# Athletica.ai Workout Reserve

I was taking a look at the Athletica.ai blog, and their concept of Workout Reserve seems to be a copy of MPA. I’m not saying this to tattle, it’s more that they’ve seen a good idea in Xert, and copy it, or ended up there on their own. I wouldn’t be surprised to see TrainerRoad trying to implement something like MPA as well.

MPA isn’t that unique to Xert, is it? Intervals.icu for instance as W bal graphs and allows you to plot them when designing workouts.

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We skipped the whole W’bal thing because it’s flawed, fundamentally so. Anyone with a basic understanding of physics and human physiology would see this once you look closely at how it works.

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Athletica.ai distinguishes between W’ and what they call Workout Reserve. It’s really quite interesting From their blog:

Q: What are the benefits of Workout Reserve vs. W’?

A: The benefit of Workout Reserve and its advantage over the W’ is that it’s a mathematical, not a physiology-derived construct. The likely fault in W’ is that It assumes an equivalent strain on the system across a variety of effort durations following the CP hyperbola. However, fatigue in the heavy and moderate domains is dynamic. Workout Reserve, therefore, is an umbrella that encapsulates physiology (integration of systems: nervous, respiratory, cardiovascular and musculoskeletal) into the performance potential without dwelling on how the performance/output is actually achieved. Beautiful in its simplicity we feel. Users might fall in love with this or may be critical. While our work is based on a deep understanding of physiology, our aim with the performance potential lies in practical application, bringing everything to a common denominator.

Q: Can I compute the Workout Reserve on my own?

A: Yes, have a look at the formulas in the pre-print paper (Zignoli 2023) at this link: they can be easily implemented in an Excel spreadsheet.

Hi there. Just to be clear, we were aware of MPA, but there is no open documentation on it as we have provided for WR (see thread below). We had to read between the lines to understand if we were building a replica of it, or a completely new feature. It wouldn’t be the first (or the last) time scientists converge to the same idea from different pathways. While there are similarities, our gut tells us that the approaches are different. As you might tell, the exponential behaviour is familiar, but you can equally find it in Wbal models. The fact that with MPA explanations there is discussion on energy and TTE (steady state to failure) makes us believe the approaches are different. WR is metabolic energy agnostic.

Thanks @CarmenV. Our latest update includes the ability to view your WR in real time on your favourite Garmin device. Version 1.4.1 for downloading here.

Thank @prof for chiming in on our forum. While some aspects of the Xert model haven’t been published, we are working with research institution and plan to publish papers covering a variety of topics with them. We are also working on a detailed Xert white paper that we expect to publish in preprint form.

As a non-scientist, one of my early aspirations was to grow the business sufficiently were we could afford to sponsor published research. Luckily, thanks to our user community, we have reached that stage. (Sadly, we had just started working with Dr. Louis Passfield and he passed away suddenly last year. Fortunately, the research group saw the opportunity and we have been working with them since then).

Look for research to get published on various aspects of Xert over the coming months and years.

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Thanks Armando @xertedbrain.
Appreciate the opportunity to communicate and collaborate ideas and great respect for your excellent platform. Louis was a friend and we all feel his loss. I look forward to reading your white paper and the research group work you are continuing. Please reach out directly if we can ever collaborate on initiatives that help our users.
Best wishes,
Paul

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