Most individuals who take their sport or training very seriously have likely heard of heart rate variability (HRV). Thanks to devices such as the Polar RS800 (Formerly S810) wrist-watch/heart rate monitor and eventually ithlete, the first (to my knowledge) commercially available smart phone HRV application, HRV data can be collected easily and affordably. The recent accessibility of HRV tools has resulted in greater usage, more data and of course greater popularity.
What most folks aren’t aware of however is that HRV is not a solitary figure or value. In fact, numerous HRV parameters exist that are supposedly representative of different autonomic variables. Below is a brief list and description of popular HRV analysis methods and values (many more values exist than described).
Time Domain Analysis: This method includes statistical and geometrical analysis of R-R interval data. Common statistical time domain values include:
- SDNN – Standard Deviation of Normal to Normal intervals.
- RMSSD – The square root of the mean squared difference between adjacent N-N intervals.
*Note: NN or “normal to normal” is used to denote that only “normal” beats originating from the sinus node are measured. Impulses from other areas within the myocardium (non-sinus node impulses) are termed ectopic beats. Ectopic beats disturb normal cardiac rhythm and can therefore affect HRV. Generally 3 or more ectopic beats within a short-term measurement meets criteria for exclusion in many research papers.
Frequency Domain Analysis: This method is considerably more complex than time domain analysis and often requires longer measurement durations. It assesses how variance is distributed as a function of frequency.
- HF – High Frequency Power: A marker of Parasympathetic Activity
- LF – Low Frequency Power: A marker of both Parasympathetc and Sympathetic Activity
- LF/HF – Low Frequncy/High Frequency Ratio: Once thought to represent the balance between sympathetic and parasympathetic activity however this remains a hot topic of debate.
As you can see, saying something along the lines of “My HRV is low today” is really vague. I’m sure I’ve been guilty of this in the past. More often than not, most people are referring to their RMSSD value as this is the same parameter provided by ithlete and BioForce (among other HRV tools).
The RMSSD is commonly used as an index of vagally (Vagus Nerve) mediated cardiac control which captures respiratory sinus arrhythmia (RSA), the frequent changes in heart rate occurring in response to respiration (Berntson et al. 2005). During inhalation, heart rate speeds up. During exhalation, heart rate slows down. RMSSD is an accepted measure of parasympathetic activity and correlates very well with HF of frequency domain analysis (discussed above).
PhD candidate and HRV researcher James Heathers provides a good explanation of why we would want to track changes in RMSSD vs. other HRV values throughout training here. I’d like to add that RMSSD is one of the few meaningful values that we can acquire with ultra-short measurement durations. It’s generally accepted that a 5 minute recording is the gold standard for HRV analysis (Task Force 1996). However, 5 minutes is entirely too long if we expect compliance from athletes or individuals. Thankfully, ample research exists that shows that ultra-short (60 seconds or less) RMSSD values (randomly selected from within a 5 minute recording) highly correlate with RMSSD from the standard 5 minute ECG recording (Katz et al. 1999; Mackay et al. 1980; Nussinovitch et al. 2012; Nussinovitch et al. 2011; Salahuddin et al. 2007; Smith et al. 2013; Thong et al. 2003). Unfortunately no research exists that tested the suitability of ultra-short RMSSD in athletic populations so my colleague Dr. Mike Esco and I went ahead and did this very recently in athletes at rest and post-exercise (paper currently in peer review). I will let you know what we found once it gets published.
Why does my HRV score (from ithlete or BioForce) look different from the values in research?
I hope you are not comparing your ithlete or BioFroce scores to data you see in published research. Simon, the creator of ithlete, decided to modify the RMSSD value collected by ithlete to make for a more intuitive and easily interpretable figure for non-expert users. The value you see from the app is the natural log transformed RMSSD multiplied by 20 (lnRMSSDx20). This modification essentially provides a figure on a 100 point scale (though ithlete scores above 100 are possible in highly fit individuals, though not common).
*Note: lnRMSSDx20 is a patented formula and therefore those interested in using this commercially must acquire a licence.
To be clear, RMSSD is only one HRV parameter. By no means was this article suggesting that other HRV values are meaningless. The purpose of this blog was to simply provide an explanation of the what and why of RMSSD since so many people are using ithlete and BioForce lately. Certainly, ECG derived HRV remains the gold standard and likely multiple HRV parameters provide a more complete picture of training status verses just one. However, for the purposes of convenience in non-expert users, the RMSSD provides an easily acquired and interpretable figure in a short period of time that reflects parasympathetic activity which is quite useful for monitoring the effects of training and in the manipulation of training loads.
Berntson, G. G., Lozano, D. L., & Chen, Y. J. (2005). Filter properties of root mean square successive difference (RMSSD) for heart rate. Psychophysiology,42(2), 246-252.
Camm AJ, Malik M et al. (1996) 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. Circ 93(5): 1043-1065
Katz A, Liberty IF, Porath A, Ovsyshcher I, Prystowsky E (1999) A simple bedside test of 1-minute heart rate variability during deep breathing as a prognostic index after myocardial infarction. Am Heart J 138(1): 32-38
Mackay JD, Page MM, Cambridge J, Watkins PJ (1980) Diabetic autonomic neuropathy. Diabetol 18(6): 471-478
Nussinovitch U, Cohen O, Kaminer K, Ilani J, Nussinovitch N (2012) Evaluating reliability of ultra-short ECG indices of heart rate variability in diabetes mellitus patients. J Diabetes Complic 26(5): 450-453
Nussinovitch U, Elishkevitz KP, Katz K, Nussinovitch M, Segev S, Volovitz B, Nussinovitch N (2011) Reliability of ultra‐short ECG indices for heart rate variability. Ann Noninvasive Electrocardiol 16(2): 117-122
Salahuddin L, Cho J, Jeong MG, Kim D (2007) Ultra short term analysis of heart rate variability for monitoring mental stress in mobile settings. Conf Proc IEEE Eng Med Biol Soc 4656-4659
Smith AL, Owen H, Reynolds KJ (2013) Heart rate variability indices for very short-term (30 beat) analysis. Part 2: validation. J Clin Monit Comput E-Pub Ahead of Print
Thong T, Li K, McNames J, Aboy M, Goldstein B (2003) Accuracy of ultra-short heart rate variability measures. Conf Proc IEEE Eng Med Biol Soc 3, 2424-2427
Does Omegawave use same variable?
I’d assume OmegaWave uses RMSSD, however I’m not certain if they include other parameters as well.
Didn’t see it explicitly but I believe Omegawave for individuals does frequency analysis too. They say their belt measures PQRST rather than just RR. And that they’ll be adding DC potential this fall. Which is all very nice but unfortunately I don’t see anyway to get at your raw measurements (so you gotta depend on their proprietary interpretation).
Thanks, I suspected that was the case. It is unfortunate that the raw data isn’t displayed. Doesn’t make it easy for cross validation.
Thanks Andrew, I believe this clarification was really helpful and long overdue.
I appreciate the feedback, thanks Julio.
You get both time-domain and frequency-domain analysis with the Omegawave Pro. The company is open so If you would like both analysis metrics, email them or post on their facebook page.
Nice article, Andrew.
Thank you for the clarification.
Reblogged this on Collins Strength and Conditioning.
Fascinating article Andrew, i have found your website very helpful. I train regularly, and believe i am in reasonable shape, but would not describe myself as an athlete. I run around 15-20km per week and also train in the gym 5 times a week plus fence regularly. Noticing a variability in my run times i bought a wahoo heart rate monitor, to see if it would give me any insights. I have a resting heart beat of around 57 and get up to 165-175 when running (i am 51). As ithlete does not work with wahoo, i got the sweetbeat hrv app. I have been surprised to find my hrv score is low – in 4 daily readings it has not got above 57 and today is 43. From your article this sounds bad – but i cannot find any database to compare with for my age and condition. Should i be concerened and if so, do you have any training suggestions? Is there a specific measure worth tracking, such as rmssd? I see that LF and HF are highly variable over a session, so they do not look like reliable indicators for short term measurements.
I’m not as familiar with the sweetbeat system. They may have a different formula than ithlete and BioForce. I’d contact them and ask what parameter they measure and if it is modified for the user. Once we know that we can try and analyze your trend.
Hi Andrew, I have not had a reply from Sweetbeat so don’t know how they calculate their HRV. I have also been using Smartbeart though, which is a great app in iTunes. It does not calculate an HRV score but does give a lot of analysis after a 10 minute monitoring period. For example, in the last 10 days my SDNN has averaged 49, with a low of 26 and high of 130. For RMSSD the numbers are average 39, low 14, high 185. For LF it is 180, 21 and 1376. And for HF 61, 18 and 361. So there seems to be quite a bit of variability. It is difficult to make sense of it all. I also see in the Sweetbeat app sudden and massive changes in LF and HF over the 3 minute monitoring period, which give me concern about how much one can conclude from these 2 measures at least.
It looks like you are getting raw values. “Difficult to make sense of” would be an accurate statement for an untrained user. Might be worth looking back over your running times/performance and cross referencing them with your HRV values. See if you notice any trends with increased RMSSD and HF and higher quality runs and vice-versa. .
Great article Andrew.
For the all those folks who like lots of data have you seen the inexpensive way of getting buckets of HRV data. Mean RR*, STD RR (SDNN), Mean HR*, STD HR, RMSSD, NN50, pNN50
RR triangular index, TINN. Time and frequency domain VLF, LF and HLF.
The hardware is cheap and the software is all free.
I’m using a Garmin Ant USB stick and an old Garmin Fr60 HRM.
Any Garmin or Polar HRV should also work.
I use a great little free Google code project
to convert the data into a data file. (Windows and Mac versions available.)
Kubios provide an excellent free (with registration) HRV analysis package
Thanks Mel. I’ll certainly check this out when I get a chance.
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Thanks for article. I have some question. Can you tell me how calculate the concentration index or relaxation index in app like this, from hrv data:
I’m sorry Fred, I am unfamiliar with those terms. Probably would be best to contact the app developer.
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What do you think about HRV short-term analysis and powerlifting training? Have you tried yourself?
Nice post, btw.
I have several posts where I discuss HRV and powerlifting training. Here are a few links:
There are several others if you look through the archives.
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I am 63 and have become a convert to using ithelete to monitor my “workout preparedness.” Invariably, when I am well rested and ready to go to the gym, my readings are ‘green’ in the 60+ to 80 range. Just after a workout, my readings drop to the 40-50 range. This is an incredible tool and I have ordered the ‘fingertip’ sensor so that I can monitor my HRV more conveniently. I am very much interested in HRV as it relates to the aging athlete–I believe it will develop increasing importance.
Glad to hear it. I think you’ll like the finger sensor. It is definitely more convenient.
Have you seen the product by WHOOP? They claim a HRV measure in Slow Wave Sleep- is this a valid measure?
I am familiar with the Whoop band and am a bit skeptical of deriving HRV from the wrist, especially given the difficulties other companies (e.g., fitbit) have with simply acquiring regular heart rate. Having said that, I have not tested this product so I cannot say for sure.
your blog is massive resource on HRV, thank you for that..
I’d be very interested in hearing your opinion on Whoop.. It looks like it measures HRV for evaluation only in deep sleep phase in night (when person is not moving). That could add to precision. When I look at modern smart watch which measure HR from the wrist it looks like its ability has improved massively over years.. For example here in comparison wrist measuring to chest band: https://www.dcrainmaker.com/2019/04/garmin-forerunner-245-music-gps-watch-in-depth-review.html
What is your take on it? Have you got any chance to try new HRV devices (Whoop, Oura ring)?
Thanks, Carlos. I appreciate the kind words. I think the wearable products certainly make HRV data acquisition more convenient, but I have concerns related to:
Cost – these are expensive for use among teams relative to apps
Filtering – can the algorithms detect sleep disordered breathing/sleep apnea?
Position – I tend to prefer HRV derived from upright positions (seated or standing) rather than supine.
Ethics – potential invasion of athletes’ privacy
Currently, i prefer them for personal rather than team use. Would be great to see more research on their efficacy.
Is it normal for heart rate variability to go up under load. I’ve been playing with hrv elite and my polar h7 for a few days.
I walk on the level (say 110bpm) with an hrv of 30-50. But when I climb stairs (150-160bpm) my variability falls to <10 ! Is that normal ?
My morning rmssd is 45 . I'm 46 years old and have only recently started training in the last 10 weeks or so but these values seem lowish.
Yes, during exercise, HRV will decrease substantially with increasing effort. Your resting HRV numbers should improve as your fitness improves over time.
Good stuff, thanks heaps
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