Suunto ZoneSense
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@Brad_Olwin yes, Brad that is exactly what I have done! I based my statements on a newer (2025) peer-reviewed study from the same authors that you have referenced. More subjects and more specific to running than previous studies. Please completely read my previous post that I have referenced before accusing me to spread “anecdotal information”! Maybe the newer data published change your mind…I am more than happy to discuss!
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@brave_dave Thanks, I’ll read the newer paper
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@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
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@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
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@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.
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@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.
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@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.
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@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…
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@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. -
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@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.
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@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.
