Heart rate variability: The variety of metrics (SDNN or rMSSD)
Welcome back to “What Does the Science Say?” This week we’re going to focus on a highly requested topic, taking a closer look at the types of HRV metrics, specifically SDNN and RMSSD.
As many of you have noticed, Athlytic’s recent updates now include the ability to choose whether you want to track your heart rate variability (HRV) in terms of SDNN (the Standard Deviation of N-N intervals) or rMSSD (the Root Mean Square of Successive Differences). With this new feature, many of us are wondering “What should I choose? Which one is right for me?”
Brief Recap
To learn more about the basics of HRV and why it is an important marker to track, check out episode 1 of the series! Put simply, the HRV helps us capture the status of our autonomic nervous system at a particular point in time.
From episode 1:
The autonomic nervous system is made of up two components: the parasympathetic (rest and relaxation) and sympathetic (fight or flight) systems. It is the balance of these two that’s important, with the sympathetic system ready to activate in case of a stressor or threat, and the parasympathetic calming you down to rest or digest. Now, just like a good tune, the tempo of the heart isn’t just one steady, solitary beat. Even during rest our heart rate has subtle variations from one beat to the next, subtle differences in timing to go from beat one to two, beat two to three, and beat three to four. This is heart rate variability, and it is determined by the balance of our unconscious nervous system, between parasympathetic and sympathetic signals.
Disclaimer: The scientific community is still performing tons of research on HRV, and its use in health and wellness is still relatively new and rapidly growing field. Currently, some of the limitations of the research (and so our understanding of HRV) come from the technology surrounding the collection of data for HRV calculations. Our data is only as good as the sensors we use to record it. As wearable technology continues to progress, the data will become more precise, and the associations and differences from such measurements will be clarified further.
SDNN
SDNN is the ‘gold standard’ for heart rate variability. This is one of the oldest studied metrics for HRV, with research predominantly focused on what it meant for long term health and mortality. Initially, measurements were made over a long period of time (such as 24 hour recordings) (1)(2).
More recently, however, data has shown that we can get reliable readings from much shorter measurements (such as 30 seconds to 5 minute readings) (3). These types of recordings are used by the Apple Watch to calculate and present your HRV.
As mentioned above, the SDNN is thought to be an accurate representation of the balance between your sympathetic nervous system (fight or flight) and your parasympathetic system (rest and digest). The balance between these two offers a unique insight into overall health and wellness, especially when looked at over the long term.
rMSSD
Another way of calculating the same data on the beat-to-beat variation in our heart rate comes in the form of rMSSD. While this is using the same information as SDNN, the way that it is calculated presents slight differences compared to SDNN. Let’s take a look at some of these differences.
First, rMSSD tends to show slightly better accuracy at these shorter samples (3). This means that when collecting heart rate data over a period of seconds to minutes, rMSSD may offer more reliable insights into HRV.
Though, as we mentioned before, SDNN still offers a fairly reliable reading at shorter intervals, as well, so what other differences might there be?
rMSSD is thought to provide a deeper insight into the parasympathetic nervous system, whereas SDNN reflects more of the sympathetic signals combined with parasympathetic signals (4). The predominance of the parasympathetic nervous system in rMSSD may offer closer insight into the body’s current state of stress. As such, rMSSD has the potential to reflect the acute changes related to stress and recovery, and thus can be a useful marker in training readiness and day-to-day recovery.
Conclusions
Both SDNN and rMSSD offer valuable tools to analyze your overall health, wellness, and recovery, and you can’t go wrong with either one, as broader trends in either metric are likely to be similar in the other. However, depending on your overall needs, some of you may prefer one over the other.
SDNN is ideal for long term health monitoring as a more global assessment of your autonomic nervous system. Monitoring this number over time can be useful on your journey for overall health and longevity.
rMSSD is better suited for day-to-day fitness tracking. Athletes can use rMSSD to track recovery and adjust their training load, providing more immediate feedback on whether the body is recovered and ready for the next workout.
As always, stay active and stay healthy!
Sources and Further Readings:
1. Frontiers | Hidden Signals—The History and Methods of Heart Rate Variability [Internet]. [cited 2024 Jan 10]. Available from: https://www.frontiersin.org/articles/10.3389/fpubh.2017.00265/full
2. Dekker JM, Schouten EG, Klootwijk P, Pool J, Swenne CA, Kromhout D. Heart rate variability from short electrocardiographic recordings predicts mortality from all causes in middle-aged and elderly men. The Zutphen Study. Am J Epidemiol. 1997 May 15;145(10):899–908.
3. Munoz ML, Roon A van, Riese H, Thio C, Oostenbroek E, Westrik I, et al. Validity of (Ultra-)Short Recordings for Heart Rate Variability Measurements. PLOS ONE. 2015 Sep 28;10(9):e0138921.
4. Khan AA, Lip GYH, Shantsila A. Heart rate variability in atrial fibrillation: The balance between sympathetic and parasympathetic nervous system. Eur J Clin Invest. 2019 Nov 1;49(11):e13174.
Note from Athlytic Developers: Once you toggle on or off to use the rMSSD method for HRV, it will take some time for the watch app as well as the widgets to sync to your preferred method to calculate your HRV.
