What is a Good HRV: Heart Rate Variability Chart by Age

HRV, or heart rate variability, describes the fluctuations in timing between successive heartbeats. It’s often said that a higher HRV equals better health, but that interpretation is incomplete. As with many complex biological processes, what truly matters is not just the  numbers, but how they change over time. Let me explain why.

I’ve been researching this topic for over a decade, since I developed the EightOS bodymind operating system and began my research in nonlinear dynamics at Nodus Labs. What I found again and again is that simplified, binary metrics do not work when applied to natural systems. The human body is a complex, interconnected network, and understanding it requires attention to patterns and dynamics, not just isolated values.

In this context, higher HRV generally reflects a more flexible and resilient nervous system, with a greater capacity to respond to stress and return to balance afterward. However, that’s not the whole story. The dynamics of that variability are equally important. Research shows that when this dynamics display fractal properties, they are associated with a lower mortality risk, increased adaptability and endurance, and even enhanced psychological well-being.

Let us explore how it works in detail.

 

HRV Chart by Age: RMSSD vs SDNN Measure

It is considered that higher HRV indicates better cardiovascular health and better integration between the parasympathetic and sympathetic nervous systems. The older you are, the lower your HRV is, so the conventional HRV chart by age looks like this:

 

If you are a healthy adult of about 40 years old, your HRV will be at 30–60 ms when measured with RMSSD (the metric used by most fitness trackers and HRV apps) or at about 35–50 ms when assessed using SDNN, which is typical for continuous monitoring devices such as the Apple Watch. This would be when you’re at the state of rest or sleeping. Women usually have slightly lower HRV than men (by about 5 ms). What this means is that your heart never beats in a steady rhythm, there are always small deviations between the beats.

 

How does it work? If your heart beats at 60 beats per minute, you won’t have exactly 1000 ms between each beat. Instead, your RR (beat-to-beat) intervals might be 981, 1019, 1010, 999, and so on. This happens mainly because when you inhale your parasympathetic vagal activity is withdrawn, so the heart speeds up. When you exhale, your parasympathetic vagal activity is restored, so the heart slows down.

High variability indicates that your vagal brake is strong and responsive:  you can shift gears quickly, responding to stress when needed but also recovering efficiently.

The 30 to 60 ms RMSSD range means that on average each beat differs from the next by about 30-60 ms. RMSSD (Root Mean Square of Successive Differences) is focused on the differences between the consecutive beats, so it is a cleaner marker of parasympathetic system’s activity. For instance, a popular fitness tracker Oura Ring uses RMSSD to measure HRV during sleep. It takes 5-minute intervals and then shows you an average value of HRV (RMSSD) over these periods of time during the night as a chart.

The SDNN (Standard Deviation of Normal-to-Normal intervals) measures the total spread of all your beat intervals around the average during the period measured.  So the 40 to 80 ms healthy SDNN range captures the changes from all sources — parasympathetic, sympathetic (response to stress), circadian rhythms, and activities. That is also why SDNN will generally be slightly higher than RMSSD and indicates a general state of your body, rather than a snapshot at the moment of measurement. Apple Watch uses SDNN in its HRV reading and that’s why it’ll be sometimes slightly higher than HRV measured using RMSSD — it might be affected by light activity or a change of state.

The younger and healthier you are, the more responsive your body will be to parasympathetic activity, which translates into higher HRV (in both SDNN and RMSSD). HRV will also be higher when you are resting or sleeping. While the standard range for a 40-year old male is 30 to 60 ms, it could easily double during sleep, prolonged rest, or as a result of regular, but not excessively straining, physical activity. As you get older (or sicker), your HRV decreases, so for a 65+ year old person this difference decreases to 15-35 ms RMSSD and 25-50 ms SDNN. So what does it tell us about your health?

 

What is a Dangerously Low HRV?

Low HRV indicates that the heart has lost some of its ability to vary its rhythm. In practical terms, the system is stuck in a single gear, with limited capacity to adjust to changing demands. Consistently low HRV has been linked to an increased risk of cardiac events, chronic illness, and premature mortality.

Physically, low HRV indicates reduced parasympathetic (rest-and-digest) tone and dominance of the sympathetic system (fight-or-flight response). This means the body remains in a stress-response state: it has a diminished ability for rest, repair, and inflammatory regulation. This can lead to the damage of cardiovascular tissue and disrupt important immune and metabolic  functions.

Since every body is unique, determining dangerous low HRV levels is best done by observing general trends rather than focusing on numbers. So it is important to know one’s baseline and look for any deviations from it, rather than compare yourself to others.

As a general rule, these HRV values can be considered to be dangerously low:

• When measured using RMSSD: below 15 ms in the state of rest is concerning for most adults. Under 10 ms in the state of rest or during sleep is red flag.
• When measured using SNDD: below 50 ms on a 24-hour recording may be linked with increased cardiac risk.
• A drop of 50% or more from your personal baseline in a single day is concerning.
• A drop of 30% or more during 3-7 days indicates that the body is under significant strain, so this should be investigated.
• A drop of 30% or more for months may require medical attention.
• An HRV that is flatlining and losing all variability can precede cardiac events.

 

HRV Dynamics: the Fractal Component

Now we arrive at the most intriguing aspect of the story: the dynamics underlying heart rate variability itself. It is well established that the fractal dynamics of HRV are associated with greater adaptability and resilience and can also be used to enhance training outcomes. Biological systems often operate at the edge between order and chaos. Chaos expresses itself through temporal fractality — self-similarity across different time scales. But what does this fractality look like?

Any temporal fractality, including HRV, can be measured using the so-called DFA (Detrended Fluctuation Analysis). DFA algorithm produces so-called alpha component indicating the level of correlation across different scales (it is closely related to Hurst component, which is also used in time series analysis). When alpha1 ≈ 1.0 the system is fractal, in the temporal sense.

What does it mean in practice and what would it look like?

Here is a graph of the heart beat intervals that are not fractal, but follow so-called brownian motion (with alpha1 component around 1.4). That is most common in everyday life or also during the periods of emotional or physical stress but also at periods of prolonged rest. The system is highly correlated and then it drifts and gets stuck in a certain state — it can’t break out of a trajectory to respond to new demands. A high correlation (alpha above 1.4) in this case is not necessarily a bad state and is usually observed at the state o rest, but it is a sign of a system that’s not highly adaptive.

 

Another extreme is when the dynamic loses all correlation and becomes random (white noise) with alpha1 component around 0.5. This means that there is no coordination across timescales. The system doesn’t have memory and can’t maintain coherent function. This dynamics is usually shown during recovery or when an athlete crosses the anaerobic threshold:

 

When DFA alpha1 is close to 1, the system has memory across different timescales. What happens beat-to-beat is connected to what happens over seconds, which connects to minutes. The system can change states but it’s not locked in a single one and can respond and adapt to perturbations across scales:

 

Why is the optimal HRV variability fractal in nature?

The heart is regulated by constantly interacting feedback loops: vagal (beat-to-beat), baroreceptors (seconds), thermoregulation (minutes), hormonal/circadian (hours). When these loops are healthy and well-coordinated, their interaction produces fractal scaling naturally. When one breaks down — disease, chronic stress, overtraining — you lose that multi-scale coordination and α1 drifts toward rigidity or randomness.

When we optimize HRV for the highest RMSSD value, we focus too much on the vagal parasympathetic aspect. It does indicate that the body is at rest, but it is not very useful as a measure of resilience and adaptability, especially during physical or daily activity.

When we optimize for the highest SDNN value, we may inadvertently give too much importance to the changes that happen due to external influences (change of activity, longer feedback loops), but that won’t give us any information about the system’s resilience and adaptability.

That’s why DFA is such a useful measure: it captures how variability at short timescales relates to variability at longer timescales. When DFA α1 is fractal (around 1.0), it indicates that the beat-to-beat fluctuations driven by parasympathetic activity follow the same statistical scaling pattern as longer-term variations. This is a signature of healthy complexity — the regulatory systems operating at different timescales (vagal, baroreceptor, hormonal) are well-coordinated with each other. The system isn’t stuck in rigid patterns or drifting randomly, but maintains adaptive flexibility across scales.