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That Triathlon Show

Advanced power training with Xert - Armando Mastracci and Stephen Cheung | EP#86

That Triathlon Show

Mikael Eriksson

Health, Swimming, Endurancesports, Run, Cycling, Health & Fitness, Fitness, Ironman, Swim, Bike, Sports, Running, Sportscience, Triathlon

4.9596 Ratings

🗓️ 21 December 2017

⏱️ 65 minutes

🧾️ Download transcript

Summary

Xert is a training and analysis software that takes use of power data to the next level. In training and fitness analysis, training planning and fitness prediction, and real-time training and racing. Armando Mastracci, founder, and Stephen Cheung, chief sport scientist, join us to discuss how triathletes can benefit from the platform.     IN THIS EPISODE YOU'LL LEARN ABOUT: Maximal Power Available (MPA) and its use in real-time as well as training planning Moving away from training zones, and towards Focus and Strain Fitness signatures and moving away from the need of formal testing Planning training and workouts to precisely predict fitness changes The adaptive training adviser     SHOWNOTES: www.thattriathlonshow.com     SPONSORS: Precision Hydration - One-size doesn't fit all when it comes to hydration. Take Precision Hydration's FREE sweat test and learn how you should hydrate. Use the discount code THATTRIATHLONSHOW for 15% off any products.       LINKS AND RESOURCES:            The Xert paltform           Baron Biosystems' website          Xert blog          Xert Youtube channel          The Xert User's Facebook community Cycling Science and Myth Busting Part 1 with Stephen Cheung | EP#74 Cycling Science and Myth Busting Part 2 with Stephen Cheung | EP#75     RATE AND REVIEW: If you enjoy the show, please help me out by subscribing, rating and reviewing: www.scientifictriathlon.com/rate/     CONTACT: Want to send feedback, questions or just chat? Email me at mikael@scientifictriathlon.com or connect on Twitter - my handle is @SciTriat.

Transcript

Click on a timestamp to play from that location

0:00.0

And the software does this for you under the covers.

0:02.0

It determines the relationship between training load and your threshold, as an example.

0:07.5

It does that under the covers.

0:09.2

When you then say, I'm going to do all this training, and this training equates to so much training against your threshold power,

0:16.4

it then says, oh, we expect your threshold power to go up by five watts as a result of that

0:21.1

trading.

0:23.4

That Traeclam show, episode 86.

0:45.2

Hey, what's up everybody and welcome back to another episode of That Traathlon show, the podcast presented by Scientific Traathlon.com. I'm your host, Michael, and today's episode is a real

0:51.1

doozy for anybody training with power on the bike or on the run,

0:55.3

and who wants to maximize the use debt and the benefits that you can get out of it.

1:00.0

So I'm joined by two guests today, Armando Mastrachi and Stephen Chung.

1:05.6

Armando is the founder and CEO, and Stephen is the chief sports scientist of Barron Biosystems, and they're more known for the name of their product, which is Exert, which is spelled X-E-R-T, so you skip the E in the word exert the way that it's commonly spelled.

1:24.8

And it's a training and analysis software that takes use of

1:28.9

power data to the next level in both training and fitness analysis, as well as in training

1:35.6

planning and fitness prediction, and also real-time training and racing. And I've been using

1:41.9

this platform by the time of this recording for just over a month

1:46.3

for so and I'm still just scratching the surface to be honest but I'm super impressed and

1:51.2

excited about the opportunities that Excerd presents so some of the things that we

1:55.3

discuss today and that you'll learn about that are key features of Excerd include

1:59.0

maximum power available or Mpa and it's used in

2:02.7

real time as well as training planning and moving away from training zones and towards

2:10.0

the use of concepts like focus and strain when it comes to analyzing and planning your training

...

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