Rings, watches, straps, and scales all promise insight into your health. But how accurate are they, and does it even matter? Dr. Brad Dieter is joined by Alex Leaf, Head of Scientific Research and Communication at FITTR USA, to cut through the noise on consumer wearable data.
Rings, watches, straps, and scales all promise insight into your health. But how accurate are they, and does it even matter? Dr. Brad Dieter is joined by Alex Leaf, Head of Scientific Research and Communication at FITTR USA, to cut through the noise on consumer wearable data.
Their core message: the value of a wearable is awareness and trend tracking, not perfect accuracy. A device that is consistently off can still show you whether you're moving in the right direction. Brad and Alex walk through body composition tools (BIA scales and DEXA), step counts and the drop in daily movement that comes with dieting, what sleep trackers measure well versus poorly, the rise of orthosomnia, and why your personal baseline matters more than anyone else's HRV or resting heart rate. They also cover where wearable data genuinely shines, including early illness detection and cycle awareness.
If you take one thing away: the numbers are never the goal. Use them to personalize your approach, and keep your eyes on the outcome you actually care about.
Key takeaways
Timestamps
Brad Dieter (00:15)
Welcome back to another episode of One Step Ahead. Today we have a special guest who's I te I guess technically not a guest, but we're gonna call him a guest since he's a guest on the episode. We have Alex Leaf. I don't know if I should call you the famous Alex Leaf or the infamous Alex Leaf. I'll let you decide if you're the the hero or the villain in in the story today, but Alex is our head of
Scientific research and communication here at Fitter Inc. here in the US. and I brought him on today because I wanted to talk about one of the larger conversations we have, both internally kind of in the work that we do, but then just broader as this industry unfolds. And we're gonna be talking about kind of wearable technology, the data that it produces, and how we should think about it more on the consumer side. so Alex, welcome to the show. It's great to have you here.
Alex Leaf (01:07)
Yeah, thanks, Brad.
Brad Dieter (01:08)
So this is a big topic and one of the things that's already come up within kind of our own community is people comparing wearable technology kind of across like brands, manufacturers, but also across like classes and technology types and generations. And so I wanted to kind of start just if we start at a very high level, when we think about kind of wearable technologies, regardless of form factor, right? Rings.
watches, chest straps, wrist straps. if we start at a very high level, how should we as consumers think about them in general? And then we'll dive into the specific pieces of data they produce.
Alex Leaf (01:47)
Well, I I would say that their biggest benefit for for most people is just awareness and then also allowing you to track patterns. So I you know, even like even outside of accuracy, if if something is just somewhat consistently off on their numbers
it should still be able to show you the direction you're moving in if you are moving in a direction, right? Because if it's consistently off, it's probably gonna be consistently off every single time for the most part, unless something like wild changes. And so, like with you have those home scales that do like bioelectrical impedance as an example.
Those aren't accurate. I wouldn't believe the body fat percent measurement it puts off at all. But, if you're on a weight loss journey and you see that number is going down week after week, then regardless of what your actual body fat is, you know that you're probably moving in the right direction. And I think that applies to everything, you know. certain things are obviously
more accurate than than other ones are. But it's like if you're tracking your steps, you know, and you just you're trying to move around more, then day over day you should be able to see
Where your steps are relative to your past numbers. And you could say that for HRV, you could say that for heart rate or whatever other metrics interest you. it's awareness of what you're currently doing or where you're at, and then how is it changing over time? And is that congruent with your goals?
Brad Dieter (03:23)
So a couple of things there I want to dive into. Let's first start with I think the scales is a good place to start because it's probably one of the more ubiquitous tools used. And I think there's an importance there there's a couple of important things to pull out there. One, when we think about the accuracy of those scales, right? You mentioned they're not accurate. We know that that's true, very much like in a research setting, right? Like we know they they just don't have great
absolute accuracy. But I think it's also important for people to realize that basically every method of body composition, whether it's the gold standard or it's a scale, they're all inaccurate, right? Because they're all estimates and they all have some level of error, right? Any sometimes as low as 3%, sometimes as high as 10%, right? And so from a research perspective, yes. But from like a practical utility standpoint, when we think about
How do we get people information about their body composition relatively reliable in terms of you get roughly the same number repeatedly if you use it the same way over time? How do these things fit in the context of that conversation? Right? 'Cause I think a lot of times people will say, Hey, don't use
Alex Leaf (04:30)
Mm.
Brad Dieter (04:31)
a scale because it's not as accurate as doubly labeled water and it's like, Well, okay, who's gonna go do doubly labeled water every week?
Alex Leaf (04:38)
Yeah, at this point in time, I'm pretty much of the opinion that I think using scales, whether they have BIA or not, just using a scale to monitor changes in body weight, is good. And if you want to monitor changes in body composition, then I think you should just go get a DEXA scan. because they've been commoditized to such an extent now that
there's there's places here in Arizona called DEXAFit where it's
Brad Dieter (05:05)
Yeah.
Alex Leaf (05:05)
just it's a hundred dollars and and then and you schedule an appointment, you go in, it takes like fifteen, twenty minutes, and and you get a whole printout that tells you, you know, all the DEXA stuff, your body composition, bone mineral density. It even breaks it down by body segment. And you know, if you were to just do that every
two to three months, even every six months, you know, I think that would be way more informative than trying to play guessing games with like a little scale that you step on. I think those scales can be helpful to to monitor trends. But if you're actually looking for like data that is much, much more likely to be closer to the truth, then I would just get a DEXA done. Because
Brad Dieter (05:51)
Yeah.
Alex Leaf (05:51)
those aren't perfect either, right? The perfect is
Dying and then having someone disassemble your body and and weighing out how much fat and muscle and everything
Brad Dieter (06:01)
Yeah.
Alex Leaf (06:02)
you have, right? That's the gold standard. obviously that's not practical because we don't know how to bring people back to life yet.
Brad Dieter (06:08)
Say that that's a one time measure.
Alex Leaf (06:10)
yeah, exactly. but you know, in in
You might be better able than me to speak about this, but in research settings too, researchers have developed kind of the next best thing, which is like called the four compartment model. And it's essentially where they use a bunch of different body composition devices that each excel kind of in in like measuring a different aspect of body comp, and then they they blend them all together. So it includes DEXA, it includes air.
Displacement. I'm I think it includes underwater weighing as well. but yeah, when DEXA's compared to you know that kind of realistic gold standard, its absolute margin of error, I believe, is two to three percent, which means that if your body fat is actually ten percent, DEXA would put you probably between eight and twelve.
Brad Dieter (07:07)
Yeah. Yeah, and I think, from a practical standpoint, you know, when we talk about coaching people and we're mentioning trends, I think just to kind of close up the discussion on scale piece, the BIA scales, you know, definitely have the kind of some of the accuracy issues and we but we're also not diagnosing anything based on these, right? So it's really the the trend and is it changing over time. The other thing that I have found really useful, 'cause we've used I've used like in body BIAs with athletes quite a bit.
Is they can give you a little bit of a proxy measure for hydration based on like how much things are changing, right? So if you like let's say you, you know, weigh and measure yourself on your BIA scale the same every day, and you see big changes in body comp, like let's say one day it says twenty percent and the next day it says fifteen percent, well sometimes that's reflective of like water or glycogen status, right? 'Cause you can see
big changes in body weight day to day, but that'll also tell you a little bit about hydration. So when we look at coaching and we see those big bounces and trends, that's usually how we interpret that data, right? It's not like, hey, Alex lost five pounds of body fat overnight. It's like, hey, we saw a big change in the water content of your body. And so
Alex Leaf (08:15)
Yeah.
Brad Dieter (08:15)
we can interpret, hey, let me guess, you probably didn't eat after practice last night or this morning, right? Yes. Okay. Your glycogen depleted, your weight's down.
This is what we're seeing, right? So you can see things like that.
Alex Leaf (08:26)
Yeah. Yeah. I I like to recommend that for people who do use the scale to monitor progress.
I think it's best, to use it at the same time every morning. Usually like right when you wake up after you use the bathroom and before you eat or drink anything, use the scale. Do it that same way every morning. But when you're comparing data points, take the average of the week. And then so like take, you know, Sunday through Saturday and then average that.
And then for the next week, you take Sunday through Saturday and then you compare those two data points rather than trying to compare the day to day because those averages should be moving in the direction that you want. And they get they get rid of that kind of day-to-day variation from things like changes in body water.
Brad Dieter (09:18)
Yeah.
So moving into some of the other wearables, especially things what that clients use for let's just start with step count, right? 'Cause that was w that was one of the things you mentioned. So before we start to talk about some of the the variances between devices and things like that, I mean one of the things that I've always talked about with clients why having, you know, some sort of wearable for a step counter, especially in today's day and age, is
One of the most repeatable findings we see in the weight loss literature is when people go in calorie deficits, they just stop moving. And it's usually a pretty large effect, right? It's usually in that range of hundreds of calories a day, right? It can be anywhere from a couple hundred to a thousand calories a day of just reductions in non-exercise activity, which we use step count as a proxy measure for that, for just general physical activity. And so regardless of
How accurate your wearable is per se. that's one of the things that's really effective, right? If you can have a client who has been monitoring their steps for weeks and then you put them on a calorie deficit, if they are aware of that number, they're less likely to see big drops because they're actually like, Hey, and now I have a target of moving, so I'm gonna do things to offset this kind of natural drop. have you seen that in your experience too?
Alex Leaf (10:32)
yeah, absolutely. when you when you don't eat, you get tired and your body just wants you to chill out. I think that's like a very fundamental kind of evolutionarily preserved thing.
interestingly though, just because you're making me think of it too, I I also notice it on the opposite end of the spectrum. Like if I eat a lot of food, then I want to not move and like go sleep as well. and
Brad Dieter (10:57)
Food coma.
Alex Leaf (10:57)
yeah, and and interestingly too, I tend to have that effect if I'm
if it's if it's hot out, even if it's sunny. it's like you go sit down under under some shade in a tree and you're like, I could just fall asleep right now. And I don't know why that is. I haven't looked into that. But yes, you know, getting getting back on on topic here, absolutely. people reducing
how active they are, usually like subconsciously. So it's not like they're purposefully trying to be less active. It's more like their habits kind of adjust to
promote less activity. with dieting, I think that's very common. Plus all the stuff that they don't really have conscious control over, like fidgeting. You know, we know that that stuff goes down. And with prolonged dieting, just reductions in that type of movement energy expenditure can be a few hundred calories a day.
So yeah, very, very common and tracking and monitoring is one way to make sure that those things don't sneak up on you.
Brad Dieter (12:02)
I have always thought that fidgeting is such an undervalued portion of caloric expenditure.
Alex Leaf (12:10)
Yep. Hundred percent.
Brad Dieter (12:12)
Yeah, and I I don't know how much work has been done on what that what the variance is amongst people in a given day, but I my parents when I was a kid hated it 'cause I always fidget. Like it's it's
Alex Leaf (12:22)
Yeah, yeah.
Brad Dieter (12:23)
constant, but I'm convinced that it's a large part of that caloric expenditure.
Alex Leaf (12:27)
yeah, I've yeah.
I I fidget a lot too. interestingly, I I tend to fidget more when I'm dieting. but I think that's just a side effect of like being kind of more adrenalized, like w having
Brad Dieter (12:39)
Antsy. Yeah.
Alex Leaf (12:41)
more stress hormones.
Mainly because, i if you're dieting and you're making an active effort to to not just sit on your butt. So it's like, yeah, I'm dieting, but I'm still going to the gym, still working with intensity, you know, I'm still getting steps and stuff, your body needs you to have energy to do that stuff. And so by the end of the day, it's like if I put my head down on a pillow, I could just pass out.
But before I do that, I'll be sitting there and it's like you wouldn't think that I would be ready to just crash. Like my body's
Brad Dieter (13:13)
Yeah.
Alex Leaf (13:14)
just, vibrating a little bit. also too, there was that one you probably know about this this study. I don't remember the authors or the year. I want to say it was done in the eighties, but they took identical twins and they overfed them by a thousand calories a day.
Brad Dieter (13:29)
Yeah.
Alex Leaf (13:30)
Right. And the range of weight gain. Like there were twins who ate over a thousand calories a day, right? It was the same amount, but they didn't gain anything. And all of their their physical activity was controlled for, which means that, you know, some of these twins literally just burned an extra thousand calories from increased fidgeting and other types of movements. Like
Brad Dieter (13:54)
Yeah.
Alex Leaf (13:54)
that's that's how big of an impact it has.
Brad Dieter (13:57)
Yeah. So when we th when we look at wearables, let's talk about kind of the step count and we'll kind of move into like heart rate and sleep data here in a minute. But there's a there's a bazillion of them, right? I mean there's Apple watches, there's Garmin watches, there's Whoop straps, there's BioStraps, there's the new is it Hume strap,
Alex Leaf (14:18)
Something like that.
Brad Dieter (14:20)
there's
rings like we have one as a company, there's the aura ring, there's the ultra human ring. we just look at step counts, right? How do people how should people think about that data in terms of accuracy and one, does the absolute accuracy matter? And then two, is there any real value or utility in comparing across devices, form factors, etcetera?
Alex Leaf (14:47)
I think for so I think step counts are probably one of the most accurate, if not the most accurate, kind of outputs that you're gonna get on a wearable device. because most, if not all of them have little accelerometers in them, which is like what they use in research studies to track steps. And so I think that they are comparable across devices.
like generally speaking, where where you run into trouble with them is that the different devices seem to have trouble picking up certain movements and like acknowledging that it's movement, or they overacknowledge. so you know, for example,
I've noticed, both with a watch and with an aura ring, if I'm walking but my hands are moving, like if I'm using a treadmill desk while I'm working, I don't get any steps. And and I actually, you know
I will take the devices off and just put them in my pocket because then they're able from that walking motion, they become more of a pedometer. And I don't get heart rate or anything, but I'm not interested in that. I'm interested in the steps. And so that's kind of how I overcome that. same thing if like, you know, when Lux was a baby and I was pushing the stroller, it would be the same thing. It wouldn't track my steps.
And it got to the point where i I would literally let my left arm just swing and I would push with one arm just to make sure I was getting the steps, which was really annoying to me because then it's like I couldn't check my phone, you know, if if like Brianna's trying to reach me or something because that's in my pocket. but yeah. I that stuff should be noticeable though. You know, I think if like
If if you're tracking your steps, most people are are going to be checking their steps kind of throughout the day. And they're going to be like, hey, I just did this, you know, my steps, like, why are they so low? They didn't move. And that's kind of the red flag where it's like, well, whatever way you were like moving your body, it was just it wasn't registering that. And so when it does register steps, you know, they're very accurate, but you have to make sure they're registering the steps.
Brad Dieter (17:00)
Yeah, and so there's I mean, you kind of mentioned there's ways around that, right? There's like, hey, when you're doing certain things, take off your wearable, put it your pocket. and there's definitely like a type of person and client that that's what they do, right? 'Cause they want to make sure their data is as accurate as possible. The other side is what we mentioned earlier is trends and changes, right? So let's say you are somebody who works from home.
you work at a computer and you use a treadmill desk all day, if you're my client, I'm probably gonna just look at your data and say, okay, we're just we just know that this is what you do, right? So I'm gonna be looking just wear your rings like you normally wear your rings. We realize we're probably missing X amount of steps per day from the actual data dashboard I'm looking at. And I'm just concerned about your changes in behavior over time. Right. So let's say we're putting you in a pretty large calorie deficit to lose weight.
Is that number that's being recorded, is that going down meaningfully and is it staying down over time, right? it it starts to become from like a utility perspective of what is your profile of movement looking like on a day to day basis for weeks, months at a time, and how is it changing based on certain things? Right? It's it's a little bit less of, Hey, when you go to the gym I need to make I need you to make sure while you're on the
the stairmaster instead of holding on so you don't die and you gotta stick it your pocket. we just we can account for those things if that's what your regular routine is.
Alex Leaf (18:22)
Yeah.
Brad Dieter (18:22)
so what about the like the other data that is more of like imputed data, right? So we talk about things like you know, sleep scores, things like that. I mean we'll we'll come back to heart rate and cause
Alex Leaf (18:35)
Okay.
Brad Dieter (18:36)
those are those aren't quite imputed. Those are more, you know, sensor driven raw data. But let's talk about some of the imputed metrics. So like, you know, recovery, stress strain, sleep.
How
do we think about these knowing that those are all algorithm based and every company's different?
Alex Leaf (18:51)
Yeah, that's a good question. Yeah, what what kind of sucks is that companies can put out their devices without necessarily having
data behind them showing that they are reliable devices compared to things like plasmography or however it is you pronounce that. with with sleep what I find really interesting is you know aura has been has been tested, Apple Watches have been tested.
and a few others I think Garmin's been tested as well and and they've been compared in systematic reviews to
the gold standard sleep study stuff. And the part that is most surprising to me is that when it comes to your total sleep time, they're actually really, really, really accurate. They're they're usually, you know, within 10 to 20 minutes for total sleep time. which already to me I find to be really helpful. Because
Brad Dieter (19:52)
Yeah.
Alex Leaf (19:53)
when you look at like
data about sleep and what are the benefits associated with sleep or manipulating people's sleep in interventions, they're not manipulating sleep stages. They're just saying, instead of sleeping six hours, sleep eight and let's see what happens, right? And so if you get a fairly accurate reading of your total sleep time, I think that's incredibly useful. Especially for
watching if your sleep is improving over time. Where where these devices really lose accuracy is sleep stages, to the extent that these devices will under or overestimate deep and REM sleep by 30 to 45 minutes. And and that might not seem like a lot, but when you consider that a typical night of sleep, you know
You're you might be getting one to two hours of those sleep stages, you know, if you have really good night of sleep. Like that's thirty percent of, you know, of air right there.
So and and you know, then there's the issue of like they come up with a sleep score, right? So they have these algorithms and they give you a sleep score and you don't know what that sleep score is based on, you don't know what their algorithm is, and you just chase this arbitrary number to be higher and higher to the extent that that researchers have actually come up with a term
Called orthopsomnia, which
Brad Dieter (21:25)
Okay.
Alex Leaf (21:26)
is like orthorexia, right? But it's somnia for sleep, to
Brad Dieter (21:30)
Yeah.
Alex Leaf (21:30)
describe people who are so worried about the quality of the sleep that they're getting that they give themselves insomnia and really crappy sleep as a result because of the anxiety. We see this with biohackers a lot. and and these other people who are like, I have to, you know, look, my sleep score is 100. I'm amazing.
And it's like it's just so ridiculous to me because there's there's no evidence out there showing that these higher sleep scores mean anything or associate with like any better outcome. it it's all it's just like a game. And and
Brad Dieter (22:04)
I don't think I've had a a
sleep score of a hundred in twenty years. Like there's just no way. Yeah.
Alex Leaf (22:09)
Never, never for me.
I mean my like my my sleep scores through my devices are usually like if I if I'm sleeping good then they're usually I would say between eighty and eighty-five. even even at times where I'm like in bed for
nine hours and I've gotten three hours of deep sleep and two hours of REM and and it's like 85. It's like, okay, well, I feel amazing. You know, my health is great. I just crushed it in the gym. Like, I don't care what the sleep score score is telling me. on an absolute basis, right? You know, if if if a good night of sleep for me is like between 80-85, that's that's my good night of sleep.
Sleep baseline. And the
Brad Dieter (22:56)
Yeah.
Alex Leaf (22:56)
usefulness for me is to then say, Hey, I've noticed on these nights where I'm staying up late because you know, like I got work I need to get done or whatever. my sleep score is dropping down to 70 or whatever. Okay, well that's yeah, that's probably not good. I don't want to make a habit of that.
Brad Dieter (23:17)
Yeah, so zooming out it's more of people should treat s the sleep data off these things and focus on h what is my total sleep I'm getting and is that getting better or worse?
Alex Leaf (23:28)
Yep.
Brad Dieter (23:28)
Yeah.
Alex Leaf (23:29)
This is this is gonna be a recurring theme in this for the rest of this talk too. Is that pe
Brad Dieter (23:33)
Yeah. Yeah. Well should we just record
ourselves and then just insert topic and just put it on loop?
Alex Leaf (23:38)
Yeah, right.
It is is I you know, I'll say it as kind of abstract as we can, but every person has their own baseline.
Brad Dieter (23:47)
Yeah.
Alex Leaf (23:48)
And even if you're looking at population level data of how certain things associate with certain outcomes,
You you can't compare person to person or measurement method to measurement method because those methods might be might be different, right? So they aren't directly comparable, but also just your baseline might be different than someone else's. And what you want to watch for is changes from your own personal baseline, not working towards this arbitrary goal. in the same way that it's like
imagine this, like imagine resting heart rate, right? An athlete has a resting heart rate of like 40. And you're like, wow, that's amazing. That indicates great cardiorespiratory fitness and all of that stuff. It's like, okay, their baseline, because of all of these different variables, is like 40. would it make sense for someone who, you know, their baseline heart rate is 60, and they're like, you know what?
That number is better than mine. I need to get there. And so I'm gonna take beta blockers because that's gonna lower my heart rate. And then once I get to 40, I'm gonna be as healthy as that guy is. Like it makes no sense. Yeah. Well, it we yeah.
Brad Dieter (25:00)
You're gonna feel terrible. Yeah. Yeah.
Alex Leaf (25:04)
It and I'll say it here too. That applies to HRV too. People are like, your HRV is so low, and it's like
No, no, no, this is totally normal for me. you know, and doing all of these different things to try and arbitrarily increase your HRV doesn't necessarily mean the things that you're doing are healthy or going to benefit you.
Brad Dieter (25:25)
Yeah, I think sometimes, we we run into this in biomedical research all the time, right? When you're designing trials, you've got to think, what is the outcome you actually care about? Right? And I think a lot of times when we think about wearable data, I think consumers often get focused on the metrics themselves as the outcome. And the reality is the metrics are individual aspects of the outcome you want. Right? So like let's
Alex Leaf (25:49)
Mm-hmm. Yep.
Brad Dieter (25:50)
say
If my goal is I want to lose weight to reduce my risk of cardiovascular disease, diabetes, and I want to look better and I want to live better longer, like you exercise, you eat well, you figure out how to have a higher level of physical activity during the day, you manage your sleep, you reduce alcohol, you do all these things and then you you look better, you feel better, all of those things. Like that's the outcome you care about.
The data that we get as coaches of like, hey, here's your step count data, here's your heart rate data, here's your sleep data. When we view those as practitioners, those tell us about the lifestyle choices you're making towards that outcome, right? We never engineer an intervention around hey, your HRV is going down. You need to do this specific type of exercise to optimize your HRV. What we're doing with that data is we're saying, okay, your HRV is normally X.
You have had three board meetings this week. You just flew somewhere. You had a bachelor at party so you were out drinking and your sleep's terrible. Well, your HRV is awful. Well, that means this week our programming and what we're gonna do is probably much less intensity. We're probably gonna be like, Hey, let's just get some steps and go to bed at a decent hour this week and we'll figure out the training piece later, right? And instead, you know, people might take that HRV data and be like, well, I know that I need to like
train this very specific way this week to bump my HR V score. It's like that's kinda missing the force for the trees.
Alex Leaf (27:13)
Yeah. Yeah. Or we get people, and I'm I'm hoping that people listening will recognize this reference, but we get people who are like, guys, I had a bad night of sleep and I need to skip the gym because a bad night of sleep increases the risk of injury. And it's just like okay, like sure it does, but
You're basing your whole life around what these numbers say i just is baffling to me. it's so counterintuitive to like
any outcome that people would actually be cared about, right? if if your outcome is is being stronger, living a longer, healthier life free of disease, then skipping the gym because you got a bad night of sleep makes no sense at all. It's like go to the gym, do your workout, you know, don't over exert yourself. Don't try to do a PR or whatever. and then just get back into things the next day.
Brad Dieter (28:07)
So
what data off of these wearables right now would you say just don't have any real utility at this point? Like they're kind of more
fun features that are interesting but are not really of a high enough value yet to be useful.
Alex Leaf (28:25)
Ooh, good question. I'm like looking at my watch right now because they're always coming out with new features. and it I I think I think I would say probably everything that that these devices track has utility for the right person looking at the right thing. So kind of a more like I think a good example of this would be like
Resting heart rate and respiratory rate overnight, right? So those are commonly tracked overnight to give you your sleeping heart rate and your sleeping respiratory rate, because that's a time when your body's completely relaxed. You know, it should be very easy to measure, very stable. and those values usually like don't really mean anything.
until you get into extremes, right? i if your respiratory rate gets above 20, you know, that's considered like a medical thing. resting heart rate above 100 is like, hey, you should probably talk to your doctor about that, right? but generally speaking, they don't have much value. Where the value comes in though, and and there have been studies that have verified this, both during COVID and during other
Just illnesses is that you you know what your baseline is, and when they start to elevate overnight, just I I mean, and we're talking small elevations, it might be a five to ten beat per minute elevation in heart rate, and your respiratory rate only goes up by like three breaths a minute. That is strongly predictive of you becoming sick with an infection.
Because they're like they're like early signs that you're completely at rest and yet your body is saying we need more oxygen, we need and we need to deliver it to the body because it is actively trying to fight off this infection. You might not even have a fever yet. you might not even be coughing yet. You might they they are early predictive changes.
And so for most people, it's like they don't matter at all. They don't really have any utility, but they do if you're trying to see, I'm starting to become sick, maybe I should intervene, you know, maybe I should increase my vitamin C or
whatever to to try and help my body battle this infection. Maybe I should not train as hard. You know, maybe I should give my body a little more time to recover. Maybe I should pull back on the dieting so that my body has more resources available to fight off this infection and then I can get back on track when I've overcome it.
So I think that, from that standpoint, those are very useful. I think HRV can be super useful to monitor kind of your recovery, very generally speaking. a lot of these markers are very easy to see if you just go out and have like a night of drinking. if if you really just want to see what your body looks like when your recovery is just absolute crap, you know,
Brad Dieter (31:24)
yeah.
Alex Leaf (31:25)
go drink.
and and then go to bed and wake up and look at your numbers. because they they will crash. Like HRV will literally it'll look like you're dying. It'll be at like 10, right? Resting heart rate could be elevated 10 to 20 beats per minute.
Brad Dieter (31:42)
Your sleep's
terrible.
Alex Leaf (31:43)
your sleep is horrendous, no REM, no deep sleep. Your sleep might have even been short, right? Cause you went to bed late. and you and and you feel it the next day. so I yeah, these things can be helpful to see, you know, if stuff like that happens. and then obviously in the long run, just over time watching those trends for depending on what your goals are.
Like
Brad Dieter (32:04)
Yeah, one
of the other ones I was thinking is interesting, so skin temperature, right?
Alex Leaf (32:09)
Yeah.
Brad Dieter (32:10)
For the average person, for me, I'm probably not gonna get much information out of skin temperature, but women it can be very helpful, right? Especially, we talk about fertility, ovulation cycles, things like that, it can be interesting.
Alex Leaf (32:23)
Yeah, yeah, it can be. that one, you know, and I say this just because you know, me and Brianna went through years of of fertility stuff trying to to have our second baby. And so it was really fun to watch how all those things change throughout her cycle because
in addition to wearable devices, she also had this thing called an inito. And this the anido, it was like you you give basically a you take some of your morning urine and and you dip the stick in it and then stick it inside of a a device. And this device was capable of measuring key metabolites of
for estrogen, progesterone, luteinizing hormone, and follicle stimulating hormone. And so while it wasn't perfect and it wasn't it wasn't a measure of actual like serum levels that you would get in a blood draw.
it you could very, very easily over the course of the cycle see the changes in these hormone metabolites that that correlated strongly with hormone levels in her actual body. So you could see prep for ovulation. You could actually see the LH peak that triggers ovulation, right?
Brad Dieter (33:42)
Yeah.
Alex Leaf (33:42)
And then it was fun because, you know, when she did become pregnant,
That LH, you see that LH surge, and then it drops back to baseline, and then it starts increasing again because HCG cross-reacts because it has a similar structure as LH. And so then you see it, you know, start to go up, up, up until it like plateaus because you've reached the maximum read value. so
Yeah, that stuff's really fun. Correlating it with body temperature and changes with progesterone, all of that. I think there's there's a lot of opportunity here for women especially to become more aware of their cycles. And I think that has a lot of value, not just from like a reproductive standpoint, but because it like it it just it lets them correlate.
Different aspects of their livelihood with different periods of their cycle where hormone changes are so extreme that they they literally do affect everything from mood to how much water you're holding, how much energy you have, what your strength is. so I yeah, I think I you know, I think there's a lot of utility there.
Brad Dieter (34:53)
So
as we close this episode out, what would you say is there anything we didn't cover or what would you say the main kind of take home is for clients in terms of like thinking about wearables, how specific do they need to be about the exact one they get versus just really understanding what the data is telling them?
Alex Leaf (35:10)
I I don't I don't think we're really at a point to suggest with you know a lot of confidence that one device is superior to another. because they they tend to some are better at sleep tracking.
Right. like for I'll give you an example. Aura tends to be better at sleep tracking than Apple Watch. Apple Watch is better at tracking heart rate and HRV. when you compare them to like gold standard kind of methods, and both of them suck at telling you how many calories you're burning.
Brad Dieter (35:43)
Yeah.
Alex Leaf (35:43)
so you know you
You can always get more than one wearable if you want. that's usually too much though. And I think the most important thing is to just kind of forget about the accuracy component, wear it for a couple weeks, see what your baseline tends to be for you, and then pick what your goals are, start working towards those goals and watch how the numbers change with them. The numbers aren't the goal.
the numbers are never the goal. they they might be a means to an end, right? Like, hey, I want to get higher steps because that takes me to my whatever my goal is. but it's not like I need my heart rate to be s X beats per minute. I need my HRV to be this, you know. It just I need my sleep score to be consistently over 90. Like, just forget about it. Just look at what your baseline is.
And do the things that you know move you closer to your goal, whether that's getting better sleep, losing weight, whatever, being more active, whatever. And then watch how the numbers change with it. And watch those trends because those trends are going to tell you if whatever it is you're doing is congruent with what you're trying to accomplish.
Brad Dieter (36:55)
Yeah, so the reality is all this data helps personalize an intervention. The goal is not for the intervention to be focused on the metrics, right? It's how do we use this data to say, hey, do I need to increase my volume, decrease my volume, increase my intensity, decrease my intensity? Do I need to, you know, change some of my lifestyle habits? And it's that data that helps, you know, like as a coach, understand what's going on in my client's life and what do I need to adjust, right? It's never we're gonna be
creating some sort of intervention for the client to meet a certain metric, other than what is the actual goal you have.
Alex Leaf (37:29)
Yep. Yeah, exactly.
Brad Dieter (37:31)
Awesome. Well, Alex, I appreciate the time today and your insights. you're always a walking dictionary of, you know, information and it's always great to have you on. So appreciate the time and we will have you back on probably pretty soon, I'm sure.
Alex Leaf (37:43)
Yeah, absolutely, man. Good to be here.
Brad Dieter (37:46)
Awesome. Thank you.