Suunto ZoneSense
-
@brave_dave Thanks, I’ll read the newer paper
-
@brave_dave Thank you for your comment and for sharing this study. It was very interesting and informative to read. From my own testing, it seems that ZoneSense results are more accurate when the intensity and the underlying terrain are stable – which is also in line with what I took away from the paper.
I do believe it could be applied for home-based testing with a defined protocol to estimate thresholds. However, I’m not fully convinced it is reliable enough for real-time training guidance.
But it’s also important to note that this method may be highly individual-dependent, and each of us could have a different experience using it. It might work very well for @Brad_Olwin and others, while for some of us the results could be less accurate. -
@Zdeněk-Hruška you are welcome!
yes, you are completely right. As the DDFA calculates the correlation of HRV over increasing HR/intensity, it needs sufficient data from intensities below the threshold and above. But as the HR reacts quite slowly to changes in intensity, it needs longer times and stable efforts to be really accurate. That’s why in their optimal test set up they use a treadmill and increase the speed every 3 min about 1 km/h to get the most noise-free data…No one though will ever have such optimal/artificial conditions on a normal run due to terrain differences or other factors, as you said.
Despite their optimal set-up, they on average/median get a deviation of ±10 BPMs from the real threshold for their 58 tested subjects.
Even under perfect conditions, this is just way too much to be really useable for Real-Time training control and trustworthy threshold estimations in my eyes. 10 BPMs above or under real threshold is just a completely different world…Yes indeed, it could be highly individual and maybe for some people it has a higher accuracy on every single run. Longitudinal measurements for individual athletes might therefore be really interesting to check for that possibility!
But looking at their data this probably only applies to very few people according to the 95% confidence intervals (Fig. 4). In the best case, less than 5% of the people get a higher accuracy of maybe ±6 or 7 BPMs.I’m hoping and looking forward to further improvements and studies on the method!
Still I need to criticize Suunto and the authors/developers to not openly communicate the actual accuracy, even if it performs better than the really bad HRmax estimation.
In my eyes, this is irresponsible as it can lead to overreaching in the worst case, when people trust in ZoneSense.
That’s why I share my information here and want to make users aware of the actual data. But that are just my two cents… -
@brave_dave said in Suunto ZoneSense:
@Zdeněk-Hruška you are welcome!
yes, you are completely right. As the DDFA calculates the correlation of HRV over increasing HR/intensity, it needs sufficient data from intensities below the threshold and above. But as the HR reacts quite slowly to changes in intensity, it needs longer times and stable efforts to be really accurate. That’s why in their optimal test set up they use a treadmill and increase the speed every 3 min about 1 km/h to get the most noise-free data…No one though will ever have such optimal/artificial conditions on a normal run due to terrain differences or other factors, as you said.
Despite their optimal set-up, they on average/median get a deviation of ±10 BPMs from the real threshold for their 58 tested subjects.
Even under perfect conditions, this is just way too much to be really useable for Real-Time training control and trustworthy threshold estimations in my eyes. 10 BPMs above or under real threshold is just a completely different world…Yes indeed, it could be highly individual and maybe for some people it has a higher accuracy on every single run. Longitudinal measurements for individual athletes might therefore be really interesting to check for that possibility!
But looking at their data this probably only applies to very few people according to the 95% confidence intervals (Fig. 4). In the best case, less than 5% of the people get a higher accuracy of maybe ±6 or 7 BPMs.I’m hoping and looking forward to further improvements and studies on the method!
Still I need to criticize Suunto and the authors/developers to not openly communicate the actual accuracy, even if it performs better than the really bad HRmax estimation.
In my eyes, this is irresponsible as it can lead to overreaching in the worst case, when people trust in ZoneSense.
That’s why I share my information here and want to make users aware of the actual data. But that are just my two cents…impressive… thanks for sharing
-
@brave_dave I share your criticism. I’ve already seen quite a few “revolutionary” metrics and supplements that were supposed to change everything. That’s why I remain a bit skeptical – in the end, most of us still rely on the good old basics like HR, pace, and RPE.
That said, I do hope that all the data Suunto and others collect through ZoneSense will help refine and improve the approach over time. They are definitely onto something – I believe it’s just a matter of finding the right use cases. Having said that, I’m very excitedly awaiting my Tymewear VitalPro – but that’s a different story
-
@brave_dave Your point about the median head to head deviation is valid but you were able to make it exactly because you could read a publicly available scientific study. So I don’t understand your criticism of the not open communication from Suunto and the developers of the method. On the other hand, there are so many more interesting findings, references and points and some impressive results in this paper that you don’t bother to mention at all. Of course you don’t have to, since your goal is just to criticise the method… That’s the reason I suggest to everyone who is interested in Zone Sense and the subject of HR zones for training to read and evaluate the article for himself.
-
@soisan I believe the main criticism comes from the way ZoneSense is presented as something that can be easily used by anyone. In reality, not many users are going to read the scientific papers behind it. To truly understand what you’re measuring – when it works, what it reflects, and how to interpret the results – you need a fair amount of knowledge.
The same logic applies to many other metrics, like VO₂max estimation, which a lot of people confuse with an actual VO₂max measurement. I remember when Stryd came out and claimed that there was only “one number you need to follow.” It quickly became clear that this wasn’t true across all use cases – ultrarunning, technical terrain, unstable surfaces, and so on. You also had to understand that a higher power output didn’t automatically mean you were improving.
I don’t think it’s a bad thing that people need to learn – in fact, it shouldn’t be too easy. But at the same time, it should be presented in a way that’s clear and intuitive at first glance. At least, that has always been my perspective. -
@Zdeněk-Hruška I agree with everything you write. The problem is the clear and intuitive presentation that you require. If this was easy we wouldn’t have all these misinformations, conspiracy theories and alternative facts that undermine communication between humans. There are subjects which are so complex and non-intuituve that only science can adequately address and investigate. Science communication is necessary but at the same time extremely demanding! In what extent is reasonable to expect this social responsibility from any company in today’s world remains to be seen. Personally I’m rather pessimistic about it. The only way to deal with it is the responsibility of the individual and its active participation.
-
@soisan my intention was not at all to criticize the paper or the method, because it is a really good and well designed study/paper in my eyes. The authors did a great job of extending their publication from 2023! Of course, there are a lot more aspects to mention/appreciate but that would go beyond a normal forum post…
But as you said, science and its results should be openly communicated and presented in a way that also non-experts can understand it and know what the results mean. Therefore, it is essential to mention what DDFA/ZoneSense in its current state can reliably do and can not do. Just mentioning the actual accuracy, as presented in the paper, in the Zone sense article on the Suunto website would be already enough to give people easy access to the facts to then judge by themselves if it is reliable enough for them to trust ZoneSense or not. But Suunto doesn’t deliver facts and in the way they present ZoneSense, and the claims they make, they give me the impression that it is way more accurate/reliable than what the facts say.
-
@brave_dave Honestly I cannot blame Suunto or any company for that matter. It’s a little bit like a debate! If I’m taking part in one in favour of a viewpoint, I will try my best to present all the arguments that support my case while at the same time I will not loose a word about any argument against it! In the same manner, Suunto understandably just mentions everything positive ZoneSense could offer to the potential user! It’s exactly what all the other competitors in the market are doing: nobody will tell you that in order to use effectively the HR zones of the watch they sell and all the indexes and the numbers that they calculate, you have to do a lactate test…
-
@brave_dave said in Suunto ZoneSense:
@Zdeněk-Hruška from a scientific perspective, ZoneSense is nothing more than a theoretical concept and a marketing move rather than something really useable in its current state. The principal behind it works but it is too unreliable in its current state. Even under perfect “lab” conditions accuracy is not good enough to be useable for most people, especially not on individual runs as they market it. Maybe in the future…
Made a summary of a recent scientific study with a link to the publication testing ZoneSense, in the thread below.
https://forum.suunto.com/post/175109
@Josaiplu “Study” by Nakan is somehow useless because such a study has already been performed with way higher n-number by the experts that developed ZoneSense. I can also only put it in the category of marketing…
I read the paper and disagree with you. DDFA is within 10bpm with a 95% confidence limit. I fail to understand why DDFA is so unreliable. Suunto did not state that ZS was a method for determining zones effectively. Instead, ZS is marketed as a tool for real time measurement. The authors clearly state “ The DDFA-based method offers a simple and accurate alternative for the estimation of the AeT and AnT, which can be implemented in real-time monitoring of the training intensity during exercise”
I completely disagree with “ ZoneSense is nothing more than a theoretical concept and a marketing move rather than something really useable in its current state.” Do not understand what you are looking for. How do you want to use ZS? Clearly ZS has limitations. I would not use ZS for short interval training (less than 10 min) but I use it extensively in long runs on trails where Pace and Power are utterly useless and HR isn’t great either. -
This post is deleted! -
@soisan After watching the videos I think Suunto does mention shortcomings, in the app the wording is very careful about whether or not you should change your zones. I agree that the short marketing videos are just that, marketing videos.
-
@Brad_Olwin It has a MEAN absolute difference (median error of means) of ±10 BPMs and a 95% confidence interval of around 4 BPMs. Meaning that for most of the 58 tested subjects the estimated value deviates by 10 BPMs and 95% of all tested subjects are in the range of 6-14 BPMs deviation. The data points also seem to be gaussian distributed. So for only less then 5% it has a lower deviation of around 6 BPMs and for some of these 5% it has even a deviation of more then 14 BPMs.
Of course, accuracy is highly subjective…but statistically seen, from the available data that I have, I do not use ZS at all.
For me personally, when I know that on average, even under perfect conditions, the ZS estimated threshold is off by 8-10 BPMs in either direction, it is not usable for me. Even the authors discuss that there set-up is highly artificial and it needs to be seen if it even works under normal run conditions and without exercise until exhaustion…also 10 BPMs above my threshold is for me all-out running for 1km while 10 BPM below, is the HR that I have on the first kilometers of a Marathon. For me that is just way too much deviation to rely on it for proper training control and also dangerous for people that believe they can trust the value and base their training on.
That I am not the only one this applies to, is already reflected by the statistics and the numerous posts from people in this forum that struggle with the value that ZS gives them and are looking for help to understand and interpret it.
Communicating the average accuracy under perfect conditions to the average non-expert consumer, would already help them a lot to interpret the value for themselves.
For the future, I am looking forward to more data and studies and maybe an improved algorithm, as the concept is really nice! But right now, it’s not there yet, in my eyes.
-
Zone Sense: Practical Questions for Confirmation
Dear All, I have some questions about Zone Sense usage and baseline management that I’d like to confirm with the community ( I have read most of the thread but it is pretty long so sorry if I have missed some already replied question, maybe a sum at the beginning could be helpful)
1. Managing the Initial Calibration Phase
I understand that Zone Sense requires approximately 10 minutes of warm-up before providing accurate readings.
My specific situation: I do trail running and my routes start with a 5-6 minute climb right from my door before reaching an undulating plateau.
My question: What’s the best approach to manage this initial climb?
- Should I intentionally go very slowly during this climb to allow proper calibration, even if it feels “too easy” (like walking when grade is higher than 15%) ?
- Or would it be better to add a 5-minute flat warm-up before starting the climb?
- Does starting with high intensity during this calibration period negatively affect Zone Sense accuracy for the rest of the workout?
For sure If I ran the first climb at a good pace it will make my data faulty for the rest of the run at least it is my impression.
2. Baseline Storage and Memory
My understanding: Zone Sense stores a personal baseline that improves over time with each workout.
Questions for confirmation:
- Is the baseline cached in the watch between workouts, or does it recalibrate from scratch each time?
- Does each workout contribute to updating this baseline, or only certain types of workouts?
3. Sport-Specific Baselines
My question: Does Zone Sense maintain separate baselines for different sports?
Context: I do both trail running and cycling (alongside others sports but those are the 2 main one) . If I do easy cycling sessions, will this help improve my Zone Sense baseline for trail running, or does each sport have its own independent baseline?
Would easy rides on the bike contribute to better Zone Sense accuracy during my trail runs, or should I focus on doing easy trail runs specifically to build that baseline?
Any insights from experienced Zone Sense users would be greatly appreciated!
-
@brave_dave I do not use ZS to set thresholds and personally I do not think that is the intent as I have stated before. I primarily use ZS in real time to assess my effort and as I have stated before it does a phenomenal job of matching my RPE. As far as I am concerned RPE is a far better estimate of effort than any thresholds I have obtained via lab testing or individual testing.
Second, I would argue that any lab test is going to have at least a 5bpm or greater error so that would be 10bpm around the median for the test. I find it rather surprising that anyone would believe their LT or AT is an absolute, our bodies do not work that way.
There is no way that 10 bpm below my LT would be a marathon pace, and 10bpm above is definitely interval effort.
So the point of ZS is not to set zones but to assess effort in real time, it does that well for me and for others. Given the lag in ZS (a couple of minutes) it is well suited for endurance efforts and not well suited for shorter efforts.
-
@brave_dave said in Suunto ZoneSense:
For me personally, when I know that on average, even under perfect conditions, the ZS estimated threshold is off by 8-10 BPMs in either direction, it is not usable for me
It is way worse for me. I’ve seen example of ZS suggesting anaerobic threshold that is even below my aerobic threshold - that is almost 30 BPM below what it should be. These are laughably inaccurate estimates.
Let’s look at another example where I actually had a steady threshold effort for a long time that was a good indicator of my anaerobic threshold. ZoneSense still managed to detect my anaerobic threshold at 148 even though I ran for 20 minutes in the 160-165 range. By the way, that closely matches multiple of Garmin’s LTHR estimates that all detected my LTHR at 162-163. How ZoneSense managed to detect that at 148 is beyond my understanding. I can’t seriously trust that at all.
-
@sky-runner this reviewer did lab tests along with ZoneSense . Among his group was DC Rainmaker. The findings suggest Zonesense is not to be trusted.
-
I only ever look at zone sense in the app after a workout, I have found it wildly inaccurate at times and others fairly accurate. For me the gold standard is RPE followed by HR however even HR can be misleading as I’m often training fatigued and when doing an hour long effort at near threshold in terms of RPE when fatigued my HR might only be in high zone 3. When fresh HR seems to align better with RPE. Zone sense has often shown nearly all green with a few tiny spurts into yellow when I’m at 9-9.5 RPE doing a threshold effort up a vertical kilometre!!!
-
@Doodoo-Runner said in Suunto ZoneSense:
@sky-runner this reviewer did lab tests along with ZoneSense . Among his group was DC Rainmaker. The findings suggest Zonesense is not to be trusted.
curious to know why, with the same HR source, the SV and SR results differ.
