Its almost always hard to say what reason on the survey is the most guilty for the answer given on the WO intensity. Sometimes there are several factors to have felt a WO very hard or all out or failed.
It would be nice to be able to pick more than one reason, lets say up to 3 and by relevance of importance (1st 2nd 3rd) to our reply.
Would really love to know the answer to this. Is it possible? Has it been considered? Could a multiple factors answer give a different response than a single reason in terms of adaptation suggestions after that WO?
EXAMPLE: yesterday evening replyed all out to a 1h30 VO2 WO when asked about the reason I doubted on wich to pick ended by picking WO intensity but thought also that hadnt slept enough the night before and that day was realy stressfull at work so in a good night sleep and low stress day the itensity of the WO could have not made me reply all out.
That all out and WO intensity reply probably made the AI to suggest me to swap the next VO2 WO by one less intense wich I’ve accepted.
Now I hope that on that next WO if I have a good performance and that is done after a good night sleep and on a less stressfull day Im not loosing the chance to improve more if I had stick with the original prescribed WO or the reasons for me all out reply had been more than only the workout intensity.
Hope this makes sense.
Probably spoken somewhere if so please can a moderator merge this or share the link to the post. Tks.
When you choose a reasoning like “sleep”, it assumes you likely could have completed the workout were it not for this temporary and unforeseen reason. So PLs will likely stay the same.
When you choose a reason like “fatigue” or “intensity”, this is not a temporary issue one-off occurrence. It means the workout is too much and your PL needs to be backed down.
So when you’re choosing your reason for not completing a workout and you’re between responses, ask yourself if you’re having a temporary or chronic issue.
I recently had thoughts along similar lines. One way to think about it is what would the exchange be like if you were reporting to a coach after a workout, and what adjustments would they make to your training based on that. The exchange with the coach would be much more nuanced than selecting from the 5 fixed choices TR gives you, and the coach would have much wider latitude in how to respond. For example, if, after some discussion, the coach senses you’re experiencing burnout, they might totally change things up for a couple of weeks to try to get you back in the groove. With TR as it is now, that’s something you’re going to have to figure out and do on your own.
I always just ask: if everything (sleep, stress, nutrition, etc) was optimal, would I have succeeded in that workout? It helps to look back on the last two weeks at workouts in the same zone and level. For example, if I nailed a 5.5 threshold last week, and completely blew up on this weeks 5.6, was there another factor that caused it? For me at least their usually is; if there isn’t, it’s probably training fatigue or intensity (hard to tell between the two most times). It’s important to distinguish, because as above poster said, those answers impacts adaptations.
I also think it can be tricky to match up workout intensity and the post work out survey. For example, high intensity workouts like V02 max are always going to be hard, but there’s a difference between working hard and barely surviving. I reserve “very hard” and “all out” for those workouts where I barely survive, and am pulling out ALL the mental tricks to get through. I almost never pick all out, because I think a race scenario is the only time I have the motivation to truly truly empty the tank (mentally and physically.)
The most likely answer has to do with data analysis. I’m a social scientist and it can be challenging to deal with multiple entry ranked data. Similarly, when you have a huge data set as TR does, the impact of one survey is not particularly meaningful. They’re seeking trends over time within and between many people and likely comparing it back to other data (eg, your power, HR) to draw conclusions that inform the machine learning.
In short, you’re in the weeds and the system does broad strokes.
I guess one thing leeding to failure is having big bumps in workout level from week to week.
Im not sure but I think that wo was an adaptation suggestion I accepted and it was wey higher level than the one I nailed the previous week and the one originaly on my calendar.
Ill have to check better the WO levels and avoid accepting big bumps when suggested.
My hope for a training app that uses machine learning and AI is that it will become more like an experienced human coach and less like a simplistic algorithm based on gross averages. The greater the amount and type of data, the more opportunity there is for machine learning/AI to recognized more subtle patterns and relationships, and then apply that to optimize structured training for the individual and their incremental experience rather than for the average.