Rmssd normal range

Rmssd normal range DEFAULT

1Sports Organisation Faculty, Autonomous University of Nuevo León, Monterrey, Mexico

2University of Seville, Seville, Spain

3Pablo de Olavide University, Seville, Spain; *Corresponding Author: [email protected]

Received 8 April 2012; revised 27 April 2012; accepted 11 May 2012

Keywords: Heart Rate Variability; Time Domain; Frequency Domain; Poincaré Plot


This study analyzed Heart Rate Variability in a large sample of active young subjects within a narrow age range (18 to 25), using time and frequency domain methods and a Poincaré plot. Heart rate was recorded (beat to beat) for 30 minutes at rest in 200 healthy subjects divided into 4 groups: 50 sportsmen (20.54 ± 1.52 years); 50 active men (21.22 ± 1.31 years); 50 sportswomen (20.10 ± 1.87 years) and 50 active women (20.92 ± 1.87 years). Significant differences were found for most parameters between athletes and active subjects (male and female) but not between genders. Percentile distributions were provided for all parameters (according to gender and physical activity level) to be used as references in future researches.


Heart Rate Variability (HRV) is considered a good indicator of autonomic control related to cardiovascular health, and has been studied in a range of situations in order to determine the variables that influence it. The most widely-reported influential variables are: age [1-6], gender [5-8], heart disease [9], neurological disease [10- 12] and exercise [6,9,13-16]. HRV is known to decrease when sympathetic activity predominates, whereas it increases when parasympathetic activity predominates. HRV thus reflects autonomic control of the cardiovascular system [17].

Different methods are available for the analysis of HRV. According to the European Society of Cardiology and the North American Society of Pacing and Electrophysiology [18], the most widely-used methods are those in the time and frequency domains, together with the Poincaré plot [19] as the only common technique in the complex field of non-linear methods.

In patients with chronic heart failure (CHF) and acute myocardial infarction (AMI) it is accepted that the best prognostic information is provided by two methods in the time domain: the standard deviation of the intervals between normal beats (SDNN) [18,20-22] and the pNN50 (a measure of the number of adjacent NN intervals which differ by more than 50 ms) [23], although the Task Force [18] recommends rMSSD better than pNN50 because its higher mathemathical robustness. A SDNN value of less than 50 ms or pNN50 lower than 3% is considered indicative of high risk; a SDNN of between 50 and 100 indicates moderate risk, while a value of over 100 ms or a pNN50 greater than 3% is considered normal [18].

Useful reference data are thus available in the time domain for the evaluation of HRV at rest, although they are related to the assessment of cardiovascular risk; it is not clear whether they can be extrapolated to healthy young people, or to what extent they are meaningful for judging the significance of other factors, such as exercise. However, there are no valid reference data for assessing parameters in the frequency domain or the Poincaré plot. It is only possible to interpret data in the light of earlier studies (mostly based on small samples in specific situations) but the values of high and low frequency (HF and LF) and the transversal and longitudinal diameters of the Poincaré plot (SD1 and SD2) are the tools used most to assess the autonomic control of the cardiovascular system.

Given the increasing use of HRV in various fields of medicine and physiology, the authors firmly believe that reference data must be provided for a large population of healthy people, taking age and gender into account. Having taking into account that age is the main factor conditioning HRV, due to a decrease of variability with aging [1-6], we thought it would be useful to provide data of a large population in a narrow age range.

The aim of the present study is to analyze HRV in a large sample of active young mexican subjects (athletes and non-athletes) from 18 to 24 years old, using time and frequency domain methods and the Poincaré plot to provide reference data for future studies.


A total of 200 subjects (100 male, 100 female) of Mexican nationality, aged 18 to 24, were selected from the student population of the Autonomous University of Nuevo León (Monterrey, Mexico).

All subjects were active and with unknown disease history. All of them were recruited voluntarily to complete the same number of males than females. All subjects were surveyed about their medical history to rule out diagnosed pathologies or drugs intake.

Half of the subjects (male and female) systematically trained for at least 6 hours a week and took part in competitions, playing for university teams. The other half of the subjects had an active lifestyle but without practicing sports regularly.

The total sample was thus divided into 4 groups: 50 sportsmen (SM group, age 20.54 ± 1.52 years, height 1.77 ± 0.14 cm, weight 73.50 ± 10.32 kg); 50 active men (AM group, age 21.22 ± 1.31 years, height 1.73 ± 0.06 cm, weight 74.59 ± 10.86 kg); 50 sportswomen (SW group, age 20.10 ± 1.87 years, height 1.63 ± 0.07 cm, weight 61.53 ± 9.53 kg) and 50 active women (AW group, age 20.92 ± 1.87 years, height 1.61 ± 0.05 cm, weight 62.07 ± 10.90 kg).

All subjects were informed of study content and gave written consent in accordance with the Declaration of Helsinki. All subjects completed a questionnaire about their medical history.

Heart rate was recorded for 30 minutes at rest in a supine position, using a Polar RS800sd® monitor in RR mode (beat to beat) [24].

These records were logged by the computer through an infrared interface (Polar IR), using the Polar Precision Performance software package (version 5). A time series was obtained and exported to Excel 2007 and SPSS Version 15.0 for analysis.

Following the recommendations of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology [18], the following parameters were calculated in the time domain: mean NN, SDNN, SDNN index (the mean of SDNN), SDANN (the standard deviation of averaged NN intervals over 5 minute periods), rMSSD (the root mean square differences of successive NN intervals) and pNN50. In the frequency domain, the frequency spectrum was assessed using the Fast Fourier Transform to determine very low frequency (VLF), low frequency (LF), high frequency (HF), the LF/HF ratio, and total power. In accordance with previous studies, other pNNx values for x = 40, 30, 20 and 10 [25,26] were also calculated.

The Poincaré scatter plot was studied as a nonlinear tool. This plot provides information on autonomic activity in the heart, since the transverse axis (SD1) is considered an indicator of parasympathetic activity and the longitudinal axis (SD2) an inverse function of sympathetic activity [27-29]. Moreover, the SD2/SD1 ratio is considered to reflect sympathovagal balance.

Average, Standard Deviation and Percentiles 5, 10, 25, 50, 75, 90 and 95 were calculated for all parameters. The Shapiro-Wilk test was used to test for normal distribution. The Kruskal-Wallis nonparametric test was used to evaluate differences between the four groups, while the Games-Howell test was used as a post-hoc test for multiple comparisons. In all cases, a p value lower than 0.05 (null hypothesis rejected) was considered significant.


Tables 1 and 2 show the time and frequency domain values and the axis of the Poincaré plot for male (SM and AM) and female groups (SW and AW), respectively.

Table 1. Time and frequency domain analysis and diameters of the Poincaré scatter plot for men (SM and AM). Values are expressed as mean ± standard deviation. *p < 0.05; **p < 0.01; ***p < 0.001.

Table 2. Time and frequency domain analysis and diameters of the Poincaré scatter plot for women (SW and AW). Values are expressed as mean ± standard deviation. **p < 0.01; ***p < 0.001.

Significant differences were found only between athletes and active subjects (both male and female). In the group of men, these differences were present in all parameters of the time domine (except SDANN and pNN10) and the diameters of Poincaré Plot (SD1 and SD2), but not in the parameters of the frequency domine. In the case of women, the differences were higher and were present in all the variables except SDANN and LF/HF ratio. When males and females were compared, no significant differences were recorded between AM and AW for any of the variables, as well as between SM and SW except for pNN50 (p = 0.044), pNN40 (p = 0.029) and pNN30 (p = 0.038).

Tables 3-6 show percentiles 5, 10, 25, 50, 75, 95 and 100 for parameters in the time and frequency domains and the Poincaré plot for male and female groups.


As indicated earlier, the Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology [18] has provided valid reference data in the time domain for the assessment of cardiovascular risk. However, it is not clear whether they can be extended to healthy young people or to other variables such as degree of exercise. There are no solid reference data for the evaluation of parameters in the frequency domain or the Poincaré plot.

A recent review [30] highlights the limitations of the Task Force references and the difficulties in implementing values from subsequent studies. In light of this review they collect values that can serve as reference, but not separated by sex or age. Morover, they do not report values for Poincaré Plot.

A common problem affecting most studies of HRV is the small size and heterogeneity of the samples used, especially when sportpeople are concerned (15). This study therefore used 200 age-matched subjects classified into four groups according to gender and to whether or not they practiced a competitive sport; all subjects were active and had no history of disease.

The main contribution of this study is to provide percentile values of HRV in the time domain, frequency domain and Poincaré plot for a population of healthy, active young people in a narrow age-range. The most important factor to control was age, since the dynamics of the NN interval are known to vary with age in healthy people [31], not only comparing childhood to old age, but also in intermediate age ranges (for example from 30 to 40 years old) [32-35].

It will, of course, be necessary to provide values for other age ranges in the future; comparable data for other age groups, provided by the present researchers or others, will help to expand the available reference data.

With respect to the nationality of the sample, the only similar study found (although with a smaller and older population) is that of Lerma et al. [36], using Mexican subjects (30 females and 20 males) aged between 21 and 36.

Regarding the duration of the time series, 30 minutes is sufficient to obtain an optimal series of data (NN intervals) for analysis [18]. Moreover, experience and theoretical knowledge for physiological interpretation are both greater for this recording period. Most of HRV studies with athletes and healthy people use short term records of ten minutes or less (5, 6, 16, 37) and it is known that the effect size is higher as the record is shorter [38].

The data obtained in the present study in the time domain showed that all parameters (except SDANN and pNN10) were significantly greater for athletes than for active subjects, both for males (Table 1) and females

Table 3. Percentiles 5, 10, 25, 50, 75, 90 and 95 for parameters in the time domain for male groups.

Table 4. Percentiles 5, 10, 25, 50, 75, 90 and 95 for the frequency domain and Poincaré plot parameters for male groups.

Table 5. Percentiles 5, 10, 25, 50, 75, 90 and 95 for parameters in the time domain for female groups.

Table 6. Percentiles 5, 10, 25, 50, 75, 90 and 95 for the frequency domain and Poincaré plot parameters for the groups of women.

(Table 2). However, there was no significant difference between men and women with the same level of physical activity, except for pNN50, 40 and 30 between sportsmen and sportswomen (females showed higher values than males). This means that, according to our results, heartbeat signal displays significantly greater variability (assessed in the time domain) in men and women engageing in competitive sports, with no gender-related differences. This could be due to greater parasympathetic activity at rest [39,40].

Other values can be used in place of pNN50 (40, 30, 20 and 10); Mietus et al [25] demonstrated that the discriminatory sensitivity of pNNx increased as the value of “x” fell. Similar findings are reported by De la Cruz and Naranjo [26] when using pNNx to differentiate between healthy subjects and cardiac patients at rest and during exercise. Here, however, the statistic pNNx with x values between 10 and 50 showed no advantage over pNN50. Refferences for pNNx statistics is one further contribution of this study.

Many other authors have reported differences in HRV between trained and untrained people [6,9,13-16,36,41]. However, in some cases sample sizes were too small to be meaningful; in other cases did not differ between gender or age ranges were too broad. Perhaps the main contribution of the present study is the statistical power derived from a large sample size in a narrow range of age.

The comparison of HRV behaviour between women and men is a controversial issue. Some studies have found gender differences while others have not [3,36]. Anyway, even when differences are found, it seems clear that age had a greater impact on HRV than sex [16].

In the present study, there was no significant difference between men and women in the time domain; the only significant difference recorded was between sportsmen and sportswomen for pNN50 (p = 0.044), pNN40 (p = 0.0299) and pNN30 (p = 0.038). In any case, the level of physical activity was more decisive than gender in the time domain.

In the frequency domain, ultra low (ULF) [42], very low (VLF) [43] and low frequency (LF) [44,45] at rest are acknowledged to be good indicators of health in cardiac patients, but there is little information about healthy young people. In the present study, sportswomen displayed significantly higher values than active women for total power (p = 0.004), VLF (p = 0.007), LF (p = 0.0001) and HF (p = 0.010), and significantly lower values for the LF/HF ratio (p = 0.009). No other differences (males vs. females or SM vs. AM) were significant. Sportswomen thus appear to display parasympathetic predominance compared to active women at rest; the authors cannot satisfactorily explain why these differences are not found for men.

A recent Meta-Analysis [38] reports a higher HF in trained subjects but they do not differ for gender or age. A paper with 145 track-and-field athletes (both sexes, from 18 to 33 years old) report values very similar to ours in the time domine but much lower than ours in the frequency domine [37].

Perini et al [46] observed no changes in HF and LF at rest in seven men and eight women over 70 years old after an eight-week training period. Lerma et al [36] reported higher HF values in sportswomen compared to sportsmen in the same condition. Other authors report significant differences between sedentary and athletic subjects, with higher total power and HF in athletes, but no difference in LF and ratio LF/HF [39], although in this study the sample comprised both men and women.

Therefore, to the observed data disparity in the literature, we must emphasize again that our study provides a proper number of dates matched by sex and age.

Results for the Poincaré plot pointed to significant differences for SD1 and SD2 between athletes and active subjects, both for males (Table 1) and females (Table 2), with much lower p values for women. There were no significant gender differences.

There are very few studies using the Poincaré plot in healthy subjects at rest but most of them report that trained people display greater parasympathetic activity than sedentary [47] or low fitness [48] subjects. Similar findings were obtained here.

Analysis of the SD2/SD1 ratio provides information on the relationship between sympathetic and parasympathetic stimuli. The present results showed a lower ratio in trained than in active subjects (male and female), although this difference was only significant for men. A lower SD2/SD1 ratio may reflect an increase in SD1, a decrease in SD2, or both. In the present sample, the diameters increased independently, but the increase in SD1 was greater than in SD2 in both men and women: SD1 increased by 35.7% in men and by 68.6% in women, while SD2 increased by 20.7% and 47.4%, respectively.

An increase in SD1 means an increase in parasympathetic activity while an increase in SD2 means a decrease in sympathetic activity [27-29], so a lower ratio implies both greater parasympathetic activity and reduced sympathetic activity at rest in trained subjects.

This is not the first report of percentile data for normal subjects: Lerma et al. [36] provided gender-related data references for HRV in the time and frequency domains, using percentile 25, 50 and 75; however, their sample comprised 30 women and 20 men ranging in age from 21 to 36. Other study reporting percentiles is that of Kim and Woo [49] with korean population between 18 and 65 years old (2478 men and 735 women). They only report data for SDNN, rMSSD, LF and HF and the records are of 5 minutes. All their data are much lower than ours even for the same range of age (168 men and 186 women).

In conclusion, the strengths of the present study, aside from percentile distributions (Tables 3-6), are: sample size, classification according to gender and physical activity level, and values not only for the time and frequency domains but also for Poincaré plot analysis, including values for pNNx statistics.


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  49. Kim, G.M. and Woo, J.M. (2011) Determinants for heart rate variability in a normal Korean population. Journal of Korean Medicine Science, 26, 1293-1298. doi:10.3346/jkms.2011.26.10.1293
Sours: https://www.scirp.org/html/4-8201618_21183.htm

What normal ranges and measurement standards we use to interpret your heart rate variability

To understand what to rely on in our calculations, we have thoroughly analyzed normal ranges and metrics to measure and evaluate heart rate variability (HRV) from an array of scientific sources.

First, there are Guidelines on Heart rate variability: Standards of measurement, physiological interpretation, and clinical use (Task Force of The European Society of Cardiology and The North American Society of Pacing and Electrophysiology).

Then, there are recent research papers on reference ranges for HRV in healthy adults, for example:

However, the above papers have their drawbacks:

  • They are small-scale. When performing data analysis, the researchers considered groups of up to 100 subjects only.

  • Scientists still argue what “normal” really means. Health is a relative and not an absolute state, and a lot of different factors can impact a healthy (i.e., normal) or unhealthy status for a particular individual. That’s why researchers still argue about what is meant by “normal ranges” or “population reference ranges.”

  • Some studies ignored additional yet critical factors that can affect your health, such as chronic illnesses or stress levels.

  • It’s next to impossible to dig up credible reference ranges for some rare scores such as AMo50 and MxDMn, because these scores are mostly used in studies focused on elite athletes, not healthy non-athletes.

Let’s take a closer look at RMSSD values (Root mean square of successive RR interval differences) to illustrate the issue. If we take a measuring time of 2.5–5 minutes, researchers have proposed the following normal ranges in different studies:

  • 13–48 ms — healthy adults aged 38–42 years

  • 35–107 ms — elite athletes

  • 53.5–82 ms — healthy men

  • 40.5–71 ms — men

  • 29–65 ms — women

  • 23–72 ms — men

  • 22–79 ms — women

These standard ranges vary considerably, and obviously can’t apply to everyone. If we try to unify them, the reference range for RMSSD would be 13–107 ms, which seems like a pretty broad generalization.

Welltory uses the reference range of 20–89 ms, which falls into the range mentioned above. Even though it's possible to get an RMSSD score that falls out of our range, it would most likely mean that your body is working overtime.

The same generalization issue happens with other not-so-common HRV scores like SDNN, pNN50, or Mean RR.

Such ranges seem too broad to be useful for everyday measurements because heart rate variability is an extremely sensitive metric. That’s why we’ve narrowed the typical normal ranges up to more specific ones.

To do so, we’ve analyzed over 60,000 RR intervals of more than 600 users and derived all the data from photoplethysmography signals in keeping with the following criteria:

Plus, our study included only the data of people who measured their HRV for at least 90 days. We then conducted data analysis to determine how the same person’s measurement results can vary depending on the time of the day, a healthy or unhealthy state, work, exercises, caffeine & alcohol intake.

It is worth pointing out that our narrowed normal ranges are also not personalized enough.

However, we realize how crucial it is to understand what’s going on with your body right now in the view of your previous trends and measurements. That’s why we offer you Productivity, Energy, and Stress scores that can reflect how you feel more accurately than reference ranges.

Sours: https://help.welltory.com/en/articles/4413231-what-normal-ranges-and-measurement-standards-we-use-to-interpret-your-heart-rate-variability
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Everything You Need to Know About Heart Rate Variability (HRV)

August 11, 2021

Heart rate variability, or HRV for short, is a measure of your autonomic nervous system that is widely considered one of the best objective metrics for physical fitness and determining your body’s readiness to perform.

By Mark Van Deusen

What is Heart Rate Variability?

Heart rate variability is literally the variance in time between the beats of your heart. So, if your heart rate is 60 beats per minute, it’s not actually beating once every second. Within that minute there may be 0.9 seconds between two beats, for example, and 1.15 seconds between two others. The greater this variability is, the more “ready” your body is to execute at a high level.

RR intervals show HRV

Heart rate variability is determined by the time between heart beats, known as RR intervals.

These periods of time between successive heart beats are known as RR intervals (named for the heartbeat’s R-phase, the spikes you see on an EKG), measured in milliseconds. WHOOP calculates HRV using RMSSD, the root mean square of successive differences between heartbeats.

HRV And The Autonomic Nervous System

Although HRV manifests as a function of your heart rate, it actually originates from your nervous system. Your autonomic nervous system, which controls the involuntary aspects of your physiology, has two branches, parasympathetic (deactivating) and sympathetic (activating).

The parasympathetic nervous system (often referred to as “rest and digest”) handles inputs from internal organs, like digestion or your fingernails and hair growing. It causes a decrease in heart rate.

The sympathetic nervous system (often called “fight or flight”) reflects responses to things like stress and exercise, and increases your heart rate and blood pressure.

Heart rate variability comes from these two competing branches simultaneously sending signals to your heart. If your nervous system is balanced, your heart is constantly being told to beat slower by your parasympathetic system, and beat faster by your sympathetic system. This causes a fluctuation in your heart rate: HRV.

Sympathetic and parasympathetic create HRV

HRV is caused by two competing branches of the autonomic nervous system, sympathetic and parasympathetic.

Why is HRV a Sign of Fitness?

When you have high heart rate variability, it means that your body is responsive to both sets of inputs (parasympathetic and sympathetic). This is a sign that your nervous system is balanced, and that your body is very capable of adapting to its environment and performing at its best.

On the other hand, if you have low heart rate variability, one branch is dominating (usually the sympathetic) and sending stronger signals to your heart than the other. There are times when this is a good thing–like if you’re running a race you want your body to focus on allocating resources to your legs (sympathetic activity) as opposed to digesting food (parasympathetic activity).

However, if you’re not doing something active low HRV indicates your body is working hard for some other reason (maybe you’re fatigued, dehydrated, stressed, or sick and need to recover), which leaves fewer resources available to dedicate towards exercising, competing, giving a presentation at work, etc.

To look at it another way, the less one branch is dominating the other, the more room there is for the sympathetic (activating) branch to be able to come in and dominate, which is why high HRV suggests you’re fit and ready to go.

Learn More:Why Athletes Should Want High HRV

What is a Normal Heart Rate Variability?

Below is an average heart rate variability chart based on age:

heart rate variability chart ms

The average heart rate variability range for WHOOP members broken down by age.

You can see that for the most part, HRV decreases abruptly as people get older. The middle 50% of 20-25 year olds usually have an average HRV in the 55-105 range, while 60-65 year olds tend to be between 25-45.

And while the figure above shows what technically falls under the umbrella of “normal HRV,” answering the question “What is a good heart rate variability?” is a lot more complicated.

Learn More:Average HRV Range by Age and Gender

HRV is Highly Individualized

Heart rate variability is an extremely sensitive metric. It fluctuates greatly throughout the day, from one day to the next, and from one person to another. People often wonder “What should my HRV be?” and “How does my HRV compare to others?”

Younger people tend to have higher HRV than older people, and males often have slightly higher HRV than females. Elite athletes usually have greater heart rate variability than the rest of us, and within that subset endurance athletes regularly have higher HRV than strength-based athletes. But, none of this is absolute. There are plenty of extremely fit and healthy people out there whose HRV is regularly in the 40s. What constitutes a healthy heart rate variability differs for everyone.

Better questions to ask are “What is a good heart rate variability trend for me?” and “What can I do to make that happen?”

Learn More:What is a Normal HRV for Me?

Heart Rate Variability Trends are What Matters

When you begin using a heart rate variability monitor, you may notice that your HRV varies greatly from day to day. This can be attributed to the many factors that affect it (more on this shortly), and is perfectly normal. If your friend has a higher HRV than you do today, that is not an indication that they are more fit than you are.

Rather than comparing your heart rate variability to others, a more practical use of HRV is to follow your own long-term trends. For example, if you’re taking steps to improve your fitness and overall health, over time you should see a gradual increase in your average heart rate variability.

positive heart rate variability trend

A positive trend in daily heart rate variability over a 3-month time period.

Similarly, a downward trend in your HRV over several days is worth paying attention to. Among other things, it might be a sign that you’re training too hard, not sleeping enough, getting sick, eating poorly, or failing to hydrate properly.

Factors that Affect Heart Rate Variability

There are a great number of things that impact your HRV. The figure below breaks them down into three categories: Training factors, lifestyle factors, and biological factors.

Factors that affect hrv

Some of the many things that can impact heart rate variability.

Training factors include the frequency and intensity of your workouts. If you go extra hard today, or for several days in a row, your HRV is likely going to take a hit. There are also many other choices you make each day (lifestyle factors) that significantly affect your heart rate variability, ranging from what you put into your body, to the quality and consistency of your sleep.

And lastly, there are biological factors that are out of your control, like age, gender and genetics–some people are just born to have higher HRV than others.

How to Improve Heart Rate Variability

Methods for increasing HRV include the following:

Intelligent Training. Don’t overdo it and push too hard for too many days without giving your body an opportunity to recover (see below).

Hydration. The better hydrated you are, the easier it is for your blood to circulate and deliver oxygen and nutrients to your body. Aiming to drink close to one ounce of water per pound of bodyweight each day is a good goal.

Avoid Alcohol. One night of drinking potentially decreases HRV for up to five days.

Steady Healthy Diet. Poor nutrition has adverse effects on HRV, as does eating at unexpected times.

Quality Sleep. It’s not just the amount of sleep you get that matters, but also the quality and consistency of your sleep. Going to bed and waking up at similar times each day is beneficial.

Auto-Regulation. In general, trying to get your body on a consistent schedule (in particular with sleep and eating to align your circadian rhythm) is helpful. Your body does things more efficiently when it knows what’s coming.

Learn More: 10 Ways to Improve Your HRV

HRV Training

Studies have shown that heart rate variability can be a valuable tool for making the most of your training. After days of strenuous activity, your HRV will dip. With proper rest and recovery, your heart rate variability will rise, letting you know when it’s once again time to push yourself.

Training adaptation with HRV

Intense training for several days will likely cause HRV to drop, but it will then increase when your body has time to recover.

Rather than sticking to a predetermined workout schedule, modifying the intensity and duration of your physical activity based on your heart rate variability will allow you to train smarter and more efficiently. When your HRV is high, your body is prepared to take on a greater workload. When it is low, it’s a sign to cut back.

Learn More: How to Use HRV to Guide Your Training

Health and Other Applications of HRV

Beyond using heart rate variability as a fitness metric, it also has many applications when it comes to our overall health and well being. Tracking your HRV can help you gain a better understanding of:

  • Nutrition
  • Sleep
  • Stress levels
  • Mental health
  • Warnings signs of sickness
  • Risk of disease

For example, if your daily routine is unchanged but your HRV drops, it may be an indicator of increased stress or oncoming illness. Or, if you’d like to see the effect a new diet has on your body, the impact will be noticeable in your heart rate variability.

WHOOP: The Ultimate HRV Monitor and Training Tool

WHOOP allows you to take heart rate variability training to the next level. As mentioned previously, HRV is an extremely sensitive metric that varies greatly throughout the day. Tracking it 24/7 (or any time when you’re active) is not very useful because it fluctuates so drastically from one moment to the next. Even changes in your respiratory rate while you’re sleeping can have a significant impact on your heart rate variability.

In order to get a consistent and reliable HRV measurement, WHOOP calculates your heart rate variability using a dynamic average during sleep. It is weighted towards your last slow-wave sleep stage each night, the time when you’re in your deepest period of sleep. This enables you to get an accurate understanding of your baseline from which to monitor your trends.

WHOOP is much more than an HRV tracker. Each morning, it uses your HRV (as well as your resting heart rate, respiratory rate and sleep performance) to calculate your daily recovery–how ready your body is to perform. WHOOP then quantifies the strain your body takes on, so you’ll know exactly how hard to push yourself to meet your fitness goals.

For an even deeper dive into everything you need to know about HRV, check out: Podcast No. 29: Heart Rate Variability (HRV)


Mark Van Deusen

Mark Van Deusen is the Content Manager at WHOOP. Before joining WHOOP, Mark served as the Managing Editor and Head Writer for CelticsLife.com. He was also a Featured Columnist for Bleacher Report and a contributor at Yahoo Sports. A former tennis coach, Mark graduated from the University of Richmond with a degree in Sociology and Leadership Studies.

Sours: https://www.whoop.com/thelocker/heart-rate-variability-hrv/

Reference Ranges for Short-Term Heart Rate Variability Measures in Individuals Free of Cardiovascular Disease: The Multi-Ethnic Study of Atherosclerosis (MESA)

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Sours: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5010946/

Normal range rmssd

Normal Values of Corrected Heart-Rate Variability in 10-Second Electrocardiograms for All Ages

Marten E. van den Berg,1Peter R. Rijnbeek,1Maartje N. Niemeijer,2Albert Hofman,2Gerard van Herpen,1Michiel L. Bots,3Hans Hillege,4Cees A. Swenne,5Mark Eijgelsheim,2,6Bruno H. Stricker,corresponding author2,7,8and Jan A. Kors1,*

Marten E. van den Berg

1Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, Netherlands

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Peter R. Rijnbeek

1Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, Netherlands

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Maartje N. Niemeijer

2Department of Epidemiology, Erasmus University Medical Center, Rotterdam, Netherlands

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Albert Hofman

2Department of Epidemiology, Erasmus University Medical Center, Rotterdam, Netherlands

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Gerard van Herpen

1Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, Netherlands

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Michiel L. Bots

3Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, Netherlands

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Hans Hillege

4Department of Cardiology, University Medical Center Groningen, Groningen, Netherlands

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Cees A. Swenne

5Department of Cardiology, Leiden University Medical Center, Leiden, Netherlands

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Mark Eijgelsheim

2Department of Epidemiology, Erasmus University Medical Center, Rotterdam, Netherlands

6Department of Internal Medicine, University Medical Center Groningen, Groningen, Netherlands

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Bruno H. Stricker

2Department of Epidemiology, Erasmus University Medical Center, Rotterdam, Netherlands

7Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, Netherlands

8Health and Youth Care Inspectorate, Utrecht, Netherlands

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Jan A. Kors

1Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, Netherlands

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Author informationArticle notesCopyright and License informationDisclaimer

1Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, Netherlands

2Department of Epidemiology, Erasmus University Medical Center, Rotterdam, Netherlands

3Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, Netherlands

4Department of Cardiology, University Medical Center Groningen, Groningen, Netherlands

5Department of Cardiology, Leiden University Medical Center, Leiden, Netherlands

6Department of Internal Medicine, University Medical Center Groningen, Groningen, Netherlands

7Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, Netherlands

8Health and Youth Care Inspectorate, Utrecht, Netherlands

corresponding authorCorresponding author.

Edited by: Mark Potse, Inria Bordeaux - Sud-Ouest Research Centre, France

Reviewed by: Bas J. Boukens, University of Amsterdam, Netherlands; Julia Ramirez Garcia, Queen Mary University of London, United Kingdom; Ana Minchole, University of Oxford, United Kingdom

*Correspondence: Jan A. Kors, [email protected]

This article was submitted to Cardiac Electrophysiology, a section of the journal Frontiers in Physiology

Received 2017 Mar 22; Accepted 2018 Apr 5.

Copyright © 2018 van den Berg, Rijnbeek, Niemeijer, Hofman, van Herpen, Bots, Hillege, Swenne, Eijgelsheim, Stricker and Kors.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.


Purpose: Heart-rate variability (HRV) measured on standard 10-s electrocardiograms (ECGs) has been associated with increased risk of cardiac and all-cause mortality, but age- and sex-dependent normal values have not been established. Since heart rate strongly affects HRV, its effect should be taken into account. We determined a comprehensive set of normal values of heart-rate corrected HRV derived from 10-s ECGs for both children and adults, covering both sexes.

Methods: Five population studies in the Netherlands (Pediatric Normal ECG Study, Leiden University Einthoven Science Project, Prevention of Renal and Vascular End-stage Disease Study, Utrecht Health Project, Rotterdam Study) provided 10-s, 12-lead ECGs. ECGs were stored digitally and analyzed by well-validated analysis software. We included cardiologically healthy participants, 42% being men. Their ages ranged from 11 days to 91 years. After quality control, 13,943 ECGs were available. Heart-rate correction formulas were derived using an exponential model. Two time-domain HRV markers were analyzed: the corrected standard deviation of the normal-to-normal RR intervals (SDNNc) and corrected root mean square of successive RR-interval differences (RMSSDc).

Results: There was a considerable age effect. For both SDNNc and RMSSDc, the median and the lower limit of normal decreased steadily from birth until old age. The upper limit of normal decreased until the age of 60, but increased markedly after that age. Differences of the median were minimal between men and women.

Conclusion: We report the first comprehensive set of normal values for heart-rate corrected 10-s HRV, which can be of value in clinical practice and in further research.

Keywords: electrocardiography, heart-rate variability, normal values, heart-rate correction, children, adults, elderly


Heart-rate variability (HRV) as measured on the electrocardiogram (ECG) is the variability of intervals between QRS complexes generated by sinus node depolarization in one continuous recording (Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology, 1996). Many studies have indicated that reduced HRV is a strong, independent, and consistent risk factor for all-cause and cardiac mortality (Billman, 2011). HRV guidelines recommend that measurements be based on 5-min or 24-h ECG recordings, but 10-s ECGs are more commonly made during routine medical care and are faster, cheaper, and more patient-friendly than longer ECG recordings. Furthermore, even though it is not possible to determine frequency-domain measurements nor some of the time-domain measurements on a 10-s signal, it is possible to obtain the two most commonly used time-domain measurements: the standard deviation of the normal-to-normal RR intervals (SDNN) and the root mean square of successive RR-interval differences (RMSSD) (Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology, 1996). It has recently been demonstrated that there is substantial agreement between 10-s and 4–5-min pulse-wave recordings for RMSSD, and to a lesser extent for SDNN (Munoz et al., 2015). As a matter of fact, time-domain HRV markers as measured on 10-s ECGs have been associated with heart failure (Rautaharju et al., 2006), cardiac mortality (de Bruyne et al., 1999), and all-cause mortality (Dekker et al., 1997) in population-based studies. In one of these studies (de Bruyne et al., 1999), both high HRV and low HRV were associated with adverse outcomes. Thus, 10-s HRV seems a possible tool in epidemiological research and risk assessment.

Heart-rate variability is known to have a strong, inverse relationship with heart rate (Coumel et al., 1994; Tsuji et al., 1996; Monfredi et al., 2014; Sacha, 2014; Gasior et al., 2015). It has been suggested by Monfredi et al. (2014) that this relationship is exponential, and that HRV parameters should be exponentially corrected for heart rate. However, the aforementioned studies either did not adjust for heart rate, or did so applying a linear adjustment. As heart-rate itself is a strong risk factor for cardiac morbidity and mortality (Greenland et al., 1999; Fox et al., 2013), the results of the earlier studies might have been confounded.

Knowledge of normal values of heart-rate corrected HRV markers from 10-s ECGs would allow to derive well-grounded thresholds for continuous variables in risk models and may be useful in establishing diagnostic criteria. A number of studies have reported normal HRV values for 5-min (Nunan et al., 2010; Kim and Woo, 2011; Seppala et al., 2014) and 24-h (Umetani et al., 1998) ECG recordings, but only one recent study reported normal values for 10-s ECGs (O’Neal et al., 2016), without investigating the age-dependence of HRV. Moreover, none of these studies applied heart-rate correction. Therefore, in this study we determine heart-rate corrected normal values for HRV as derived from 10-s ECGs across all age groups.

Materials and Methods

Study Populations

In this study we combined data from five population studies conducted in the Netherlands. The 10-s 12-lead ECGs from these studies were digitally recorded and stored at sampling rates of at least 500 Hz, up to 1200 Hz in the pediatric group. ECGs were recorded with the subjects in supine position. All data were anonymized.

(1) Pediatric Normal ECG Study (Rijnbeek et al., 2001). The population of this study consists of 1,912 children, their ages ranging from 11 days to 16 years. The children were recruited in the year 2000 at three child health centers, three primary schools, and one secondary school in the city of Rotterdam. The children’s height and weight, measured before ECG recording, corresponded well with the Dutch growth standard. ECGs were recorded with a portable PC-based acquisition system (Cardio Control, Delft, Netherlands).

(2) The Leiden University Einthoven Science Project (Scherptong et al., 2008). The population of this study contains 787 medical students of Leiden University. The ages of the participants range between 17 and 29 years, and all attested to be in good health. The ECGs were recorded from 2005 until 2007 with Megacart electrocardiographs (Siemens, Erlangen, Germany).

(3) The Prevention of Renal and Vascular End-stage Disease (PREVEND) Study (de Jong et al., 2003). This study, which started 1997, has as its goal to investigate the natural course of microalbuminuria and its relation to renal and cardiovascular disease in the general population. The PREVEND population consists of 8,592 participants aged 28–75 years, from the city of Groningen. Medical records, including medication use, were available for all participants. ECGs were recorded with CardioPerfect equipment (Welch Allyn Cardio Control, United States).

(4) The Utrecht Health Project (Grobbee et al., 2005). This ongoing study started in 2000 in Leidsche Rijn, a newly developed residential area of Utrecht. All new inhabitants were invited by their general practitioner to participate. The population of this study consists of 6,542 participants. Written informed consent was obtained and an individual health profile was made by dedicated research nurses. Baseline assessment included physical examination, ECG, blood tests, and interview-assisted questionnaires. Pharmacy records were used to obtain medication use. ECGs were recorded with CardioPerfect equipment (Welch Allyn Cardio Control, United States).

(5) The Rotterdam Study (Hofman et al., 1991). This study, which started in 1990, investigates determinants of a number of age-related disorders in an elderly population, prominently among them cardiovascular disease. The Rotterdam Study population consists of 10,994 inhabitants of Ommoord, a suburb of Rotterdam, aged 55 years or older. Participants were visited at home for an interview and were subsequently examined at the research center. Detailed information was collected on health status, medical history, and medication use. ECGs were recorded with an ACTA electrocardiograph (Esaote, Florence, Italy).

From these five populations, totaling 28,827 participants, we selected a subgroup of participants with no indication of cardiac disease. Reasons for exclusion were a history of myocardial infarction, heart failure, coronary bypass surgery, coronary angioplasty, or pacemaker implantation. Other exclusion criteria were hypertension and diabetes mellitus. Hypertension was defined as a systolic blood pressure ≥160 mmHg or a diastolic blood pressure ≥100 mmHg or use of antihypertensive medication, including use of beta-blockers. Diabetes mellitus was defined as a non-fasting serum glucose ≥11 mmol/l or use of glucose-lowering drugs. After applying these criteria, 15,248 individuals were available. We further removed ECGs with disturbances that resulted in QRS-detection errors or potentially affect accurate measurement of RR intervals, such as excessive noise, excessive baseline wander, sudden baseline jumps, or spikes. We also removed ECGs with premature ventricular beats, premature supraventricular beats, and second or third degree atrioventricular block. This resulted in 13,943 participants with a low-noise ECG containing only normal beats available for analysis.

This study was approved by the Medical Ethics Committee of the Erasmus University Medical Center. Since all data were anonymized and retrospectively collected, informed consent of the subjects was not required according to Dutch legislation.

HRV Measurement and Correction

RR intervals for all ECGs were automatically determined by the Modular ECG Analysis System (MEANS), an ECG computer program that has been evaluated extensively (van Bemmel et al., 1990; Willems et al., 1991). The QRS detector of MEANS operates on multiple simultaneously recorded leads. The simultaneous leads are transformed to a detection function, which brings out the QRS complexes among the other parts of the signal. The detection signal is gauged against an adaptable threshold to detect the occurrence of a QRS complex. MEANS signals all the abovementioned signal disturbances, and recognizes premature ventricular complexes, supraventricular complexes, and atrioventricular blocks. All ECGs were visually inspected to validate the automatic processing results. We calculated two time-domain HRV markers: SDNN and RMSSD.

To correct the HRV markers for heart rate (HR), we investigated and compared four different models:


From these, the following HRV correction formulas can be derived, taking 60 beats per minute as the reference:


To determine the correction parameter β, we used linear regression to fit each of the models (the parabolic and exponential models were log-transformed prior to the regression analysis). To deal with possible confounding by age and sex, regression was performed in predetermined age groups as specified in Table ​1, and for men and women separately. For each correction model, we determined the R-squared value as a measure of model fit for each of the 17 age groups, for SDNN and RMSSD and for men and women separately (17 × 4 = 68 combinations). A mean R-squared per model was computed by weighting the R-squared values per age group by the number of subjects in that age group.

Table 1

Age and sex distribution of the study population.

Age groupNo. of boys/menNo. of girls/womenTotal
Younger than 1 month11819
1 to 2 months272350
3 to 5 months343872
6 to 11 months6954123
1 to 2 years5152103
3 to 4 years6062122
5 to 7 years120104224
8 to 11 years115164279
12 to 15 years140108248
16 to 19 years156382538
20 to 29 years4509081,358
30 to 39 years1,3761,9693,345
40 to 49 years9621,1122,074
50 to 59 years9761,2452,221
60 to 69 years9951,2432,238
70 to 79 years295472767
80 to 89 years51106157
90 years and older145

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Estimation of Normal Values

Centile curves were estimated using the Box-Cox t distribution in a semi-parametric model for location, scale and shape (Rigby and Stasinopoulos, 2005; Rigby and Stasinopoulos, 2006). The Box-Cox t distribution allows for modeling of the distribution of the median, skewness, and kurtosis as functions of age. The lms function of the R-package gamlss was used for the creation of the centile curves. The 2nd percentile was taken as the lower limit of normal (LLN) and the 98th percentile as the upper limit of normal (ULN). Normal values for all age categories were estimated using the predict.gamlss function of the gamlss package. The normal values of all age categories in Tables ​3, ​4 were estimated based on the modeled Box-Cox t distribution, taking the central age in the age group. For example, normal values for the category of 16 to 20 years were based on the values of participants aged 18 years.

Table 3

Normal values for heart-rate corrected SDNN (in ms) per age group and for both sexes.

Median (2nd percentile; 98th percentile)
Age groupBoys/menGirls/women
<1 month99.6 (33.6; 265.6)109.2 (35.1; 282.2)
1 to 2 months99.4 (33.4; 265.1)108.8 (35.0; 281.6*)
3 to 5 months98.8 (33.2; 264.1)108.1 (34.7; 280.3)
6 to 11 months98.1 (32.9; 262.7)107.1 (34.3; 278.3)
1 to 2 years95.4 (31.8; 258.0)103.8 (33.1; 271.9)
3 to 4 years91.3 (30.0; 250.4)98.6 (31.2; 261.9)
5 to 7 years86.0 (27.8; 240.5)92.3 (28.9; 249.8)
8 to 11 years78.3 (24.7; 225.7)84.0 (25.8; 233.5)
12 to 15 years69.3 (21.1; 208.0)75.2 (22.7; 215.7)
16 to 19 years60.7 (17.8; 190.9)67.3 (20.0; 199.2)
20 to 29 years48.5 (13.9; 161.4)56.0*** (16.6**; 172.7)
30 to 39 years37.5 (11.0; 129.2)43.4*** (13.3**; 137.8)
40 to 49 years30.4 (8.8; 113.7)33.3* (10.6; 109.5)
50 to 59 years24.4 (6.9; 103.4)25.6 (8.4***; 90.2*)
60 to 69 years20.4 (5.6; 104.8)20.7 (6.9**; 82.8)
70 to 79 years17.8 (4.7; 120.9)17.9 (5.9; 89.5*)
80 to 89 years15.6 (3.9; 158.3)16.1 (5.1; 126.1)

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Table 4

Normal values for heart-rate corrected RMSSD (in ms) per age group for both sexes.

Median (2nd percentile; 98th percentile)
Age groupBoys/menGirls/women
<1 month153.1 (53.0; 440.2)161.9 (56.9; 463.9)
1 to 2 months152.4 (52.7; 438.7)161.1 (56.6; 462.2)
3 to 5 months150.9 (52.1; 435.7)159.6* (56.0; 458.6)
6 to 11 months148.8 (51.2; 431.1)157.3* (55.1; 453.2)
1 to 2 years141.9 (48.4; 416.3)150.0 (52.1; 435.8)
3 to 4 years131.4 (44.1; 393.1)138.9 (47.6; 409.4)
5 to 7 years118.8 (39.1; 364.6)126.0 (42.5; 378.3)
8 to 11 years102.1 (32.8; 324.9)109.7 (36.1; 338.1)
12 to 15 years84.8 (26.5; 280.3)93.6 (30.1; 297.1)
16 to 19 years70.1 (21.6; 239.3)80.4 (25.3; 261.8)
20 to 29 years51.9 (16.0; 182.7)63.7*** (19.8**; 212.9)
30 to 39 years37.7 (12.1; 134.4)47.7*** (15.3*; 158.4)
40 to 49 years29.9 (9.8; 111.5)35.8*** (12.1; 118.5)
50 to 59 years24.1 (7.7; 102.5)27.3*** (9.5***; 95.6)
60 to 69 years20.7 (6.2; 114.6)22.6** (8.0*; 92.2)
70 to 79 years19.0 (5.4; 157.1)20.3 (7.0; 112.1*)
80 to 89 years17.9 (4.9; 230.1)19.2 (6.3; 166.7)

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Differences in LLN, median, and ULN between men and women were tested per each age group, using nonparametric estimates and a bootstrap approach with 5,000 bootstrap samples (Johnson and Romer, 2016).


Table ​1 shows the age and sex distribution of the study population. All age groups contain more than 100 ECGs, except groups below 6 months and the very sparsely populated group of 90 years or older. Overall, 42% of the subjects are men, varying between 31 and 51% in the individual cohorts (Supplementary Table S1). The age distributions of the cohorts partially overlap (Supplementary Figure S1). Within age groups, the distributions of the HRV markers in the overlapping cohorts are comparable, with small but statistically significant differences between the Leiden and PREVEND cohorts on the one hand, and the Rotterdam and Utrecht cohorts on the other (Supplementary Figures S2, S3).

For each of the four correction models, we determined the R-squared values for the 68 combinations of age group, gender, and HRV marker. The exponential model had the highest R-squared value for 40 combinations, the parabolic model for 17, the hyperbolic for 9, and the linear model for 2 combinations. The combinations with the highest R-squared values of the hyperbolic and linear models were obtained in the four age groups of children less than 1 year, whereas the highest R-squared values for the parabolic model were dispersed over all age groups. When the exponential model did not yield the highest R-squared, differences with the best model were small (mean ± SD R-squared parabolic-exponential 0.008 ± 0.009, hyperbolic-exponential 0.032 ± 0.025, linear-exponential 0.009 ± 0.002). A mean R-squared per model was computed by weighting the R-squared values per age group by the number of subjects in that age group. Table ​2 indicates that the exponential model overall had the best fit, although differences with the parabolic model were small. Quantile-quantile plots for each of the age groups showed that the residuals for the exponential and parabolic models are roughly normally distributed, contrary to the residuals for the linear and hyperbolic models (Supplementary Figures S4–S7). We therefore decided to use the exponential model for correction of the HRV markers in all age groups. While the estimated parameters α considerably differed across age groups, the parameters β were largely similar (Supplementary Figures S8, S9). We computed an aggregate β as the weighted mean of the age-specific estimates, taking the inverse of the variance of the estimates as the weights (Becker and Wu, 2007). This resulted in the following correction formulas:

Table 2

Weighted average R squared for different models that estimate HRV as a function of heart rate.


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Figures ​1, ​2 show scatterplots of the corrected HRV markers against HR. There was a residual association between corrected HRV markers and HR (linear regression coefficient 0.55 for SDNNc and 0.93 for RMSSDc). If only subjects with HR < 120 beats per minute were considered, the coefficients dropped to 0.37 for SDNNc and 0.61 for RMSSDc. The residual association per age group was generally smaller [median (inter-quartile range) regression coefficients -0.03 (-0.35; 0.05) for SDNNc, and -0.29 (-0.59; 0.70) for RMSSDc]. The highest positive residual associations were found for the age groups of children less than 1 year; the highest negative residual associations occurred for the age groups of children of 5–8 years and for the oldest age group.

We further investigated the effect of using an aggregate β in the correction formula as compared to age-group specific β’s. By definition, the age-group specific β’s minimize the difference between the estimated and observed HRV markers in each separate age group, and there is no residual association between the corrected HRV markers and HR in that age group. However, when we performed age-group specific corrections, the overall residual association only slightly decreased, to 0.47 for SDNNc and 0.64 for RMSSDc.

The median, LLN and ULN of the heart-rate corrected HRV markers, stratified by sex, are shown in Table ​3 (SDNNc) and Table ​4 (RMSSDc) per age group, and in Figure ​3 (SDNNc) and Figure ​4 (RMSSDc) as continuous age-dependent curves. For comparison, non-parametric percentile estimates of the HRV markers grouped by age decade are shown in Supplementary Figures S10, S11. Other percentile values of SDNNc and RMSSDc are provided as Supplementary Tables S2–S5. SDNNc and RMSSDc display the same age-dependent pattern. The median and LLN of both markers steadily decrease from childhood to the years of middle and older age. The ULN also decreases till the age of 50–60, after which both markers show a marked increase of their ULN, resulting in a greater range of normal values in the elderly. The differences between men and women appear to be small, but are statistically significant mostly for the LLN and median in the age groups of 20–70 years (see Tables ​3, ​4), with lower values for men than women. The differences in the ULN between men and women are generally not statistically significant.

Figures ​5, ​6 illustrate the effect of heart-rate correction on the normal values of SDNN and RMSSD, respectively. The marked difference between the corrected and uncorrected HRV markers in infancy and adolescence can be explained by the strong age-dependence of heart rate in children (Supplementary Figure S12). The lower the age, the higher the average heart rate, and thus the larger the difference between corrected and uncorrected HRV markers. Since average heart rates in middle-aged and elderly people are fairly constant (65–70 beats per minute, see Supplementary Figure S12), the correction factor in the correction formulas is also fairly constant (e.g., for SDNN 1.12 for 65 bpm and 1.25 for 70 bpm). As a result, the corrected values for these age groups, on average, differ from the uncorrected by a small, constant factor.


This is the first population-based study to provide heart-rate corrected normal values for SDNN and RMSSD as derived from 10-s ECGs across all ages and for both sexes. Our study shows that in both men and women, the LLN of SDNNc and RMSSDc decreases continuously from birth to old age, whereas the ULN decreases at the same rate until the age of 50–60 and then starts to rise again. The differences in SDNNc and RMSSDc between men and women are small, with men generally having slightly lower LLN and median values than women in the age groups of 20–70.

Several studies calculated uncorrected normal values for HRV from 5-min or 24-h ECG signals (Umetani et al., 1998; Nunan et al., 2010; Kim and Woo, 2011; Seppala et al., 2014). Nunan et al. (2010) published normal values for middle-aged and elderly people in a systematic review of 5-min SDNN and RMSSD using 44 studies containing 21,438 participants. However, their data were not stratified by age. Seppala et al. (2014) reported normal HRV values but only for children aged 6–8 years. Additionally, two studies looked at the effect of age. Kim and Woo (2011) found that 5-min SDNN and RMSSD decreased between the age of 18 and 50 in both men and women. Umetani et al. (1998) also found that 24-h SDNN decreases in adults, as recorded in 260 healthy participants aged 10–99 years. The decrease of uncorrected 5-min or 24-h HRV in aging adults was similar to the decrease in uncorrected 10-s HRV found in our study, as illustrated in Figure ​5. There is one other study that calculated normal values of uncorrected 10-s HRV, for middle-aged and elderly participants, but this study did not take age into account (O’Neal et al., 2016).

When HRV is not heart-rate corrected, we find a sharp increase between birth and adolescence, a pattern that was also observed by others (Silvetti et al., 2001; Karlsson et al., 2012). However, this increase is connected to heart rate, which is strongly age-dependent in the young (Rijnbeek et al., 2001). After heart-rate correction, SDNNc and RMSSDc decrease continuously from birth to adolescence, and further into higher ages. A continuous decrease in HRV indices was also observed in a recent study in children aged 6–13 years (Gasior et al., 2015), after correcting the indices by different powers of the heart rate. These findings underline the fact that meaningful comparison of HRV measurements, and their possible association with adverse outcomes, can only be made if the relationship between HRV and heart rate is properly taken into account. Our correction formulas for SDNN and RMSSD can be applied to deal with this issue.

We compared several types of formulas to correct the HRV markers for heart rate. The exponential correction formula yielded the best model fit for most of the age groups, although differences with a parabolic correction formula were small. For some of the age groups below 1 year, linear or hyperbolic formulas gave a better model fit, but differences with the exponential formula were small. Moreover, quantile–quantile plots showed that the residuals of the exponential model were more normally distributed. We therefore recommend use of the exponential correction formula. It should be noted though that this correction is not perfect as there remained some residual association with heart rate, especially for high heart rates as often seen in children. Use of a separate correction formula per age group turned out to reduce this residual association only slightly. This may be explained by differences in the distribution of the residuals between age groups, which when combined can still result in an overall residual association. For practical reasons, we decided to use a single correction formula for all ages. A correction formula without overall residual association can be obtained by fitting the model on all data together, i.e., without taking age into account, but this model turned out to have a very poor fit (R-squared 0.08 for SDNN and 0.15 for RMSSD) and was therefore not further considered.

We made the observation, not previously reported in the literature, that after the age of 60 the ULN turns upwards, in men even more than in women, while the median and LLN continue their downward course. This finding implies a growing instability in sinus node activity in a part of the aging population. This is perhaps caused by incipient dysfunction of the cardiac excitation and conduction system.

Our study has a number of strengths. We are the first study to report HRV normal values that are corrected for heart rate. We have a total of 13,943 ECGs with wide age coverage, from children of 11 days to 90-year old, both male and female. All ECGs from the five included study cohorts were analyzed automatically by a well-validated program, MEANS, which eliminates intra-observer variation that may result from manual measurement of RR intervals. The use of the 10-s ECG may be seen as a strength and as a limitation. Admittedly, the 10-s ECG contains less information than longer recordings, and may sometimes contain only a few RR intervals for HRV calculation. Also, HRV estimates based on 10-s ECGs will have a larger variability than HRV estimates from longer recordings. In that respect, longer recordings are to be preferred over 10-s ECGs. On the other hand, the 10-s ECG is in universal use, cheap, and easily and quickly obtained. For many cohort studies, 5-min or longer ECG signals are not available, and 10-s HRV would be the only option to study HRV in these cohorts. A further limitation of our study is the low number of ECGs in the extremes of the age distribution. For this reason, the normal limits of the groups younger than 6 months and older than 90 years should be used with caution. Another limitation is that the R-squared values for the exponential model, while being better than for the other models, are still rather low. Thus, the exponential model is not optimal. Finally, we excluded individuals who used beta blockers, probably the most commonly used drugs that affect heart rate and HRV. Other drugs, such as tricyclic antidepressants, have also been reported to influence HRV (Noordam et al., 2016) but were not excluded because this medication information was not available in our study. However, in previous studies that showed an effect of medication on HRV, the HRV markers were not heart-rate corrected as we propose here, which may explain part of the observed effect.


Normal limits have been established for heart-rate corrected SDNN and RMSSD, derived from 10-s ECGs, using a consistent and automatic methodology for all ages and both sexes. Our coverage of the pediatric population allows age-specific comparisons of HRV of the pediatric ECGs, from birth to puberty, independent of the rapid change in heart rate in this period of life. Using these normal values, both researchers and clinicians have a tool to decide upon cut-off values of HRV.

Author Contributions

MvdB, PR, and JK: conception and design of the work. MvdB, PR, MN, AH, MB, HH, CS, ME, BS, and JK: data collection, cleaning, preprocessing. MvdB, PR, GvH, and JK: statistical analysis and interpretation and drafting of the manuscript. All authors: critical review of the manuscript and final approval.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Funding. This work was supported by grants from the Netherlands Organisation for Health Research and Development (ZonMw) [Priority Medicines Elderly 113102005 to ME and PR; and HTA 80-82500-98-10208 to BS]. The Utrecht Health Project received grants from the Ministry of Health, Welfare and Sport, the Utrecht University, the Province of Utrecht, the Dutch Organisation of Care Research (ZON), the University Medical Center Utrecht, and the Dutch College of Healthcare Insurance Companies (CVZ). The Rotterdam Study is supported by the Erasmus MC and Erasmus University Rotterdam, the Netherlands Organisation for Scientific Research (NWO), ZonMw, the Research Institute for Diseases in the Elderly (RIDE), the Netherlands Genomics Initiative (NGI), the Ministry of Education, Culture and Science, the Ministry of Health, Welfare and Sport, the European Commission (DG XII), and the Municipality of Rotterdam.


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Heart rate variability

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