that's it

it's the whole game the friction you feel when you weigh and measure your food that's not a bug

not as a feature and it's where awareness lives it's where the learning happens and it's what

builds the kind of knowledge that eventually sets you free from needing any of these tools at all

if you want more of this clarity of course check out the consistency project podcast or visit

three pillars method dot com which is where you can learn even more from EC thank you to her for

letting us share these conversations thank you to you for being here thank you so very much

for tuning into the show this week two quick things before you go first if you want even more

from this episode go check out the full five three one breakdown we give you the five big ideas

from the episode three reflection questions to help you process it all and one key takeaway that

you can put into action right now the link is in the show notes the second thing while you're there

check out the chase club it's our premium membership where we include bonus content add three episodes

and a brand new chasing tracker app check it all a chasing excellence

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