Shownotes
In this investigative “Wellness Girl chat,” Dr. Kelsy Vick breaks down how fitness trackers estimate caloric expenditure—and why those numbers can be misleading for assessing workout effort, effectiveness, or weight loss. She defines calories (including the difference between food-label “calories” and kilocalories), explains total daily energy expenditure, and outlines what wearables typically model: basal metabolic rate (BMR) plus active energy expenditure, while often failing to capture thermic effect of food and non-exercise activity thermogenesis (NEAT). She reviews how BMR is estimated from static equations using age, sex, height, and weight (with limitations and common error ranges), then explains how active calories rely on proprietary algorithms driven largely by heart rate, supported by sensors like accelerometers, gyroscopes, barometers, GPS, and (for Oura) skin temperature—often switching sensor priorities based on the selected activity.
The episode also connects heart rate to oxygen consumption and energy expenditure, explains VO2 and the lab-based VO2 max testing process, and shows how wearables use heart-rate-based VO2 relationships to estimate calories. Dr. Vic compares common devices: Apple Watch (Move vs Total Calories, activity classification, VO2 feature accuracy cited within 4% when using VO2 integration), Whoop (step-free strain model, continuous heart rate monitoring, algorithm shifts based on heart rate reserve), Oura Ring (finger-based sensing, emphasis on resting metrics, temperature and sleep inputs for BMR, MET-based intensity categories with user-reported effort), Garmin (GPS- and pace-informed VO2 estimates, cycling power-based calculations, with studies showing wide accuracy ranges and underestimation at low and very high intensities due to heart-rate lag and anaerobic work), and Fitbit (older models leaning more on accelerometer data, newer models incorporating heart rate).
Dr. Vick summarizes research findings showing large real-world error in wearable calorie estimates, including a Stanford Benchmark study (most accurate device averaging 27% off; least accurate 93% off, with factors like skin color and BMI affecting results), a 2025 head-to-head comparison reporting varying under- and over-estimation across devices, and a systematic review where no brand consistently met acceptable accuracy limits. She notes that resting heart rate and step count are generally more accurate than calorie burn estimates, while sleep duration is moderately accurate and sleep staging is weak. The takeaway: calorie metrics may be useful for broad trends, but shouldn’t drive dietary or training decisions; she recommends emphasizing other wearable metrics such as heart rate, VO2/VO2 max, heart rate variability, and recovery instead.
Resources:
https://www.empirical.health/blog/apple-watch-calories-accuracy/
https://www.apple.com/health/pdf/Heart_Rate_Calorimetry_Activity_on_Apple_Watch_November_2024.pdf
https://pmc.ncbi.nlm.nih.gov/articles/PMC5738849/
https://pmc.ncbi.nlm.nih.gov/articles/PMC9950693/
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00:00 Calories Culture Backstory
02:49 What Trackers Claim
04:05 What Is A Calorie
04:51 TDEE Basics BMR Active
06:41 BMR Formula Limits
08:28 Sensors And Algorithms
11:55 Heart Rate And VO2
12:55 VO2 Lab Gold Standard
15:35 Apple Watch Breakdown
17:14 Whoop Strain Model
18:57 Oura Ring And METs
22:14 Garmin VO2 And GPS
26:03 Fitbit And Accuracy Studies
29:01 How To Use Calories
31:08 Wrap Up And Takeaways
Transcripts
Let's do a little investigative journalism and Wellness Girl chat
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about calories and fitness trackers.
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Welcome back to Wellness the Pod.
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I'm your host, Dr.
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Kelsey Vic, a board certified
orthopedic doctor, physical therapy,
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and a pelvic floor physical therapist.
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And if you're like me, you have
come from an era hyper-focused on
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calories and caloric expenditure.
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It was the only thing I used to track on
MyFitnessPal back in college to make sure
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I didn't gain the freshman 15 or have
any weight gain from being out on my own.
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I feel like so much of the
messaging back then was fear-based
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messaging about weight gain.
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When you go out on your own and are
having to cook and you're a little
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more stressed in college and you're
having to exercise on your own, you're
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no longer in high school sports.
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Like it was all fear-based messaging
around keeping your calories and
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your energy expenditure relatively
level so that you did not become
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a victim of the freshman 15.
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As a side note, I had a TA in college.
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I loved her, she was from Canada,
and she said when she came to
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Texas she was unaware of how much.
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Chips and salsa and queso was served at
every meal at Tex-Mex restaurants, and so
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she called it the Texas 10 for her because
it was so much chips and salsa and queso
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that she was not used to and would just
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devour at every meal.
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So she called it the Texas 10,
but whatever you called it, I feel
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like calories were such a focus.
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And back when I was in college,
Fitbit was like the big thing.
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So I would always see what my
caloric expenditure was on Fitbit and
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compare that with my MyFitnessPal.
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And this was not a healthy
way to do it at all, but.
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It was what was common at the time.
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A lot of girls were using MyFitnessPal
and Fitbit and trying to make those
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calories equal or even less and go
more into a caloric deficit in order to
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maintain what they thought was a healthy
weight or a healthy body composition
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for this new stage in our lives.
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So I thought it would be fun to actually
look at how different fitness trackers,
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common fitness trackers today actually
look at and calculate caloric expenditure.
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I know Apple has moved calories.
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Whoop has strain.
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So today we'll dive into some of
the context into how different
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fitness trackers actually
estimate caloric expenditure.
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And it's not the same across the board.
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They use different algorithms
and different formulas to
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get caloric expenditure.
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We're gonna talk about why it differs so
drastically compared to what's actually
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happening within our bodies and why we
shouldn't use calories as measured on a
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fitness tracker as the primary objective
measure that we're looking at, especially
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as it pertains to workout effectiveness,
effort of the workout or weight loss.
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So I know there are so many
fitness wearables out there.
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The ones we'll talk about today are the
common ones, I would say today, or a ring.
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Whoop Apple, watch Garmin and Fitbit.
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Which I don't see as many Fitbits
nowadays, but I still figured since
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it was kind of the OG one that I
feel like a lot of people knew.
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I'd still put it in there just in case.
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Someone was curious on how their old
Fitbit and how some of the newer Fitbit
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models track caloric expenditure compared
to more popular fitness wearables today.
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So let's go over a few like basics.
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starting with what even is a calorie.
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So a calorie with a big C is the
energy to heat one gram of water
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by one degree Celsius at standard
pressure, which equals about 4.184
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joules.
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Such a complicated definition
for what a calorie is.
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in food labeling and nutrition
labeling, when we talk about a calorie
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with a little C, what we're really
referring to are kilo calories.
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And kilo calories represent the energy
your body gets from oxidizing nutrients
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like carbs, proteins, and fats.
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For example, a 250 calorie item
on a label means 250 kilo calories
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or 250,000 small calories Now what
are the basics behind how fitness
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trackers actually measure calories?
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So every fitness tracker
estimates the total daily energy
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expenditure using two variables.
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And remember, energy expenditure is a
fancy term for calories expended because
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calories is a unit used to measure energy.
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So those two variables that go
into total daily energy expenditure
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are your basal metabolic rate.
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The calories required to sustain
basic life functions at rest.
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Basically breathing, digestion,
circulation, cellular repair,
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which typically accounts for about
60 to 75% of daily calorie burn.
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And the second variable is
active energy expenditure.
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So the additional calories burned
through physical activity and
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movement from walking around
the house to intense exercise.
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What fitness trackers
don't actually track.
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That also goes into our physiologic
total daily energy expenditure are
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what we call thermic effect of food and
non-exercise activity thermogenesis.
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So the thermic effect of food
accounts for about 10% of daily
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caloric energy expenditure, and it's.
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The amount of calories used to just
digest the food that we're consuming.
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And then neat or non-exercise activity.
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Thermogenesis includes fidgeting,
gesturing while talking.
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This is actually really cool.
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There's studies done on
even, , like a soleus raise.
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So like when you're sitting and
just lifting your heel up and how
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many extra calories that burns.
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For people who are just
fidgeters, naturally fidgeting.
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So the fidgeting, the hand gesturing,
all of that goes into this neat or
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non-exercise activity thermogenesis,
which fitness trackers do not track.
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It's a lot harder to track hand
gestures and movements and little
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fidgets throughout the day with
something you wear on your wrist or
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your fingers, so that makes sense.
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But those are the two variables that
fitness trackers don't accurately
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estimate or include into their
calculations for total energy expenditure.
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So BMR or basal metabolic rate.
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most fitness trackers estimate this
from a few well-known equations based
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on your age, sex, height, and weight.
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So a lot of the time when you're setting
up an account, this is what you will
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enter, and this helps to estimate your
BMR And there's a few standard equations.
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There's differences amongst all of them,
but there's a few, I guess, well-known
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equations that estimate BMR based on the
variables that you enter when you create
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your account on these fitness trackers.
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However, even these equations have their
own drawbacks, oftentimes citing high
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accuracy for the given value of your BMR
within about 200 calories, plus or minus
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So there's a high amount of accuracy
within a 200 plus or minus calories of
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what you're actually expending each day.
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So the formulas that help estimate BMR
that are a part of these fitness trackers
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already have some limitations to them.
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So a few drawbacks for some of these
equations that these fitness trackers use
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is number one, it's a static equation.
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our BMR is affected by a
lot of different factors.
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Things like our hormones, our sleep, our.
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Stress are environmental temperatures.
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So it's not this static measurement
that we can just copy paste every day.
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It fluctuates day to day, week by week.
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It's not this static measurement.
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So that's one of the
drawbacks of these equations.
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And another drawback is that the
studies that are been used to come up
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with these equations aren't inclusive
of all variations in our genetics.
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So these formulas basically already
have their limitations and these fitness
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trackers are using these formulas, which
are researched and are the best ones when
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it comes to what has been researched.
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But since they're already using
these to get your BMR you're already
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starting from this limited viewpoint
of your actual caloric expenditure.
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So the second variable that fitness
trackers track when it comes to
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total daily energy expenditure is
your active energy expenditure.
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So this is that movement on top of
what is required to sustain basic life.
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So active calorie estimates are really
the bread and butter of these fitness
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trackers, and they are very secretive
on what their algorithms are because
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that's usually the r and d that has
gone into creating some of the best
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algorithms for their fitness trackers
based on the sensors involved and based
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on the data that they have from all
of us as fitness wearable consumers.
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So it's very secretive, but there's a few.
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Tools that a lot of these fitness
trackers use to help estimate
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active energy expenditure.
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most of them use a combination
of an accelerometer, which is
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movement and velocity changes.
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So this is something like step count
they oftentimes use an optical heart
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rate sensor, so this is that like
red flashing light that picks up
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heart rate via light absorption.
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Some of them have a gyroscope or
a tracker sensor for rotational
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movements, especially used in activity
classifications like swimming or
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different rotational movements of the arm.
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Especially, a lot of the time the
trackers are primarily wrist based, or
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if it's the R ring, it is finger based.
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So different rotational movements
sensed via the arm typically.
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A barometer, which helps detect
incline decline, A-G-P-S-G-N-S-S,
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geolocation, outdoor pace and distance
estimator, and then skin temperature.
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So general metabolic state, and
this one's primarily used by Aura.
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So these are all of the different
sensors that fitness trackers can
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utilize to estimate total daily energy
expenditure or caloric expenditure.
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And these aren't all included in all
sensors, but these are just sort of
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the basic categories of sensors that
are integrated into fitness wearables.
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what I found really cool based on my
research was that the reason they have you
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choose different activity levels whenever
you're recording a workout is because
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their algorithms will switch to different
sensors and prioritize those sensors
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or the data coming from those sensors
more than data coming from other sensors
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based on the activity that you're doing.
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So.
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If you're doing something like swimming,
they're going to use the data coming
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from the gyroscope more, or weight
that heavier than the data coming from
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the barometer or the incline decline.
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So if you think about it.
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It makes sense to weight certain
data a little bit higher based on
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the activity that you're doing.
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So this might be why some of those
Caloric estimates change once you
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change your activity after the fact.
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So if you just start exercising without
actually going in the app change or going
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on your watch, changing the activity
that you're doing and then performing the
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workout and then stopping the workout,
if you go in after the fact, this is why
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some of those estimates might change,
is because their algorithms pull or.
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weight certain data heavier based on
the activity that you're doing, which
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I thought was actually pretty cool.
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It makes sense.
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But once I realized like, oh yeah,
of course they're getting this data
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from so many different sensors and
it makes sense to weight data coming
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from the gyroscope for swimming higher
than step count data, of course.
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So I thought that was pretty cool.
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But overall, heart rate is typically
the primary driver of caloric
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expenditure across all models.
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they will weight other data a little
more depending on the activity.
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But overall heart rate is one of the
primary factors that goes into how these
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fitness wearables and how this fitness
tech estimates caloric expenditure.
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so, why is that?
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Heart rate actually correlates
with oxygen consumption, which
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correlates with energy expenditure.
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And there's a formula for this, although
the formula is said to be more accurate at
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greater than 50% of your max heart rate.
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So again, another estimation formula
that has error built in that will affect
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the accuracy of the caloric data that
these fitness wearables are giving you.
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So oxygen consumption or VO O2 is a
measure of how much oxygen your muscles
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can actually utilize, not necessarily how
much oxygen they're receiving, but how
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efficient are your muscles at actually
utilizing the oxygen that is sent to them.
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it goes a step further and takes into
account things like mitochondrial
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density and how much your muscles
can actually utilize the oxygen
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at more of a cellular level.
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There's a lot of formulas that can look
at heart rate and estimate your VO O2.
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And many of the fitness trackers use
a variation of this relationship in
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order to estimate caloric expenditure.
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But before we dive into that, let's
talk about the gold standard way of
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measuring your VO two max so that we
know how these fitness trackers are
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getting the estimate compared to that
gold standard testing in the lab.
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00:13:07,263 --> 00:13:12,783
So to measure VO O2, you typically are
using a treadmill or a cycle like a,
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a stationary bike, depending on the
activity that you perform most often.
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00:13:17,433 --> 00:13:20,613
A lot of the time they're doing these
in elite athletes, and so specificity
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00:13:20,613 --> 00:13:24,323
is very important, but we'll use the
treadmill as an example, you'll put a
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mask on and it's a very uncomfortable
mask that is tied up to a lot of different
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sensors to measure and estimate different
gas concentrations of O2 and CO2.
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00:13:33,233 --> 00:13:37,223
So what you'll do is you'll start
running on a treadmill and over time the.
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00:13:37,723 --> 00:13:41,833
Treadmill will increase
in incline and speed.
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00:13:41,863 --> 00:13:44,563
So you're basically ramping
up intensity really quickly.
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It's usually a pretty quick test because
your goal is to reach this relative
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fatigue point or a certain ratio of.
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Oh two to CO2 before the
test is actually terminated.
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00:13:55,923 --> 00:13:58,893
So you put the mask on, you run
on the treadmill, the mask starts
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measuring different gas concentrations,
primarily CO2 as it's expelled.
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00:14:02,883 --> 00:14:05,073
As intensity increases
your heart rate rise.
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00:14:05,563 --> 00:14:11,083
You consume more O2 and expel more CO2,
at some point, the intensity of the
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00:14:11,083 --> 00:14:15,073
workout will exceed the amount of oxygen
that your heart and lungs can supply.
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00:14:15,573 --> 00:14:19,173
So CO2 will continue to increase
as you're expelling, air, as
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you're exhaling and O2 plateaus.
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So that test is terminated
once that O2 plateaus, but.
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Ideal.
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00:14:26,468 --> 00:14:31,868
It also might terminate if the intensity
gets too high for the testing subject or
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if that O2 CO2 ratio reaches a certain
number and then the test is terminated.
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00:14:36,758 --> 00:14:38,228
So it's a very challenging test.
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00:14:38,228 --> 00:14:41,558
It requires a good amount of lab
gear, a good amount of sensors.
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00:14:41,558 --> 00:14:44,708
So a lot of the time we just
try and estimate VO two based
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00:14:44,708 --> 00:14:45,728
on some of these formulas.
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00:14:46,228 --> 00:14:50,028
And just to link it back to this
entire episode, V two and heart rate
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00:14:50,028 --> 00:14:54,708
are linked together, and both of those
variables, along with certain other
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00:14:54,708 --> 00:14:58,458
variables and data coming from other
sensors are utilized to estimate caloric
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expenditure in these fitness wearables.
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Okay, let's dive in to each of the
different fitness wearables and if you
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guys have one that I didn't mention.
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00:15:06,788 --> 00:15:07,868
I know Microsoft has one.
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00:15:08,228 --> 00:15:09,608
I know Samsung has one.
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00:15:09,848 --> 00:15:14,078
I'm sorry I haven't gotten to these, but
I feel like a lot of them have similar, I.
247
00:15:14,578 --> 00:15:18,028
Technologies and ways of calculating,
but I'd be happy to do another episode,
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00:15:18,028 --> 00:15:20,428
bonus episode of just those devices.
249
00:15:20,428 --> 00:15:22,648
If you guys tell me
which ones you utilize.
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00:15:22,678 --> 00:15:25,968
I haven't seen too many of my own
friends using like the Samsung or
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00:15:25,968 --> 00:15:28,878
the Microsoft or, I know I keep
getting ads for the new earrings,
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00:15:28,878 --> 00:15:30,138
so I'm interested to see that one.
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00:15:30,138 --> 00:15:32,538
I don't think it's out yet,
but if you guys have any other
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00:15:32,538 --> 00:15:34,848
ones that you'd like me to dive
into, I'd be more than happy to.
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00:15:35,348 --> 00:15:40,028
But today we'll just cover Apple
Watch Garmin Ora Ring, whoop, and the
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00:15:40,058 --> 00:15:41,998
Fitbit, starting with the Apple Watch.
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00:15:41,998 --> 00:15:46,428
So some of the key features move
calories on the Apple Watch.
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00:15:46,488 --> 00:15:49,068
Looks just at active energy expenditures.
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00:15:49,068 --> 00:15:52,223
So remember there's the BMR basal
metabolic rate, and then there's
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00:15:52,228 --> 00:15:55,398
active energy expenditure, and these
move calories on your Apple Watch.
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00:15:56,028 --> 00:15:58,128
Constitute the active energy expenditure.
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00:15:58,628 --> 00:16:03,158
Total Calories combines BMR and
Active Energy expenditure, and the
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00:16:03,158 --> 00:16:05,258
Apple Watch has a VO O2 integration.
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00:16:05,258 --> 00:16:07,778
So it's different from calories,
but it gives a good picture
265
00:16:07,778 --> 00:16:09,068
of overall heart health.
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00:16:09,638 --> 00:16:14,168
so the VO O2 feature specifically has
been found to be accurate within 4% of
267
00:16:14,168 --> 00:16:19,058
the actual value of your actual VO O2
if you're using that VO O2 integration.
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00:16:19,058 --> 00:16:23,698
So I think you actually have to go in
and do the VO O2 testing in order to get
269
00:16:23,698 --> 00:16:26,398
that more accurate within 4% estimate.
270
00:16:26,398 --> 00:16:27,268
But that's super cool.
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00:16:27,768 --> 00:16:31,488
So the Apple Watch basics, it uses the
heart rate, the optical heart rate sensor.
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00:16:31,488 --> 00:16:35,178
It uses the accelerometer, the
gyroscope, the barometer, and GPS data.
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00:16:35,678 --> 00:16:39,218
It also has activity based
individualization, so it uses machine
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00:16:39,278 --> 00:16:43,058
learning based activity classification
to better estimate caloric expenditure
275
00:16:43,388 --> 00:16:45,428
based on activity specific formulas.
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00:16:45,678 --> 00:16:49,248
So it weights data coming from the
accelerometer, the gyroscope, GPS data,
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00:16:49,248 --> 00:16:51,198
et cetera, based on that activity.
278
00:16:51,198 --> 00:16:52,788
So super important.
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00:16:52,788 --> 00:16:56,358
If you want the most accurate estimate to
actually start your workout for most of
280
00:16:56,358 --> 00:17:00,558
these fitness wearables with the workout
that you're actually doing that weight, it
281
00:17:00,558 --> 00:17:01,888
can weight some of the data a little bit
282
00:17:02,388 --> 00:17:04,488
more appropriately for the
activity that you're doing.
283
00:17:04,988 --> 00:17:07,928
So if you're swimming, it will weight data
coming from gyroscope, a little higher.
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00:17:07,928 --> 00:17:11,468
GPS and accelerometer will be weighted
higher for walking and running, and
285
00:17:11,468 --> 00:17:14,377
then yoga, Pilates, et cetera, with
heart rate weighted a little bit more.
286
00:17:14,377 --> 00:17:18,637
For the whoop the key features, it
notably rejects step counts entirely in
287
00:17:18,637 --> 00:17:22,238
favor of a strain model, which integrates
continuous heart rate monitoring.
288
00:17:22,238 --> 00:17:24,548
That's a big thing for whoop,
continuous heart rate monitoring.
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00:17:24,968 --> 00:17:27,758
BMR calculations based on
height, age, weight, and sex.
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00:17:27,758 --> 00:17:30,218
And this is how all of them
get their BMR calculations.
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00:17:30,578 --> 00:17:33,698
Whoop claims their product doesn't
count steps because step count
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00:17:33,698 --> 00:17:35,618
ignores intensity and other movements.
293
00:17:36,118 --> 00:17:39,298
With their preferred method being
strain, which accounts for heart
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00:17:39,298 --> 00:17:40,828
rate alongside physical activity.
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00:17:41,328 --> 00:17:45,648
So to calculate the active energy
expenditure for Whoop, they utilize your
296
00:17:45,648 --> 00:17:49,638
heart rate to estimate strain data and
therefore active caloric expenditure.
297
00:17:50,028 --> 00:17:53,358
They utilize heart rate reserve,
which is max heart rate, minus your
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00:17:53,358 --> 00:17:55,638
resting heart rate to gauge intensity.
299
00:17:55,698 --> 00:17:59,838
Inactivity and when to switch algorithms
to measure caloric expenditure.
300
00:17:59,838 --> 00:18:04,848
So once it goes higher than about 30% of
your heart rate reserve, which is your max
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00:18:04,848 --> 00:18:09,018
heart rate, minus your resting heart rate,
then it will switch algorithms to be more
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00:18:09,018 --> 00:18:14,058
of an active exercise based algorithm to
calculate that active energy expenditure.
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00:18:14,558 --> 00:18:17,768
And just as a reminder, we've
done an episode on Max heart rate
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00:18:17,768 --> 00:18:22,208
and rest and heart rate, but max
heart rate is typically estimated.
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00:18:22,208 --> 00:18:25,838
Again, there's a few formulas for
this, but it's estimated to be two 20
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00:18:25,868 --> 00:18:27,758
minus your age is your max heart rate.
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00:18:27,758 --> 00:18:30,848
So intensity is based on a
percentage at that max heart rate.
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00:18:31,148 --> 00:18:34,448
It's definitely affected by heart rate
medications, blood pressure medications,
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00:18:34,448 --> 00:18:35,738
any cardiovascular medications.
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00:18:35,738 --> 00:18:37,148
So that's a little disclaimer.
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00:18:37,648 --> 00:18:41,098
If you're a normal, healthy adult,
you can use two 20 minus age to
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00:18:41,098 --> 00:18:42,238
estimate your max heart rate.
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00:18:42,268 --> 00:18:44,608
And then your resting heart rate
is typically the lowest heart rate
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00:18:44,608 --> 00:18:46,018
that your body reaches at rest
315
00:18:46,518 --> 00:18:50,148
and heart rate reserve is the max heart
rate minus your resting heart rate.
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00:18:50,398 --> 00:18:55,018
so whoop will start calculating
active energy expenditure at 30%
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00:18:55,138 --> 00:18:57,328
of that heart rate reserve number.
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00:18:57,828 --> 00:19:01,908
For the R ring key features, it utilizes
the gyroscope, accelerometer heart
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00:19:01,908 --> 00:19:06,048
rate, sensing body temp, and then user
entered body metrics again, specifically
320
00:19:06,048 --> 00:19:08,448
for that basal metabolic rate.
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00:19:08,698 --> 00:19:12,298
So it's worn on the finger rather than
the wrist giving it better heart rate.
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00:19:12,298 --> 00:19:14,128
Sensing from what Aura says.
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00:19:14,128 --> 00:19:17,698
I still question this because
when we take pulse metrics,
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00:19:17,698 --> 00:19:18,778
we're taking it at the wrist.
325
00:19:18,778 --> 00:19:23,128
So I question this a little bit, but I
do agree with them saying the fingers
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00:19:23,128 --> 00:19:26,428
might move a little bit less so the data
that they're getting from the finger
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00:19:26,428 --> 00:19:29,278
might be a little bit more accurate
because you're not necessarily having
328
00:19:29,278 --> 00:19:31,138
some of these hand gestures at the wrist.
329
00:19:31,138 --> 00:19:33,598
Your fingers are typically
not moving as much as.
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00:19:34,098 --> 00:19:38,238
Your wrist, so I can see that a
little bit, but I still, we definitely
331
00:19:38,238 --> 00:19:42,468
take pulse measurements and heart
rate measurements at the wrist, so I
332
00:19:42,528 --> 00:19:43,848
question those claims a little bit.
333
00:19:43,848 --> 00:19:48,128
And if anyone has some of the science to
back that up, I would love to read it.
334
00:19:48,128 --> 00:19:52,148
But I still question the claim
that the finger has better
335
00:19:52,148 --> 00:19:53,618
blood flow than the wrists.
336
00:19:54,118 --> 00:19:58,588
The R ring emphasizes passive resting
metrics and uses a combination
337
00:19:58,588 --> 00:20:01,288
of finger heart rate, sensing,
temperature, and accelerometer
338
00:20:01,538 --> 00:20:02,833
to estimate calorie expenditure.
339
00:20:03,333 --> 00:20:07,923
So passive calories or that BMR
is derived from user entered body
340
00:20:07,923 --> 00:20:09,993
metrics, body temp, and sleep pattern.
341
00:20:10,473 --> 00:20:14,973
So this takes a more holistic
approach to estimate BMR.
342
00:20:15,683 --> 00:20:19,313
As we said earlier, some of those
BMR formulas do not take into
343
00:20:19,313 --> 00:20:22,763
account stress and sleep in some
of the dynamics of your BMR.
344
00:20:23,033 --> 00:20:25,103
They use it as more of
a static measurement.
345
00:20:25,103 --> 00:20:28,433
So Aura tries to account for that
with some of its sleep technology
346
00:20:28,433 --> 00:20:32,153
sensing and body temp sensing, although
we'll talk about it later, that
347
00:20:32,153 --> 00:20:33,983
technology isn't quite accurate either.
348
00:20:33,983 --> 00:20:37,253
So take all of that with a grain of
salt, but at least they're trying
349
00:20:37,253 --> 00:20:40,903
to integrate some of those more
holistic components into your BMR
350
00:20:40,903 --> 00:20:42,133
into some of their measurements.
351
00:20:42,633 --> 00:20:46,653
For active calories, it uses
mets to classify activities as
352
00:20:46,653 --> 00:20:48,303
low, moderate, or high intensity.
353
00:20:48,803 --> 00:20:50,753
So a MET is kind of a confusing thing.
354
00:20:50,753 --> 00:20:52,943
I think it's meant to be simple,
but to me it's confusing, but
355
00:20:52,943 --> 00:20:54,263
it's a metabolic equivalent.
356
00:20:54,533 --> 00:20:57,323
So mets are used as a
multiplier of your BMR.
357
00:20:57,323 --> 00:21:01,733
So one met is the energy
required to sustain you at rest.
358
00:21:01,733 --> 00:21:06,833
So if something is three mets, it's
three times as intense as at rest.
359
00:21:06,833 --> 00:21:08,603
If it's five mets, it's
five times as intense.
360
00:21:08,603 --> 00:21:09,653
So it's a multiplier.
361
00:21:10,043 --> 00:21:11,783
I think it's a little bit more confusing.
362
00:21:12,033 --> 00:21:13,168
I have never really used it, but.
363
00:21:13,848 --> 00:21:18,468
I also understand that in order to
gauge intensity, they can use it as a
364
00:21:18,468 --> 00:21:22,218
multiplier based on low effort, moderate
effort, or high intensity effort.
365
00:21:22,338 --> 00:21:23,898
And I have used an ora ring.
366
00:21:23,898 --> 00:21:26,838
And after the workout, I'll say,
what is your intensity effort?
367
00:21:27,338 --> 00:21:31,568
And this is why, is because they will use
a multiplier for low intensity, moderate
368
00:21:31,568 --> 00:21:36,008
intensity, and high intensity to calculate
your caloric expenditure for that workout.
369
00:21:36,508 --> 00:21:41,818
However, if it's a highly intense
upper body day, I have found that
370
00:21:41,818 --> 00:21:45,208
if I put high intensity, I don't
necessarily think that I burned as
371
00:21:45,208 --> 00:21:47,338
many calories that the AA ring says.
372
00:21:47,338 --> 00:21:49,143
So I feel like there is some.
373
00:21:49,643 --> 00:21:52,553
Definitely subjectivity to
that measurement based on
374
00:21:52,553 --> 00:21:53,933
your own gauge of intensity.
375
00:21:53,933 --> 00:21:57,803
So the fact that you are the one
deciding whether that workout was
376
00:21:58,253 --> 00:22:02,723
low, moderate, or high intensity
to then calculate the active energy
377
00:22:02,723 --> 00:22:07,633
expenditure, there's a little bit of
a limitation in that calculation Then.
378
00:22:08,133 --> 00:22:12,813
So basically your intensity ranking
then helps them to calculate the
379
00:22:12,813 --> 00:22:14,253
estimated caloric expenditure
380
00:22:14,753 --> 00:22:16,793
for Garmin, some of the key features.
381
00:22:16,793 --> 00:22:20,933
It uses GPS when available to supplement
accelerometer data for outdoor
382
00:22:20,933 --> 00:22:24,693
activities and incorporates VO two
max estimates, which it calculates
383
00:22:24,693 --> 00:22:28,833
from heart rate versus that pace
data to improve calorie estimates
384
00:22:28,833 --> 00:22:30,783
for trained athletes specifically.
385
00:22:31,283 --> 00:22:34,733
So it gets those VO O2 max
estimates from heart rate and
386
00:22:34,733 --> 00:22:36,383
running pace or cycling power.
387
00:22:36,863 --> 00:22:41,393
If your heart rate is lower at a given
pace, they assume higher aerobic fitness.
388
00:22:41,393 --> 00:22:42,773
So they're taking your pace.
389
00:22:43,073 --> 00:22:47,183
If you've got a relatively quick
pace and your heart rate is a little
390
00:22:47,183 --> 00:22:51,773
bit lower of your max heart rate,
they assume your VO O2 max based on.
391
00:22:52,273 --> 00:22:57,053
That data that they're collecting and then
can use that to help estimate total active
392
00:22:57,053 --> 00:22:59,183
caloric expenditure or energy expenditure.
393
00:22:59,683 --> 00:23:03,853
So because of this integrated GPS data,
it can actually estimate your VO O2
394
00:23:03,853 --> 00:23:05,713
that you're working at during a workout.
395
00:23:06,213 --> 00:23:09,963
So once Garmin actually has that VO
O2 max estimate, based on your heart
396
00:23:09,963 --> 00:23:13,683
rate at a certain pace, it can then
calculate how much oxygen you are
397
00:23:13,683 --> 00:23:15,363
consuming at a given heart rate.
398
00:23:15,833 --> 00:23:18,533
so this relationship is
pretty linear, above 50%.
399
00:23:19,033 --> 00:23:22,663
They also reference PACE data to
help layer that into the equation to
400
00:23:22,663 --> 00:23:26,023
make it a little bit more, or try and
make it a little bit more accurate.
401
00:23:26,523 --> 00:23:29,853
In general, one liter of oxygen
consumed is estimated to take
402
00:23:29,853 --> 00:23:31,893
about five kilo calories of energy.
403
00:23:31,893 --> 00:23:34,973
It's a little different depending
on the fuel source, so whether fats
404
00:23:34,973 --> 00:23:38,913
or carbs, but they estimate it to be
about Five kilo calories of energy.
405
00:23:39,413 --> 00:23:43,673
So once VO O2 max is established as
a ceiling, the current VO O2 that
406
00:23:43,673 --> 00:23:46,793
you're working at is estimated by
seeing where your real time heart
407
00:23:46,793 --> 00:23:50,633
rate falls relative to your heart
rate reserve, which is again, is that
408
00:23:50,633 --> 00:23:52,643
heart rate max minus heart rate rest.
409
00:23:53,143 --> 00:23:56,673
So the VO two max is your ceiling
heart rate during the workout
410
00:23:56,673 --> 00:23:59,553
tells you at what fraction of
that ceiling you are working at.
411
00:23:59,853 --> 00:24:03,903
and since we have that formula of one
liter of oxygen consumed is estimated to
412
00:24:03,903 --> 00:24:08,613
be about five kilo calories of energy,
we can convert some of that VO O2 data,
413
00:24:08,613 --> 00:24:13,353
some of that heart rate data to then
get total active energy expenditure.
414
00:24:13,853 --> 00:24:15,323
So for cycling, it's a
little bit different.
415
00:24:15,323 --> 00:24:19,283
It switches the algorithm to estimate
direct power output rather than
416
00:24:19,283 --> 00:24:22,403
heart rate, so it's more accurate,
especially if it's actually
417
00:24:22,403 --> 00:24:24,573
connected to a direct power meter.
418
00:24:25,073 --> 00:24:27,413
So there's a wide range
in accuracy for Garmin.
419
00:24:27,413 --> 00:24:32,363
One study says they found accuracy within
7% of lab measurements for calories,
420
00:24:32,363 --> 00:24:34,883
specifically with some as low as 6.7%
421
00:24:34,883 --> 00:24:37,013
during medium to high effort activities.
422
00:24:37,283 --> 00:24:39,953
So that's the bottom part of the range.
423
00:24:40,163 --> 00:24:43,223
Another study reported up to 49.3%
424
00:24:43,223 --> 00:24:47,273
error with the use of what they call
this first beat technology, which
425
00:24:47,273 --> 00:24:48,593
is a company that Garmin acquired
426
00:24:48,843 --> 00:24:51,273
to put this energy expenditure
data into their products.
427
00:24:51,773 --> 00:24:56,693
Overall, Garmin seems to underestimate
at lower and higher intensities
428
00:24:56,693 --> 00:24:57,863
for your caloric expenditure.
429
00:24:58,163 --> 00:25:02,303
At lower intensities of effort, say
yoga or an upper body day, your heart
430
00:25:02,303 --> 00:25:06,293
rate, which is a key measurement in
the Garmin active energy expenditure
431
00:25:06,293 --> 00:25:10,793
formula, won't elevate despite
the high amount of muscular effort
432
00:25:10,793 --> 00:25:13,733
that your body's putting in, which
would then underestimate calories
433
00:25:13,793 --> 00:25:15,473
and then at the higher intensities.
434
00:25:15,473 --> 00:25:18,563
Like during high intensity interval
training or sprints, we are reaching
435
00:25:18,563 --> 00:25:21,683
about 80 to a hundred percent of our max
heart rate, depending on the workout.
436
00:25:22,183 --> 00:25:25,183
But the energy system within our
bodies that we utilize during all out
437
00:25:25,183 --> 00:25:29,533
sprints is our anaerobic system or a
system that doesn't utilize oxygen.
438
00:25:29,783 --> 00:25:34,343
Heart rate lags about 15 to 30 seconds
behind a sudden high intensity effort
439
00:25:34,523 --> 00:25:38,363
because the cardiovascular system
takes time to ramp up and during
440
00:25:38,363 --> 00:25:41,333
that window, the muscles are already
working anaerobically, so without
441
00:25:41,333 --> 00:25:43,583
oxygen at near maximal output.
442
00:25:44,033 --> 00:25:46,913
So since the algorithm utilizes
oxygen consumption as a
443
00:25:46,913 --> 00:25:48,803
factor for deciding caloric.
444
00:25:48,803 --> 00:25:50,753
Expenditure and takes
into account heart rate.
445
00:25:50,753 --> 00:25:53,923
It would underestimate caloric
expenditure at these higher intensities
446
00:25:53,983 --> 00:25:56,713
due to the use of our anaerobic
systems and that heart rate lag.
447
00:25:57,213 --> 00:25:57,633
Overall.
448
00:25:57,633 --> 00:26:01,443
Garmin is more accurate for people using
GPS because it gives the pace data that
449
00:26:01,443 --> 00:26:03,693
helps in the VO two estimate equation.
450
00:26:04,193 --> 00:26:07,103
For the Fitbit, some of the key
features are it uses accelerometer
451
00:26:07,103 --> 00:26:09,833
and heart rate sensing if the
device has a heart rate sensor.
452
00:26:10,073 --> 00:26:13,603
Older models typically rely a little
bit more on accelerometer data over
453
00:26:13,603 --> 00:26:15,313
the heart rate and newer models.
454
00:26:15,313 --> 00:26:18,283
With heart rate sensors now utilize
that heart rate data a little bit more.
455
00:26:18,638 --> 00:26:22,148
So some of the studies that looked at
a wide variety of fitness trackers,
456
00:26:22,148 --> 00:26:26,648
what do they say when it comes to
caloric expenditure and how accurate
457
00:26:26,648 --> 00:26:31,208
these fitness trackers are at measuring
total daily energy expenditure with
458
00:26:31,208 --> 00:26:34,658
that BMR and those active energy
expenditure part of the equation.
459
00:26:35,158 --> 00:26:38,548
So overall devices over or
underestimate energy expenditure
460
00:26:38,548 --> 00:26:40,438
by greater than 30% on average.
461
00:26:40,938 --> 00:26:44,538
And the error exceeds the 10%
acceptable threshold for all
462
00:26:44,538 --> 00:26:46,278
devices in real world conditions.
463
00:26:46,698 --> 00:26:51,648
So there was a Stanford Benchmark
study in 2017, and the two devices,
464
00:26:51,678 --> 00:26:56,238
they looked at, I think six or seven
devices, but not all of them were.
465
00:26:56,738 --> 00:26:59,378
Either around or talked about
like the R ring wasn't included,
466
00:26:59,378 --> 00:27:00,308
the Garmin wasn't included.
467
00:27:00,548 --> 00:27:03,698
They did include the Apple Watch and
the Fitbit surge, which were part of our
468
00:27:03,698 --> 00:27:07,838
investigative journalism, but the most
accurate device was off by an average
469
00:27:07,838 --> 00:27:13,328
of 27% when comparing how it estimated
total daily energy expenditure, and
470
00:27:13,328 --> 00:27:14,243
then also what was found in the lab.
471
00:27:14,743 --> 00:27:19,153
The least accurate device was off
by 93% factors including skin color,
472
00:27:19,153 --> 00:27:21,253
and BMI affected those measurements.
473
00:27:21,753 --> 00:27:25,673
There's a 2025 head-to-head
comparison done that looked at
474
00:27:25,673 --> 00:27:28,823
some of the more modern fitness
trackers compared to lap testing.
475
00:27:29,353 --> 00:27:31,783
So they looked at some of the
results as a percentage of
476
00:27:31,783 --> 00:27:33,883
actual lab measured calorie burn.
477
00:27:34,183 --> 00:27:40,783
So whoop was 66% of the actual calorie
burn, so it underestimated by about 34%.
478
00:27:41,293 --> 00:27:45,193
Fitbit charge was 72%, so it
underestimated it by about 28%.
479
00:27:45,493 --> 00:27:51,763
The Samsung Galaxy Watch was about
78%, or Ring four was about 86%.
480
00:27:51,763 --> 00:27:55,763
And Apple Watch Series 10 92%
Which is the closest to lab result,
481
00:27:55,823 --> 00:28:00,893
underestimating it by only about 8%,
and then the Garmin vivo active six
482
00:28:00,893 --> 00:28:05,583
was 112%, so it actually overestimated
caloric expenditure by about 12%.
483
00:28:06,083 --> 00:28:10,133
So then there was a systematic review
done that looked at 158 publications
484
00:28:10,133 --> 00:28:12,443
covering nine brands of fitness wearables.
485
00:28:12,503 --> 00:28:16,223
And among the studies, no brand
fell within the acceptable accuracy
486
00:28:16,253 --> 00:28:17,813
limits for energy expenditure.
487
00:28:18,433 --> 00:28:21,733
There was an overall tendency to
underestimate the energy expenditure,
488
00:28:22,003 --> 00:28:26,333
and only about 18% of the comparisons
fell within plus or minus that 10%
489
00:28:26,333 --> 00:28:30,223
error, 53% fell below that 10% error.
490
00:28:30,223 --> 00:28:34,663
So the bottom line, the metric
resting heart rate has excellent
491
00:28:34,663 --> 00:28:37,933
accuracy for these fitness
wearables, about two to 5% error.
492
00:28:38,293 --> 00:28:41,023
Step count is pretty
good, eight to 10% error.
493
00:28:41,263 --> 00:28:43,213
Heart rate variability is good for.
494
00:28:43,753 --> 00:28:45,183
But it . Varies by device.
495
00:28:45,423 --> 00:28:46,953
Calorie expenditure is poor.
496
00:28:47,223 --> 00:28:51,523
27 to 93% error range across various
studies and across various devices.
497
00:28:52,023 --> 00:28:55,263
Sleep duration had moderate
accuracy, often greater than 10%.
498
00:28:55,323 --> 00:29:01,593
Overestimation and sleep staging was
pretty weak, about 79% accuracy at best.
499
00:29:02,093 --> 00:29:03,323
So what does all this mean?
500
00:29:03,323 --> 00:29:06,383
What I found very interesting
was the algorithm switching
501
00:29:06,383 --> 00:29:07,553
with different activities.
502
00:29:07,553 --> 00:29:11,303
I should have probably assumed that
based on, especially the R ring, where
503
00:29:11,483 --> 00:29:14,333
if I did a strength training day and
it was an upper body strength training
504
00:29:14,333 --> 00:29:17,333
day and I put high intensity compared
to low intensity or moderate intensity,
505
00:29:17,573 --> 00:29:22,003
it definitely changed that caloric
expenditure variable that was going
506
00:29:22,003 --> 00:29:23,833
into that equation a little bit more.
507
00:29:23,833 --> 00:29:27,133
So I thought that was interesting
in how they pull from different
508
00:29:27,133 --> 00:29:28,783
data from these different sensors.
509
00:29:29,283 --> 00:29:32,103
Weight, different data, a little
heavier depending on the activity,
510
00:29:32,103 --> 00:29:33,333
which I think is kind of cool.
511
00:29:33,833 --> 00:29:35,663
But how would I recommend to use calories?
512
00:29:35,663 --> 00:29:38,723
Like why are we even tracking
them on these fitness trackers if
513
00:29:38,723 --> 00:29:40,193
the accuracy is relatively low?
514
00:29:40,193 --> 00:29:43,523
Across a lot of studies, I think we're
probably getting closer and closer,
515
00:29:43,523 --> 00:29:44,933
and I think that's probably the goal.
516
00:29:45,203 --> 00:29:47,723
But if your goal is something like.
517
00:29:48,223 --> 00:29:54,073
Understanding intensity of your workout
or looking into different trends
518
00:29:54,223 --> 00:29:57,433
of your workouts mixed with your
activity level throughout the day.
519
00:29:57,433 --> 00:30:01,723
Even just walking, I think calories can
give you a good trend, but I wouldn't
520
00:30:01,723 --> 00:30:08,583
necessarily look at it and use it as a
way to base your dietary modifications
521
00:30:08,583 --> 00:30:12,873
on if you're trying to go more into a
caloric deficit or a caloric surplus.
522
00:30:12,873 --> 00:30:16,023
Again, there's high amounts of error
in these calculations, in these
523
00:30:16,023 --> 00:30:18,813
estimations, in these formulas that
these fitness wearables are using.
524
00:30:18,813 --> 00:30:22,713
Based on the research, there's error
at every step, which only compounds the
525
00:30:22,743 --> 00:30:24,423
amount of error in that final answer.
526
00:30:24,428 --> 00:30:24,438
So.
527
00:30:24,938 --> 00:30:28,988
I wouldn't necessarily use it as the
subjective data to base any changes
528
00:30:28,988 --> 00:30:34,088
in my nutritional patterns or exercise
patterns off of, but I think if we look
529
00:30:34,088 --> 00:30:38,198
at trends, it might be a nice way to
have just another variable to track.
530
00:30:38,698 --> 00:30:43,648
Overall, I feel like calories is not
something I look at or rely heavily on.
531
00:30:43,978 --> 00:30:45,178
For data.
532
00:30:45,178 --> 00:30:48,718
I like to use heart rate
or VO two or my heart rate
533
00:30:48,718 --> 00:30:50,038
variability, like how I recover.
534
00:30:50,538 --> 00:30:55,688
My VO two max, I like to use some of those
variables to gauge certain performance
535
00:30:55,688 --> 00:30:57,368
improvements and training improvements.
536
00:30:57,368 --> 00:30:59,648
So I don't necessarily
like to use calories.
537
00:30:59,648 --> 00:31:02,708
That's not saying that you shouldn't, but
this just gives a little bit of a rundown,
538
00:31:02,708 --> 00:31:06,628
a little bit , of the science behind how
different fitness trackers are calculating
539
00:31:06,658 --> 00:31:08,638
calories and the accuracy amongst them.
540
00:31:09,138 --> 00:31:10,788
So I hope you enjoyed this episode.
541
00:31:10,788 --> 00:31:13,038
This was a fun little
investigative journalism episode.
542
00:31:13,038 --> 00:31:16,158
Again, I hope you learned a little
bit about caloric expenditure, how
543
00:31:16,158 --> 00:31:21,408
calories are counted, how the different
formulas involved in estimating caloric
544
00:31:21,408 --> 00:31:25,668
expenditure and your basal metabolic
rate and mets metabolic equivalents.
545
00:31:25,668 --> 00:31:27,588
That might be something
new for, for you guys too.
546
00:31:27,588 --> 00:31:30,873
And a reminder on VO two, like
we got to incorporate a lot of.
547
00:31:31,268 --> 00:31:34,208
Fun exercise physiology stuff
in today's episode, but I
548
00:31:34,208 --> 00:31:34,988
hope you guys learned a lot.
549
00:31:34,988 --> 00:31:37,388
I hope you're able to bring this up in
conversation with some of your friends
550
00:31:37,388 --> 00:31:40,908
who are like, oh, my move calories are at
this, and you'll be like, you know, hang
551
00:31:40,908 --> 00:31:45,558
on, that's cool, but maybe look at this
variable instead, given the inaccuracy
552
00:31:45,558 --> 00:31:48,558
in some of these fitness trackers
and their ability to count calories.
553
00:31:48,558 --> 00:31:53,278
So maybe a fun conversation topic to bring
up With your other health and wellness
554
00:31:53,278 --> 00:31:57,208
e friends, but I enjoyed this look into
fitness trackers and caloric expenditure.
555
00:31:57,208 --> 00:31:59,108
I hope you guys learned a lot
and I'll see you guys again.
556
00:31:59,108 --> 00:32:00,938
On the next episode of
Wellness is the pod.
