I saw recently the announcement recent of a whoop like offering from Polar and I had an idea as to how this could enhance the Trainer Road ecosystem. I’ve pretty much been using Trainer Road back to when it started with virtual power and Kinetic Road Machines. I’m really appreciating the new features, AI FTP detection, Adaptive Training and now Red-Light Green-Light.
I feel that the adaptions I see are sensible and I seeing good outcomes as this is very much a trust the process approach which I’m happy to follow.
In terms of adaptive training Red Light Green light this would be based on one side of the equation which is your exertion and colored with survey metrics ( The secret source as Trainer Road refer to it ) . The other side of the equation would be your non recorded exercise / exertion and sleep recovery. My thinking was it would be great if this data could be used to as a factor to further tailor and tune AI recommendations.
Whilst there are a plethora of products on the market ( Oura ring, Whoop, Garmin Polar etc…) measuring sleep and recovery, integrating each of their proprietary backends would be a huge undertaking for Trainer Road and as far as I’m aware the is no standard format for exchanging the data.
I personally struggle to play this data against my training. For instance, I might have a productive session schedule but have had a poot night sleep so want to dial the effort back a bit.
I saw an article on DCRainmaker’s site re a new offering from Polar (Polar 360) Polar (Sorta) Launches Whoop-like Competitor: The Polar 360 | DC Rainmaker and I thought of TR. It is basically a Whoop like activity tracker; however, it is not going to be marketed as a retail product. It is aimed at corporates as a sort of White Label product and it looks like it uses the functionally rich Flow backend and provides SDK or API interface for integration.
This data could initially used to further color the Red-Light Green-Light / Train now recommendations based the Polar Flow backend via the SDK integration. As the data set grow the TR Machine learning could be played over the rest / recovery data to further tune the model.
I have used Polars sleep data for a few years and its very good and rich terms of the metrics , but all I end up doing is lifting some key markers into Training Peaks but it doesn’t actively color my training.
This could move TR into a totally unlike space where they would have a 360 view of the user’s activity and not just training exertion and making training recommendations base on a more complete picture.
Depending on the commercial model on offer from Polar it could be an interesting bolt on for TrainerRoad in that they may not have to carry stock and worry about carrying stock and the supply / warranty problem that hardware supply typically entail. As a TrainerRoad user you could buy a Polar / TR 360 band from Trainer Road and the resulting data would then be incorporated into your training plan suggestions.
I would wait for this device to be released and tested heavily. I also would not use any sleep data from Polar, except sleep duration. I don’t believe any device on the market gets very good sleep phase data outside of the OURA ring and the Apple Watch, but even those are not 100%. There are also a lot of ways you can get bad data from these sensors (loose band, sweat on sensor, tattoos, position on wrist, etc). I would not want to miss a great workout because of bad readings.
Sleep duration, HRV and RHR overnight seem to be how current apps get a “readiness score”. Perhaps something like that can be used to err on the side of caution (e.g. recommending an endurance workout if you’ve had a bad nights sleep). But it’s likely a lot of work for their devs and I only really see it doing more harm than good to a training plan.
EDIT: I think a feature to connect a device, e.g. the polar, or an apple watch or OURA ring (not sure how permissive their API is), so that TR can collect this data, would be a great idea. Having more data is always a good thing.
I’m quite surprised there is such little interest in this topic: I’ve been using Oura ring for about 4 years now and wear it all the times. It has been extremely effective in predicting when I’m about to get sick and when I’m reaching the upper limit of my training.
Recently Apple has introduced “Vitals” that should do something similar, but the watch is not nearly as good as measuring HRV as the ring.
No decision can be made on a single datapoint, but the same is true for TR which bases the yellow and red days mostly on workload and past history. A bad night of sleep, an increased body temp, elevated RHR or respiration rate, are all signals that should not be ignored.
Right now when I get a yellow day I look at the Oura dashboard to get a second opinion. Conversely if Oura flags a red day I adjust my TR workout accordingly.
The sauce of adaptive training is looking at different signals and weigh them to draw one probable conclusion.
Integration could be very simple.
I’ve been using the Breakaway on my iPhone which basically does this, I found it to be a great guide to daily training.
Alternatively, I regularly see counter-intuitive indications from my Oura data, either saying I am primed for a productive day when I am clearly fatigued or vice versa. One specific example I can remember was in the Fall of 2021, I was really, really sick (still think it was COVID, but I kept testing negative so… ) but my Oura ring was giving me Readiness scores in the high 80’s / low-90’s.
the science around HrV and sleep data is foggy, at best. A few years back Jonathan mentioned sitting next to a physiologist on a plane flight who was deeply researching HRV and their conclusion at the time was was “it is murky, at best, to use HRV as a predictive metric for physical performance.”
I have yet to make any workout related decision based on my Oura data.
I would argue that there has been quite a bit of advancement in this field since 2021, but more importantly is not just HRV to be taken into consideration, but type and quality of sleep and other physiological indicators as RHR, blood oxygen and respiratory rate.
Those are all signals, non of them can uniquely indicate your trainable state, but together with the rest of the information available can help making better decisions.
I would argue, for instance, that the Adaptive Training does not take into account my current elevation. If I suddenly move to 9k feet I can guarantee you that my next workout needs to be adjusted.
More information always lead to better decisions, that is a general principle.
More data is not always better, especially when it’s bad data. You may be having an awesome experience with your ring, but look around and you can find lots of people whose experience with Whoop, Oura, Apple, and Garmin data re: HRV/Sleep is terrible. I’m one of them. I’ve owned all those devices (except the Oura) and every one of them will tell me I had an amazing night of sleep or my HRV is in a great place, when in fact I feel like absolute crap in the morning.
If Sleep/HRV tracking data was reliably good, and if there was scientific consensus that HRV was a quality metric, I would agree with you, but having watched this space for years now, it’s just not there.
I know most of TR’s competition are using this stuff as a selling point right now, but I respect TR for holding out and not implementing it until they are sure they can do it right…even if that is never.
Yeah if anyone has the machine learning framework in place to be able to use this data in a meaningful way, it’s TR, but I don’t think the data is good enough to be useful yet. There’s so much inconsistency out there in terms of how the data is collected, recorded, and reported, that I suspect it’s still about as effective as a random number generator at this point. I’d bet even if they got all our wearable data and fed it into their modelling the ML processing wouldn’t be able to draw any meaningful conclusions from it at this point.
And even when the data does start to get reliable enough to use, it’s probably going to be several more generations of product development beyond that point before it’s possible to treat the same data from different devices/platforms in a similar way. I use Oura as my primary tracker but also wear a Garmin watch most of the time, and the sleep data is very rarely the same between the two, let alone the recovery insights.
Sure TR could see if they can come up with one input model for Oura, another for Whoop, another for Garmin, another for Apple, etc. but I’m guessing that’s a wildly more resource-intensive project than if they could treat all the data as being equal and process it collectively.
Saying that, I would love it if they did start collecting the data - then even if it was a lone engineer’s pet project at least we’d know they have the ability to check and see whether their model is able to interpret anything useful from all the TR/Whoop users or whatever.
There are probably other way simpler metrics that would be more immediately useful though - for instance I’m surprised that they still don’t have any integration with smart scales. I’d expect weight trends to be helpful for the AT model right now, even if they only became relevant in certain conditions (gain/loss above a certain percentage for 2+ weeks while either nailing workouts or failing workouts could potentially be useful for the system to know).
I’m a longtime Oura ring user and I like the red/yellow indicators from TR as well. I track my training load and watch my stress balance in Intervals.icu to see if I’m going deep in the red. While I find value in all of those tools and the metrics they provide, none of them are making decisions for me. I’m not going to alter/skip training on a given day just because my readiness was low or I dipped into the red.
Sure, it’s interesting to see how much sleep I got last night or where my RHR or HRV is sitting, but in my opinion the primary value of those things is long term trends, not what a specific day is indicating. I will alter my training when my body is telling me I need rest and I will often look at the metrics to see where they are trending, but there isn’t much evidence I’ve seen that any of these metrics are able to give clear indication of whether it’s time to rest or not from a daily perspective. Case in point - I don’t even bother to look at any metrics on race morning, they are always terrible from carb loading, etc… And that’s when I’m at my most ready.
All that said, I am a fan of incorporating as many data points as possible into the analytical and ML tools. The tools will only get smarter and the more data they have, the more they can find patterns and causation relationships. I still think we are years away from an app dictating training interventions as effectively as listening to your body, but I expect we could get to that point if the system had enough data (might require blood markers, etc.). In the meantime, I’m happy to keep using these tools to keep an eye on long term trends.
Just to be clear I’m not suggesting that TrainerRoad should develop their own recovery models based on HRV or similar.
I have used Oura ring for 4 years now (and tried many others like Whoop, Apple Watch, Garmin etc).
Sure TR could see if they can come up with one input model for Oura, another for Whoop, another for Garmin, another for Apple, etc. but I’m guessing that’s a wildly more resource-intensive project than if they could treat all the data as being equal and process it collectively.
An Oura integration would only look at the high level metrics, like Readiness Score and Sleep Score (and at least Oura has an API for that). Again this may just improve the accuracy of red/yellow day predictions
I don’t know anyone that has used one of these wearables when the output was unreliable or unusable, you simply stop using it (like it is for every Garmin user I know)
As much as I would use this feature, making every platform a hub for every external device or measurement might not be a good use of TRs development time.
TrainingPeaks sucks in metrics from Garmin and I imagine it might be useful to a coach to see amounts of sleep for a client.
FWIW I’m a Garmin user who finds the sleep, HRV, body battery, etc output aligns pretty well with how I’m feeling. Not perfect but good enough to be useful.
I listened to this Ross Tucker pod yesterday, which speaks a lot to the points you make around data comparison/validity across different platforms, and also the problem of opaqueness in underlying algorithm methodologies used by the tech companies involved. Worth a listen if you want something to pass the time on an endurance session.
As a long time Oura user, I agree with this. A one-day change is not something to use a sole indicator to change training. A trend over 5ish or more days - I think that should be given more weight and more generally aligns with how I feel.
Some of the point of TR ‘using’ this data is to start collecting it to see if it is valuable - this is as much a learning/development process for them as it is about providing new features in the near term. They have a platform where they could be advancing the state of the art, not just waiting for established methods of using HRV/recovery/etc to be applied.
I wear my Garmin for many reasons that aren’t tracking sleep so I’m not stopping, but this week there is a definite sensible chuckle at my sleep scores. Sunday night was horrible and I got an 89? Monday night I was so passed out that I thought my partner was crazy when she told me my alarm went off at 5am, I thought it was like 2am, I got like a 40. Last night also slept solidly and it was a 29.
I don’t want any of this data telling TR what to do with my plan.
Didn’t read too far back so apologies if this has already been mentioned. Apple also started incorporating this kind of feature into their Fitness app. Seems fairly accurate.
Ok, so today, after a red and 2 yellow days TR gave me green light after I took a rest day yesterday.
Today I was prescribed South Twin +2 (VO2Max 5.7)
But Oura flagged a red day.
Some objective metrics are above baseline, so definitely not a good idea to go hit a hard effort. What would you do?
The thing is: I don’t need TR to integrate Readiness metrics in their recommendation, I’m already looking at my Oura app and will adjust training accordingly. (Would be good if it was all in one place tho)
I’m saying that the red light/green light in TR is very limited (just based on workload and RPE, maybe age and some other static parameters) and by not looking at additional recovery data from hundred thousand of users they are missing out on improving their adaptive training models.
I’ll take TR’s RLGL recommendations a LONG time before I take Oura’s. At least when I see a yellow or red day, I know I have previously had hard rides and can make the correlation.
As noted, way too often I get recommendations from Oura that are 180* wrong.
Also, Jonathan specifically addresses the issue of metrics from devices like Oura or Whoop in the Nero Show podcast he was on a few weeks ago, and why they opted for RLGL.
Clearly there are many people who engage in different type of activities than others and the way that is weighted by the RLGL algo is obviously questionable.
I find it difficult to accept that you think that quality and duration of your sleep has less relevance than the workload of your previous days. That seems counterintuitive.
With that said, trust what works for you: don’t know if I have to say it one more time, TR is missing out in not collecting that collateral info.
) but my Oura ring was giving me Readiness scores in the high 80’s / low-90’s.
