Calipers to camera: Collecting body composition data 

This interview with Jason Moore was recorded via remote video on November 12th, 2024.

Jason Moore (00:00) If you have too much midsection fat or not enough muscle or too much fat to muscle ratio type things, these basic things can really give you a lot of understanding as to what your baseline capacity will be to just deal with life.

Peter Bowes (00:02) Hello again. Welcome to the Live Long and Master Aging podcast. I’m Peter Bowes. This is where we explore the science and stories behind human longevity. Now, if you’re data focused like me and enjoy graphs and charts and spreadsheets, especially if they map your progress towards better health and your longevity in particular, well, I think you’re going to enjoy, least I hope you’ll enjoy this conversation. Jason Moore is the CEO and founder of Spren, which is a camera-based, and that’s the camera in your phone, camera-based technology that measures body composition, body fat percentage, muscle mass, and so on, with quite remarkable accuracy. Jason, welcome to the podcast.

Jason Moore (01:06) Peter, thank you so much for having me. And I’m excited to be here because something that you talk about a lot is sort of increasing the health span and quality of life. And that’s something that resonates deeply with our mission. So happy to be here to share whatever information could be helpful.

Peter Bowes (01:25) That’s great. yes, you’re absolutely right. It’s all about health span. Don’t talk so much about lifespan, but just maximising those healthy years free of chronic diseases, which is why at least I think it is important to look at the data. And you do, Jason, don’t you have to be a little bit geeky to enjoy diving into all of this data? I mentioned the spreadsheets and the Excel sheets that I’ve well, I’ve kept them for many, many years and I’ve notebooks full of data and it’s nice that we’re moving into an age when just collecting that data is so much easier.

Jason Moore (01:56) Yeah, you know, it’s funny you say that because working in the software industry, one of the things that software people try to do is to kill spreadsheets. And the reason why I bring that up is not because they’re not useful. Obviously, they’re extremely useful. But to sort of just sort of…automate things a little bit, make it easier, take some of the work and the legwork and the complexity out of analyzing data. That’s a big part of our mission too. But of course, the other half of it is making that data more accessible to more people, which could either fill up your spreadsheets or it could replace them depending on what stage of the journey you’re at.

Peter Bowes (02:35) And it’s fascinating, isn’t it? Looking back over the years, how we’ve collected that data from traditional sort of mechanical methods to measure body fat in a doctor’s office to, well, we’ve got smart scales now, we’ve got DEXA machines, which of course you have to go to unless you’re rich enough to own one yourself. But these are very elaborate X-ray machines and generally considered to be the gold standard in the industry in terms of recording the composition of your body. But the beauty of your technology and you can explain it to me is that really we can do all of this at home now.

Jason Moore (03:09) Yeah, you know, and it’s that’s again, that’s sort of that underlying mission there is making things more accessible. People like billionaires and very wealthy people or maybe even elite athletes or something like that, they have these whole elaborate setups around them to measure their body and understand what’s going on and the team of experts to interpret all of that information for them and to tell them what to do. We’re trying to make all of that as accessible to everyone as we can through automation and software, but also retaining the quality of the experience. Now we haven’t figured out how to have a massage therapist show up at your house every day after work or anything like that yet, but you know we’re filling in parts of that journey at least.

Peter Bowes (03:59) And you’ve hinted at this, you come at this from the computational side as opposed to the medical side from your own personal, educational and career background.

Jason Moore (04:11) Right, yeah, so a lot of kind of my background is in data building databases, data decision support systems, kind of is one thing that they call them. I’ve spent a lot of time trying to figure out how to make data serve people in what they’re trying to do. But when it comes to the health and well-being and longevity space, I got into it through personal interest at first, and then I became a coach for a number of years, and then I became an educator. And I’ve actually taught thousands of other coaches and practitioners now how to use different biomarkers and physiological markers to improve outcomes for their clients. And again, I’m not a doctor, although I’ve collaborated with many doctors, but I do look at it from how to make sense of data and translate that into real world action that has sort of measurable results basically. And on the tech side, we believe that that by making the results easily measurable, accurately from your phone, you can actually find what works for you instead of kind of being beholden to more generic guidance and recommendations.

Peter Bowes (05:24) So let’s dive into what Spren is, how it works and how you get it and how you can incorporate it into your daily regime.

Jason Moore (05:24) Yeah, so it starts with our app. We have an app in the app store called Spren, S-P-R-E-N, and really that’s kind of the hub for your data where you can use your smartphone camera to measure things like body composition or even heart rate, HRV, and respiration. We have a number of markers in there. You can also integrate wearables and other data that you might be tracking, whether that’s blood labs or other biomarkers, etc. And then we basically help you analyze that. Like I said, make it so that you may not need a spreadsheet as much because we’re pulling insights out for you and kind of guiding you on what these things mean and what you should be paying attention to. But our core technology that makes us really different is that camera technology that we have been grateful to partner with. I think now it’s like 43,000 volunteers that have collaborated with us on our R &D process to develop these high quality camera sensing technologies.

Peter Bowes (06:38) We will. Just a little digression because you mentioned HRV, heart rate variability, which I know is something that you are something of an expert in, having looked at this over a number of years. Why is that? And I mention it because I follow my HRV using my aura ring here, like I know so many millions of other people do as well. And interestingly, I just noticed in the last few weeks, and I’ve seen a lot of chatter online about this, about how it’s dropped as we’re moving into the shorter days of the winter. And I’m wondering, is that something that you’ve come across? see anecdotally a lot of people talking about that as to whether there’s any reason for it.

Jason Moore (07:15) Yeah, you know, I think this is interesting because there is a lot of anecdotal evidence for that. When we look at the population data, it actually doesn’t seem to play out quite so predictably. That being said, it’s been a little while since I’ve looked at that specific data myself, although we have looked at it many times. There could be some confounding factors in that, in the sense that the southern hemisphere will be going into a different season versus us. Obviously, the populations are weighted more heavily in the northern hemispheres. And then different countries and cultures have different seasonal kind of emphasis on whether or not it’s more stressful stressful or less stressful or, you know, so just a lot of factors when you’re looking at global population data. But I do think there’s really strong anecdotal evidence that would lead me to believe certain seasons are definitely just inherently, you know, more taxing on the system than others.

Peter Bowes (08:22) Yeah, interesting. Let’s get back to Spren and talk about it. Let’s start from the beginning then. So you get the app, you have the app on your phone. In terms of the practical process that’s involved. What do we do?

Jason Moore (08:33) Right, so basically body composition is one of our markers that people are really excited about because as you mentioned, a Dexa machine or other kind of hardware-based solutions for measuring body comp. They require you to go to a lab or they require you to buy something expensive, et cetera. With this, you can basically just use your phone. And what you do is you start to scan and you put your phone down. And then there’s real time computer vision feedback to guide you through the process of standing in frame while your phone scans your body. Now, a couple of things. You do have to basically get in your underwear or into some really tight-fitting clothing, you know We’re not saving or storing or sharing those images Those are just for the scans purposes and we delete them as soon as we give you your results and which happens basically in real time within a minute or so usually And so you face the camera and then you turn to your side we do two snot two snapshots and it’s kind of like depositing a check or doing something like that at a bank where if you’ve ever scanned something with your phone and you’re like trying to match the corners to the frame, we guide you to do that in real time so you can put your arms up and we can get the right view to get an accurate measurement. it takes all of about, you know, probably the first time people do it, they spend maybe five minutes on it because it’s just a new experience and… Once you kind of get going with it, it takes one to two minutes. And so some people choose to do it every single day, although I would say that’s overkill. You don’t lose fat or gain muscle at a meaningful enough rate on a daily basis. But lots of people will choose to measure it once a week or maybe once a month, depending on kind of what they’re monitoring. And those are both really good cadences, I think.

Peter Bowes (10:38) Yeah, and I’ve done it myself and you’re absolutely right. There is a little bit of trial and error the very first time in terms of your precise positioning and you figure it out. There’s quite a fast sort of learning curve there in terms of how to do it quickly. So once we’ve done that, what is the data that we get?

Jason Moore (10:55) Yeah, so body composition breaks down into a number of different metrics. So one that lots of people are very interested in is body fat percentage. So is it 20 %? Is it 40 %? Is it, I don’t know, for some people it’s 5%. That’s for if they’re extremely lean. But basically, that’s a key pillar metric. The other half of it, though, is lean mass or muscle mass. Especially in the longevity and health span space, think muscle is getting a lot more attention now and for in which is great. And so that’s really what separates body composition from things like measuring body weight is it’s not just about pounds or kilos on a scale. It’s what’s the proportion of muscle to fat in the body and are you maintaining or preserving muscle or even increasing muscle while also of course managing the fat. Now you also benefit from breaking it down a little further than that. Not just fat and muscle, but where is that fat and that muscle on the body? And so we have a metric called android to gynoid ratio and android and gynoid. And basically that’s looking at trunk fat versus limb. It’s kind of like fat around your midsection or your belly, or fat around the hips and kind of the legs and the limbs. And this is important because really the dangerous fat is what’s called visceral adipose tissue. And that’s the fat that’s around your belly and around your organs in your midsection. And that’s what’s really gonna be the most predictive of health span and longevity. Having a little bit of softness around the body that’s kind of evenly distributed is really not a big deal. I think we’re finding the evidence is pretty clear on that. It’s more of an aesthetic preference than it is a health preference. But we don’t want it concentrated around the midsection. So we try to help people understand if that’s happening. And then also from a muscle perspective, generally speaking, more lean mass, more muscle mass is generally better for health span and longevity. Of course, there’s diminishing returns on that at some point, but for the average person, most people are under-muscled basically, and so we kind of want to see what’s the distribution of muscle on the body as well.

Peter Bowes (13:26) And how accurate is Spren? Have you done real-time comparisons with the more traditional ways of getting this data for body fat, for example? In other words, using Spren and in the same moment using another device as well to make a comparison?

Jason Moore (13:45) Yeah, yeah, this is a great question. One of our core philosophies is that garbage in, garbage out. So we really strive hard to have high accuracy and reliability with all of our measurements. And so earlier you had mentioned DEXA being kind of the gold standard for body composition. The funny thing is, is that’s actually more of the gold standard of what’s accessible to people. The real gold standard is more like an autopsy or an MRI. And so these things are just obviously no one wants to get an autopsy. But an MRI is usually cost prohibitive or just inaccessible to people as well. But those are kind of more true gold standards, so to speak. DEXA itself even has a margin of error between DEXA machines or between scans, which is plus or minus a few percent. And so that has been though clinically proven to be more than sufficient for pretty much every case of tracking changes in these important tissues in the body. I will say one thing real quick. We do not provide bone mineral density estimates. That is one difference. If someone’s heard of a DEXA, usually they either follow a podcast like this or their doctor has tried to get them to measure their bone mineral density for osteopenia or osteoporosis. And so that we cannot do using just the camera of your phone yet today. We’re focused on body composition. But anyways, we are within the margin of error of DEXA itself when it comes to the accuracy and reliability of our scans.

Peter Bowes (15:32) Yeah, and you make a very good point, actually, most people actually, perhaps a large number of people watching and listening to this podcast probably have never even heard of DEXA. The only reason I’ve experienced it is that I was involved in a clinical trial once a nutrition clinical trial and part of the measurements involved a DEXA scan. in my, let’s say, ordinary private life, I haven’t come close to having my body tested using that kind of device. So it isn’t something that most people experience. But I think one fascinating point to emphasize is the ease of use of this and the safety issues. You are literally just using the camera in your phone. There’s no x-ray involved, which I know will concern some people if they repeatedly need to have an x-ray.

Jason Moore (16:19) Right, right, exactly. DEXA is an x-ray machine basically and it’s a big $30,000 machine that you lie in and you get your whole body x-rayed from top to bottom basically. And it’s a great innovation for what it does, but yeah, it’s the convenience factor and other potential downsides other than cost and all of that, you know, make this much easier to do from your phone. And then one thing that’s also, we continuously get surprised by this, but every time we kind of replace an older, more expensive, more clunky technology, we see this sort of explosion of use cases that occurs as well, because suddenly you’re not just measuring once a year or something like that, you can measure again once a week. And then we have our community share with us all these amazing things they learned that they never learned before even if they were doing DEXAs. And so that’s kind of a fun side effect as well.

Peter Bowes (17:19) Yeah, I was actually just going to ask you about that in terms of the anecdotal response that you’ve had from your community of people experiencing this and applying it to their lives. What kind of things have they told you?

Jason Moore (17:30) Well, know, there’s, especially when we get into the realm of body composition, one of the kind of controversial topics are GLP-1s and a lot of medically assisted kind of weight loss tools. And we’ve had people on those things using our platform. And for the first time, they were measuring the actual difference between changes in lean mass and changes in body fat percentage. And one person, Gary, I remember, reported that he didn’t believe at first that he was losing all muscle mass when he was doing an aggressive weight loss diet and with some assistance. And so he went and got it confirmed by the lab. And the lab said, you should keep tracking with this app because it brought up a really important point here. He then shifted his nutritional patterns to make sure to get adequate protein and to incorporate a little bit of resistance training. Then he was able to reverse that trend, maintain his muscle mass while then losing a lot of body fat percentage that he had to lose. And so we get anecdotes like that all the time and we’re just, that’s kind of what drives us every day. And, but you kind of get some other unexpected things too. We tend to not emphasize the absolute number as much as the relative change in the numbers over time. And that I think breaks down a lot of psychological barriers for people when it comes to improving healthspan, longevity, wellbeing, or even your own body image or the confidence that you have in your body is it’s to say like, look, we all have a starting point and there’s no shame in where you’re starting. What we’re trying to do is to say, are you going in a direction you want to be going or not, right? And so by making it easier to measure more granularly, we also get lots of people reporting back that kind of the demotivating swings that that they used to have are now not as big of a deal because they can see that those things actually are part of a bigger trend. everyone has ups and downs every day, even every week. But over time, that trend is telling you if you’re going in the right direction or not. So we get a lot of anecdotes around that as well.

Peter Bowes (20:01) Yeah, and I think some of the motivating, motivating as opposed to demotivating swings can be the way that because we can frequently test ourselves now with devices like this and others, that we can look at the data and correlate that with our behaviour in the previous 24 hours, the previous week and behaviour, I mean, what we’ve eaten, what time we stopped eating the night before, how much alcohol we had the night before or no alcohol for a long period of time. All of these issues have a very subtle impact, don’t they, on the data over a period of time. that’s the key, that we can record it over that long period of time. And it’s a very personal thing. But look at how we’ve been behaving and relate that to actually how we are, how we feel, but how the data says we are at any particular time.

Jason Moore (21:48) 100 % and that’s actually really ultimately one of the hardest things about healthspan and longevity is basically that the feedback loops are so long typically. And so it’s really difficult to stay motivated and stay on it or to know if what you’re doing is even really moving the needle at all. And so you’re hitting the nail on the head, so to speak, in the sense that if we can decrease that cycle time for feedback, then you can actually just save tons of time. And I kind of like maybe we can use this podcast as the first time for me to kind of publicly say how I think about this, but it’s like, it’s almost like a time machine cubed to put like a kind of nerdy label on it because not only are you getting more time, but the time that you are getting is much better. And we’re having you spend less time to get those more time and better time. Right? And so giving you time back on the effort side while also increasing the quality and productivity of your life, as well as giving you more time on the back end as well. So Time Machine Cubed.

Peter Bowes (22:05) Yeah, I like that. I like that a lot because getting time back as you this is a bigger conversation probably, but getting time back as you age really strikes a nerve. It’s crucially important because I don’t know about you, but I feel now that I’m older, I’m over 60 now that every day is getting shorter. At least it goes faster. mean, a lot of old people talk about this, about how time, the days, the weeks, the decades even just move faster. we’re getting into philosophy a little bit here, but just the idea of getting some time back is, I think, hugely valuable.

Jason Moore (22:37) Yeah, like, this is where I think there’s a lot of interesting, in some ways, overlap, but also sort of divergence between the idea of optimizing to the infinite degree, and also getting, you know, just high quality time for sort of a minimum input possible, right? And so they’re both educational and there’s no right or wrong on which way somebody wants to lean. But I think for most people, they’re starting with that first one, which is what’s the kind of minimum I can put in to get the max return on investment for my time? And then once you’ve gotten through those things, you have the energy and the capacity to think about optimization from there.

Peter Bowes (23:25) Let me ask you about AI, artificial intelligence. I’ve had a lot of fascinating conversations about it just recently and the increasing role, the growing role that it is playing and is going to play in our medical lives, our relationship with our health professionals, relationship with the doctor, relationship with technology and how we can use it to our benefit. How do you see AI, especially as it relates to the kind of data that this technology is gathering for us.

Jason Moore (23:56) Yeah, yeah, it’s a great question. I’m a believer that AI is going to dramatically change our lives in ways that we can’t even imagine today. I don’t know the timeline. I’m not necessarily an expert on forecasting that. But interestingly, so we use AI in multiple ways in the traditional sense of the word. The camera scanning technology that we’ve developed uses machine learning underneath the hood and uses computer vision to recognize where you are in real time. And those are forms of AI. Also, we use data science and algorithms to connect all of these different data points and serve up insights to you so that you kind of know what to pay attention to. And again, that’s a form of AI. Those things are going to continue to just keep getting better to make it easier for you to get more data out of your phone or whatever device that we end up carrying around with that smart glasses or something else, as well as sort of the taking a lot of the analysis load off of us. So it’s like we’re doing all these things. What should I be paying attention to? I just want it to tell me. Do I need to stand up right now, drink a glass of water? Or do I need to be worried about what my next meal is? Or what is the thing for me right now that I need to be paying attention to? And that’s a really powerful use case for AI that’s going to use. again, AIs that already kind of exist in the world, but just making them better. Now there’s this other half of it that’s exploding onto the scene with chat GPT and conversational AI and things like that that are leading towards, you know, theoretically artificial general intelligence and some, and some other kind of topics like that. One thing is very certain it’s going to change the way that we interact with technology. And so, instead of having interfaces with lots of buttons and cells and tables and things on them, you’re going to be able to just ask your computer or your phone for things, or it’s going to be able to just tell you things in more conversational language. And that actually lowers the barrier even further for people. It’s kind of imagine like that billionaire again with their team of experts in their back pocket and the team of experts are saying like, hey, you you’ve been eating a lot of McDonald’s lately, you know, maybe you like it. maybe it’s good for your psychological health in the short term, whatever, it’s probably not doing you too many favors on this longevity healthspan journey. Why don’t we cycle in some other things right here? And you can have a conversation about that that kind of humanizes it a little bit and recognizes the fact that… McDonald’s is not inherently evil. It probably shouldn’t be on the menu for most people who care about healthspan, but there are psychological reasons that people still eat there, right? Or convenience reasons or whatever, social conditioning.

Peter Bowes (27:06) Well, yeah, it’s a complicated subject, isn’t it? I don’t eat fast food, I don’t eat convenience food, but I do occasionally like some comfort food, which isn’t necessarily going to be the best meal that I have in a week, but it actually does make me feel good. Maybe on the end of a long, tough day, grey weather outside, some nice hot comfort food, whatever your comfort food is, whether it’s fish and chips or whatever it is. You know, there can be a psychological…advantage to you in doing that. you know, we shouldn’t always be, I think, tied to the constraints of always eating a healthy diet. We’re digressing again a little bit here. But it’s a complex issue. I think AI, as you just explained, can help us kind of sort that out in our minds. And I think the other thing AI can do, talking of healthspan, is human to human communication. And I mean, our individual communication to our medical experts and health experts that are looking after us, because that has traditionally been a very poor form of communication. In other words, we haven’t been able to tell our doctors accurately about our symptoms, about what we’ve been doing. There’s been a sort of honesty factor there that you’re not always telling your doctor the truth. I think AI is going to sort of equalize all of that and that it will treat us all in the same way. There’ll be no emotional side to it. And the doctor, the health practitioner will get the facts that can be potentially very useful to us.

Jason Moore (28:33) Yes, you know, I think the other thing too is that AI will actually hopefully give time back also to all of our supporting services, right? So in our well-being or healthspan journey, will interact with doctors, we will interact with practitioners and coaches and other types of experts and people. And those people have limited and finite capacity, right? you’re thinking like, okay, I want the best doctor, right? But the best doctor is busy and expensive as well. And so AI can give those people time back to where they can reach more people and serve more people more effectively and spend more time refining their craft versus sort of filling out paperwork, for example. And so that’s kind of an optimistic view potentially because I know there’s also economic pressures and geopolitical pressures that are going to be colliding around all of this. But if we give more time back to all humans as much as possible, we can also spend more time supporting each other. And it feels like human-human relationships and contact and support are going to be as essential, if not more essential, going into the future than they are today or in the past as well.

Peter Bowes (29:55) Let me ask you the next step with SPREN. What is your immediate goal? What’s the next step with the technology? Do you have a vision of something that you’d like to do with the technology that in the future that you can’t do with it right now?

Jason Moore (30:08) Yeah, definitely. mean, there’s a couple of different things. I’ll just throw a few highlights out there and then let me know if you want to dig into any of them. One is that we can continue to push the envelope on sort of real time understanding of the body, right? So we actually love wearables. We are happy for users of Oura or Whoop or Apple devices or Garmin or Polar or just the number kind of goes on of devices that you can use. There’s also kind of expansion in that space of, you know, continuous glucose monitors and other types of sweat patches and all sorts of things. Those are all wonderful and we would love to collaborate with those devices, but we also think that there’s a lot you can do with devices you already have. And so whether that’s your phone camera or your webcam on your computer, one example of that is that we have technology already that we just haven’t had time to publish yet, but it’s fully working that allows us to measure heart rate and respiration and stress levels in real time on a web call like this. And so this could be extremely useful for a number of cases. For example, telehealth. If you’re talking to your doctor and they want to know what your vitals are, they don’t have to send you a device necessarily. They’ll be able to just get it right there, right during the consultation. obviously with your permission. so things like that, we want to continue to push the envelope on, see if we can unlock blood pressure and blood glucose and a bunch of other markers over time. And then on the other side of it is helping people make sense of the data and knowing what to actually do with it. So we are very partnership oriented in our approach to that. So we think that Our team studies these subjects quite in great depth, but the best path is if we partner with experts in different fields to really deliver the best content and guidance. To know, again, kind of coming back earlier, it’s like, what role does McDonald’s play in somebody’s life? I’m not necessarily personally an expert on that. I have a lot of principles that I could maybe share with the world about it, but depending on the context of that person’s life, they may need to have a very specific conversation around those decisions. And we want to partner and get down into the nitty gritty granularity of that personalization. Because I’ll leave one thought with you on that subject, which is that personalization is a word that gets tossed around in the industry quite a bit. And I think it’s great that there’s a lot of conversation around it. but I just have not seen true personalization delivered in a scalable way anywhere yet. I think we’re scratching on the surface of it, but a lot of times it’s more like take a quiz and you get option A or option B. And it’s not very nuanced and granular, and we want to be able to empower a much more nuanced and granular conversation to happen around day-to-day living.

Peter Bowes (33:23) It all sounds exciting. The one thing that I would like to just dive into a little bit more, the one thing you mentioned there about the potential for video consultations with your doctor and the real time sharing of data. That to me sounds fantastic. It’s hugely futuristic if you imagine that you and I are talking into cameras right now and you’re the doctor and somehow I can share my heart rate with you, the doctor. How would that work?

Jason Moore (33:49) Yeah, well, it’s interesting. We’ve already developed it. It’s already kind of on our shelf of R&D that as a kind of growing company, we have to prioritize our resources on what we get out there to the world. And we’re focused a lot on body composition right now. But there’s actually really interesting data in this video feed right now that we’re using to talk to each other. As our heart beats, blood is flowing up through our face and it’s actually changing the color of the pixels that the camera is detecting as it flows through our face. And we can use machine learning and computer vision to actually zoom in on the pixels and track individual pixels to remove the noise of our heads moving and light reflecting from the window outside or whatever it is and extract your heart rate signals, your respiration signals, and your HRV signals right out of this video. And it’s actually fundamentally quite similar to how wearables detect heart rate. It’s called PPG, basically, at its core. But with the computer vision based with the camera, there’s actually a much richer signal and a lot more noise in that signal. So it’s a little bit more complicated. In essence, takes us about 10 seconds to calibrate the detection and then it happens in real time from there.

Peter Bowes (35:19) Amazing. Yes, exciting. Let me ask you, Jason, as we close this, what are your own personal longevity aspirations? This is a podcast about looking to the future, maximising our health span. Some people, lot of people I talk to have very specific goals as to what they want to achieve and what they want to do. So how do you see the decades ahead and how do you live your life now based on the knowledge that you have and the data that you have to try to achieve it?

Jason Moore (35:48) Yeah, it’s, you know, I have some philosophies, but what I find is that day to day, my emotions and feelings end up driving my behavior more than my philosophy of longevity. So what I try to do is trick myself to where my feelings and emotions then create the right behaviors. Right? So that might sound kind of esoteric or funny, but I have young children right now and a five-year-old and a nearly two-year-old. And over the past five years of being a parent, it has really brought longevity and healthspan fully onto my radar for the first time in a visceral way, like where I really feel it day to day. So I was always kind of into fitness and sports and wellness and things for my whole life. So I was lucky to kind of get ahead of some of the challenges of aging and health span. But as soon as I had children, I started to get hit with reality a little bit more in the face. And so, you my goal is to basically feel energetic every day as much as possible, to feel confident that I can do the things that I want to do, and that I’m not just settling because of sort of lack of preparation. So when my kids, as my kids get older, you know, I want to go rock climbing with them. And it’s not because I want to be an elite rock climber. In fact, I’ll be very cautious, most likely, not taking exceptional risk necessarily doing it. But I want to feel confident that I can go do something like that. And I’m not going to walk away with, you know, an injury or shaving off, you know, months of my life. And so that’s kind of just how I’m thinking about it day to day and then I like to also try to measure sort of big picture things that matter and so that’s what originally led me to HRV I was looking for what’s like one metric that can give me a really good understanding of whether things are going well or not well. Even though I’m personally a data nerd and geek and tech geek and all that. Day to day, I kind of want to take the thinking out of it as much as possible and just say like, okay, things are going in the right direction or the wrong direction. HRV is super helpful for that. And again, body composition is another thing that really is helpful for that. So when it comes to metabolic health, that underpins energy, that underpins, you know, your body’s ability to function. and drive his immune system as well as a number of other systems. If you have too much midsection fat or not enough muscle or too much fat to muscle ratio type things, these basic things can really give you a lot of understanding as to what your baseline capacity will be to just deal with life going forward. And so. I just look for stuff like that. Probably one thing that we’re missing today is more like musculoskeletal, things like balance, strength, mobility, stuff like that. Those things are incredibly important as well to look at. And I do monitor those personally about myself. We just don’t have any automated tools on the platform yet for those, keyword yet. We’re just trying to make those things as easy for people to track as possible so that you can kind of live and know that you’re kind of living in a way that’s supportive of what your philosophies are as well.

Peter Bowes (39:43) And talking, as you were, about having younger people in your life, your children, if you could speak to your younger self, maybe this will be advice that you will eventually give to your children. Is there something that you would say to your younger self, some advice that is based on your lifetime so far of knowledge and research and professional activities and what you understand now that you didn’t know then?

Jason Moore (40:08) Yeah, know, it’s really basic stuff, most likely, to my younger self. My younger self was horrible with sleep, and my younger self was horrible with nutrition. And even though I was into sports and fitness and things, I did a lot of stuff that was a waste of time and spent not enough time on the basics that actually mattered. And so…In many ways, it’s kind of a cliche, but I would tell my younger self to focus on the basics and really get sleep right, really try to get enough protein, but not overeat in general. I’m personally sensitive to a lot of foods, but what I have found is that by doing an elimination diet for a period of time, I’ve actually been able to reincorporate more foods over time. So I would teach my younger self that all that friction that I had and that resistance that I had to trying an elimination diet early, like, don’t worry, if you do this, you’ll actually have a more inclusive diet over the long run. So just telling myself things like that.

Peter Bowes (41:22) Jason, this has been a really fascinating conversation. I’m excited to see what you do next with the technology, what the data can tell us. Thank you very much indeed.

Jason Moore (41:30) Thank you, Peter.