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Harnessing artificial intelligence to provide the next generation of personalized fitness

Prescription, analysis and individualization of physical activity for health and performance

EnduCloud assist health providers and coaches in the prescription of aerobic physical activity plan for health and performance based on the individual targets and constrains. EnduCloud monitors the response to training and modifies the plan based on the individual response. EnduCloud combines advanced (an

d unique) advanced analytical tools and artificial intelligence to analyze the training load, the response to the load and the individualized adaptation. EnduCloud uses the wide range of wearable devices that are already in use by the people from sport and smart watches with training time or 24*7 monitoring, which is collected through common applications including Strava™ Garmin Connect™ and Polar Flow™, and will gradually add more vendors and sensor types. EnduCloud include specific modules customized to the needs of specific populations starting with cardiac patients and continuing to general health, recreational athletes and oncological patients. At its initial stage EnduCloud supports walking and running, that will be gradually expand to include cycling, swimming, triathlon, rowing, cross country ski and more for indoor and outdoor sport. EnduCloud primary audience is health providers and coaches that uses EnduCloud to provide higher quality personalized service to their customers. Additionally, EnduCloud offers a free tier of unsupervised personalized service to the general public, that can be complemented by paid supervision services through EnduCloud. EnduCloud elaborate database which includes comprehensive information regarding the trainee, the training plan and the trainee response to the plan will use both for the next stages of EnduCloud’s development and to improve our understanding on the consequences of physical activity.

22/04/2026

Three related questions (and pieces of advice) tend to come up again and again in slightly different forms. They’re hard to explain precisely because they’re built on a correct principle – implemented poorly:
• “Am I running at too high a heart rate?” / “How do I lower my heart rate while running?” / “You’re running at too high a heart rate.”
• “Most training should be in Zone 2.”
• “How should I set my zones on my watch?” / “Is it possible that my entire run is in Zone 5?”
The question about running at a “low heart rate” comes up frequently, and the fact that most of us wear heart rate monitors reinforces it. Heart rate is simple, measurable, and gives a sense of control. But from a physiological and training perspective, this is an imprecise interpretation of a valid concept.
It’s hard to overstate the importance of easy training. A large and consistent body of research shows that most improvements in aerobic capacity, especially in elite athletes, but also in recreational ones, are associated with a high volume of low-intensity work. Training at lower intensities is linked to increases in mitochondrial density and function, improved fat oxidation, and greater cardiovascular efficiency.
However, translating “easy training” into “low heart rate” is a problematic shortcut.
Heart rate is an indicator of intensity, not the intensity itself. It reflects physiological strain at a given moment, but it’s neither the only metric nor necessarily the most accurate one. Running pace, power output, breathing rate (for example, the “talk test”), and perceived exertion (RPE) are all valid measures of intensity. In practice, they should complement, if not sometimes replace, heart rate in assessing true intensity, both from an objective standpoint (external load) and a subjective one (internal physiological stress relative to the athlete’s capacity).
Heart rate is also highly individual. It’s influenced by age, s*x, genetics, fitness level, recovery status, temperature, hydration, sleep, stress, and even medications (such as beta-blockers). In other words, what counts as a “low heart rate” for one person might represent an easy effort, while for another it could be moderate or even high.
This gap becomes particularly evident in three populations:
• Beginners or athletes returning from injury or a break, whose aerobic systems are not yet well developed, so even easy efforts can produce relatively high heart rates.
• Individuals with atypical heart rate responses, whether naturally or due to medication.
• Well-trained athletes, whose physiological thresholds are high, so what’s labeled “Zone 2” is not necessarily easy at all.
So the goal is not a “low heart rate,” but a low-intensity effort.
In practice, that means running at an intensity where breathing remains comfortable, conversation is possible, and volume can accumulate without significant fatigue. Heart rate can be a useful tool, but only when it’s calibrated to the individual, not based on absolute values and formulas. Lowering heart rate should never be the goal in itself; rather, it’s a byproduct of improved aerobic fitness.
A key point to remember: aerobic fitness, reflected in a low resting heart rate (sometimes approaching 40 bpm or even lower) and relatively low heart rates during easy efforts, is not the result of targeted “low heart rate training.” It’s the inevitable outcome of years of consistent, high-volume training. Instead of trying to force heart rate down, the focus should be on consistent training over time. The heart rate will follow.
When using heart rate as a measure of intensity, it’s best to interpret it relative to the individual profile: athlete physiological profile as reflected by the 1st and 2nd ventilatory/lactate thresholds, lactate threshold heart rate (LTHR), zones determined through field or lab testing, or even consistent longitudinal analysis of training data. General formulas like “220 minus age” may serve as a rough starting point, but they are far too imprecise for managing training load and can lead to misleading conclusions.
Once we add structured zones, like “Zone 2 training”, we introduce another layer of confusion, largely because multiple zone models exist, and some (including the default settings in many sports watches) are not aligned with individual physiological differences.
The table below illustrates how heart rate zones can vary between athletes of different levels—and how they compare to typical watch-based zones:

(See Image)

A few key observations:
• The upper limit of Zone 1 rises significantly with fitness (from ~62% to ~75% of max HR). Elite athletes often perform easy runs at intensities their watch would label as “Zone 2.”
• Zone 3 (the Norwegian threshold zone) is where “double threshold” sessions take place. For professionals, it spans a wide range (80–90%), while for beginners it’s narrower and starts much lower.
• Default watch zones tend to underestimate Z1 and Z2 for trained runners. A truly easy run for an elite athlete (Z1) may appear as Z2 or even Z3 on the watch—potentially misleading less experienced runners into going too hard.
• Z4 and Z5 become compressed at the upper end in elite athletes because LT2 is very close to maximal heart rate, leaving little room above it.
Anchor points (approximate):
• LT1 (~1.5–2 mmol/L lactate):
o Beginners: ~62–65%
o Trained: ~70–73%
o Elite: ~76–80%
• LT2 (~3.5–5 mmol/L lactate):
o Beginners: ~76–80%
o Trained: ~84–87%
o Elite: ~89–93%

14/04/2026

Still plenty of room to improve, but Coach Endy’s answer highlights something important:
Even a really strong language model, one that can look at a workout and break down all the data from an analytics platform, isn’t enough on its own.
A workout never stands alone. You have to look at it in context: what happened over the past week, the past month, how you’ve been sleeping and recovering, and what’s going on outside of training. More than anything, it’s about how all of that comes together over time and lines up with the goal.
Bottom line: fitness isn’t quite there yet, and Andy still has room to improve, but there’s definitely something to feel good about here too.

Your 4x4-minute workout intervals held a rock-solid 172 W (CV≈0.06%), which is precisely at the lower boundary of your prescribed Z4–Z5 range (149–198 W) and appropriate for lactate-threshold work, but the intensity sits at the conservative end—you had substantial headroom to push harder without violating the zone ceiling. The 19 bpm cardiac drift (HR rising from 106 to 125 bpm while power remained stable) is a clear sign of aerobic decoupling, likely amplified by your severely restricted sleep (4.2 h) and low HRV (59 ms), both of which suppress parasympathetic recovery and elevate cardiovascular strain at fixed workloads.
Your short-interval (15 s / 60 s) ex*****on was excellent—four repeats hit 210–213 W, well into the anaerobic-capacity zone—but the main-set intervals could have targeted 185–195 W to extract more training stimulus without exceeding your prescribed ceiling. Given your current undertrained state (ACWR 0.79) and compromised recovery markers, prioritize sleep restoration over intensity escalation; once you establish 7+ hours consistently, you’ll see the cardiac drift diminish and gain clearer feedback on true sustainable threshold power.
Your workout satisfaction (5/5) reflects the physiological stress signals and good mental resilience, but trust the HRV and sleep data over RPE—this session cost more than it should have.
AI-generated based on health and activity data at time of activity

11/04/2026

One of the rare privileges in working on a product like EnduCloud is stepping back from day-to-day ex*****on and looking at the bigger picture – not just what to build next, but where the market is actually going, and where the gap lies between technological progress and real user needs.
Over the past decade, wearables have evolved dramatically. What started as simple tracking devices has become a fragmented ecosystem of highly specialized tools:
sport watches focusing on performance, platforms like WHOOP and Oura analyzing recovery and lifestyle, and increasingly, medical-grade sensors such as CGMs and lactate monitors entering the consumer space.
Each of these is powerful. But each is also a single stream of data.
The human body, of course, doesn’t work in silos. Performance, recovery, metabolism, and fatigue are tightly interconnected. And when we start putting these signals side by side, the limitations become clear:
a session may look “good” mechanically while metabolic strain is rising,
HRV may suggest readiness while fatigue is accumulating,
glucose data may reveal suboptimal fueling despite solid ex*****on.
The issue today is no longer lack of data – it’s lack of integration.
More data increases complexity, often creating contradictions and shifting the burden of interpretation onto the user – athlete, coach, or clinician. The real challenge is turning data into context, and context into decisions.

06/04/2026

Coach portal nearing completion, with tracking tools for athletes and teams, both on athletic and operational aspects

03/04/2026

Contrary to what many people assume, building training plans is only a relatively small part of developing a hybrid training system like EnduCloud. A much larger share of the work goes into more fundamental questions: what fitness really is, how external load, such as running a given distance at a certain pace, translates into the internal stress the body experiences, how to measure changes in fitness over time, and how to adjust training difficulty so it continues to provide an effective stimulus.

Even that description does not fully capture the complexity of one of the hardest problems in endurance training – a problem we are still far from defining clearly, let alone solving. As athletes improve, their physiological thresholds shift. What was easy yesterday can eventually become too easy. If reference points are not updated, training stops being challenging, and progress stalls.

To address this, EnduCloud evaluates training load against a continuously updated physiological profile for each athlete. At the same time, we still need to communicate fitness in familiar terms such as VO2max-related thresholds, lactate threshold, and training intensity zones. Because athletes come from different training backgrounds, and because zones are more of a descriptive tool than a fundamental one, the system allows users to work with different intensity metrics, be it heart rate, pace, or power, and to choose their preferred zone model.

At first glance, this may seem like a purely technical, even cosmetic, feature. In practice, though, trying to align different models reveals a deeper issue: systems that look almost identical on the surface, with the same “five zones” and similar labels, can actually represent very different intensities.

To understand why, it helps to step back. Intensity is not something we can measure directly. There is no intensity meter attached to the body. Instead, we rely on proxies such as heart rate, pace, and power. Each captures a different aspect of effort: cardiovascular response, external output, mechanical work. But none of them directly measures metabolic intensity. So when we talk about intensity, we are really talking about an approximation.
To make that approximation useful, we divide it into zones. And that is where the problems begin. The way these zones are defined is not consistent. It depends on the anchor chosen, and on how the zones are structured relative to that anchor. Zones can be based on maximum heart rate, threshold heart rate, threshold power, or physiological markers such as VT1 and VT2. Each approach creates a different system.

In many consumer systems, for example, the division is based on percentages of maximum heart rate. Zone 2 is often defined as 60–70% of HRmax, Zone 3 as 70–80%, and Zone 4 as 80–90%. This is simple, intuitive, and easy to apply. But it rests on a strong assumption: that there is a fixed relationship between a percentage of HRmax and a specific metabolic state. In reality, that relationship varies significantly between individuals and across fitness levels.
Other models, such as the Norwegian approach, anchor zones to physiological thresholds instead. VT1 marks the point at which lactate production and ventilation begin to rise more noticeably, while VT2 marks the point at which the system can no longer maintain steady-state balance. The zones are defined relative to those points: below VT1, between VT1 and VT2, around VT2, and above it. In this model, there are no fixed percentages, because the thresholds themselves shift as fitness improves.

This highlights something that sports science has long understood, but that has not fully made its way into coaching practice, consumer devices, or even popular science discussions: the “aerobic zone” does not sit at the same percentage of HRmax for everyone. For a beginner, VT1 might occur around 65% of HRmax. For a trained athlete, it might be closer to 75–80%. For an elite athlete, it may be even higher. In other words, the zone itself moves as fitness improves.

When you try to map one system onto another, the mismatch becomes obvious. “Zone 2” on a watch, defined as 60–70% of HRmax, does not necessarily align with “Zone 2” in a threshold-based model, which is the model most discussions of Zone 2 are actually referring to. Sometimes it is lower, sometimes it overlaps partially, and sometimes it sits entirely below the intended aerobic training range. That means two athletes can both be training in “Zone 2” while doing fundamentally different workouts.

This is not just a theoretical issue. It directly affects how training programs are built. A large part of the professional and popular discussion around endurance training is framed in zone language: how much time to spend in Zone 2, how much in Zone 4, and what the balance should be between low and high intensity. But if the zones themselves are not defined in the same way, those recommendations lose much of their meaning.

Zone 2 is the most obvious example, but the same problem exists at higher intensities as well. In Joe Friel’s widely used 5- or 7-zone heart rate model, “Zone 4” refers to work around threshold — hard, but still relatively sustainable. In the Norwegian 5-zone model, “Zone 4” sits above threshold, in the range where anaerobic contribution becomes more significant and fatigue accumulates quickly. Same label, very different physiological demand.
When we chose to let users select their preferred intensity model in EnduCloud, the goal was not simply convenience. It was to align the language people use with the underlying physiology. At the same time, that flexibility comes with a responsibility: understanding that numbers and labels are not absolute. They are context-dependent, and the quality of their connection to physiology varies.

First, it is important to choose a consistent anchor. If you are using maximum heart rate, you need to understand its limitations. There is substantial research showing that heart-rate-based zones do not reliably map onto physiological thresholds. That means one athlete’s Zone 2 could be below VT1, while another athlete running at exactly the same heart rate might already be above it. If you are working with thresholds, on the other hand, you need to make sure they are current and not based on a test from a year ago, or five years ago, as someone once suggested.

Second, you should be cautious about transferring recommendations from one system to another. A guideline like “spend 80% of your time in Zone 2” is meaningless unless you know how Zone 2 is defined. It can mean very different things depending on whether you are using watch-based zones or a threshold-based model, even if both systems have five zones.
The differences between these systems are not mistakes. They are different ways of simplifying a complex reality. The problem begins when we treat the simplification as if it were the reality itself. When your watch says Zone 2, it is using one definition. When a coach talks about Zone 2, they may be using another. Sometimes they overlap. Sometimes they only partly overlap. Sometimes they barely overlap at all.

If there is one takeaway, it is this: do not focus on the name of the zone. Focus on what it actually represents. Without understanding the anchor behind it, “Zone 2” is just a label. Its real meaning comes from the reference point it is tied to, and that is what ultimately determines whether it becomes a precise and useful training tool.

18/03/2026

Step by step the coach portal is coming online:
- Team & Athlete Dashboard
- Athlete & Activity analytics
- Field mode
- Basic CRM

14/03/2026

Training by feel is costing you results. Here's what the data says.
Most athletes train hard. Few train smart. The difference isn't about effort, it's information. At EnduCloud, we've built a performance intelligence platform that turns your raw training data into actionable insight, so every session moves you closer to your goal instead of just adding miles to your legs.
Here's a look at the science behind what we do.

Knowing your threshold isn't enough
You've probably heard of FTP (Functional Threshold Power) or lactate threshold. These are real, useful numbers, but they're snapshots, not the full picture. EnduCloud tracks your metabolic profile: the full power-duration curve that describes how you perform across every effort from a 10-second sprint to a four-hour run or ride. That profile changes as you train. We track it continuously, so your plan evolves with you, not against you.

Your aerobic engine has a fuel tank – and we track it
One of our most powerful tools is W′BAL (pronounced "W-prime balance") – a real-time model of your anaerobic energy reserves. Think of W′ as a rechargeable battery. Hard efforts drain it. Easy riding recharges it. Most athletes don't know how depleted that battery gets during a hard workout, or how long it takes to recover.
EnduCloud models this in real time using the Skiba/Clarke formulation, the same approach used in elite sport science, so we can design interval sessions that stress exactly the right system, then let it recover completely. No more junk miles. No more digging a hole you can't climb out of.

Readiness isn't just about sleep
How you feel on any given morning is the product of dozens of variables: sleep duration and quality, yesterday's training load, cumulative fatigue built over weeks, even your resting heart rate trend. EnduCloud's Smart Readiness system synthesises all of these into a single daily signal, including our proprietary Sleep Adequacy Index, which benchmarks your sleep against your personal baseline rather than a population average. Because what's "enough sleep" for you isn't the same as what's enough for someone else.

AI that coaches, not just calculates
Numbers without context are just noise. EnduCloud's AI coach, powered by Claude Opus and GPT 4.6, reads your full training history, your goal timeline, your life schedule, and your fatigue state before recommending anything. It generates daily micro-challenges calibrated to where you are today, not where the plan assumed you'd be three weeks ago.
When life gets in the way with illness, travel, a bad week, the adaptation engine reflows your plan forward, preserving your long-term stress curve while absorbing the disruption. No manual shuffling. No guilt. Just a plan that still works.

The athletes who reach their goals aren't the ones who train the hardest. They're the ones who train with the best information.
Ready to see what your data is telling you? Register to EnduCloud trial for free →

enducloud.com

16/01/2026

One step closer to production

22/12/2025

Join the EnduCloud Pilot

EnduCloud is moving to the Pilot stage. Whether you're walking for health, jogging for fitness, or a recreational runner striving to achieve new goals, we invite you to join the development of an innovative hybrid AI-human coaching platform.

https://enducloud.com/static/pilot.html

20/12/2025

Step by step Coach Endy is building its personality

05/12/2023

We are happy to announce that EnduCloud has been accepted into Garmin Connect Developer Program - another major step toward the limited launch of EnduCloud later this year.

08/02/2023

One of several studies that have been published in recent years and repeatedly demonstrate that the derivation of training ranges from one maximum or arbitrary value differs from person to person (and in particular between women and men), therefore leading to an incorrect definition of the training ranges, and as a result a failure to achieve the desired metabolic (and health) adaptations.

One of the main challenges facing trainers and developers of systems for recording training is finding a mechanism that will allow individual adjustment of the training ranges according to the characteristics of the specific trainee.

(Link to the article in the first comment)

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