Suunto ZoneSense
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I’m using ZoneSense (Race 1) since it was released. Until recently (I guess before the last update) it worked quite well. Now I observe the same that has been reported here, as long as it’s below LT1, it totally makes sense.
As soon as I cross LT1 it doesn’t, From LT1 to LT2 there is roughly a spawn of max. 15 beats. It also doesn’t correlate to the felt effort. E.g. LT2 comes too early.To make sure that my belt is still ok, I use the app “HRV Monitor” on Android. It’s a quite simple app that counts the artifacts while it’s recording a 2min HRV session. It’s still ok for my belt, so I doubt it’s related to a worsen quality of the R-R signals.
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My experience with ZoneSense has been quite mixed. I have over a decade long experience running on hilly and mountainous terrain and know my body well. Either I am an outlier who doesn’t fit DFA-alpha1 model or there are factors that it doesn’t take into account, or perhaps I don’t interpret it correctly.
For me, it seems to shift into the red zone way too easily - at an effort that I know I can sustain for a long time and wouldn’t characterize as anaerobic. But on some other runs it seems much more reasonable and fitting to the level of effort. Also, it seems to have improved when I changed from Polar H10 HRM to Suunto HRM. Perhaps my Polar strap wasn’t new so the HRV data contained more glitches. But if that is the case, the algorithm should try to deal with that by filtering the data and excluding outliers.
Furthermore, when it detects Aerobic and/or Anaerobic thresholds for me, these numbers always seem ridiculously low - like everything is shifted by one zone down. It consistently detects AeT around 130 and AT around 150. Yes, I am 55 years old, and for an untrained person of my age these would be fine numbers. But I am in the top 5% percentile for my age, if not top 1%. I regularly take podium in my age group. I easily sustain 140 after multiple hours of running. I did a number of tests that all suggested that my AeT is in low-mid 140s. Similarly, I did a number of tests, including a few guided LTHR tests with Garmin, that all consistently detected my AT around 161-163.
I asked Gemini (Google’s AI) if DFA-alpha1 (the algorithm behind Zone sense) can thrown off by muscles being fatigued or by running on a hilly terrain. Here are some quotes:
When an athlete is fatigued, the normal relationship between DFA-alpha1 and exercise intensity can be altered. Instead of reflecting a rise in exercise intensity, the DFA-alpha1 values may be lower than expected for a given workload due to shifts in the autonomic nervous system balance.
Perhaps this is fine and this is what Zone sense is supposed to do, but it shouldn’t suggest aerobic and anaerobic thresholds. I think those should reflect a fresh recovered state.
Running on hilly terrain introduces variations in vertical motion and effort, which can affect the accuracy of DFA-alpha1 readings, according to Scientific Triathlon. This is because DFA-alpha1 relies on consistent heart rate variability patterns, and the sudden changes in effort associated with inclines and declines can disrupt these patterns.
I run on a hilly terrain a lot. Two days ago ZoneSense detected my AT at 155, which is closer to actual than usual but still closer to what I believe the low end of my Z4. I already knew during the run that that would be what it would detect it at. There was a moderate hill that I ran at the end of the run, and that it where my HR stabilized just as the climb started to flatten. In fact, later in that run my HR reached almost 160 and I still wasn’t in what I would classify as Z5 and not even breathless. I know how Z5 feels, and that wasn’t it.
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@sky-runner I’ve had a similar experience. As with most metrics, the numbers are not as simple as companies make them seem – it takes some knowledge and practice to really understand and apply them. For now, I see ZoneSense as a helpful tool to keep me from pushing too hard on easy days. That alone can be very valuable, assuming it works as we believe it does.
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Has someone already used Zonesense, especially ZS based TSS with Cross Country Skiing? Winter is coming in the northern hemisphere and I am curious how it works compared to Heartrate
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@Zdeněk-Hruška said in Suunto ZoneSense:
the numbers are not as simple as companies make them seem.
Totally agree. After my last race (trail running), I have to chose between MET at 101, ZS at 139, power at 158 and HR at 186. How to be confident with CTL and co.?
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Interesting video on zone sense on the nakan. ch channel
Sorry it’s French but YouTube now allow live translation hope it will help some to better understand zone senseThere is also a blog post to read that I did not had time yet to discover
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Did some of you tried a (ramp) test to validite LT1 LT2 thresholds ? What would you recommand ?
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@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…
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@tomtom73 When I am fresh, my ZonseSense L1 and L2 are very close to validated laboratory tests. Know that the ZS thresholds will change depending on your physiology and fatigue, that is the point! It is not meant to be a tool to set HR zones but a tool to use in real time to assess your effort.
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@brave_dave Have you read the scientific peer-reviewed manuscripts for ZS and HR arrhythmias or exercise? It works well. I would rely on peer-reviewed published data and not anecdotal information. I find ZS to work extremely well for me given the limitations and the intended use for ZS.
Here is the DOI link for peer-reviewed data on ZS.
10.3389/fphys.2023.1299104 -
@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.
