All right, welcome back team to the building a life on these podcast. Thanks so much for stopping by. I really appreciate it. If we haven't had a chance to meet yet, my name is Jordan Renicky. I'm a dual board certified physician in family and sports medicine, and the goal of this podcast is to keep you active and healthy for life. Through actionable evidence-informed education. Today, we're talking all about smart watches, smart devices, all those things, these health trackers, right? It's a big thing. So, watches, rings, fitness bands, whatever it is, you know, that's the big thing. We're going to dive into A lot about these. How they work, what's their core technology, something called photoplathysmography or PPG? We'll just say PPG from here on out. We're going to talk about How the sensor works, what metrics they produce, what they can look at. And really, I want you to understand how this technology works: what are the benefits of it, the limitations, what we do with these numbers, what they mean, and from a medical perspective, how do we kind of all put it in, right? And so. Hopefully, today the goal is going to let cautious optimism, right? I'm going to kind of talk to you about how we have real physiologic insight with these potentially. These devices, but also a lot of unvalidated things and kind of there. So, at the end of the day, we'll talk more and more about that. And so, let's dive in here first. So, first, let's talk about How your wearable actually works. So, PPG that technology, right? So, the core technology behind this, this is the core in pretty much all the things: rings or watches, whatever, it detects blood volume changes in peripheral tissues. So, right, it's an optical technique. What happens? There are a sensor here. So, essentially, there are two main components. One, first, is an LED light that projects light into your skin. So, you think about your wrist. It is projecting these green light, usually for wrist, into the skin. And then a photo detector is also on that watch or band or whatever, and it detects and measures the amount of light that bounces back to that sensor, right? So, in the same thing. On the watch, going into the skin and coming back to the watch as a photo detector sees how much is getting bounced back. Very similar. We have lots of other things that work like that. But the LED shines light in, the tissue will either absorb or scatter it, and the photo detectors measures what's left. So, there's classic diffraction patterns and absorption patterns that we expect to see. And so, they use that idea. Typically, in the wrist, they use green light. So, green light, the reason is a lot of times There's certain absorptive properties of hemoglobin in red blood cells, right? So, specifically, a lot of these are based on looking at blood flow. That's what they're looking at. So, the green light. Seems to do that best in the finger ones. You sometimes need red because it's not as important. Either way, a lot of times it's green. But when the heart beats, so during systole, right? So that pumping of blood, blood volume in the artery increases, right? Because it's pumping out from your heart into your body. And this leads to absorbing more of that green light, so less of it's coming back. Between beets or diastole, the blood volume decreases, which leads to reflecting more green light back. And so we think about that's how we're looking at it. So a photo detector captures this rhythmic change in reflected light. Creating this PPG waveform. So it goes, you know, blood there, then not back. So the pattern of more green light versus less green light reflected or coming back, that's what it's detecting. It's detecting this change. And that essentially is. Standing as a heartbeat for us. You know, obviously, you're not measuring electrical rhythm necessarily. We'll talk more about that, but that's what's going on. And as I mentioned before, we also have the green light not only Because of just the way hemoglobin absorbs things in the red blood cells, but also theoretically, it's less susceptible to motion artifacts during daily activities, which is really important. Because we're going to have lots of noise, we'll learn about that more. But yeah, as I mentioned, rings a lot of times will have red, which can penetrate deeper into the tissues. Red, and I'm sorry, the rings typically have a little higher benefit because there's more capillaries and a more stable placement leading to a usually cleaner signal. Rings might actually be superior to risk-borne devices in some certain senses. I'm not everyone's slam dunk there, but for the main ones, that's kind of what we look at. And I do want to mention talking about motion artifacts, right? So, this is a huge and important thing to talk about because it's a source of a lot of errors. So, this is probably the number one enemy of a clean PPG signal is motion artifact, meaning like when you're moving around, right? So, physical movement. Causes a sensor to shift, disrupting the light path and creating noise that can be mistaken for a heartbeat, right? So it kind of creates this noise. It's not this clean pattern, it's not used to it. And you know, these are all have algorithms that are trying to like make noise of this, right? They're trying to say, hey, Quiet it down, like, oh, I found this little piece of noise here. What now? This signal, like, they're trying to interpret that, and sometimes it gets misinterpreted into a heartbeat when it really shouldn't be. Other things that can affect it is skin tone and tattoos. So melanin is a strong absorber of green light, and higher melanin concentrations and darker skin tones can reduce the amount of light reaching the blood vessels, weakening the signal, which could lead it off. On top of that, tattoo ink, particularly dark colors, can completely block the light, making reading very challenging as well. On top of that, fit matters. So if you have a loose-fitting device, it can allow ambient light to leak into the sensor. Corrupting the data, so it's not just the single light it's looking for. And also, if it's too tight, it can lead to like poor circulation, right? And poor blood flow, and that can throw off your results as well. Other things that can affect it, your body temperature, if it's low body temperature, certain cardiovascular conditions, all those things can lead to weaker, unreliable signals. So there's a lot of variables that can be adjusted and that kind of can affect that as well. And so that's generally what we're looking for. And the reason I talk about that is because I'm going to talk about something called heart rate variability here or HRV. And this is really important. So, what we're doing with the optical light, that is essentially trying to calculate this HRV through a proxy. And we'll talk about that in one second. So, HRV, what is it? It is not your heart rate, heart rate variability. It's the variation in time between consecutive heartbeats. So, most people think, like, yeah, I have a regular rate and it's 60 and whatever, whatever, like, that's normal. Yes, it's 60. It's regular, you know. If you look at EGG, it's relatively normal, but it's not precisely normal, like every single time down to the millisecond, right? So there's a little variation in each, and that's what heart rate variability is. And it will be measured in milliseconds, so very, very small, right? But there is a kind of weird, counterintuitive idea, right? So, a healthy, resilient state in your body is marked by high variability. Not like a metronome. So it's not like your heart's just a metronome, same beat every single time. You actually have more variability the more like ready you are, is essentially how we think about it. A high HRV suggests adaptability, readiness, you're not as stressed, you're just in a good state, whereas a low HRV actually suggests stress, fatigue, or your system's potentially understrain, right? So that's what it is. Think about the nervous systems, right? So, we're going to talk real quick about those. So, we have two main nervous systems. First is the autonomic overarching umbrella, autonomic nervous system. Inside that, we have sympathetic and parasympathetic. So, the autonomic nervous system. Is like what makes things go. It's kind of the kind of the balance between the parasympathetic and sympathetic. So it's a tug-of-war between, right? So the sympathetic is, you know, what everyone talks of, like the fight or flight. So the body's gas pedal. Here, when we were trying to go and we need to get revved up, we're in a life-saving situation, or you're about to get out in the field, you're amped up, or you got a big test coming, whatever it is. Like, that's when you feel that your heart's picking up, and you're feeling that adrenaline run, right? That's the sympathetic nervous system. And for here, this decreases HIV. If you are sympathetic nervous system predominant, typically we're gonna have a decrease in your HIV. Then we have the parasympathetic nervous system. So, this is our quote-unquote rest and digest, although there's a lot more than that. And this is the body's break. So we had sympathetic being the gas. This is kind of the break for a not perfect analogy, but it works. And this is usually driven by the vagus nerve and dominance here, meaning if we have more parasympathetic tone, that's going to increase our HRV. And then HRV is the output between this balancing act between the two systems, right? It's kind of letting us know where are we between there. And so we have that, but. How do we get true HRV? How do we measure it versus wearables? So, to truly measure HRV, we mentioned how we are looking at electrical signals, right? So, true HRV is measured with an EKG or ECG. You know, when you go to the hospital, you get hooked up. That's truly it because it's measuring the electrical impulses in your body. It's real time, it's the gold standard. It calculates the precise time. Between something called the R peaks. So the R waves. So essentially, if you look on an EKG, we have certain PQRS complexes and whatnot. There's specific spots on the EKG that is measuring like point to point, point to point. It's not really that important, but it's the RR interval. But that's. The gold standard, that's what it's measuring from. Whereas for your wearable, it's a little different. So, your watch or your ring uses that light, like I talked about, that PPG to detect pulse wave arriving at your finger or your wrist. And this means that. It measures the time between what's actually going on between the electrical impulse versus the blood flow more peripherally. So it kind of gives us something called the pulse rate variability. So PRV, not true HRV. So it's measuring. The difference between pulse waves, so it's not quite the electricity we measure in the HRV, it's a pulse rate variability. But they're kind of very similar, that's why we use them. But it does matter, right? So there is lag time between the heart's electrical contraction, you know, that R peak. In our wave, versus the blood pressure wave reaching the sensor. So there's that little delay there. And the lag isn't constant, right? It can be affected by blood pressure, body position, arterial stiffness, a bunch of different things. And so Bottom line is while PRV and HRV are very closely related and healthy people at rest, they're not completely interchangeable. So that's the thing. So the data from your wearable is an estimate of HRV. It's not your true HRV. I know it sounds like I'm being pedantic, but it's just important to know that, like, hey, this is where it's coming from and what we're looking at. And so, how does this actually, HRV, how is this actually calculated? Well, there's a bunch of different ways you can calculate it. So, you take this raw data of just measuring it, then your device calculates the time between these beats, right? So, inner-beat intervals. And then you use something called, and there's a bunch of different ones, but one most common probably calculation is called the root mean square of successive differences or RMSSD. Not that important, but it's valuable because it kind of helps solidify and validate and kind of have a consistent value of what we're looking at, all of these things. And yeah, it's what we look for. And it's. Nice because it's predominantly driven by the parasynthetic system. So it kind of gives us an understanding of where we're at there. But that's there's multiple ways you calculate HRV using different things. It's not quite as just like, oh, it's this single number. You get multiple of them, then you put them in an equation and spit it out. So. Either way, RMSD is probably the most common. There are other ways to do that as well. But regardless, that's not the most important thing in this podcast. Just understand that it's once again another calculation. Your HRV value that you're getting from your watches are not First of all, it might actually be HRV. It's usually not. It's an estimate of it, but then it's an estimate of like an average and a calculation. So it's just kind of an interesting thing. Like, that's what we're actually looking at. And HRV is important, right? There's lots and lots of studies on actually can be very, very helpful, but like what actually changes or moves your HRV? That's a whole other topic. I actually have plan on doing a podcast all about HRV in the future. And we'll talk more about it because it's interesting. But how do we change this overall? If we understand that, hey, higher HRV is probably good, lower is not as good. What can we do it? Well, it's influenced by like almost everything, is really what it comes down to: physical stress. So, whether that is You know, training and intensity, your volume, how long you do it for, psychological stress. Are you busy at work? Are you stressed? Do you have emotional events going on? Lifestyle, alcohol consumption, late-night meals, hydration, sleep is a huge thing as well. Quantity and quality are also huge drivers. So, there's lots of things going on. It's worth mentioning as well, HRV, a low score after like a hard workout is normal, right? Because we know you just stress it. You had a big sympathetic drive, like we're going to have a Dukes in them. That is normal and expected. Physiologic response. The big insight is like how quickly you respond or what your trends are. That's if you take anything away from this, it's all about trends, right? And one value pretty much doesn't matter. But that's kind of HRV. I do want to talk about how we use these wearables for sleep tracking, right? So that's another big thing. So the gold standard for sleep studying is a polysemography, right, or a PSG. And this is like a sleep lab. You send firefirst someone to get a sleep study. This is where they're going and they get a sleep study. It's formal. It's the only and true, a true way to accurately measure sleep in different stages. And it involves multiple different direct measurements: EEG for brain waves, EOG for eye movements, and EMG for muscle tone. So lots of things are hooked up. And this is the scientific benchmark and all things that are compared to, right? So if you go to sleep lab and get this, I've trod formal sleep study. This is what we're comparing it to. But wearables are obviously not doing that, right? So we're just estimating your sleep. So your watch or your ring has no access to your brain waves, right? You can't understand what's going on there, but it uses an algorithm to make an educated guess to based on a combination of things, right? So. Movement, meaning you're using your accelerometer. Are you moving your heart rate, your heart rate variability? Your sometimes they can quantify your respiration and respiration rate, potentially body temperature as well, these newer ones. And so. Your sleep report is an algorithmic inference, not a direct measurement of actually how much you slept. So, there are flaws there. And there are some core flaws here worth mentioning. First is like stillness versus sleep, right? As we've kind of talked about before, there's some built-in there. It's not perfect, right? It's not measuring everything, it's not gold standard. But the primary input is movement for this, meaning if you are just not moving, they'll equate that with sleep. So if you're just laying there very still trying to sleep, They may actually accidentally say, Hey, you've been sleeping. And it may look more sleep than actually what's going on. And so, yeah, so if you're just laying there, even reading a book, maybe meditating, lying still, who knows what? It could be incorrectly logged as light sleep, giving you more credit for sleep than you actually had. On top of that, also we are inferring brain states from body data, right? So the core issue extends to sleep staging. The device is guessing at a complex brain state like REM or D. Sleep, whatever, using only peripheral body data. And that's a, you know, that correlation can be weak sometimes. On top of that, algorithms often misclassify periods of very still sleep as light sleep. Or deep sleep, and they're gonna get the stages wrong, so it's not perfect. So, there's a lot of overestimation of what's going on, and then also REM is also probably the most problematic stage for wearables. Its characteristic erratic heart rate can look very similar to wakefulness, leading to significant misclassifications as well. Um, and yeah. Obviously, these algorithms are not perfect. It's what we use. They perform best in healthy individuals with consolidated sleep and they're less accurate for anyone who has fragmented sleep patterns. People with sleep apnea, chronic pain, you wake up a lot, and new parents like. This is one I've talked about all the time. Like, I am a parent, and I will soon be a parent to a second child shortly. And I just know my sister can be jacked up, so I'm not going to measure my sleep. Because, like, what would I say? Like, it's like, oh, you woke up. Yeah, like, I got a human to take care of. Of course, we're going to do that. But yeah, it's one of those things we have to think about. And it's also worth mentioning something called orthosomnia. So essentially, it's an unhealthy obsession with achieving a perfect sleep score based on data tracker, right? Which can ironically cause sleep-related anxiety. So. People look at their score like, oh no, I didn't sleep well last night. And then they worry about it. And then it causes them to not sleep well the next night. And it's a real thing, right? People look at it and say, oh, my sleep score wasn't good. We need to work. That's kind of interesting. So, overall, if we are going to use this for sleep data, though, I think it's important to maybe look at total sleep time and schedule consistency. Sleep stages are going to be a rough, rough estimate, and use your data for correlation with your daytime behaviors, right? Not as a standalone diagnosis. So we'll kind of talk all about that. Going into the next section here, I do want to talk about like the black box that is like readiness scores or the number that these give you. So, a lot of times, these companies package all this data into a single digestible number, right? Your readiness score, your recovery score, your body battery, whatever it is. They say, Hey, this is it. And it's incredibly appealing, right? Like, most things, I mean, this is what I say all the time on podcasts. Most things, like, people love clear-cut things. Like, you're ready, or you're not ready, here's your number. People love that. They want the answer, right? The one definitive question: Am I ready to work out today or ready to train today? That's what people are aimed at here. And the target demographic here are people who want to. Work hard, or you're you know, either lifelong athletes, your everyday athletes, or people who are just really, really into this and want to do it. So that's what to look for. But the problem is, there's a lot going into these numbers. Right, so what's actually inside these? So, the first thing is they're proprietary, right? So, like whoop versus aura versus garment, like whatever, they're all proprietary. Nobody knows, so nobody knows what's actually inside. It's a trade secret, and so we don't know the specifics. But we know a lot of common ingredients, right? So specifically, HRV is one really big one, a very heavily weighed component, often looking at nightly average compared to your long-term baseline. So that's a big thing. It's like, oh, it does establish your baseline. And then the day of you had bad, and they say, oh, it's actually down because of that. Or why your readiness is down. It looks at your resting heart rate a lot of times. It can be really, it's a primary kind of cardiac marker, can be very helpful. And then it looks at sleep performance, like total sleep time and You know, it does look at sleep stages potentially, maybe looking at that, and other newer ones incorporate things like potential body temperature or the previous day strain-meaning like how much did you work out? What did you move based on accelerometer data? Lots of things. So there's Some combination of all these things. Who knows how they weigh it? We're not necessarily sure. But because it's a black box, we don't know and we can't independently scrutinize or validate this based on what we know from like traditional HRV stuff. So, you know, we have traditional. HIV studies with ECGs, all that fun stuff. And we can't really corroborate: like, hey, how does this readiness score compare to this validated study? Like, we just don't necessarily know because it's, you know, it's a Subjective number, just a random number. And so it's kind of this one-size-fits-all approach, right? So weighing the inputs of lots of data into, you know, they have their algorithm, but then for every person, it's kind of like the same thing. Like, oh, for this person, we're going to use the exact same, you know. Algorithm we have because that's what they got, and there's no customization at all, right? So the algorithm's idea of perfect recovery balance might not match your individual physiology, right? Like you might be affected much, much more by sleep. Or something like that, and it might not reflect upon that. So that's what to think about. And once again, these scores are rarely, if ever, validated against real-world performance outcomes and peer-reviewed independent research, right? So the validation is typically done internally by the company, selling the advice, saying, hey, like, It's good. Like, trust us, it's good. And I understand that, right? You have to have trade secrets. And I totally get that. But it's just a challenge to, you know, how do we tell people and patients and athletes to say, hey, we should use this because this one's better, because it's Seems like it. And so you'd have to do the independent validation yourself. But we have to think about it. And at the end of the day, a single score strips away like all the nuance, right? It tells you, like, What, but doesn't tell you why, right? So if you had a lower score, like, oh no, I'm at 58%, like, what's going on? And what caused that, right? Was it a hard workout? And that's like kind of normal. We're going to recover back. Did you sleep poorly? Were you, you know, drinking? Are you stressed? Like, who knows? It can kind of lead to a flaw in decision making, right? So, like, you might skip a workout when you're actually perfectly able to do that or going too hard. If you had some bad intel or something like that when really you didn't feel good, but your data said you were good. So there's lots of things. And so we'll talk more about recommendations at the end. But overall, like I'd pretty much like ignore your final readiness score. That really isn't like a big thing you should be basing your day off. It can be helpful to see, it's kind of fun. Um, but I really wouldn't put too much into that. I, you know, I think looking at the raw ingredients might be better, like your HRV entry trends, your resting heart rate, like those things. Would be better. Maybe check your total sleep time if you can. Don't worry about stages. It kind of gives you more actual content than a single arbitrary number can really give you. So I think that would probably be one thing to think about, which would be more helpful. And, you know, I'm probably a pretty conservative person in terms of interventions. I try not to have too many. I think in the world today, we have lots of people saying, hey, the more you know, the better. And I don't know if that's necessarily the case. And so I did want to talk about another thing, though, that's like, You know, free and easy. So that's the bad thing, too. These cost money. A lot of times, these things are really expensive to start, and then you pay a monthly fee on top of that. So you're like Oh, I'm paying money to like get anxiety about how I sleep. Like, that's not the ideal situation, right? I don't want you paying a button of money and having recurrent subscription to then have more health anxiety. That's not the goal. And so, there's something here we can do. So, more like subjective measures. The oldest, these are old, right? In the literature, they're there, but they're most tested. There's asking questions, like, specifically, like, how do you feel? Like, when you wake up, like, how do you feel? It's really important because your brain is super Important, right? It's a huge, powerful processor that's integrating countless signals that a wearable can't measure, right? It's measuring your life stress, your emotional state, all those things, how you really truly feel. And subjective perception can be a remarkably accurate predictor of your performance. And so it actually may be even better than any number that they give you. And so there are different. Tools we can use. There's ones for psychometric questionnaires, standardized validated tools for quantifying mood recovery. A couple of them are the Palms, the Rescue Sport, and then you also have simple wellness scales where you can just say more practical for daily use: like, hey, A Likert scale of one to five, one to ten, like, how are you feeling? There's also like other, like, big ones. There's kind of like the big five questions out there for daily check-ins. That people use is, you know, one is how is your mood? Two is what are your stress levels? Three is, how is your sleep quality, regardless of what your watch said? Like how do you feel? Four, what are your energy levels? And five is how like sore are you? You know, how is your muscle soreness? And answering those questions before you work out or when you get up in the morning will probably be all you really need. Like, quite honestly, if you did those, you could probably go what's going on there. And consistency is key, right? Answering them every morning helps you establish a base on, like, hey, this is what I normally feel like, and kind of go from there. But that is a cheaper and probably just as good alternative to getting a specific number. But based off of that, I think a powerful thing is doing that and then basing your training on a rating of perceived exertion. So, this is another big thing I talk about with Um, exercise, and you know, I don't think everything has to be done to RPE, but RPE can be a very, very helpful tool. So it's essentially a one to ten scale to rate how difficult a workout felt. You know, like after you did it, like, man, that's a 10 out of 10. I could not have gone any harder. I am wife, whereas one's like, yeah, I like. Existed. I just walked around. And so it's been used in sports science for decades, not necessarily the one to 10. They have different scales, but one to 10 is I think just easier. And it kind of captures the internal load of a workout, which is often more important than the external load that we see on the bar. Because, like, a lot of times, You one day might be very stressed, right? And you're like, going, you normally can, let's say you can squat 225 for five or something like that. And then you put 225 and it feels like 400. You're like, that's. But I can do these numbers, but then you do them and you grind through that. But that is a very, very different experience than when you're fresh and you can do it no problem. And so, Understanding RP changes day to day, and also it allows you to change weights day to day, right? You let's say you slept terribly and didn't feel great. You can RP, like get it down so that you have the correct stimulus for that day. And so I love, love. Love RPE because it can help you auto-regulate, right? So, the idea is like we don't need to grind through things when we're having a bad day. If we're having a bad day, you can auto-regulate, go to you know, your target RPE for the day or whatever it is, it might be way less. Or, if you're feeling good, you can go a little higher, and that's how we build in kind of the stimulus that we want. Once we're adapted, we can kind of continue to move there. So, I love RP, can be used, can be very helpful. The subjective approach can be nice because it's cost-effective and accessible, right? It's completely free, requires no technology. It's great. It adds context as well, right? It captures the why behind your number. So your HRV might be low. But your subjective feeling of stress tells you it's from a work deadline, not necessarily overtraining, understanding that, hey, when that's gone, I'll probably be better back and ready to go. So that can be there. And also, it can be empowering, right? So it trains. You to be more in tune with your body signal rather than outsourcing your feelings to a device, right? Which doesn't know all the things that are going on. It can't read your brain. And so, what is best, though? Going objective versus subjective, here's kind of a comparison. I mean, The big thing is, like, is one better than the other? And at the end of the day, it can work well together. That's really what it comes down to. You know, Is the unbiased quote unquote number from your watch more truthful than your feelings, right? Some people say, well, like, I can't trust myself. I just want a number. But really, the answer isn't either or. They kind of measure different things and it can be powerful when used together if you want to use that. Once again, I'm not saying you have to use a health tracker by any means. Like, I don't think you have to at all. But if you want to, it can be helpful. And so, the wearables, let's talk about them first. They definitely have strength, right? So, passively collected. Removing daily reporting bias, meaning, hey, you don't have to get up and answer questions necessarily, it's just there. It can be helpful. It definitely can detect physiologic changes before you even sometimes you might see your HRV dropping before you feel anything. That's something to think about. May theoretically act as an early warning sign for like an infection or illness or something like that. Also, it is excellent for tracking just quantifiable long-term trends and metrics, like your heart rate and your HIV. That's what it's fantastic at, right? You get all this data and it's great to see trends. Weakness is though it does lack context, right? It shows your autonomic neural system may be stressed, but it can't differentiate between, was it a workout? Do you have a deadline? You know what's going on? And that can be good sometimes, too. Like, you don't really care. I just want to know the number, and that can be, you know, that can be a pro and a con necessarily. But then, also, weakness is susceptible to a lot of errors, you know. Movement errors, poor fit, any of those things that are going on. And now, from the subjective side, what are the strengths? Well, the strengths are: it's rich with context, right? It understands why you're feeling the way you're feeling. It's free, it's simple, it helps you become more in tune with your body's signals and can be a highly sensitive and accurate predictor of performance, sometimes outperforming other objective metrics as well. But the weaknesses can be influenced by different biases. Obviously, it's just subjective feelings. It can feel like a chore if you have to answer questions every day. You're like, oh, I hate this. And then it lacks the numerical precision of physiologic data, which is what we go there. So I've used all the above. I've had trackers, I kind of add questions. I think a lot of times the best trackers do incorporate questions as well, saying, hey, like, how do you feel? How do you, you know, what's your sleep? What's your overall feeling today? How's your energy? Are you sore? All those things, which can be helpful to kind of combine the two. And usually they still do that and then give you a score at the end of the day, too, which is there. But as okay, but either way, it is interesting that Yes, there's lots of things going on. And what do you do, though, when you're doing both these things? So let's say, for an example, like, hey, you are checking your data objectively and You're saying subjectively, you're kind of doing some sort of check-in, whether it's just a question of, like, hey, how do I feel today? Like, literally, that could be that question. So, when they agree, it's pretty straightforward, right? You have all your metrics that look good, you feel good. And you're ready to roll. So, like, that's great. You know what's going on. If you feel terrible and your HIV is terrible, then, like, okay, you know what to do. Maybe you have to pull back. But, like, once again, if they're both saying good to go, then like, good to go. Like, don't waste any time, just go. If you both say bad, might be a time to kind of back up. But, like, when they diverge, is like the biggest thing that I see, right? So, this is like the biggest issue I see with objectives, like in the wearables in general. It's like, what do you do when your objective is bad? Like, your data says you're quote unquote not ready or doing bad, but you feel like fine and you feel great. My question is, what do you do there, right? So your numbers are poor, but you feel great. Is this an early warning sign or a data anomaly, right? We don't know. And so for me, I lean on trusting your body in that situation. You know, if you said, You feel good, go for it. Also, a single day of bad data is not a reason to cancel workout, right? So, use it as a kind of a prompt to be mindful and check in with yourself during the day or the next couple of days and subjective feelings. That's what's going on. Um, I think about that specifically. We're like, hey, like, if I feel good, but my numbers are bad, what do I trust? Well, I trust usually how I feel. And one day, really, another trend is like, you should just Pay attention to trends. Like one day is pretty much nothing. Like one day of bath, like that's, I don't know what to make of that. It's really trends. If you start noticing your HRV is dropping over time, over time consistently, then we have to think about what's going on. But one night you wake up and it says red, but you feel good. It's like I'd still send it. I think there'd be no problem going forward there. But on the flip side, let's say your measures say you're ready. You are ready, but you wake up and go, dude, I just feel terrible. Like, I feel sore. I do not feel good. What do you do there? Well, once again, I say you listen to your body, right? It might be a big burden of emotional fatigue that you're not seeing the numbers or whatnot. But for me, I prioritize a lighter workout, rest, or recovery, something like that. You know, I still usually work out pretty much any day that I'm supposed to work out, I'll work out because that's just what I do. But I kind of can pivot, right? If I feel terrible, I'm just like, okay, I might be doing 30 minutes of steady state on the bike here today. That's what we're doing. Whereas if it feels good, then I might go a little harder or whatnot. That's the big thing for me is like using these in tandem with each other. And so I kind of use them as maybe a partnership. And so the objective data is telling you maybe what's happening in your body. Subject data tells you why it might be happening and kind of using those two may be helpful together. Once again, I don't know if I need to pay $50, you know, $40 a month to know this information when you can just like ask your body how it feels, and it's probably just as good. But overall, I think how do we integrate this? I kind of mentioned this before. I want you to just be informed potentially. So data informed, not necessarily data-driven robot. I see people online who are obsessed with getting perfect sleep scores or this or that. Like the data serves you, you don't serve the data. That's everything I talk about with health. Like health. Like, health shouldn't be your life, like, it should be a part of your life. Obviously, and that might be counterintuitive. Like, well, Jordan, if I don't care, I'm gonna die. Like, okay, I get that. I get that, but I'm just saying, like, you shouldn't obsess. Like, when you can get The vast, vast, vast majority of your benefits, like until you start hitting that point where you start like you're worrying about things more consistently than you need to be, like, you're probably good to go in terms of like you're not on an unhealthy level. But if you start noticing You're worried about, like, oh, this specific thing I ate, or this specific thing, or I'm worried about this trend or whatnot, but like, objectively, everything about you is healthy, then I think we're getting to that point where we don't necessarily go a little overboard. And so the data should serve you. You don't serve the data, right? You're not going to integrate your whole life about this. And I've seen this, I've seen people online say, like, you pretty much like, don't live life like you need to get perfect sleep. Like, sleep is the most important thing in the world. You need to get that. Like, guess what, man? Sometimes weddings happen and you got to go out and you do that and you come back, it's a little later. That's okay. Like, we're humans and we have human experiences. And so, I'll never apologize for that because we're much more than just the robots, right? We're not just simple numbers. And so That being said, having an integrated approach might be helpful going forward. Once again, they are tools, and I use them occasionally in different times in my life. I have right now with the craziness of just life in parenthood. It doesn't do any good to me, right? I go up how I feel. If I, you know, need a quick workout, I do it and you get what you get. But for someone who's really locked in and training and caring about recovery, like absolutely kind of a great place. I would definitely recommend doing that. It can be very, very helpful. But this is one thing we can think about, kind of this three-tiered system hierarchy of everyone. So, tier one, this is like the top, you know, the most important thing that I think is your subjective feelings, right? This is the ultimate ground of like what's actually going on in your body, right? Your perception of mood, energy, stress, soreness, I think it's the most important input. If you feel terrible, it doesn't really matter if your device says you're 95% recovered. You're probably not. You're not feeling good. You should probably take it easy. You know, if you feel fantastic and a single day of objective data is bad, I think it shouldn't stop you. I think overall, the most important thing is how do you feel? Because Yeah, and if you listen to readiness numbers all the time, you might be, yeah, leading a little ball, a little astray, I should say. You're kind of led astray a little bit, but. Yeah, two is next like the physiologic trends, right? So, what are your wearables showing you, right? So, don't focus on a single day's number, but what are your trends, your seven-day, your 30-day trends of your Heart rate ability or your resting heart rate, something like that. Is your HRV baseline consistently trending down over a week or something or more? That potentially is powerful. Maybe an actual insight that you're Subjective feelings haven't even picked up on yet. So just be aware of: hey, that's going down. Do I need to change something? It hasn't been two weeks. Maybe I need to look into it. And then, overall, like the bottom of the pyramid is really the scores they give you. Right. So, these validated scores, these are the least trustworthy and have the most noise, in my opinion. So, you know, this is black box of algorithms of saying, hey, this is your number based on What who knows? Is it based on you? No, it's not really. It's based on an algorithm that you know they've looked at. And I'm sure there's a lot of people who are a lot smarter than me doing this. And so I'm not saying they're all worthless. I'm just saying. I wouldn't necessarily look at that. It's kind of like my third step down, but it is very easy to look at. So I get that. I get it. When you see the number, you're like, oh, green. I mean, I'm guilty of it too. You're like, oh, green, we're good to go. But I kind of use this as a kind of a check-in, right? Maybe a simple check-engine. If it's on, it might prompt you to look a little deeper to say, hey, what's going on? But overall, I wouldn't necessarily have to dictate your day-to-day behavior. That's kind of what I think about. If you do have these first, you know, first thing, wake up in the morning, kind of do your maybe big five. You know, how's your mood, stress, sleep quality, energy, soreness? Or just if you're wanting to be quick, like, how am I feeling today? Single question might do that. Just to check in with yourself, see how you're doing. Then, you know, we can look at your. App after and look at your HRV, resting heart rate, and then you can kind of look at those, synthesize those two, figure out are they in line, and then kind of go from there. And so that's kind of what it is. I don't want to get too bogged down with those, but this is just to say that the numbers that we get from these things are definitely not perfect, right? We like to think that objective means ultimately the best. And we've seen in some research that sometimes objective is just as good as objective in terms of. Performance outcomes and indicators there. And so for me, it's like: if I can do the cheap thing and the free thing, I'm a frugal guy. So if I can do the thing and not pay a certain fee a month, I'd probably do that. And I just don't know. How much better are you going to have of an outcome by tracking any of this? That's the big thing, right? Like, is this going to lead you to be a healthier person? Is meticulously tracking your sleep and your strain and your recovery scores is that going to lead you to a healthier life? And healthier outcomes? I don't know that. And nobody can say that, right? That's like the big thing. And so they like to market to you that for sure that's the case, but we just don't know if that's the case. Or if you could have just taken care of yourself, had less stress, maybe check in subjectively, higher feeling, like did that lead to a better outcome? I don't know. It'd be a great study. But that's kind of what it was. So, today we've talked about a lot. We've gone from understanding, you know, PPG, the metrics of how we use it. We understand HRV. We looked at what that's like. We talked about the black box there. So, lots of different things. And so, at the end of the day, Technology is amazing. It's only even it better, right? And this is not the boo-poo on technology. I love technology. I have a podcast. I love, I'm a nerd on this stuff. I love these things. But it's just still very in the early stages, and I'm not quite ready to say we should be relying completely on it, right? So, the data is a tool to enhance your own self-awareness, not a medical device. To deliver a diagnosis, that's another big thing, too. Right, these companies are not medical devices, they literally, because they're not medical devices, they pretty much say, Hey, we can't be responsible for anything you find here, like, it's not our fault. And if it's not right, that's not her fault either. So, just understanding that is also helpful. So, I want you to be informed and a critical user to understanding this and not just saying, hey, this is. The thing. But rule number one, focus on trends, non-absolutes. The most valuable insights come from tracking your data over time, right? Compared to your baseline over weeks and months. And then looking at that to see what's going on. And then context is always king, right? So, rule number two: data without context is just noise. So, always interpret your numbers in the context of your life, your training, your stress, your nutrition. Your sleep, all that stuff. So, if you use random marathon, you're low score, like that's probably expected, right? Score after a week of rest means something else might be going on entirely different. So And then three, use data as a conversation starter, not a command, right? So let the data prompt curiosity, not conclusions. Ask, you know, to use it to ask better questions. Like, am I, you know, I see this, my resting heart rate is trending up. What might be going on there rather than making declarations like, oh shoot, it says I'm sick, like I'm getting sick. Like, that's just not a helpful thing. And then, you know, rule number four, my final rule: once again, trust your body over the algorithm. This is the most important rule. Your subjective feeling of well-being sits on the top of my kind of pyramid of what's important, it's the final, most important input for your decision-making. Or you can just do none of this, right? You can just keep working out, man. Just be healthy, eat a good diet, sleep, exercise, do all those fun things, and you can still be healthy. But this is something I see a lot of online. So I wanted to address it. And so ultimately. Yeah, the goal isn't to become like built on these things. I want you to be not reliant on necessarily anything. Use them as a guide or as a way to reach your goals if you want to. But yeah, it can be helpful, but definitely not necessary for a healthy life. But that's going to be it for today. I really appreciate it. If you did enjoy this, it would mean the world to me. If you either like this on your podcast platform of choice or share with a friend, or if you liked it on YouTube, that'd be wonderful as well. But either way, I really it does mean the world to me you listening here. But that's gonna be it for today. Now get out your phone and get outside. Have a great rest of your day. We'll see you next time.
