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You are listening to an on the move

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media c production.

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Inside tracker published a paper on the dataset

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in 2018,

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and that looked at

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thousand users that had, like, at least 2

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longitudinal blood for us, and it only looked

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blood. And so

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This is essentially going to be the hu

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paper to that, and now we're looking at

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20000 people that have least 2 blood draws.

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And then within that cohort, some folks have

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Dna data,

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some folks have fitness tracker data, some info

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them both. So

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we were looking at really the combination of

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blood fitness tracker and

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Dna

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information

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And, you know, so for example,

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with Dna and

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and statins, for example, you we can look

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and see that there are certain people who

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haven't an elevated,

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like, a higher genetic risk of having high

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Ldl.

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And

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what we see is that for those folks,

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it's actually harder for them to improve their

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Ldl. So they're just like, bumping up against

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a wall, and it's you know, those are

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also folks that might have to go to

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a stat earlier right? Or, you know, the...

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Even if they get more steps, it's it's

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not gonna take their L nail down because

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they're just, you know, there's something in their

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genetics that makes them feel resistant to this.

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So

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kind of looking at that integration between, like,

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genetics fitness tracker nina and

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blood is really

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cool.

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That was Renee D.

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This is Mar Sal. Thanks for tuning into

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my podcast, Mar on the move. Each week,

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I will be inviting interesting, innovative,

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movers and shaker to join me on the

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show and share their story. You will discover

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and hear from thought leaders, experts, influencers, and

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entrepreneurs

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from the world's of wellness sports beauty fitness,

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fashion and more. Morning on the move will

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feature an ec mix of people I know,

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work with and think are generally doing cool

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things. On each episode, I sync up with

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my guests about life, career and training and

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showcase their expertise and story. Hello. Welcome and

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welcome back to the Mar on the move

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podcast.

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I am your host, Mar Sal.

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Today on the podcast, I'm syncing up with

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inside trackers,

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Renee D.

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Sv of science and Ai.

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We are talking all about her role at

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inside tracker,

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how and when she started working with the,

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how inside tracker leverages the power of generative

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Ai,

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what's new and

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exciting at the company.

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We also do a deep dive into

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Renee training and fitness,

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talking about road versus track versus trail running,

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Ash Yoga,

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v 2 max and lac threshold tests.

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If you are a data geek, you're gonna

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love this. We get into the weeds.

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But if you're not a data geek, you're

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also gonna love it because we're talking about

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health, and wellness and inside tracker from biomarkers

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to fitness data and Dna.

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Inside tracker is the ultra personalized

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nutrition platform

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that analyzes your blood, Dna, and lifestyle to

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help you optimize your body from the inside

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out. It transforms your body's data into meaningful

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insights and a customized

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action plan of the science back

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nutrition recommendations,

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you need to optimize your health. If you

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like what you hear,

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leave us a 5 star review on Apple

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or Spotify.

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Share this conversation on your social channels.

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Wherever you like to get social. And don't

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forget. Shop our partners and use our codes.

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Hope you have an awesome fourth of July

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and holiday weekend if you're listening to this

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night or over the weekend now onto my

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conversation with Renee.

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Talk to me a little bit about your

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role at inside tracker and how long you've

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been there and what you do, etcetera.

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Thank you. It is an absolute pleasure and

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an honor to be here. So I'm Yeah.

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Happy to. So I've been an inside tracker

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for

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a couple of years now, but I have

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known inside tracker since its inception.

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Think close to 14 years ago. So the

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the

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founder and chief scientific officer Gil Bland, and

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I actually worked together at my first job

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out of graduate school.

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At this company where we did a lot

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of personalized medicine systems biology analysis for pharma

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and biotech, but

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really at the heart of all of this

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analysis that we would do, for these companies

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was this knowledge based,

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and

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logic

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kind of reasoning

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engine

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that was built by a number of individuals

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there, but a particular 1 that I would

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love to mention is this person dexter or

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pratt. Really kinda taught me everything I know

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about building these these types of systems and

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what they're really good at is

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taking a whole bunch of data, which we're

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drowning the data. Right?

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And

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systematically

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using

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knowledge that we have about

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science in in the case of inside tracker,

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our knowledge we have about

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lifestyle interventions like,

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you know, changes in your diet or an

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exercise program or meditation or something like that?

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What is the influence? How do you... How

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do you match all of that information that

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you get from all these clinical studies with,

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you know, a lot of blood and fitness

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tracker and Dna data that kind of creates

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a biological picture of your yourself. So my

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role it inside tracker

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is

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definitely really on the science side science and

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Ai, and

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we care a lot about this particular engine

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that we

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use to kinda connect

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in individual's blood, fitness tracker Dna results to

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known clinical studies to create evidence backed recommendations.

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And, you know, also working together really closely

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with the, you know, marketing teams and

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business development and and trying to use and

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harness our own data asset in a way

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that is, you know, kinda useful for science

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and,

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useful for the company and,

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hopefully, it's helpful to people overall. So you're

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basically the woman behind the engine. I am

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I'm am a woman behind the engine. I

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all put it that way. You are 1

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of the women behind you engine that looks

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at all the data from all the users

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of inside tracker,

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and then

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looks at that versus, like, all the clinical

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studies and trials and kind of matches it

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all altogether. And I would say that, like,

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kind of a mix of, like, classic science

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and data and also generative Ai. So, yeah.

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No. That's absolutely correct. And the type... This

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particular

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engine, like I said, if 1 1 of

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many women,

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1 that I'll mention is this woman, Michelle

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Ka, who

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heads up our science team and she,

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She's a nutrition, and

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she and her team, really, they source and

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aren't constantly looking for new peer reviewed you

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know, publications about clinical studies

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that might look at different interventions like, our

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cold plunge is actually good for you and

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when should you do that? Great. I remember

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when they they were, like, Ash uganda has

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been around for centuries.

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And

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there. But there was no... Like, there was

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finally a scientific,

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like, clinical study that came out around the

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use case of Ash uganda, an inside tracker

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was 1 of the first

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companies I saw talking about that, because it

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was science backed. Yeah. It was a while

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ago, but, yeah.

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Yeah. And I I

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you know, I didn't build this engine. Right?

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I just, like, kate rolled on the scene

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2 years ago. So a lot of the,

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you know, hard set work was done before

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I got there.

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But I have to say that,

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and my background is is in

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molecular biology and engineering. So I'm not a

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nutrition.

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I'm not an exercise anesthesiologist epidemiologist, although I

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would love to be 1.

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And

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I...

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So I myself. Right? Like, if I go

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on Instagram, and I'm just scrolling through.

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It's really hard. Like, I find it very

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confusing because

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every influencer has, like, kind of a different

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idea, Like, what is their hot take? What

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is their angle on particular nutrient or a

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supplement or something like that. And

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what I very much like about these types

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of... We call them expert Ai

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systems.

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What I like about them is that they

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hold more information than any 1 human ever

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could. Right?

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So in our knowledge base now, we have

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hand curated over 7000 clinical studies.

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So there is no way that I can

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remember the results of 7000 clinical studies,

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and then apply those results to even my

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own data that I know really, really well.

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So

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in that sense, you know, this is where

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I think

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this is a very white box Ai,

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and it's where

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this type of Ai

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doesn't get as much press as, like,

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chat.

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L

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or machine learning,

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but it's been around for decades. Yeah a

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hundred... It's the og GAI.

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Yeah. That's why... Yeah. Definitely 1 of the

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Og. I'm trying to bring it back. I'm

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trying to be, like, this is the new

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black ice. Well, interesting because it wasn't part

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of the brand language.

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Right? It was not really over the years

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with inside tracker that I'm aware of. You

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know, it was like, it was just

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it just was what it was. Because... I

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mean, essentially,

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right? Like, inside tracker

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does more than, like, your general

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doctor results. Like, if you see your vitamin

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D level is low. It's not because they're

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comparing you to the entire

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national

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grid. Right? Like, they're comparing you or, you

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know, to people in America. They're comparing you

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to other athletes specifically

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in your age range. And it's not a

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general

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sort of,

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I don't know what Trying to say. It's

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not a general.

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It's more of

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and finally be able to have, like, that

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have kind of information. And enough data to

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be able to do some, you know, some

277
00:10:39,551 --> 00:10:42,100
of our own kind of scientific discovery on

278
00:10:42,100 --> 00:10:43,772
this dataset and actually,

279
00:10:44,664 --> 00:10:46,125
we are wrapping up

280
00:10:46,825 --> 00:10:49,404
literally today or tomorrow a paper for

281
00:10:49,705 --> 00:10:50,504
submission. So

282
00:10:51,065 --> 00:10:53,120
once we submit though, I'd actually be happy

283
00:10:53,239 --> 00:10:55,089
happy to talk about. There's a lot of

284
00:10:55,383 --> 00:10:56,359
fun and interesting

285
00:10:56,971 --> 00:10:59,297
results that have... We've been able to pop

286
00:10:59,988 --> 00:11:01,044
pop up from

287
00:11:01,593 --> 00:11:04,546
interrogating these data. And probably nothing, like, too

288
00:11:04,546 --> 00:11:07,099
crazy surprising either, you know, but

289
00:11:08,136 --> 00:11:10,547
are still are you submitting these papers to,

290
00:11:10,707 --> 00:11:12,805
like, where do you to, like, research

291
00:11:13,263 --> 00:11:15,900
Exactly. So we're looking at, like, different digital

292
00:11:15,900 --> 00:11:18,621
health journals because it is essentially a digital

293
00:11:18,621 --> 00:11:19,519
health platform,

294
00:11:19,895 --> 00:11:20,395
and

295
00:11:20,930 --> 00:11:22,385
as you know, like, you're

296
00:11:22,760 --> 00:11:26,214
you are your triathlete, Like, you're very knowledgeable

297
00:11:26,665 --> 00:11:29,521
about, you know, yourself and your performance and

298
00:11:29,521 --> 00:11:31,028
testing all of these markers and all of

299
00:11:31,028 --> 00:11:31,845
that and

300
00:11:32,471 --> 00:11:33,924
you know, what is

301
00:11:35,013 --> 00:11:36,919
amazing is that, like, folks like you are

302
00:11:36,919 --> 00:11:39,937
definitely early adopters, like, very tip of the

303
00:11:39,937 --> 00:11:42,279
spear ahead of the curve on things and,

304
00:11:42,495 --> 00:11:44,348
you know, we think a lot about how

305
00:11:44,723 --> 00:11:46,258
this... It's essentially

306
00:11:46,633 --> 00:11:47,349
health. Right?

307
00:11:48,463 --> 00:11:51,586
Some people wanna call bio hacking. Some people

308
00:11:51,586 --> 00:11:53,499
wanna call it health span optimization, but, like,

309
00:11:53,898 --> 00:11:55,731
essentially, what we are doing is preventive health.

310
00:11:55,891 --> 00:11:57,246
We're saying, alright. Well,

311
00:11:57,978 --> 00:12:00,762
Your H hvac level is kinda climbing towards

312
00:12:00,762 --> 00:12:02,751
6 percent and 6 percent, for between in

313
00:12:02,751 --> 00:12:05,713
the pre prediabetes category, and it's easier to

314
00:12:05,713 --> 00:12:08,111
reverse that now. Right. Then when you got

315
00:12:08,111 --> 00:12:08,990
6 and a half,

316
00:12:09,869 --> 00:12:12,506
so you have a great opportunity here where

317
00:12:12,506 --> 00:12:13,405
you can just

318
00:12:14,279 --> 00:12:16,759
you know, do... Incorporate some horizontal zone 2

319
00:12:16,759 --> 00:12:18,440
walks, and let's see what happens. You know,

320
00:12:18,600 --> 00:12:20,120
we'll... Does your H hvac once you go

321
00:12:20,120 --> 00:12:21,320
down or not? You know,

322
00:12:21,974 --> 00:12:24,131
So it's a very good facilitator for kind

323
00:12:24,131 --> 00:12:25,510
of, like, n of 1

324
00:12:26,289 --> 00:12:27,827
studies, but also just

325
00:12:28,206 --> 00:12:29,345
kind of pointing out

326
00:12:29,897 --> 00:12:31,644
where are things going well where things not

327
00:12:31,644 --> 00:12:33,073
going well, where do you need to focus

328
00:12:33,073 --> 00:12:34,582
on is a time to talk to a

329
00:12:34,582 --> 00:12:37,043
doctor? Right. You know, if you're... If you've

330
00:12:37,043 --> 00:12:38,632
been trying a bunch of lifestyle?

331
00:12:39,922 --> 00:12:42,338
Types of interventions, and they're not working. So

332
00:12:42,397 --> 00:12:44,712
maybe... So for example, that that was the

333
00:12:44,712 --> 00:12:47,506
case with me. So my cholesterol has been

334
00:12:47,506 --> 00:12:48,006
high

335
00:12:48,475 --> 00:12:49,981
ever since I was in my my twenties.

336
00:12:50,218 --> 00:12:52,516
I mean, kinda borderline high, like, not enough

337
00:12:52,516 --> 00:12:53,388
to concern

338
00:12:53,942 --> 00:12:54,576
physician. But.

339
00:12:55,623 --> 00:12:57,636
Of course, now, you know, there's

340
00:12:58,092 --> 00:13:00,640
a lot more talk about how your lifetime

341
00:13:00,640 --> 00:13:03,348
exposure to high cholesterol. It means a lot

342
00:13:03,348 --> 00:13:05,538
more than just that 1 number. So

343
00:13:06,397 --> 00:13:08,474
eventually, just from kind of tracking my Ldl

344
00:13:08,474 --> 00:13:11,031
cholesterol levels over the course of years and

345
00:13:11,031 --> 00:13:12,150
given my age,

346
00:13:12,802 --> 00:13:14,153
you know, I did end up going on

347
00:13:14,153 --> 00:13:16,298
a stat, which... My whole family's is on

348
00:13:16,298 --> 00:13:18,762
statins, like, we're all on statins, but no

349
00:13:18,762 --> 00:13:20,765
amount of diet and exercise was gonna get

350
00:13:20,765 --> 00:13:22,356
me where I needed to go. But... Okay.

351
00:13:22,595 --> 00:13:24,664
But I not been tracking my Ldl and

352
00:13:24,664 --> 00:13:26,733
in Ap b levels, I wouldn't have been

353
00:13:26,733 --> 00:13:28,745
able to figure that out, you know? And

354
00:13:28,745 --> 00:13:30,424
you went on a stand, You had no

355
00:13:30,424 --> 00:13:30,985
other choice.

356
00:13:32,345 --> 00:13:35,084
Yes. Yeah. So, you know, tell

357
00:13:36,039 --> 00:13:38,194
You'll you'll see folks who are, like,

358
00:13:39,711 --> 00:13:42,665
you know, I... Because you're very athletic. I

359
00:13:42,665 --> 00:13:45,161
mean... I somewhat athletic. I'm

360
00:13:45,790 --> 00:13:48,172
I am not gore. I Shut No. Do

361
00:13:48,172 --> 00:13:49,997
you don't understand? Like, I was like, oh.

362
00:13:50,235 --> 00:13:50,735
You

363
00:13:51,267 --> 00:13:53,410
job to post pictures of me running, but,

364
00:13:53,569 --> 00:13:53,728
like,

365
00:13:54,378 --> 00:13:56,045
I didn't run today. I went to the

366
00:13:56,045 --> 00:13:56,863
physical therapist.

367
00:13:57,236 --> 00:13:57,474
Like,

368
00:13:58,427 --> 00:13:58,927
I'm.

369
00:13:59,935 --> 00:14:00,173
Yeah.

370
00:14:00,888 --> 00:14:02,420
And we can talk about my track

371
00:14:02,793 --> 00:14:04,804
workouts to my my ham pulls and all

372
00:14:04,804 --> 00:14:06,965
of that. Yeah. Good stuff that happens after,

373
00:14:07,205 --> 00:14:09,845
you know, third 40. But but... Yeah. So

374
00:14:10,004 --> 00:14:11,304
I I essentially

375
00:14:11,764 --> 00:14:14,437
had tried kind of all of the lifestyle

376
00:14:14,575 --> 00:14:17,446
interventions. Yeah. I tried cutting out, like, all

377
00:14:17,446 --> 00:14:19,462
saturated fad. I've

378
00:14:19,839 --> 00:14:23,863
tried, you know, obviously, I exercise so that's

379
00:14:23,999 --> 00:14:26,624
good. I held sleep gosh, but whatever hurts...

380
00:14:27,022 --> 00:14:28,692
It's not the asleep, but I ended up

381
00:14:28,692 --> 00:14:29,170
seeing

382
00:14:30,856 --> 00:14:32,928
highly recommend this if you live in the

383
00:14:33,007 --> 00:14:35,557
Boston area, but at mass general, there's a

384
00:14:35,557 --> 00:14:36,057
women's

385
00:14:36,433 --> 00:14:37,411
preventive cardiology

386
00:14:38,345 --> 00:14:40,752
unit at mass general hospital at all. And

387
00:14:40,752 --> 00:14:42,029
so I went, and I saw 1 of

388
00:14:42,029 --> 00:14:44,582
the cardiologist there. My Pcp, I love was,

389
00:14:44,662 --> 00:14:46,577
like, you're asking me too any questions. I'm

390
00:14:46,577 --> 00:14:47,669
just gonna send you

391
00:14:48,267 --> 00:14:49,006
my doctor.

392
00:14:49,621 --> 00:14:50,896
Like love this stuff. I'm like, okay.

393
00:14:52,410 --> 00:14:55,198
And this woman, doctor was awesome, but she

394
00:14:55,198 --> 00:14:57,110
was... You know, I was, like, alright. Look.

395
00:14:57,444 --> 00:14:58,500
Should I just go

396
00:14:58,954 --> 00:15:00,147
be super restrictive?

397
00:15:00,704 --> 00:15:02,850
And she was... She just said if you

398
00:15:02,850 --> 00:15:03,407
do that?

399
00:15:04,123 --> 00:15:06,667
You'll probably lower your Ldl by another 20

400
00:15:06,667 --> 00:15:08,596
points. But we really need you down another,

401
00:15:08,676 --> 00:15:10,671
like, 50 or 60 points. And I was

402
00:15:10,671 --> 00:15:12,347
like, put me on the stat and Yeah,

403
00:15:12,586 --> 00:15:13,884
totally. And

404
00:15:16,031 --> 00:15:17,861
I'll take a down thanks. I'm just like,

405
00:15:18,258 --> 00:15:20,167
brick and even and then not get there.

406
00:15:20,486 --> 00:15:21,758
And restrict was like,

407
00:15:22,648 --> 00:15:23,836
I don't need a ton of me. Like

408
00:15:23,916 --> 00:15:24,946
I'm not a vegetarian, but I don't need

409
00:15:24,946 --> 00:15:26,056
a ton of me, but I get a

410
00:15:26,056 --> 00:15:27,799
lot of protein from dairy, and I'm like,

411
00:15:27,879 --> 00:15:29,464
ugh, I have to cut all of that

412
00:15:29,464 --> 00:15:32,019
out. It's that's it's getting hard. Yeah. I

413
00:15:32,019 --> 00:15:34,163
mean, it's a big... It's a big choice.

414
00:15:34,402 --> 00:15:36,229
But, you know what's great like, and I

415
00:15:36,387 --> 00:15:37,658
I feel like you know this because you

416
00:15:37,658 --> 00:15:39,167
work there, but you get an option to

417
00:15:39,167 --> 00:15:41,095
see what's going on. And then you can

418
00:15:41,095 --> 00:15:43,169
make an educated decision because I love the

419
00:15:43,169 --> 00:15:44,307
inside tracker also

420
00:15:44,923 --> 00:15:47,396
send you different article lengths, like when you

421
00:15:47,396 --> 00:15:51,644
get your blood biomarkers back also offers nutritional

422
00:15:51,644 --> 00:15:54,144
advice and insight, and there are,

423
00:15:54,684 --> 00:15:55,004
you know,

424
00:15:55,725 --> 00:15:57,884
nutrition scientists that work at inside tracker that

425
00:15:57,884 --> 00:15:58,384
are

426
00:15:58,779 --> 00:16:01,167
there for you to... I know they don't

427
00:16:01,167 --> 00:16:03,714
do 1 on 1 consulting, like, with... Or

428
00:16:03,714 --> 00:16:06,342
do they now with people getting their blood

429
00:16:06,342 --> 00:16:06,794
tests

430
00:16:07,153 --> 00:16:08,986
We can hook you up with Okay. Let's...

431
00:16:09,145 --> 00:16:09,782
They're not... Yeah.

432
00:16:10,500 --> 00:16:12,970
For sure. Yeah. It's not... At this time,

433
00:16:13,129 --> 00:16:15,280
it is not a canonical part of, yeah,

434
00:16:15,440 --> 00:16:17,048
program where if you sign up. You get,

435
00:16:19,118 --> 00:16:21,347
you automatically get a consult. But Ashley has

436
00:16:21,347 --> 00:16:23,576
been on my podcast a few times, and

437
00:16:23,576 --> 00:16:24,792
she's like, the cholesterol

438
00:16:25,420 --> 00:16:26,870
vanishing queen. So

439
00:16:28,113 --> 00:16:30,094
No. And actually, that was the 1 thing,

440
00:16:30,253 --> 00:16:32,648
and I I still am, like, Let me...

441
00:16:32,888 --> 00:16:34,485
I wonder if I could just wash off

442
00:16:34,485 --> 00:16:36,959
the stat and just go Ashley. Just do

443
00:16:36,959 --> 00:16:37,917
the Ashley protocol.

444
00:16:38,556 --> 00:16:40,472
She's got a lot take tiktok followers 1

445
00:16:40,472 --> 00:16:43,198
saying Didn't cry. You didn't try Ashley. I

446
00:16:43,198 --> 00:16:44,713
didn't try the Ashley protocol a call. You've

447
00:16:44,713 --> 00:16:48,300
been dialed into this scene well before you

448
00:16:48,300 --> 00:16:49,495
came on board. So it talked to me

449
00:16:49,495 --> 00:16:52,089
about like, what you were doing before and

450
00:16:52,389 --> 00:16:53,829
why you came on board and when you

451
00:16:53,829 --> 00:16:54,389
came on board.

452
00:16:55,029 --> 00:16:56,709
Feeling and I met at that original company

453
00:16:56,709 --> 00:16:58,089
that had that kind of

454
00:16:59,360 --> 00:16:59,860
knowledge

455
00:17:00,233 --> 00:17:00,947
representation reasoning,

456
00:17:01,344 --> 00:17:02,796
expert Ai type of system

457
00:17:03,168 --> 00:17:04,993
engine. That 1 wasn't an expert Ai, but

458
00:17:04,993 --> 00:17:06,818
it, you know, there were some structural elements

459
00:17:06,818 --> 00:17:08,343
at early similar to what we do here

460
00:17:08,343 --> 00:17:09,717
at Inside tracker. But

461
00:17:11,045 --> 00:17:13,588
after that job, I worked at 2 other

462
00:17:13,588 --> 00:17:14,088
places

463
00:17:15,509 --> 00:17:17,808
1 of them was this company called patients

464
00:17:17,808 --> 00:17:20,979
like me, which is really interesting because patients

465
00:17:20,979 --> 00:17:22,193
like me was essentially

466
00:17:22,739 --> 00:17:25,151
forum, It still exists. It's a forum for

467
00:17:25,687 --> 00:17:27,599
people who have a condition. So and it

468
00:17:27,599 --> 00:17:28,555
started with Als.

469
00:17:29,511 --> 00:17:31,264
So if you add, you could meet other

470
00:17:31,264 --> 00:17:33,273
people with Als you know, other our family

471
00:17:33,273 --> 00:17:35,612
members, you know, caring for people with Als

472
00:17:35,751 --> 00:17:38,468
and for information. It was it was awesome.

473
00:17:39,108 --> 00:17:41,585
So what was super interesting about patients like

474
00:17:41,585 --> 00:17:43,914
me was that and we worked with what

475
00:17:43,914 --> 00:17:46,467
we call patient generated health data. So people,

476
00:17:47,184 --> 00:17:50,215
you know, patients themselves were providing information about

477
00:17:50,215 --> 00:17:51,651
what symptoms they had, what drugs they were

478
00:17:51,651 --> 00:17:53,654
taking, like, side effects all that kind of

479
00:17:53,654 --> 00:17:54,948
stuff. And

480
00:17:56,354 --> 00:17:57,807
it really gave me a healthy

481
00:17:58,181 --> 00:17:59,952
respect for patient centric.

482
00:18:00,658 --> 00:18:02,965
And caring about the patient voice.

483
00:18:03,602 --> 00:18:06,067
So there was this cool example. I know

484
00:18:06,783 --> 00:18:07,283
where

485
00:18:07,831 --> 00:18:09,814
They were working with a pharma company who

486
00:18:09,814 --> 00:18:12,931
had a lupus drug and, you know, lupus

487
00:18:13,779 --> 00:18:14,096
sucks.

488
00:18:14,651 --> 00:18:15,048
Yeah.

489
00:18:15,618 --> 00:18:18,268
So it's a challenging condition to have, and

490
00:18:19,122 --> 00:18:21,112
I I can't remember which way this went,

491
00:18:21,351 --> 00:18:21,851
But

492
00:18:22,386 --> 00:18:23,363
the original

493
00:18:24,074 --> 00:18:26,572
endpoint of the study was going to be

494
00:18:26,791 --> 00:18:27,670
about pain,

495
00:18:28,549 --> 00:18:31,665
like improvements in pain, but they actually interviewed

496
00:18:31,665 --> 00:18:33,830
lupus patients and lupus patients were, like, well,

497
00:18:34,069 --> 00:18:35,819
actually, we care a lot more about brain

498
00:18:35,819 --> 00:18:36,319
fog

499
00:18:37,250 --> 00:18:39,238
than pain. So they ended up changing the

500
00:18:39,238 --> 00:18:41,862
clinical study, but you would be either surprised

501
00:18:41,862 --> 00:18:43,491
or not surprised by how

502
00:18:44,582 --> 00:18:46,650
little patients actually have a voice in the

503
00:18:46,650 --> 00:18:49,116
way that clinical trials are, like, developed and

504
00:18:49,116 --> 00:18:51,046
design and stuff like that. So, you know...

505
00:18:51,445 --> 00:18:52,562
That's super helpful.

506
00:18:53,041 --> 00:18:56,394
That's really a cool company. Yeah. Yeah. Yeah.

507
00:18:56,633 --> 00:18:59,041
No. That... That element of it was really

508
00:18:59,041 --> 00:18:59,541
beautiful.

509
00:19:00,632 --> 00:19:02,461
And then I worked with another company called

510
00:19:02,939 --> 00:19:04,871
precision for Medicine, which is essentially doing

511
00:19:05,739 --> 00:19:08,849
you know, consulting services and managing. The the

512
00:19:08,849 --> 00:19:10,125
role I had there was

513
00:19:10,763 --> 00:19:12,997
also working again with Pharma and biotech and

514
00:19:12,997 --> 00:19:15,444
kind of, like, helping to analyze large data

515
00:19:15,565 --> 00:19:16,284
that. So

516
00:19:17,724 --> 00:19:20,524
you know, all in all, it was more

517
00:19:20,524 --> 00:19:21,345
in the

518
00:19:21,724 --> 00:19:22,384
kind of

519
00:19:22,684 --> 00:19:23,505
precision medicine

520
00:19:23,884 --> 00:19:24,044
place.

521
00:19:25,013 --> 00:19:26,782
Rather than wellness

522
00:19:27,155 --> 00:19:31,200
and preventive health with lifestyle intervention. So, you

523
00:19:31,200 --> 00:19:32,335
know, instead of

524
00:19:32,643 --> 00:19:35,663
changing your diet or, you know, having a

525
00:19:35,663 --> 00:19:38,922
particular exercise routine, the interventions were drunk. Right.

526
00:19:40,289 --> 00:19:42,049
But it's really the same principles that we're

527
00:19:42,049 --> 00:19:43,809
just able to apply, but with different... You

528
00:19:43,809 --> 00:19:45,650
know, now the drug is exercised or the

529
00:19:45,650 --> 00:19:47,250
drug is food or the drug is.

530
00:19:48,222 --> 00:19:50,205
Sleep and meditation and recovery. And...

531
00:19:51,395 --> 00:19:53,219
I think that it's it's become, like, health

532
00:19:53,219 --> 00:19:55,440
and wellness. It's and medicine have become more

533
00:19:55,440 --> 00:19:57,841
well rounded it. And so I think, you

534
00:19:57,841 --> 00:20:00,150
know, presenting people with all their options is

535
00:20:00,150 --> 00:20:01,901
always a good thing, then they can decide,

536
00:20:02,060 --> 00:20:04,345
which direction they wanna go, but I think

537
00:20:04,781 --> 00:20:06,449
like you said, like, you're taking a stat.

538
00:20:06,607 --> 00:20:09,069
Like, sometimes you have to do those things.

539
00:20:09,307 --> 00:20:11,531
Right? I mean, there's only so much you

540
00:20:11,531 --> 00:20:12,007
can do.

541
00:20:12,578 --> 00:20:13,931
Through food and medicine, like,

542
00:20:14,568 --> 00:20:16,955
sometimes you have to take an antibiotics. Sometimes

543
00:20:16,955 --> 00:20:18,785
you need to do certain things that are

544
00:20:18,785 --> 00:20:21,013
not gonna be, like, your natural go to,

545
00:20:21,252 --> 00:20:23,980
but I think just having the information, getting

546
00:20:23,980 --> 00:20:25,896
ahead of it and being able to, like,

547
00:20:26,055 --> 00:20:28,131
you said, like, stop it early or change

548
00:20:28,131 --> 00:20:30,366
it earlier is like, a game changer.

549
00:20:31,498 --> 00:20:34,278
Totally. And I... Like, I'm very pro. Right?

550
00:20:34,516 --> 00:20:36,105
Like, if you need to take drugs, like

551
00:20:36,105 --> 00:20:38,567
you should take the drugs there. Great. Really

552
00:20:38,567 --> 00:20:38,884
helpful.

553
00:20:39,853 --> 00:20:41,445
Yeah. No. I think it's great. And... But

554
00:20:41,525 --> 00:20:43,755
I think also, like, for me through inside

555
00:20:43,755 --> 00:20:45,369
tracker. Like, I've just learned

556
00:20:46,143 --> 00:20:49,738
so much about, like, my biomarkers and my

557
00:20:49,738 --> 00:20:50,056
health,

558
00:20:50,692 --> 00:20:51,192
and

559
00:20:51,645 --> 00:20:54,188
just like, even through some of the articles

560
00:20:54,188 --> 00:20:56,747
and through, like, just learning about the clinical

561
00:20:56,747 --> 00:21:00,088
studies and going on pub med and really

562
00:21:00,088 --> 00:21:02,338
getting ahead of stuff from

563
00:21:02,809 --> 00:21:05,442
just an everyday athlete perspective has been...

564
00:21:06,160 --> 00:21:08,017
I mean, incredibly empowering

565
00:21:08,394 --> 00:21:09,911
the education behind it.

566
00:21:10,564 --> 00:21:12,736
And I think it's interesting that,

567
00:21:13,113 --> 00:21:14,648
you know, with your Ai

568
00:21:15,183 --> 00:21:18,449
machine with the data analytics that you guys

569
00:21:18,449 --> 00:21:20,317
are doing that you're able to really,

570
00:21:20,690 --> 00:21:20,928
like,

571
00:21:21,880 --> 00:21:24,578
pigeon... Like, not like, to to streamline the

572
00:21:24,578 --> 00:21:26,720
results so that you're not looking at it...

573
00:21:26,958 --> 00:21:29,941
I'm not compared to someone who's, like, 52

574
00:21:30,718 --> 00:21:33,431
living somewhere else that doesn't exercise. Right? Like,

575
00:21:33,670 --> 00:21:35,027
I'm compared to people that are like me.

576
00:21:35,186 --> 00:21:35,880
And I think

577
00:21:36,395 --> 00:21:39,991
you've seem to have really expanded your audience

578
00:21:40,285 --> 00:21:42,746
beyond just athletic, like, triathletes athletes and runners,

579
00:21:42,904 --> 00:21:44,333
but now it's more longevity,

580
00:21:44,984 --> 00:21:46,899
and general health seekers? Which I think is

581
00:21:46,899 --> 00:21:49,532
really interesting. So how do you do that

582
00:21:49,532 --> 00:21:51,687
with the data? Like, how did you kind

583
00:21:51,687 --> 00:21:54,012
of build the next tier of data as

584
00:21:54,012 --> 00:21:56,479
you're growing the company. Yeah. That's a great

585
00:21:56,479 --> 00:21:57,695
question. And,

586
00:21:58,150 --> 00:22:00,083
you know, our data is really from

587
00:22:00,553 --> 00:22:02,946
from all come. Right? The the the data

588
00:22:02,946 --> 00:22:04,802
that we're looking at is is

589
00:22:05,578 --> 00:22:07,275
in a completely blinded

590
00:22:07,732 --> 00:22:09,965
way because we don't actually care about anybody's.

591
00:22:10,299 --> 00:22:12,854
Personal information, but just, you know, we might

592
00:22:12,854 --> 00:22:15,328
do a blinded analysis of all of, you

593
00:22:15,328 --> 00:22:17,722
know, all of the inside tracker consumers, blood

594
00:22:17,722 --> 00:22:18,601
values and say,

595
00:22:19,731 --> 00:22:21,425
look at which biomarkers

596
00:22:22,357 --> 00:22:24,665
tend to be correlated with age or, you

597
00:22:24,665 --> 00:22:26,257
know, an interesting thing we looked at.

598
00:22:27,067 --> 00:22:28,575
Was, for example,

599
00:22:29,052 --> 00:22:30,877
we saw that people that were able to

600
00:22:30,877 --> 00:22:32,489
improve their Ldl values

601
00:22:33,020 --> 00:22:33,973
compared to didn't.

602
00:22:34,767 --> 00:22:35,267
They

603
00:22:35,814 --> 00:22:38,230
you know, after joining on tracker, they

604
00:22:39,007 --> 00:22:41,242
took more steps over the course of, you

605
00:22:41,242 --> 00:22:43,158
know, in between their blood draws, like, the

606
00:22:43,158 --> 00:22:45,647
ones that improved actually took more steps. That's

607
00:22:45,647 --> 00:22:48,039
not, like, a prospective clinical trial. We can't

608
00:22:48,039 --> 00:22:50,590
say that taking that those transaction caused the

609
00:22:50,590 --> 00:22:52,742
decrease in Ldl, but, like, it was a

610
00:22:52,742 --> 00:22:53,561
very interesting

611
00:22:54,273 --> 00:22:58,204
an interesting retrospective association. And so we do

612
00:22:58,661 --> 00:23:01,269
have... You, sometimes we'll do an analysis then

613
00:23:01,389 --> 00:23:03,544
We'll do exactly that. Right? Like, we might

614
00:23:03,544 --> 00:23:03,943
compare...

615
00:23:04,422 --> 00:23:05,938
And this is a paper that was published

616
00:23:06,496 --> 00:23:08,252
by Anti tracker last year.

617
00:23:09,384 --> 00:23:11,545
Looking at runners. Right? So we were able

618
00:23:11,545 --> 00:23:12,605
to compare people

619
00:23:12,985 --> 00:23:14,664
who... And this is all from self reported

620
00:23:14,664 --> 00:23:16,765
data, who reported themselves as sedentary,

621
00:23:17,958 --> 00:23:19,255
you know, versus

622
00:23:20,429 --> 00:23:24,197
modern... You know, moderately active running, and then

623
00:23:24,414 --> 00:23:26,424
who they would be considered kind of elite

624
00:23:26,424 --> 00:23:29,620
or pro runners, And volume amateur. So there

625
00:23:29,620 --> 00:23:31,697
are 4 different categories that really allowed us

626
00:23:31,697 --> 00:23:32,517
to compare,

627
00:23:32,976 --> 00:23:34,449
you know, across those

628
00:23:34,825 --> 00:23:37,929
across those different categories. So now there's really

629
00:23:37,929 --> 00:23:39,942
enough data in the

630
00:23:40,397 --> 00:23:41,773
database to be able to do

631
00:23:42,324 --> 00:23:43,839
analyses like that, and I'll tell you, for

632
00:23:43,839 --> 00:23:46,551
example, I never updated my profile data.

633
00:23:47,268 --> 00:23:49,422
So I was actually considered to be 1

634
00:23:49,422 --> 00:23:51,655
of the sedentary people. And I'm not setting.

635
00:23:51,909 --> 00:23:54,063
Right. Right? But when you have enough people

636
00:23:54,063 --> 00:23:54,780
in the cohort,

637
00:23:55,498 --> 00:23:57,652
mistakes like these kind of... You were using

638
00:23:57,652 --> 00:23:59,566
inside tracker or way before you start working

639
00:23:59,566 --> 00:24:01,102
there. Well, I started

640
00:24:01,733 --> 00:24:03,558
And I got, like, my first instance I

641
00:24:03,558 --> 00:24:05,860
tracker her blood test. And then around the

642
00:24:05,860 --> 00:24:08,876
same time, I actually... Gonna, like, plug garmin

643
00:24:08,876 --> 00:24:10,740
right now for no reason, but,

644
00:24:11,192 --> 00:24:12,462
I don't I know. I plug them all

645
00:24:12,462 --> 00:24:13,731
the time. They should pay me too.

646
00:24:16,763 --> 00:24:18,197
I have to say, like, I bought this

647
00:24:18,197 --> 00:24:20,609
watch. My brother and I bought it at

648
00:24:20,747 --> 00:24:22,978
think, like, the Phoenix 6, we bought it

649
00:24:22,978 --> 00:24:25,789
during, you know, whatever Black Friday, and

650
00:24:26,660 --> 00:24:28,900
I just wanted to get good feedback from

651
00:24:28,900 --> 00:24:30,900
the stupid watch. So I was like, whoa,

652
00:24:31,059 --> 00:24:32,660
I guess he should start running again, and

653
00:24:32,660 --> 00:24:34,259
there's are so many runners at inside trackers.

654
00:24:34,420 --> 00:24:36,992
So It was actually pretty see. Yeah. The

655
00:24:36,992 --> 00:24:38,830
joke is, like, I hate running. I'm, like,

656
00:24:38,990 --> 00:24:40,188
I hate running, but I'm just gonna do

657
00:24:40,188 --> 00:24:42,185
it. Now it's, like, 2 years later, and

658
00:24:42,345 --> 00:24:42,640
I

659
00:24:43,156 --> 00:24:46,494
still kinda hate road running, but I like

660
00:24:46,494 --> 00:24:49,037
running. Now I can't stop running. Yeah. So

661
00:24:49,037 --> 00:24:50,626
you like... So where do you like running

662
00:24:50,626 --> 00:24:51,634
that you said you

663
00:24:52,075 --> 00:24:54,154
my heart belongs of the trail. Really? I

664
00:24:54,154 --> 00:24:56,075
am... I just like to... I like to

665
00:24:56,075 --> 00:24:58,154
be in the woods and I'm walking the

666
00:24:58,154 --> 00:25:00,174
dog. I are walking

667
00:25:00,648 --> 00:25:01,919
slash running from the dog.

668
00:25:02,634 --> 00:25:03,508
I'm in the woods,

669
00:25:04,064 --> 00:25:07,321
it's quieter. There's trees, you know. So so

670
00:25:07,321 --> 00:25:08,671
you do a lot of trail running? Is

671
00:25:08,671 --> 00:25:11,628
that your thing or trail walking, trail needs

672
00:25:11,788 --> 00:25:13,855
Yeah. I try to be on the trail

673
00:25:13,855 --> 00:25:14,673
as much as

674
00:25:15,286 --> 00:25:17,592
as much as possible. But I... It's hard

675
00:25:17,592 --> 00:25:19,439
to get onto the trail I live in

676
00:25:19,519 --> 00:25:21,037
Cambridge, so it's hard for me to get

677
00:25:21,037 --> 00:25:21,936
to a trail

678
00:25:22,714 --> 00:25:25,670
on a weekday, so most weekdays are done.

679
00:25:26,881 --> 00:25:29,351
So where do you usually run? Oh, so

680
00:25:29,510 --> 00:25:30,626
I run...

681
00:25:31,422 --> 00:25:33,197
If I'm in the Boston area, I always

682
00:25:33,255 --> 00:25:35,667
run in the fe because it's the closest

683
00:25:35,978 --> 00:25:38,436
to me the middle sex bells in Med,

684
00:25:38,595 --> 00:25:41,052
and I love it there. I just absolutely

685
00:25:41,052 --> 00:25:41,607
love it there.

686
00:25:43,763 --> 00:25:45,753
It... My dad lives on Cape cod and

687
00:25:45,753 --> 00:25:47,606
if I'm there, there's the for,

688
00:25:50,688 --> 00:25:52,280
recreation area. I don't know. There's a lot

689
00:25:52,280 --> 00:25:54,204
of mountain biker there. In on the cape?

690
00:25:55,318 --> 00:25:56,692
Yeah. And this particular,

691
00:25:58,101 --> 00:26:00,327
in this particular, like area where I'll where

692
00:26:00,487 --> 00:26:02,316
I'll trail run. But, like, my dream is

693
00:26:02,316 --> 00:26:03,923
to be able to walk out to my

694
00:26:03,923 --> 00:26:06,236
backyard and run on a trail there. Like,

695
00:26:07,033 --> 00:26:08,947
I'm living in the wrong environment for me.

696
00:26:09,346 --> 00:26:11,101
I I feel the same way living in

697
00:26:11,180 --> 00:26:12,695
New York City, Like, even though I don't

698
00:26:12,695 --> 00:26:15,025
really like trail running. I do wish I

699
00:26:15,025 --> 00:26:17,100
could go outside and just be on my

700
00:26:17,100 --> 00:26:19,096
bike or go for run and not have,

701
00:26:19,176 --> 00:26:20,474
like, a million people

702
00:26:21,810 --> 00:26:23,899
outside like, in the way on their way

703
00:26:23,899 --> 00:26:25,894
to do stuff, like, you know, and have

704
00:26:25,894 --> 00:26:27,888
to try stop at traffic lights and go

705
00:26:27,888 --> 00:26:29,346
through potholes. But?

706
00:26:30,696 --> 00:26:32,453
Yeah. You know, like, during the pandemic, I

707
00:26:32,453 --> 00:26:34,129
was like, maybe we can move. And my

708
00:26:34,129 --> 00:26:34,927
partner is was like, no.

709
00:26:35,645 --> 00:26:37,481
No. I've worked hard my whole life to

710
00:26:37,481 --> 00:26:38,772
live in new york city. Like oh my

711
00:26:38,772 --> 00:26:39,830
god. And just

712
00:26:40,206 --> 00:26:42,198
upstate. So, like, I get it. I grew

713
00:26:42,198 --> 00:26:44,030
up here. At the same time, it's like,

714
00:26:44,189 --> 00:26:45,942
oh, do you wanna meet for a cocktail

715
00:26:45,942 --> 00:26:48,348
at a great restaurant? Okay. You in and

716
00:26:48,348 --> 00:26:50,743
hour Right? Like, and I'm gonna walk there?

717
00:26:50,982 --> 00:26:53,537
And it's you. There's there's there's, like, pros

718
00:26:53,537 --> 00:26:55,213
and cons to living in air in a

719
00:26:55,213 --> 00:26:57,224
city. What are sports you do? Look. Hi,

720
00:26:57,384 --> 00:26:59,464
Ski. Life ski is my favorite great thing

721
00:26:59,464 --> 00:27:01,625
in the world. That's... I would I would

722
00:27:01,625 --> 00:27:03,224
give up running for skiing in a heartbeat

723
00:27:03,224 --> 00:27:04,505
beat if I had to pick 1, not

724
00:27:04,505 --> 00:27:05,144
even a question.

725
00:27:05,705 --> 00:27:07,478
It pick skiing your time. What were you

726
00:27:07,478 --> 00:27:08,994
doing before you were running? Because you said

727
00:27:08,994 --> 00:27:10,671
you didn't love running and you kinda just

728
00:27:10,671 --> 00:27:12,746
started, like, 2 years ago? I mean, I

729
00:27:12,746 --> 00:27:14,443
feel like you have a lot of fancy,

730
00:27:14,901 --> 00:27:17,086
you know, very accomplished athletes

731
00:27:17,936 --> 00:27:20,319
on your podcast and I... I'm just, like,

732
00:27:20,478 --> 00:27:21,193
I'm like, you're...

733
00:27:21,828 --> 00:27:23,099
If you have a listener who who's, like,

734
00:27:23,178 --> 00:27:24,860
just an average person.

735
00:27:25,495 --> 00:27:27,478
That's what I A lot of my listeners

736
00:27:27,478 --> 00:27:29,938
are just average people. Trust me. It's like,

737
00:27:30,096 --> 00:27:31,921
I am in... I am a solid, like,

738
00:27:32,333 --> 00:27:34,481
I I could work and maybe get to

739
00:27:34,481 --> 00:27:36,390
middle of the pack, you know,

740
00:27:36,947 --> 00:27:39,038
a little bit, like... But

741
00:27:39,429 --> 00:27:41,500
I'm never... I am I'm probably... I might

742
00:27:41,500 --> 00:27:43,174
win something when I'm 90 if I can,

743
00:27:43,254 --> 00:27:44,768
like, last that long.

744
00:27:46,442 --> 00:27:49,482
But Yeah. Like not not now. But I...

745
00:27:51,313 --> 00:27:53,541
I mean, I grew up doing crew and

746
00:27:53,541 --> 00:27:54,280
flying soccer.

747
00:27:55,228 --> 00:27:57,936
But then, honestly, like, there was a pull

748
00:27:57,936 --> 00:28:00,086
period in my thirties where I just didn't

749
00:28:00,086 --> 00:28:01,918
really do much of all of anything. Like,

750
00:28:02,157 --> 00:28:03,613
I ran occasionally

751
00:28:04,321 --> 00:28:06,863
or, you know, I ski whatever I whenever

752
00:28:06,942 --> 00:28:08,848
I could, because that is actually my... That's

753
00:28:08,848 --> 00:28:10,199
my real love. But, like,

754
00:28:10,913 --> 00:28:12,684
I really go come from

755
00:28:13,629 --> 00:28:14,369
a major

756
00:28:15,707 --> 00:28:16,527
sports background,

757
00:28:17,066 --> 00:28:18,824
you know, outside of, like, high school and

758
00:28:18,824 --> 00:28:19,324
college

759
00:28:19,703 --> 00:28:21,621
stuff. I think a lot of my guests

760
00:28:21,621 --> 00:28:23,714
over the years like, different Like I've had

761
00:28:23,714 --> 00:28:26,351
a lot of fashion guests and also, like,

762
00:28:26,591 --> 00:28:28,449
doctors and scientists that,

763
00:28:28,828 --> 00:28:31,146
you know, they may run here and there

764
00:28:31,146 --> 00:28:31,865
or do yoga,

765
00:28:32,358 --> 00:28:34,826
they're not always athletes or they don't at

766
00:28:34,826 --> 00:28:36,737
least identify as athletes. I would say, like,

767
00:28:36,816 --> 00:28:38,965
if you run your runner, but some people

768
00:28:38,965 --> 00:28:40,834
don't identify as that. So

769
00:28:41,213 --> 00:28:43,232
I am happy to identify as an athlete

770
00:28:43,371 --> 00:28:45,928
because I am the slowest, like, middle of

771
00:28:45,928 --> 00:28:48,246
the pack person, and but I just like

772
00:28:48,246 --> 00:28:50,173
my whole lifestyle is like that. So I

773
00:28:50,173 --> 00:28:51,681
feel I look at it like, as a

774
00:28:51,681 --> 00:28:53,507
lifestyle. Right? So it's not just, like, being

775
00:28:53,507 --> 00:28:54,221
fast. It's like,

776
00:28:54,935 --> 00:28:56,682
I'd look at my health. I look at

777
00:28:56,682 --> 00:28:57,737
wellness. I

778
00:28:58,523 --> 00:29:00,831
base my day around, you know, some kind

779
00:29:00,831 --> 00:29:03,958
of exercise or, you know, my travel around

780
00:29:04,015 --> 00:29:06,005
getting good food, but I also, you know,

781
00:29:06,244 --> 00:29:07,358
I go off the other end too.

782
00:29:08,248 --> 00:29:10,947
Honestly, I think I literally... Anybody who moves

783
00:29:10,947 --> 00:29:13,250
as an athlete. Right Like, if you're...

784
00:29:13,964 --> 00:29:15,417
And actually, I did do

785
00:29:16,683 --> 00:29:18,521
I practiced a lot of my s yoga

786
00:29:18,521 --> 00:29:19,800
for a long time, and,

787
00:29:20,439 --> 00:29:22,277
right actually, in my thirties, like, that's what

788
00:29:22,356 --> 00:29:23,795
I was doing. I doing on Cha yoga

789
00:29:23,795 --> 00:29:25,502
beds. I practiced Ash.

790
00:29:26,113 --> 00:29:28,256
You did. Oh my god. I still do.

791
00:29:28,891 --> 00:29:30,875
I love it. I still do too. I

792
00:29:30,875 --> 00:29:32,882
practiced. I started when I was, like, in

793
00:29:32,882 --> 00:29:34,877
my twenties in New York, and I started

794
00:29:34,877 --> 00:29:35,834
with Eddy Stern.

795
00:29:36,553 --> 00:29:37,430
Oh, of course.

796
00:29:38,228 --> 00:29:39,585
He was definitely no.

797
00:29:39,999 --> 00:29:41,056
Went there forever,

798
00:29:41,512 --> 00:29:43,502
and then he moved to Brooklyn and I

799
00:29:43,502 --> 00:29:44,936
don't really... I don't... I didn't go to

800
00:29:44,936 --> 00:29:47,404
brooklyn, and he's back in the city, but

801
00:29:47,484 --> 00:29:48,121
I haven't gone.

802
00:29:48,694 --> 00:29:51,015
Practice. I love ash stronger. That's like, that

803
00:29:51,015 --> 00:29:53,894
keeps me together. It's really hard. Right? Like,

804
00:29:54,134 --> 00:29:57,095
is... That's athletic. It's not, like a roll

805
00:29:57,095 --> 00:29:57,994
on the floor

806
00:29:58,308 --> 00:30:01,092
relax yoga? No. No. In as far. Where

807
00:30:01,092 --> 00:30:02,683
do you practice it, where you live?

808
00:30:03,717 --> 00:30:04,217
So

809
00:30:05,404 --> 00:30:06,842
I started doing it when I was living

810
00:30:06,842 --> 00:30:09,799
in Berkeley, so I went to...

811
00:30:10,917 --> 00:30:13,015
I think, Ash Yoga Berkeley. It was called

812
00:30:13,730 --> 00:30:15,490
my teacher there was Fancy over.

813
00:30:16,369 --> 00:30:17,809
And then when I moved to Boston, I

814
00:30:17,809 --> 00:30:19,670
practice with George White sides for

815
00:30:20,384 --> 00:30:22,945
a long time. And now I just... I

816
00:30:22,945 --> 00:30:23,184
don't.

817
00:30:25,585 --> 00:30:28,245
And I have been actually thinking about trying

818
00:30:28,305 --> 00:30:28,805
to

819
00:30:29,439 --> 00:30:31,747
pull something in a little bit more regularly,

820
00:30:31,986 --> 00:30:33,737
whether... You know, I can still practice on

821
00:30:33,737 --> 00:30:35,090
my own. I feel like I have enough,

822
00:30:35,169 --> 00:30:37,000
you know, of a of a background that

823
00:30:37,079 --> 00:30:39,045
I feel safe practicing on my own and

824
00:30:39,244 --> 00:30:39,883
I love...

825
00:30:40,681 --> 00:30:41,001
I...

826
00:30:42,358 --> 00:30:44,354
Like, I am trying to, like, actually have

827
00:30:44,354 --> 00:30:45,253
a very regular

828
00:30:45,631 --> 00:30:48,026
sitting meditation practice, but Ash is so much

829
00:30:48,026 --> 00:30:50,903
easier. Right? Because moving meditation, and that's for

830
00:30:50,903 --> 00:30:53,680
us, like, sc people. Yeah. I can't. I

831
00:30:53,680 --> 00:30:56,242
don't meditate. Yeah. It's hard. It's too hard

832
00:30:56,456 --> 00:30:57,091
for me.

833
00:30:57,424 --> 00:30:58,933
For me to sit. But you don't have

834
00:30:58,933 --> 00:31:00,284
this it. Right? You can walk. You can

835
00:31:00,284 --> 00:31:02,190
do our stronger guy, like, whatever. You can

836
00:31:02,190 --> 00:31:03,564
you don't... You know, but

837
00:31:04,748 --> 00:31:06,123
I am trying

838
00:31:06,578 --> 00:31:08,249
to do the sitting because it is hard

839
00:31:08,249 --> 00:31:09,204
for me, but,

840
00:31:10,557 --> 00:31:12,626
yeah, Like, I don't know. Ash is just

841
00:31:12,626 --> 00:31:14,812
a very good mind body

842
00:31:15,425 --> 00:31:17,175
connection. Yeah. And you can just do it

843
00:31:17,175 --> 00:31:18,925
your own series at home. Like, it's a

844
00:31:18,925 --> 00:31:20,809
it's like the alphabet. Right? So you just

845
00:31:20,929 --> 00:31:22,042
I mean, that's what I was doing. There

846
00:31:22,042 --> 00:31:23,952
was a great during the pandemic. There was

847
00:31:23,952 --> 00:31:26,758
a great teacher that I practice with who

848
00:31:27,292 --> 00:31:30,195
she just rallied. Like, nobody was teaching classes

849
00:31:30,252 --> 00:31:32,482
online in early days, and she just like,

850
00:31:32,721 --> 00:31:33,835
I don't know how she did it. I

851
00:31:33,835 --> 00:31:35,427
don't know what because it's... She's such a

852
00:31:35,427 --> 00:31:35,746
yogi.

853
00:31:36,319 --> 00:31:38,552
Erica H during the pandemic, but she just

854
00:31:38,552 --> 00:31:40,067
started doing, like, zoom classes.

855
00:31:40,705 --> 00:31:43,577
And it was great because even though I

856
00:31:43,577 --> 00:31:45,411
know my own practice, it was great to

857
00:31:45,411 --> 00:31:47,743
have someone like, talking and leading the practice

858
00:31:47,743 --> 00:31:49,741
and, like, you know, there's little things you

859
00:31:49,741 --> 00:31:51,259
do where someone can correct you.

860
00:31:51,898 --> 00:31:54,056
So I don't know. Online was now... I'm

861
00:31:54,056 --> 00:31:56,148
thinking about going back, although I keep I'm

862
00:31:56,148 --> 00:31:57,104
gonna go back to Eddie,

863
00:31:57,981 --> 00:32:00,691
but that requires me to physically, like, get

864
00:32:00,691 --> 00:32:03,002
to, like, the Yoga studio at 7AM.

865
00:32:03,974 --> 00:32:06,212
Which I've totally spoiled now that we all

866
00:32:06,212 --> 00:32:08,529
are, like, kinda working from home and it's

867
00:32:08,529 --> 00:32:08,769
like...

868
00:32:09,648 --> 00:32:11,406
Although I would love to go study with

869
00:32:11,406 --> 00:32:12,845
that know. If I if I looked in

870
00:32:12,845 --> 00:32:13,976
into new I would just be like you

871
00:32:13,976 --> 00:32:16,048
anyone's got. For fine onboarding. Please do. I

872
00:32:16,048 --> 00:32:17,960
know. He... I mean, it's so easy. Right?

873
00:32:18,199 --> 00:32:19,713
Like, it's so. I could, like, walk there.

874
00:32:19,952 --> 00:32:21,546
Now you've got me thinking. I might have

875
00:32:21,546 --> 00:32:22,024
to go back.

876
00:32:23,550 --> 00:32:26,086
Ash is pretty pretty intense. Like, do you

877
00:32:26,086 --> 00:32:28,066
feel like there's a lot that you've learned

878
00:32:28,066 --> 00:32:30,309
over the years from your practice that you

879
00:32:30,538 --> 00:32:32,449
have brought into, like, your day to day

880
00:32:32,449 --> 00:32:34,677
work and, like, all that you do in

881
00:32:34,677 --> 00:32:34,916
life?

882
00:32:36,587 --> 00:32:37,542
Definitely. Like,

883
00:32:38,832 --> 00:32:40,049
Honestly, I

884
00:32:42,578 --> 00:32:45,288
I love skiing for very different reasons, but

885
00:32:46,259 --> 00:32:46,759
Tonga,

886
00:32:47,135 --> 00:32:49,125
and I should say, like, I tried a

887
00:32:49,125 --> 00:32:51,752
couple different kinds of yoga and what I

888
00:32:51,752 --> 00:32:52,889
love about Ash

889
00:32:53,424 --> 00:32:53,924
and

890
00:32:55,031 --> 00:32:56,860
you know, for people that don't know.

891
00:32:57,815 --> 00:33:00,042
Our strongest practice my style, which means you

892
00:33:00,042 --> 00:33:00,599
show up,

893
00:33:01,569 --> 00:33:02,127
to class,

894
00:33:02,924 --> 00:33:05,157
everybody has, like, a set sequence poses that

895
00:33:05,157 --> 00:33:07,071
they're going to do. Nobody talks. Right? Like,

896
00:33:07,310 --> 00:33:09,463
there's no instruction. Nobody's, and I love that.

897
00:33:09,623 --> 00:33:10,442
Like, I

898
00:33:10,834 --> 00:33:12,270
I don't wanna talk to anybody at 07:00

899
00:33:12,270 --> 00:33:13,945
in the morning. Right? I just wanna pro,

900
00:33:14,104 --> 00:33:15,481
and I wanna do my practice

901
00:33:16,417 --> 00:33:16,917
quietly,

902
00:33:17,693 --> 00:33:20,086
and I don't wanna be lecture by, like,

903
00:33:20,818 --> 00:33:22,485
don't know. Somebody's, like, 23 and talk about

904
00:33:22,485 --> 00:33:24,391
their relationship problems for, like, the first 15

905
00:33:24,391 --> 00:33:26,399
minutes of class, which is what, like, every

906
00:33:26,455 --> 00:33:28,122
yoga class I went to when living in

907
00:33:28,201 --> 00:33:30,359
San Francisco was like Oh, yeah. Like I

908
00:33:30,359 --> 00:33:32,262
just can't. I can't, like I can't. I'm

909
00:33:32,262 --> 00:33:34,086
here to not talk. I'm not here to

910
00:33:34,086 --> 00:33:37,520
talk. Yeah. And then, you know, you focus

911
00:33:37,909 --> 00:33:39,981
on your breathing the entire time. So it's,

912
00:33:40,061 --> 00:33:41,597
like, a breathing moving

913
00:33:42,133 --> 00:33:43,807
meditation. So I learned...

914
00:33:45,002 --> 00:33:47,087
I... That was That was my first real

915
00:33:47,087 --> 00:33:49,893
experience with meditation. But what is amazing

916
00:33:50,507 --> 00:33:52,893
about Ash is because you go and you

917
00:33:52,893 --> 00:33:53,927
do the same sequence.

918
00:33:54,499 --> 00:33:56,095
I mean, you know this. Right? Yeah. You

919
00:33:56,095 --> 00:33:57,372
as you go and do the same sequence

920
00:33:57,372 --> 00:33:58,010
every day.

921
00:33:58,728 --> 00:34:00,004
It is like this...

922
00:34:00,643 --> 00:34:02,254
And by the way, I hate team. Like,

923
00:34:02,493 --> 00:34:04,092
I... This is... It's the opposite of what

924
00:34:04,171 --> 00:34:05,849
I would ever see myself doing,

925
00:34:06,808 --> 00:34:10,096
but it's the most incredible measuring stick. Right?

926
00:34:10,414 --> 00:34:13,193
Because I'm like, oh, today, I'm really focused.

927
00:34:13,829 --> 00:34:15,496
Today, my breathing is off.

928
00:34:16,449 --> 00:34:18,832
Today, you know, I am able to get

929
00:34:18,832 --> 00:34:20,755
into this pose, like, I never have before.

930
00:34:21,074 --> 00:34:23,540
What is going on. And then tomorrow, everything

931
00:34:23,540 --> 00:34:24,653
falls apart, but like,

932
00:34:25,369 --> 00:34:26,219
it is just

933
00:34:26,658 --> 00:34:28,732
because you're doing the same thing over and

934
00:34:28,732 --> 00:34:29,450
over again,

935
00:34:30,008 --> 00:34:32,002
the other thing that I realized to is

936
00:34:32,002 --> 00:34:32,242
like,

937
00:34:33,119 --> 00:34:34,155
I don't know. I feel like this is

938
00:34:34,155 --> 00:34:35,692
very, like, first child's

939
00:34:36,803 --> 00:34:40,174
probably, you know, socialize as a female problem

940
00:34:40,950 --> 00:34:41,450
where

941
00:34:42,067 --> 00:34:44,061
you don't wanna do anything unless you're really

942
00:34:44,061 --> 00:34:46,190
good at it. And so you're afraid to

943
00:34:46,389 --> 00:34:49,522
sort of mediocre at any, which also

944
00:34:49,980 --> 00:34:52,793
why I hated running, really mediocre. But

945
00:34:54,945 --> 00:34:57,264
But what's crazy is I'm like, oh, if

946
00:34:57,425 --> 00:34:59,264
I actually just put work into something, I

947
00:34:59,264 --> 00:35:00,065
get better at it.

948
00:35:01,759 --> 00:35:03,672
Go figure. Yeah. Well, like I had to

949
00:35:03,672 --> 00:35:05,985
be, like, in my twenties and doing this

950
00:35:05,985 --> 00:35:07,978
practice for years to, like, sort... Just sort

951
00:35:07,978 --> 00:35:09,255
of figure that out. Do you have, like,

952
00:35:09,334 --> 00:35:10,131
your Aha moment.

953
00:35:10,864 --> 00:35:13,095
Wow. I was like, oh, look, I practiced,

954
00:35:13,254 --> 00:35:14,130
and then I got better.

955
00:35:14,768 --> 00:35:17,715
03:60 view. Like, you also, like, analyze data.

956
00:35:18,207 --> 00:35:20,036
A little bit like Stronger. In my opinion

957
00:35:20,036 --> 00:35:21,808
because you're, like, doing the same thing over

958
00:35:22,342 --> 00:35:24,489
and, like, thinking about all the things that

959
00:35:24,489 --> 00:35:27,215
could possibly be different than the traditional

960
00:35:27,924 --> 00:35:29,523
sort of things you're looking at that should

961
00:35:29,523 --> 00:35:30,481
always be the same.

962
00:35:31,840 --> 00:35:33,539
You have, like, baseline,

963
00:35:34,158 --> 00:35:36,316
you have measurements on your practice that you're

964
00:35:36,316 --> 00:35:38,728
taking every single day. And that was, like,

965
00:35:38,888 --> 00:35:40,566
that is Insight tracker, by way. And that

966
00:35:40,566 --> 00:35:42,164
is 1 thing that I... Like, part of

967
00:35:42,164 --> 00:35:43,842
why I wanted to join in Insight tracker

968
00:35:43,842 --> 00:35:45,840
was I just wanted to do experiments on

969
00:35:45,840 --> 00:35:48,670
myself. And it's really easy to do experiments

970
00:35:48,728 --> 00:35:49,367
on yourself.

971
00:35:50,324 --> 00:35:52,239
We work against the tracker or just, like,

972
00:35:52,318 --> 00:35:54,486
in life in general, but But that's what

973
00:35:54,645 --> 00:35:56,655
Ash s is. It's 1 big long

974
00:35:57,030 --> 00:35:59,733
experiment on yourself, and you think a whole

975
00:35:59,733 --> 00:36:01,085
lot of things out. Right? Like,

976
00:36:02,214 --> 00:36:04,234
So, yeah. What did you learn from your

977
00:36:04,295 --> 00:36:05,414
your os practice?

978
00:36:06,135 --> 00:36:07,974
I just think, like, you really have to

979
00:36:07,974 --> 00:36:10,614
breathe through hardship. That's for running to. Use

980
00:36:10,614 --> 00:36:11,309
your breath

981
00:36:11,668 --> 00:36:14,539
because it will help you. Just inhale, exhale.

982
00:36:15,097 --> 00:36:17,250
Sometimes I say it out loud. If I'm

983
00:36:17,250 --> 00:36:19,483
not listening to myself, but I do that

984
00:36:19,483 --> 00:36:19,801
a lot.

985
00:36:20,454 --> 00:36:21,034
But it...

986
00:36:21,414 --> 00:36:23,815
A great folk, like, way to focus. Right?

987
00:36:24,054 --> 00:36:24,214
It's...

988
00:36:25,335 --> 00:36:26,934
I don't know. III

989
00:36:26,934 --> 00:36:29,275
actually should ask you this. You're you're a

990
00:36:29,589 --> 00:36:32,146
the season track athlete. So when you're running

991
00:36:32,146 --> 00:36:33,604
around that track and you're

992
00:36:33,983 --> 00:36:34,803
absolutely miserable,

993
00:36:35,980 --> 00:36:37,826
because I feel like I started running these

994
00:36:37,826 --> 00:36:39,574
track workouts. I'm like, they're so fun and

995
00:36:39,574 --> 00:36:40,606
then, like, a month and a half into

996
00:36:40,606 --> 00:36:41,877
it and I'm like, oh, I feel terrible.

997
00:36:42,036 --> 00:36:44,418
Like, I'm just at the point at all

998
00:36:44,418 --> 00:36:46,086
times. That's the point of it.

999
00:36:47,294 --> 00:36:47,771
Right.

1000
00:36:48,248 --> 00:36:50,497
That's a how. How do you like, mentally

1001
00:36:50,793 --> 00:36:53,338
handle that when you're at that point? So

1002
00:36:53,338 --> 00:36:55,897
for me, I I love the track because,

1003
00:36:56,135 --> 00:36:58,520
again, I like to look at, like, numbers.

1004
00:36:59,077 --> 00:37:01,064
And so Mh. When you're out on a

1005
00:37:01,064 --> 00:37:02,438
run, Like, my my

1006
00:37:02,987 --> 00:37:04,976
quarter mile time, like, my 400 time, like,

1007
00:37:05,135 --> 00:37:07,282
running on the West side highway is so

1008
00:37:07,282 --> 00:37:08,953
different than it is running on the track.

1009
00:37:09,684 --> 00:37:11,614
So I like the mono not mono

1010
00:37:12,068 --> 00:37:14,317
or mono Yeah of being

1011
00:37:15,009 --> 00:37:16,916
able to, like, just focus on, like, the

1012
00:37:16,916 --> 00:37:18,609
numbers. So it's the same thing with Ash.

1013
00:37:18,919 --> 00:37:20,032
Right? Like, you're going,

1014
00:37:20,589 --> 00:37:22,497
You're doing the same thing every time,

1015
00:37:23,372 --> 00:37:26,076
and things will happen differently every time, But

1016
00:37:26,076 --> 00:37:28,223
it's the same exact thing. Like, there's no

1017
00:37:28,223 --> 00:37:28,620
changes.

1018
00:37:29,191 --> 00:37:31,650
Like, outside of maybe if you're on a

1019
00:37:31,650 --> 00:37:33,658
track the weather. But I like I like

1020
00:37:34,507 --> 00:37:37,695
measuring that kind of stuff. So I just

1021
00:37:37,695 --> 00:37:39,519
started getting back on the track though recently.

1022
00:37:39,757 --> 00:37:40,868
I mean, I haven't been doing a lot

1023
00:37:40,868 --> 00:37:43,089
of track work. I've been really just doing

1024
00:37:43,089 --> 00:37:43,644
road running.

1025
00:37:44,692 --> 00:37:46,996
And, like, just logging miles for half marathons.

1026
00:37:47,234 --> 00:37:49,141
But I do wanna get faster, and I

1027
00:37:49,141 --> 00:37:50,889
think get that being on the track is

1028
00:37:50,889 --> 00:37:51,548
is really

1029
00:37:52,335 --> 00:37:54,717
the best way besides strength training to get

1030
00:37:54,717 --> 00:37:55,114
faster?

1031
00:37:55,987 --> 00:37:58,449
Yeah. A hundred percent. But, I mean, what

1032
00:37:58,449 --> 00:37:59,878
do you like about it? What don't you

1033
00:37:59,878 --> 00:38:00,434
like about it?

1034
00:38:02,037 --> 00:38:04,101
I mean, I I like it for the

1035
00:38:04,101 --> 00:38:05,952
same reason. So my

1036
00:38:07,834 --> 00:38:08,334
current

1037
00:38:09,840 --> 00:38:12,719
coaches, like, who's rang running prescriptions for me

1038
00:38:12,719 --> 00:38:15,360
is this 1 Roma Van Malt. She's amazing.

1039
00:38:16,414 --> 00:38:18,752
She actually started a super cool company

1040
00:38:19,530 --> 00:38:21,847
that looks at women's health. It's a great,

1041
00:38:22,007 --> 00:38:22,906
like, digital

1042
00:38:24,179 --> 00:38:26,417
app in the women's health space, And she,

1043
00:38:30,013 --> 00:38:33,184
hat... She was a national, like, modern task

1044
00:38:33,304 --> 00:38:35,462
lead and has done a lot of very

1045
00:38:35,462 --> 00:38:36,841
fast running. And so

1046
00:38:37,380 --> 00:38:38,659
she was the 1 who...

1047
00:38:39,298 --> 00:38:40,417
I was talking to her, and I was

1048
00:38:40,417 --> 00:38:41,216
like, you know,

1049
00:38:42,906 --> 00:38:44,099
all the races, I... It's like,

1050
00:38:45,053 --> 00:38:47,200
I like long races. Like, I'm comfortable in,

1051
00:38:47,279 --> 00:38:48,733
like, a long, slow

1052
00:38:49,347 --> 00:38:51,993
type of environment. Right? So

1053
00:38:52,383 --> 00:38:53,519
whether it is

1054
00:38:54,372 --> 00:38:56,043
kind of in the, like, half marathon or

1055
00:38:56,043 --> 00:38:57,396
longer. Right? And...

1056
00:38:58,351 --> 00:39:00,022
But I just kinda knew. I knew the

1057
00:39:00,022 --> 00:39:01,866
same thing. I was, like, I there was

1058
00:39:01,866 --> 00:39:03,532
actually... The the first rate that I did

1059
00:39:03,532 --> 00:39:05,197
was a 50 k, and I was pretty

1060
00:39:05,197 --> 00:39:05,673
slow. Right?

1061
00:39:06,704 --> 00:39:08,053
It was great. It was a great...

1062
00:39:08,941 --> 00:39:11,403
Race, but they had 2 options. There was

1063
00:39:11,403 --> 00:39:13,627
the 50 k or the 50 mile. And

1064
00:39:13,786 --> 00:39:15,851
I knew from my 50 k time that

1065
00:39:16,010 --> 00:39:18,130
I actually would have been disqualified from the

1066
00:39:18,248 --> 00:39:20,949
50 mile event. So it wasn't even like,

1067
00:39:21,028 --> 00:39:23,331
oh, Let me try that next because I

1068
00:39:23,331 --> 00:39:24,920
would had to gotten faster. So I was

1069
00:39:24,920 --> 00:39:27,081
like, I should probably just should probably work

1070
00:39:27,081 --> 00:39:28,992
on Speed work. So Roman and I talked

1071
00:39:28,992 --> 00:39:30,584
about it, and she was like, yeah, let's

1072
00:39:30,584 --> 00:39:31,699
get you on the track. So,

1073
00:39:33,132 --> 00:39:35,855
we did. And I And, yeah. No. It's...

1074
00:39:36,015 --> 00:39:38,565
It is super fun and it's fast. Right?

1075
00:39:38,725 --> 00:39:40,558
Like, you are faster on the track,

1076
00:39:41,515 --> 00:39:43,362
than outside or at least I... Tend to

1077
00:39:43,362 --> 00:39:45,030
be those nice spring.

1078
00:39:46,778 --> 00:39:48,288
It's good to run on the track with

1079
00:39:48,288 --> 00:39:50,115
other people or at least, like, have some

1080
00:39:50,115 --> 00:39:51,228
accountability while you're there.

1081
00:39:51,958 --> 00:39:52,277
Yeah.

1082
00:39:53,312 --> 00:39:54,131
This is miserable.

1083
00:39:54,587 --> 00:39:57,693
It's handy. Yeah. I'm afterwards. You're like, I'm

1084
00:39:57,693 --> 00:39:58,910
amazing, but

1085
00:39:59,764 --> 00:40:02,086
I feel like that. Has been... Yeah. I

1086
00:40:02,086 --> 00:40:03,517
mean, it's hard. It's hard to, like,

1087
00:40:04,233 --> 00:40:06,696
get there, and I don't know. I always

1088
00:40:06,696 --> 00:40:09,019
try to... I always overthink it. Is there

1089
00:40:09,019 --> 00:40:11,340
anything, like any new biomarkers that you guys

1090
00:40:11,340 --> 00:40:13,739
are looking at or, you know, data and

1091
00:40:13,739 --> 00:40:14,559
you're analyzing

1092
00:40:14,940 --> 00:40:16,940
business that use your in your like,

1093
00:40:17,753 --> 00:40:19,986
expertise that you are... I tracker come up

1094
00:40:19,986 --> 00:40:21,842
pipeline or in the future. That

1095
00:40:22,460 --> 00:40:23,896
in 2018,

1096
00:40:24,215 --> 00:40:25,499
the and that looked at,

1097
00:40:26,134 --> 00:40:28,855
thousand users that had, like, at least 2

1098
00:40:29,068 --> 00:40:30,893
longitudinal to blood draws and it only looked

1099
00:40:30,893 --> 00:40:31,845
at blood. And so...

1100
00:40:32,654 --> 00:40:34,327
This is essentially going to be the update

1101
00:40:34,327 --> 00:40:36,478
paper to that, and now we're looking at

1102
00:40:36,478 --> 00:40:38,867
20000 people that have at least 2 blood

1103
00:40:38,867 --> 00:40:39,106
draws.

1104
00:40:40,316 --> 00:40:43,342
And then within that cohort, some folks have

1105
00:40:43,502 --> 00:40:44,458
Dna data,

1106
00:40:45,015 --> 00:40:47,325
some folks have fitness tracker data, some folks

1107
00:40:47,325 --> 00:40:48,121
have both. So...

1108
00:40:48,854 --> 00:40:50,931
We were looking at really the combination of

1109
00:40:50,931 --> 00:40:52,550
blood fitness tracker and

1110
00:40:54,447 --> 00:40:55,186
in Dna

1111
00:40:55,565 --> 00:40:55,885
information,

1112
00:40:56,619 --> 00:40:58,134
and, you know, so for example,

1113
00:40:59,092 --> 00:41:00,230
with Dna and

1114
00:41:02,123 --> 00:41:04,530
and statins for example example, You we can

1115
00:41:04,530 --> 00:41:06,282
look and see that there are certain people

1116
00:41:06,282 --> 00:41:07,896
who have an elevated,

1117
00:41:08,591 --> 00:41:11,298
like, a higher genetic risk of having high

1118
00:41:11,378 --> 00:41:13,087
Ldl. Example and

1119
00:41:13,461 --> 00:41:15,131
what we see is that for those folks,

1120
00:41:15,370 --> 00:41:17,676
it's actually harder for them to improve their

1121
00:41:17,755 --> 00:41:18,153
Ldl.

1122
00:41:18,630 --> 00:41:20,698
So they're just, like, bumping up against a

1123
00:41:20,698 --> 00:41:23,520
wall, and it's you know, those are also

1124
00:41:23,577 --> 00:41:25,726
folks that might have to go to a

1125
00:41:25,726 --> 00:41:28,512
stat and earlier, Right? Or, you know, the...

1126
00:41:29,070 --> 00:41:31,155
Even if they get more... Steps. It's it's

1127
00:41:31,155 --> 00:41:33,387
not gonna take their Ldl down because they're

1128
00:41:33,387 --> 00:41:35,379
just, you know, there's something in their genetics

1129
00:41:35,379 --> 00:41:37,053
that makes them sort of resistant to this.

1130
00:41:37,212 --> 00:41:37,712
So

1131
00:41:38,504 --> 00:41:40,981
kind of looking at that integration between, like,

1132
00:41:41,300 --> 00:41:43,159
genetics, fitness tracker data and,

1133
00:41:44,497 --> 00:41:46,015
blood is really,

1134
00:41:46,589 --> 00:41:46,909
cool.

1135
00:41:47,867 --> 00:41:49,785
I would say that the other big thing

1136
00:41:49,785 --> 00:41:50,285
that

1137
00:41:51,063 --> 00:41:53,880
I am frankly really excited about is

1138
00:41:54,994 --> 00:41:57,894
you, inside tracker does an incredible

1139
00:41:58,195 --> 00:41:58,695
job

1140
00:41:58,994 --> 00:42:01,494
of providing a lot of

1141
00:42:02,449 --> 00:42:05,250
information in education to folks.

1142
00:42:07,090 --> 00:42:07,590
And

1143
00:42:07,969 --> 00:42:09,570
there are some people like you that are,

1144
00:42:09,650 --> 00:42:11,043
like, going to var

1145
00:42:11,898 --> 00:42:13,036
kinda, like, read,

1146
00:42:14,211 --> 00:42:16,285
if you want all of that information.

1147
00:42:17,003 --> 00:42:18,359
And then there are other people who are,

1148
00:42:18,439 --> 00:42:21,006
like, y. This is a lot. Like, I

1149
00:42:21,006 --> 00:42:22,920
just just give me 1 thing to work

1150
00:42:22,920 --> 00:42:23,239
on.

1151
00:42:24,276 --> 00:42:25,712
I really wanna do this. Like I really

1152
00:42:25,712 --> 00:42:28,344
wanna get healthier. I I wanna be, like,

1153
00:42:28,663 --> 00:42:30,668
running with my grandkids when 9 years old,

1154
00:42:31,382 --> 00:42:31,882
but,

1155
00:42:32,256 --> 00:42:32,653
you know,

1156
00:42:33,685 --> 00:42:35,511
I don't I don't wanna wave through all

1157
00:42:35,511 --> 00:42:37,337
of this stuff. So we're trying to think

1158
00:42:37,337 --> 00:42:38,607
about different ways to, like,

1159
00:42:39,498 --> 00:42:43,489
essentially provide more engagement tools for folks in

1160
00:42:43,489 --> 00:42:45,724
between the different blood draws? So how do

1161
00:42:45,724 --> 00:42:47,720
we have leverage fitness tracker our data?

1162
00:42:48,293 --> 00:42:48,793
To

1163
00:42:49,958 --> 00:42:52,337
give you a clue about whether you're on

1164
00:42:52,337 --> 00:42:53,234
track or not

1165
00:42:54,240 --> 00:42:55,930
in between these blood. So

1166
00:42:56,398 --> 00:42:58,552
we're going to launch something that we're calling

1167
00:42:58,552 --> 00:42:59,828
the Health band habit score.

1168
00:43:00,546 --> 00:43:02,939
Somebody else might think of a better name

1169
00:43:02,939 --> 00:43:04,614
for this. But essentially what it'll do is

1170
00:43:04,614 --> 00:43:06,724
it'll be a weekly report summarizes,

1171
00:43:08,142 --> 00:43:09,980
your fitness tracker data over the last 6

1172
00:43:09,980 --> 00:43:11,818
weeks. It looks at it kind of a

1173
00:43:11,818 --> 00:43:13,416
way similar to training peaks would look at

1174
00:43:13,576 --> 00:43:16,694
Ct. Right? Like, I I love training peaks.

1175
00:43:17,012 --> 00:43:19,972
It'll... It'll essentially, you'll get a weekly score

1176
00:43:20,583 --> 00:43:21,718
and then they'll summarize,

1177
00:43:22,979 --> 00:43:25,067
on, like, all of these different areas

1178
00:43:25,679 --> 00:43:28,219
that not blood right now. So really just

1179
00:43:28,219 --> 00:43:29,989
like fitness tracker data and

1180
00:43:30,299 --> 00:43:32,215
if you are a person that, like, logs

1181
00:43:32,215 --> 00:43:34,530
into the app and provides information about how

1182
00:43:34,530 --> 00:43:35,988
crest you are or

1183
00:43:36,366 --> 00:43:39,240
whether you've actually done your recommendations or not.

1184
00:43:39,812 --> 00:43:41,877
It'll take all that data. But, basically say,

1185
00:43:42,195 --> 00:43:44,895
here's what went really well last week. Here's,

1186
00:43:45,054 --> 00:43:47,436
you know, that went less well last week.

1187
00:43:48,090 --> 00:43:50,329
And, you know, here are some things for

1188
00:43:50,329 --> 00:43:52,730
you to work on. So my my last

1189
00:43:52,730 --> 00:43:53,929
kind of report that we had.

1190
00:43:54,489 --> 00:43:55,530
My steps were great.

1191
00:43:57,057 --> 00:43:57,876
But my

1192
00:43:59,523 --> 00:44:01,989
bedtime was trash because it was like...

1193
00:44:03,039 --> 00:44:04,713
10:31AM,

1194
00:44:04,952 --> 00:44:07,424
like, night. And you'll be able to... Like

1195
00:44:07,424 --> 00:44:09,497
for people who are diligent about, like, putting

1196
00:44:09,497 --> 00:44:11,331
in all their data and information, you'll be

1197
00:44:11,331 --> 00:44:12,781
able to kind of, like, look at that

1198
00:44:12,781 --> 00:44:14,536
and give them a report. Yes. But you

1199
00:44:14,536 --> 00:44:15,732
don't you don't have to...

1200
00:44:17,646 --> 00:44:19,800
You... If you have a fitness tracker connected,

1201
00:44:20,039 --> 00:44:21,315
you should be able to get a report.

1202
00:44:21,968 --> 00:44:24,280
What I actually really like about it is

1203
00:44:24,280 --> 00:44:24,780
that

1204
00:44:26,432 --> 00:44:28,824
this is actually funny. So on Garmin, you

1205
00:44:28,824 --> 00:44:30,272
know, you get the v 2 Max. Estimate,

1206
00:44:30,431 --> 00:44:31,860
but it doesn't give you a decimal point.

1207
00:44:32,654 --> 00:44:34,322
But I can see on this that they

1208
00:44:34,322 --> 00:44:36,386
actually calculate the decimal point, and I wanna

1209
00:44:36,386 --> 00:44:37,045
know that

1210
00:44:37,418 --> 00:44:39,340
information So what is this V 2 max

1211
00:44:39,340 --> 00:44:41,882
on our garmin Where that from? I love

1212
00:44:41,882 --> 00:44:44,026
this question, and I love talking about this.

1213
00:44:44,820 --> 00:44:46,489
So I'm glad. I'm glad we had talk

1214
00:44:46,489 --> 00:44:47,465
about it. So

1215
00:44:48,974 --> 00:44:51,714
the the... What you get on the

1216
00:44:52,014 --> 00:44:52,514
garmin

1217
00:44:52,815 --> 00:44:54,275
or apple or

1218
00:44:55,869 --> 00:44:58,127
Aura is now doing it. And

1219
00:44:58,746 --> 00:44:59,784
or fitbit,

1220
00:45:00,983 --> 00:45:03,960
is you do get AV2 max estimate

1221
00:45:04,831 --> 00:45:05,331
And

1222
00:45:05,704 --> 00:45:06,737
what they're basically...

1223
00:45:07,372 --> 00:45:09,936
They have some model that they're using. And

1224
00:45:09,992 --> 00:45:11,579
none of these places have publish their models.

1225
00:45:11,738 --> 00:45:13,978
So like, I can't actually tell you is

1226
00:45:13,978 --> 00:45:15,893
going on. There is a white paper for

1227
00:45:15,893 --> 00:45:18,946
apples. Where basically, they're looking at

1228
00:45:19,882 --> 00:45:20,382
probably

1229
00:45:21,650 --> 00:45:24,429
your heart rate while you exercise. If you're

1230
00:45:24,429 --> 00:45:28,002
running outdoors, how, like, what's your pace and

1231
00:45:28,002 --> 00:45:30,067
for how long and, you know, how that

1232
00:45:30,067 --> 00:45:32,630
running essentially, or biking because I think Garmin

1233
00:45:32,630 --> 00:45:34,563
only does it for, like, outdoor runs

1234
00:45:35,257 --> 00:45:35,757
and

1235
00:45:36,134 --> 00:45:39,733
trail... Yeah, outdoor runs and cycling, I but,

1236
00:45:39,893 --> 00:45:41,330
like, you won't get it from a treadmill.

1237
00:45:42,128 --> 00:45:42,767
But anyway,

1238
00:45:43,406 --> 00:45:45,162
so they're making... And I think Aura is,

1239
00:45:45,322 --> 00:45:46,620
they're just... They just

1240
00:45:47,078 --> 00:45:49,488
launched a 6 minute walk test. So they're

1241
00:45:49,488 --> 00:45:51,962
probably looking at heart rate and how fast

1242
00:45:51,962 --> 00:45:53,398
you lock in 6 minutes.

1243
00:45:55,632 --> 00:45:56,590
And from that,

1244
00:45:57,083 --> 00:46:00,343
they're trying to correlate that information with...

1245
00:46:01,377 --> 00:46:02,649
I don't know. Hopefully, we they have a

1246
00:46:02,649 --> 00:46:05,114
dataset set of, like, actual real V 2

1247
00:46:05,273 --> 00:46:07,038
Max values that were measured in the which

1248
00:46:07,038 --> 00:46:09,030
is the only way that you can of

1249
00:46:09,030 --> 00:46:11,659
the... Your your true V max can only

1250
00:46:11,659 --> 00:46:12,398
be measured

1251
00:46:12,774 --> 00:46:14,459
in the lab. Have And this is what

1252
00:46:14,538 --> 00:46:17,098
I would say is if you care

1253
00:46:18,420 --> 00:46:20,639
a lot about knowing what that number is.

1254
00:46:21,608 --> 00:46:22,963
Just go to the lab and do it.

1255
00:46:23,441 --> 00:46:25,513
It does cost money. It's not cheap. It's

1256
00:46:25,513 --> 00:46:27,187
gonna be somewhere between 75 and a hundred

1257
00:46:27,187 --> 00:46:29,100
and 25 dollars probably in the United States.

1258
00:46:30,309 --> 00:46:31,901
But you just have to go, it takes

1259
00:46:31,901 --> 00:46:33,413
15 minutes, then you're done, And then you

1260
00:46:33,413 --> 00:46:35,005
have your number, and that number is real.

1261
00:46:35,641 --> 00:46:37,154
So what I can tell you is I've

1262
00:46:37,154 --> 00:46:38,746
done this, I actually have another 1 schedule

1263
00:46:38,746 --> 00:46:41,698
for july second. But I'm curious if you

1264
00:46:41,698 --> 00:46:43,921
need work on, like, is gonna... Has helped

1265
00:46:43,921 --> 00:46:44,239
or not.

1266
00:46:46,065 --> 00:46:46,755
But by

1267
00:46:47,985 --> 00:46:49,812
I've done it in the lab, and I've

1268
00:46:49,812 --> 00:46:50,789
had estimates

1269
00:46:51,242 --> 00:46:51,742
from

1270
00:46:53,863 --> 00:46:54,974
garment and apple.

1271
00:46:55,862 --> 00:46:57,711
And my lab value...

1272
00:46:58,797 --> 00:47:01,574
My garmin estimate was maybe 2 units now

1273
00:47:01,891 --> 00:47:02,684
My lab value.

1274
00:47:03,492 --> 00:47:04,842
So it was really close.

1275
00:47:05,875 --> 00:47:10,663
My Apple estimate was, like, 17 units lower

1276
00:47:11,830 --> 00:47:12,330
than

1277
00:47:12,799 --> 00:47:16,731
like actual. So whatever model Apple is using

1278
00:47:17,109 --> 00:47:19,902
for me personally is Yeah. Dead wrong. Like,

1279
00:47:20,141 --> 00:47:20,755
it is

1280
00:47:21,349 --> 00:47:21,849
wildly,

1281
00:47:22,540 --> 00:47:24,603
wildly off. So I always tell people who

1282
00:47:24,603 --> 00:47:26,746
are like, why is my a new 2

1283
00:47:26,746 --> 00:47:28,730
max system it's so bad, And I'm like,

1284
00:47:28,809 --> 00:47:30,965
it's it's it's probably not. You should sort

1285
00:47:30,965 --> 00:47:31,917
of lab and I'm a checked.

1286
00:47:33,027 --> 00:47:35,088
I think it, like, I think when when

1287
00:47:35,167 --> 00:47:37,565
I did... Whenever I do those tests, I

1288
00:47:37,804 --> 00:47:39,324
I love it and I hate it because

1289
00:47:39,324 --> 00:47:41,085
it's really, like, when I did my Lac

1290
00:47:41,085 --> 00:47:44,465
tape threshold test, I was so depressed after

1291
00:47:44,764 --> 00:47:45,264
because

1292
00:47:45,739 --> 00:47:47,657
it was so low for my zone 2.

1293
00:47:47,896 --> 00:47:50,054
Like, I don't know. The it was not

1294
00:47:50,054 --> 00:47:52,451
good for me, like, it psychologically, like, I

1295
00:47:52,451 --> 00:47:53,409
thought I was much better.

1296
00:47:54,382 --> 00:47:56,397
And it kinda took me down and

1297
00:47:56,775 --> 00:47:58,689
I it was way before I started inside

1298
00:47:58,689 --> 00:48:00,545
tracker. Like I had a coach for Triathlon

1299
00:48:00,842 --> 00:48:02,438
that had me do, like, all these tests.

1300
00:48:02,677 --> 00:48:04,546
I didn't do the Bo Max test, but

1301
00:48:04,762 --> 00:48:06,434
I did the Lac hate threshold test on

1302
00:48:06,434 --> 00:48:09,059
the bike. And I mean, it was hard.

1303
00:48:09,457 --> 00:48:10,890
You know, now you can just do with

1304
00:48:11,143 --> 00:48:12,812
A test like that on zwift and you

1305
00:48:12,812 --> 00:48:14,481
don't need to test your blood, but I

1306
00:48:14,481 --> 00:48:16,093
was looking at my blood. And,

1307
00:48:16,706 --> 00:48:18,239
like, it was it was definitely

1308
00:48:18,787 --> 00:48:20,616
like, a lot lower than I wanted it

1309
00:48:20,616 --> 00:48:21,808
to be, and I haven't done it again.

1310
00:48:21,967 --> 00:48:23,160
But I would love to do it again

1311
00:48:23,160 --> 00:48:24,989
because I'm so into, like, the numbers. And

1312
00:48:24,989 --> 00:48:26,022
if you can improve,

1313
00:48:26,594 --> 00:48:28,832
then I wanna see the improvement over time

1314
00:48:28,832 --> 00:48:30,910
over, like, what's doing it. So I like

1315
00:48:30,910 --> 00:48:32,429
that you do that, and you're going back

1316
00:48:32,429 --> 00:48:34,187
on July second to get another test to

1317
00:48:34,187 --> 00:48:36,285
see if it improved from your track workouts

1318
00:48:37,159 --> 00:48:38,597
I'm a little nervous about it as well,

1319
00:48:38,916 --> 00:48:41,313
but I I actually bought a lac tape

1320
00:48:41,313 --> 00:48:43,710
meter, and I haven't done the lac tape.

1321
00:48:44,123 --> 00:48:44,623
Testing.

1322
00:48:45,157 --> 00:48:46,429
I spent a lot of money on this.

1323
00:48:46,667 --> 00:48:49,155
Like, those strips are super expensive too. But

1324
00:48:49,291 --> 00:48:50,642
every once a while, I'll get... On a

1325
00:48:50,642 --> 00:48:53,130
treadmill, and I'll I'll do it, and it's

1326
00:48:53,998 --> 00:48:55,136
but it also

1327
00:48:56,949 --> 00:48:58,703
it's really hard to do.

1328
00:48:59,820 --> 00:49:00,320
And

1329
00:49:01,335 --> 00:49:02,872
I have to say that

1330
00:49:04,542 --> 00:49:05,021
the...

1331
00:49:05,499 --> 00:49:07,335
When you go and you get AV2 Max

1332
00:49:07,335 --> 00:49:10,048
test, they'll also, like, re zone you. Like,

1333
00:49:10,208 --> 00:49:10,867
they'll give

1334
00:49:11,245 --> 00:49:13,660
rates. Times. But to your point, like, the

1335
00:49:13,660 --> 00:49:15,679
only way you're really going to know

1336
00:49:15,980 --> 00:49:16,480
where

1337
00:49:17,019 --> 00:49:19,099
that threshold is is by doing the black

1338
00:49:19,099 --> 00:49:19,900
cake testing.

1339
00:49:21,432 --> 00:49:23,365
I think that there are some

1340
00:49:23,820 --> 00:49:25,650
continuous, like, you you have a kind of

1341
00:49:25,650 --> 00:49:26,923
a continuous Glucose monitor.

1342
00:49:27,559 --> 00:49:28,992
Or maybe you know about this. Like, I

1343
00:49:28,992 --> 00:49:31,158
feel like companies are working on a continuous

1344
00:49:31,158 --> 00:49:32,914
lac monitor And I'm like, that is what

1345
00:49:33,073 --> 00:49:34,749
I want. That I want. Well, it would

1346
00:49:34,749 --> 00:49:36,346
have to check your blood though. So I

1347
00:49:36,346 --> 00:49:37,702
don't know how it would work, but I

1348
00:49:37,702 --> 00:49:39,554
am so on board with that, Do you

1349
00:49:39,554 --> 00:49:41,635
do it yourself? You doing the lac tape

1350
00:49:41,635 --> 00:49:42,755
testing yourself on your finger?

1351
00:49:43,474 --> 00:49:45,954
Yeah. And it's it's like, it's hard. It's

1352
00:49:45,954 --> 00:49:47,795
hard to do, and a lot of times,

1353
00:49:47,954 --> 00:49:48,930
like, your...

1354
00:49:49,650 --> 00:49:51,410
Because you're not supposed to, like, squeeze your

1355
00:49:51,410 --> 00:49:53,570
finger for the blood lac tape 1, and

1356
00:49:53,570 --> 00:49:54,469
it's it's

1357
00:49:54,769 --> 00:49:56,610
it isn't definitely a rig role, and then

1358
00:49:56,690 --> 00:49:58,533
I have, like, practice for a little while

1359
00:49:58,533 --> 00:50:00,278
to make sure that I do it, and,

1360
00:50:00,437 --> 00:50:02,817
you know, you're literally, like, running, and then

1361
00:50:02,817 --> 00:50:04,007
you have to stop and then do this.

1362
00:50:04,166 --> 00:50:05,222
So that's affecting

1363
00:50:06,007 --> 00:50:08,072
your results and I don't know. It's very

1364
00:50:08,072 --> 00:50:09,684
finicky. So A continuous

1365
00:50:10,455 --> 00:50:12,203
continuous 1 be good. Do you, like, since

1366
00:50:12,203 --> 00:50:14,682
we're, like, talking about data and analytics. And

1367
00:50:14,682 --> 00:50:16,677
all those fun things. Like, do you think

1368
00:50:16,677 --> 00:50:19,149
the Garmin has, like, accurate zones for your

1369
00:50:19,149 --> 00:50:21,702
heart rate and for your base? Because... I

1370
00:50:21,702 --> 00:50:23,536
mean, there's, like a general formula. But then

1371
00:50:23,536 --> 00:50:25,465
when you start training and running it kinda

1372
00:50:25,465 --> 00:50:26,682
shifts based on

1373
00:50:27,219 --> 00:50:29,372
you. But do you think it's... Yeah. Yeah.

1374
00:50:29,692 --> 00:50:30,192
That

1375
00:50:30,568 --> 00:50:32,823
that is a great question. And I

1376
00:50:34,409 --> 00:50:35,705
I know on garmin,

1377
00:50:36,479 --> 00:50:37,833
it is very... And I don't know if

1378
00:50:37,833 --> 00:50:40,221
this is my true lac tape thresholds.

1379
00:50:42,067 --> 00:50:45,332
But I know that that estimate keeps changing,

1380
00:50:46,447 --> 00:50:49,393
depending on, like, how much work I'm doing

1381
00:50:49,393 --> 00:50:49,791
frankly.

1382
00:50:52,117 --> 00:50:53,789
For the actual zones,

1383
00:50:54,586 --> 00:50:56,338
I don't know, and I need to check

1384
00:50:56,338 --> 00:50:58,410
my settings because I don't know if my

1385
00:50:58,410 --> 00:50:59,547
zones are

1386
00:51:00,257 --> 00:51:02,887
particularly dynamic. But what I can tell you

1387
00:51:02,887 --> 00:51:04,901
is that the garmin zones

1388
00:51:05,675 --> 00:51:06,574
for me

1389
00:51:07,444 --> 00:51:08,581
were fairly

1390
00:51:09,356 --> 00:51:13,022
accurately matched with the zones that I got

1391
00:51:13,022 --> 00:51:14,695
when I did my Left V 2 Max

1392
00:51:14,695 --> 00:51:15,108
test test.

1393
00:51:15,984 --> 00:51:16,484
So

1394
00:51:16,939 --> 00:51:19,509
so those zone twos actually

1395
00:51:20,203 --> 00:51:22,693
matched up. And I think I might have

1396
00:51:23,244 --> 00:51:24,596
don't remember if it was garmin, but I

1397
00:51:24,596 --> 00:51:25,790
think it might be Garmin where you get

1398
00:51:25,790 --> 00:51:26,847
to pick which

1399
00:51:27,699 --> 00:51:29,928
method in training piece. Right?

1400
00:51:31,138 --> 00:51:32,653
Yeah. I don't know about... I don't think

1401
00:51:32,653 --> 00:51:34,487
it's... I think training peaks you can go

1402
00:51:34,487 --> 00:51:35,843
into the back end, and then you can

1403
00:51:35,843 --> 00:51:37,836
pick, like, whether you're doing 5 zones, 7

1404
00:51:37,836 --> 00:51:38,315
zones.

1405
00:51:39,272 --> 00:51:41,280
I don't know. But I I have to...

1406
00:51:41,520 --> 00:51:42,955
I always have to update it, You know,

1407
00:51:43,115 --> 00:51:44,412
like, I feel like sometimes...

1408
00:51:45,108 --> 00:51:46,544
I don't know. I just feel like sometimes

1409
00:51:46,544 --> 00:51:47,979
the data on the back ends wonky. Like,

1410
00:51:48,139 --> 00:51:50,067
I forget about it. Like, I'll put in,

1411
00:51:50,226 --> 00:51:52,160
and I do all the training, and then

1412
00:51:52,616 --> 00:51:54,527
I'm, like, I am still stuck in the

1413
00:51:54,527 --> 00:51:57,076
mindset that my heart rate, my zone 2

1414
00:51:57,076 --> 00:51:59,485
running heart rate is like, 40, but it

1415
00:51:59,485 --> 00:52:00,605
might be higher and now that I'm older,

1416
00:52:00,765 --> 00:52:01,965
but I did it when I was younger.

1417
00:52:02,445 --> 00:52:04,845
And I'm hanging on to it. So but

1418
00:52:05,005 --> 00:52:06,925
Garmin would tell me I think Garmin would

1419
00:52:06,925 --> 00:52:08,617
be like, you are not like, you're are

1420
00:52:08,617 --> 00:52:09,414
in zone 4?

1421
00:52:10,609 --> 00:52:12,203
I would be so upset. That's what happened

1422
00:52:12,203 --> 00:52:13,717
with the Lac test? I think I thought

1423
00:52:13,796 --> 00:52:15,549
I was, like, morph fit, and I bought

1424
00:52:15,549 --> 00:52:17,701
it. Then did you do... Did you, like,

1425
00:52:18,114 --> 00:52:20,692
do a bunch of zone 2 kinda training

1426
00:52:20,830 --> 00:52:22,289
after that and try

1427
00:52:22,748 --> 00:52:24,960
like, did it work out. I I got

1428
00:52:25,319 --> 00:52:27,876
I was a new athlete, Like, the beginner

1429
00:52:27,876 --> 00:52:29,555
athletes like hate zone 2.

1430
00:52:30,274 --> 00:52:32,272
And Yeah. And I my coach was, like,

1431
00:52:32,432 --> 00:52:33,790
we're gonna be spending a lot of time

1432
00:52:33,790 --> 00:52:35,643
in zone 2, and I'm like, yeah. I'm

1433
00:52:35,643 --> 00:52:37,561
not gonna be working with you. He was

1434
00:52:37,561 --> 00:52:39,959
right. Of course. But, yeah. No. I think

1435
00:52:39,959 --> 00:52:41,398
it's hard. I think it's, like, I don't

1436
00:52:41,398 --> 00:52:43,156
know. So... And then... So that's the kind

1437
00:52:43,156 --> 00:52:44,846
of stuff that you're doing though, looking at

1438
00:52:44,846 --> 00:52:46,993
the back end of, like, the data inside

1439
00:52:46,993 --> 00:52:48,185
tracker? And kind of...

1440
00:52:48,901 --> 00:52:50,014
Do you think inside tracker,

1441
00:52:50,729 --> 00:52:52,024
would they ever add

1442
00:52:52,494 --> 00:52:54,655
that kind of testing into the back end,

1443
00:52:54,735 --> 00:52:56,015
like, I don't know how they would do

1444
00:52:56,015 --> 00:52:56,914
it, but,

1445
00:52:57,535 --> 00:52:58,434
like, from a

1446
00:52:59,135 --> 00:53:01,785
to, like, compare, like, your lac threshold or

1447
00:53:01,785 --> 00:53:04,251
your V 2 Max to, like, your heart

1448
00:53:04,251 --> 00:53:05,547
rate, like, your biomarkers

1449
00:53:05,922 --> 00:53:07,376
or your heart rate variability,

1450
00:53:07,751 --> 00:53:07,831
like,

1451
00:53:08,719 --> 00:53:09,196
that coming?

1452
00:53:09,831 --> 00:53:12,452
We do look at V Max and the

1453
00:53:12,452 --> 00:53:14,834
connection between V Max and biomarkers and V

1454
00:53:14,834 --> 00:53:17,081
to Max actually is

1455
00:53:18,268 --> 00:53:18,768
wonderful

1456
00:53:20,426 --> 00:53:21,326
in that

1457
00:53:22,583 --> 00:53:25,300
if you had to pick 1 biomarker to

1458
00:53:25,300 --> 00:53:25,860
focus on,

1459
00:53:26,751 --> 00:53:27,228
improving,

1460
00:53:27,865 --> 00:53:30,172
just pick the T Max. Pick your V

1461
00:53:30,172 --> 00:53:31,047
max estimate because,

1462
00:53:31,843 --> 00:53:32,480
first of all,

1463
00:53:33,355 --> 00:53:34,332
it is correlated

1464
00:53:35,677 --> 00:53:38,326
in the right direction with, like, nearly

1465
00:53:39,577 --> 00:53:42,363
nearly every biomarker that we measure in blood.

1466
00:53:42,761 --> 00:53:44,932
So it's a, you know, a

1467
00:53:45,403 --> 00:53:48,297
it can somewhat predict what your blood biomarker

1468
00:53:48,754 --> 00:53:51,068
value is Are oh interesting. It's not the

1469
00:53:51,068 --> 00:53:51,546
only thing.

1470
00:53:52,518 --> 00:53:55,245
Yeah. Like, it's connected it associated

1471
00:53:56,018 --> 00:53:56,518
where

1472
00:53:57,052 --> 00:54:00,173
many, many of the like key markers in

1473
00:54:00,173 --> 00:54:01,073
there. Like

1474
00:54:01,532 --> 00:54:05,207
glucose. They want see insulin, ldl DLBH

1475
00:54:05,207 --> 00:54:05,686
hdl,

1476
00:54:07,619 --> 00:54:09,054
how much sleep you get? Like,

1477
00:54:10,091 --> 00:54:13,303
every... You're just... Yeah. You name it? Probably

1478
00:54:13,521 --> 00:54:14,956
related with V 2. So the higher your

1479
00:54:15,036 --> 00:54:16,951
V 2 max is the better.

1480
00:54:17,602 --> 00:54:19,907
And if it's low, then you might wanna

1481
00:54:19,907 --> 00:54:21,974
be looking at your blood to see what's

1482
00:54:21,974 --> 00:54:22,791
going on

1483
00:54:23,245 --> 00:54:23,745
there

1484
00:54:24,199 --> 00:54:24,597
as well.

1485
00:54:25,968 --> 00:54:27,966
Yeah. If it's low. I mean, the good

1486
00:54:27,966 --> 00:54:29,724
thing and and you know this. The good

1487
00:54:29,724 --> 00:54:32,121
thing the good thing about a low V

1488
00:54:32,121 --> 00:54:34,370
2 Max is that, like, you can... Yeah.

1489
00:54:34,609 --> 00:54:36,360
Get it Yeah. There's always room for moment.

1490
00:54:36,599 --> 00:54:36,917
Just, like,

1491
00:54:37,952 --> 00:54:38,929
there's always

1492
00:54:39,702 --> 00:54:41,950
always room for improvement bit. So... And you

1493
00:54:41,950 --> 00:54:44,190
can get it, like, you can... There are

1494
00:54:44,190 --> 00:54:46,110
definitely some newbie gains there. Right?

1495
00:54:46,910 --> 00:54:49,150
Like, if you're if you're sedentary, and then

1496
00:54:49,150 --> 00:54:50,690
all of a sudden, like, you

1497
00:54:51,084 --> 00:54:53,322
safely be an roanoke running program. Like, your

1498
00:54:53,402 --> 00:54:55,160
V 2 Max is definitely gonna jump.

1499
00:54:55,879 --> 00:54:57,717
And that's where it doesn't matter. So, like,

1500
00:54:57,877 --> 00:54:59,849
even though I'm mad at my app,

1501
00:55:00,446 --> 00:55:01,321
V02 Max.

1502
00:55:01,958 --> 00:55:03,310
If Apple was the only...

1503
00:55:04,105 --> 00:55:05,401
If Apple was the only

1504
00:55:06,014 --> 00:55:08,255
my my god device that I had was

1505
00:55:08,255 --> 00:55:10,105
using. Like, the whole point isn't

1506
00:55:11,034 --> 00:55:13,179
the number itself. Like, the whole point is

1507
00:55:13,179 --> 00:55:15,739
actually is the number getting higher. Over time.

1508
00:55:15,898 --> 00:55:17,757
Like, that's it. That's all that actually

1509
00:55:18,216 --> 00:55:20,394
really and truly matters unless

1510
00:55:21,333 --> 00:55:24,070
you are, like, in a lead athlete and

1511
00:55:24,463 --> 00:55:27,246
or you're really kind of performance driven.

1512
00:55:27,802 --> 00:55:29,154
But if you're just doing it for health,

1513
00:55:29,313 --> 00:55:31,619
Like, just just try and get that number

1514
00:55:31,619 --> 00:55:33,544
higher or whatever it Yeah. I think so.

1515
00:55:33,703 --> 00:55:35,852
I mean, I think I... I'm, like, speaking

1516
00:55:35,852 --> 00:55:37,762
of numbers. I have to do my inside

1517
00:55:37,762 --> 00:55:39,775
tracker test and I've been, like, a afraid

1518
00:55:39,911 --> 00:55:42,003
because I've been eating so poorly

1519
00:55:42,393 --> 00:55:44,378
and have not. I used to eat oatmeal

1520
00:55:44,378 --> 00:55:44,853
every day,

1521
00:55:45,647 --> 00:55:46,282
every day.

1522
00:55:47,155 --> 00:55:49,218
I would eat, like for years, and I

1523
00:55:49,218 --> 00:55:50,409
had great cholesterol.

1524
00:55:51,059 --> 00:55:52,971
And I have not. I just got too

1525
00:55:52,971 --> 00:55:54,723
sick of it, I just stopped eating it.

1526
00:55:55,201 --> 00:55:57,989
And now I'm afraid to take my test.

1527
00:55:58,148 --> 00:55:59,661
Because if my cholesterol is up,

1528
00:56:00,313 --> 00:56:03,253
mean, nothing interesting experiment though I I did.

1529
00:56:03,492 --> 00:56:04,309
I literally

1530
00:56:04,763 --> 00:56:07,957
was eating oatmeal probably every day. For, like,

1531
00:56:08,116 --> 00:56:10,344
10 years because I was had gluten issues.

1532
00:56:10,661 --> 00:56:12,730
And and by the way, like, those things

1533
00:56:12,730 --> 00:56:14,320
kind, a lot of those things for me.

1534
00:56:14,894 --> 00:56:16,652
After not eating them went away.

1535
00:56:17,291 --> 00:56:19,469
I mean, I don't have Celiac, but

1536
00:56:19,849 --> 00:56:21,287
some of them are still here, but in,

1537
00:56:21,447 --> 00:56:23,739
I can eat things in moderation now, but

1538
00:56:24,415 --> 00:56:26,401
but, yeah, I was eating oatmeal like, every

1539
00:56:26,401 --> 00:56:29,204
single day for years. And,

1540
00:56:29,578 --> 00:56:31,564
like, still cut oatmeal, and I just kinda

1541
00:56:31,564 --> 00:56:32,755
got, like, so over it.

1542
00:56:34,364 --> 00:56:36,201
During the yeah. And all... It started up

1543
00:56:36,201 --> 00:56:39,077
upsetting my stomach before runs. And so I

1544
00:56:39,077 --> 00:56:39,577
just

1545
00:56:40,848 --> 00:56:42,275
And now I haven't done my blood in,

1546
00:56:42,354 --> 00:56:43,940
like, I don't know, like, 4 months, and

1547
00:56:44,098 --> 00:56:46,556
I'm so nervous because it's... I... Yeah. Yeah.

1548
00:56:46,873 --> 00:56:48,141
I'm gonna do it. I have to, like,

1549
00:56:48,474 --> 00:56:50,939
But now I've been, like, binging oatmeal. I

1550
00:56:50,939 --> 00:56:53,961
started eating Oatmeal again. Just like, I'm preparing

1551
00:56:53,961 --> 00:56:55,949
for the inside tracker test. I like, started

1552
00:56:55,949 --> 00:56:57,937
taking Vitamin D again. I'm like, trying to,

1553
00:56:58,016 --> 00:57:00,497
like, you know, get everything back up to

1554
00:57:00,497 --> 00:57:01,372
a positive level.

1555
00:57:02,088 --> 00:57:04,077
Totally. It's like, when you start floss because

1556
00:57:04,077 --> 00:57:06,715
the dentist appointment it's you're? Yeah. I've totally

1557
00:57:06,715 --> 00:57:08,544
been doing this every single day twice a

1558
00:57:08,544 --> 00:57:10,770
day the whole time. Yep. Definitely. So do

1559
00:57:10,770 --> 00:57:12,439
you do you check your blood like every

1560
00:57:12,439 --> 00:57:12,837
3 months?

1561
00:57:13,804 --> 00:57:15,627
I have an appointment on Saturday because I

1562
00:57:15,627 --> 00:57:16,578
too am late.

1563
00:57:17,688 --> 00:57:19,986
But I try I try to do it

1564
00:57:19,986 --> 00:57:20,486
every

1565
00:57:20,794 --> 00:57:23,125
every 3 months or so. And

1566
00:57:24,137 --> 00:57:27,401
there's always like something. It's like, whack mole.

1567
00:57:27,640 --> 00:57:30,282
Like, I alright. I solve that problem, but

1568
00:57:30,282 --> 00:57:32,274
something else is... I think, like, last time

1569
00:57:32,274 --> 00:57:33,469
my cortisol is all high.

1570
00:57:34,106 --> 00:57:36,453
Normally, my cortisol is not high. And

1571
00:57:37,465 --> 00:57:39,534
I think I know why might have been

1572
00:57:39,534 --> 00:57:42,159
navy, but I'm actually really curious to see

1573
00:57:42,159 --> 00:57:45,277
what happened specifically with that marker because plus

1574
00:57:45,277 --> 00:57:47,901
it's not normally up. Although it is high

1575
00:57:47,901 --> 00:57:50,230
in many women, not

1576
00:57:50,684 --> 00:57:51,184
not.

1577
00:57:51,638 --> 00:57:54,064
So, sometimes it's like retest specific biomarker?

1578
00:57:54,443 --> 00:57:55,721
Like, can you do that on your own

1579
00:57:55,721 --> 00:57:57,878
not through inside tracker. Do you... Or can

1580
00:57:57,878 --> 00:57:59,796
you just do it? You personally through an

1581
00:57:59,796 --> 00:58:02,609
inside tracker? Or I mean, I have...

1582
00:58:04,449 --> 00:58:05,269
I did

1583
00:58:05,570 --> 00:58:06,070
retest,

1584
00:58:06,690 --> 00:58:07,989
like, a couple

1585
00:58:08,289 --> 00:58:09,109
a handful

1586
00:58:09,650 --> 00:58:10,150
of

1587
00:58:10,943 --> 00:58:11,443
biomarkers

1588
00:58:13,096 --> 00:58:13,596
because

1589
00:58:14,133 --> 00:58:15,887
I don't have a thyroid. And so I

1590
00:58:15,887 --> 00:58:16,786
have to take le,

1591
00:58:17,323 --> 00:58:19,157
and I had forgotten to take it for,

1592
00:58:19,237 --> 00:58:20,010
like, 2 weeks

1593
00:58:20,769 --> 00:58:23,670
So I took a Ts h test, and

1594
00:58:24,130 --> 00:58:25,170
it was out of control,

1595
00:58:26,210 --> 00:58:27,329
and and actually,

1596
00:58:27,809 --> 00:58:28,309
interestingly,

1597
00:58:29,023 --> 00:58:30,929
my lipid values also went up.

1598
00:58:32,358 --> 00:58:33,867
So when you're hypo thyroid,

1599
00:58:34,581 --> 00:58:37,123
you have a higher chance of, actually also

1600
00:58:37,123 --> 00:58:37,440
having

1601
00:58:37,996 --> 00:58:41,041
Less strong because they're... Yeah. It's... So it's

1602
00:58:41,041 --> 00:58:43,992
really it's really interesting. Like, if somebody's cholesterol,

1603
00:58:44,152 --> 00:58:46,225
just all of a sudden kinda gets higher

1604
00:58:46,225 --> 00:58:49,271
than normal, like I would go do a

1605
00:58:49,271 --> 00:58:52,940
that to see because that could could possibly

1606
00:58:52,940 --> 00:58:53,440
be

1607
00:58:53,818 --> 00:58:56,290
part of the issue. But, yeah, Like, I

1608
00:58:56,290 --> 00:58:58,536
literally forgot to take my thyroid for a

1609
00:58:58,536 --> 00:59:00,756
couple of months, I had tested was like,

1610
00:59:00,914 --> 00:59:03,293
oh, whoa, and then I retest that

1611
00:59:03,689 --> 00:59:05,433
particular marker. I take a couple weeks later.

1612
00:59:06,083 --> 00:59:07,140
And everything...

1613
00:59:07,675 --> 00:59:09,982
Everything went back down because I retest that

1614
00:59:09,982 --> 00:59:12,210
and the lipids because I... That was also

1615
00:59:12,210 --> 00:59:13,801
something that I saw was I also saw

1616
00:59:13,801 --> 00:59:14,676
the spike in lipids.

1617
00:59:15,168 --> 00:59:17,313
Been super awesome to connect with you. Well,

1618
00:59:17,472 --> 00:59:18,266
thank you so much.

1619
00:59:19,538 --> 00:59:21,444
Thanks again for tuning into Mar on the

1620
00:59:21,444 --> 00:59:21,603
move.

1621
00:59:22,334 --> 00:59:23,927
If you like what you hear, leave us

1622
00:59:23,927 --> 00:59:26,795
a 5 star review in Apple podcasts.

1623
00:59:27,672 --> 00:59:29,026
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1624
00:59:29,679 --> 00:59:32,000
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1625
00:59:32,400 --> 00:59:35,359
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1626
00:59:35,359 --> 00:59:36,559
the move dot com.

1627
00:59:37,135 --> 00:59:39,855
For more info on this episode, links in

1628
00:59:39,855 --> 00:59:41,375
the show notes, and of course,

1629
00:59:41,934 --> 00:59:44,494
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1630
00:59:44,974 --> 00:59:47,648
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