What is the RMSSD and Why is it Important to HRV Biofeedback?

The RMSSD is the root mean square of successive differences between normal heartbeats. Let's unpack this. We obtain the RMSSD by first calculating each subsequent time difference between adjacent interbeat intervals in milliseconds. Then, we square each of these values and average the result before taking the square root of the total. Athletes use the RMSSD to monitor workout intensity and their recovery. Graphic © Josep Suria/Shutterstock.com.

Female athlete running hurdles

Many consumer HRV apps (Apple Health, Elite HRV, Fitbit) use the RMSSD or the Ln RMSSD to measure HRV. Ln means the natural logarithm (i.e., log to the base e).

Caption: In this Elite HRV screenshot, note the breathing pacer at the top of the screen. The bright blue line at the bottom shows that Ln RMSSD increased across the session. The RMSSD is expressed in milliseconds. Nunan et al. (2010) reported a mean of 42 ms (SD = 15 ms, range = 19-75 ms) in their sample of 21,438 healthy adults.

Nunan et al. (2010) HRV norms table

At least a 5-minute sample is required. However, researchers have proposed ultra-short (< 5 minutes) periods of 10 seconds (Salahuddin et al., 2007), 30 seconds (Baek et al., 2015), and 60 seconds (Shaffer, Meehan, & Zerr, 2020).

The RMSSD reflects rapid beat-to-beat variance in heart rate and better estimates vagal activity than the SDNN (Shaffer, McCraty, & Zerr, 2014). The RMSSD is conceptualized as vagally-mediated HRV (vmHRV; Jarczock et al., 2021). The RMSSD is the best overall measure of short-term HRV because it is less affected by outliers and artifacts than SDNN (Gevirtz, 2020).

The RMSSD is identical to the nonlinear metric SD1, reflecting short-term (~ 5 minutes) HRV (Ciccone et al., 2017). While the RMSSD is correlated with high-frequency power (Kleiger et al., 2005), the influence of respiration rate on this index is uncertain (Schipke et al., 1999; Pentillä et al., 2001). The RMSSD is less affected by respiration than is respiratory sinus arrhythmia across several tasks (Hill & Siebenbrock, 2009). The RMSSD is more influenced by the PNS than SDNN (Gevirtz, 2017).

The RMSSD has been linked to cancer and epilepsy deaths. A novel ratio of short-term RMSSD and C-reactive protein predicted survival in cancer patients and the general population (Jarczock et al., 2021). Lower RMSSD values are correlated with higher scores on a risk inventory of sudden unexplained death in epilepsy (DeGiorgio et al., 2010).

Summary The RMSSD, widely used by consumer apps to index HRV, is arguably the best overall measure of short-term HRV. Learn More

References Baek, H. J., Cho, C. H., Cho, J., and Woo, J. M. (2015). Reliability of ultra-short-term analysis as a surrogate of standard 5-min analysis of heart rate variability. Telemed J E Health, 21, 404–414. https://doi.org/10.1089/tmj.2014.0104 Ciccone, A. B., Siedlik, J. A., Wecht, J. M., Deckert, J. A., Nguyen, N. D., & Weir, J. P. (2017). Reminder: RMSSD and SD1 are identical heart rate variability metrics. Muscle Nerve. https://doi.org/10.1002/mus.25573 DeGiorgio, C. M., Miller, P., Meymandi, S., Chin, A., Epps, J., Gordon, S., Gornbein, J., & Harper, R. M. (2010). RMSSD, a measure of vagus-mediated heart rate variability, is associated with risk factors for SUDEP: The SUDEP-7 Inventory. Epilepsy Behav, 19(1), 78-81. https://doi.org/10.1016/j.yebeh.2010.06.011 Gevirtz, R. N. (2017). Cardio-respiratory psychophysiology: Gateway to mind-body medicine. Gevirtz, R. N. (2020). The myths and misconceptions of heart rate variability: HRV in biofeedback training. Association for Applied Psychophysiology and Biofeedback annual meeting. Hill, L. K., & Siebenbrock, A. Are all measures created equal? Heart rate variability and respiration – biomed 2009. Biomed Sci Instrum, 45, 71-76. PMID: 19369742 Jarczok, M. N., Koenig, J., & Thayer, J. F. (2021). Lower values of a novel index of vagal-neuroimmunomodulation are associated to higher all-cause mortality in two large general population samples with 18 year follow up. Sci Rep, 11, 2554. https://doi.org/10.1038/s41598-021-82168-6 Kleiger, R. E., Miller, J. P., Bigger, J. T., & Moss, A. J. (1987). Decreased heart rate variability and its association with increased mortality after acute myocardial infarction. American Journal of Cardiology, 59, 256-262. https://doi.org/10.1016/0002-9149(87)90795-8 Nunan, D., Sandercock, G. R. H., & Brodie, D. A. (2010). A quantitative systematic review of normal values for short-term heart rate variability in healthy adults. Pacing and Clinical Electrophysiology, 33(11), 1407-1417. https://doi.org/10.1111/j.1540-8159.2010.02841.x Pentillä, J., Helminen, A., Jarti, T., Kuusela, T., Huikuri, H. V., Tulppo, M. P., Coffeng, R., & Scheinin, H. (2001). Time domain, geometrical and frequency domain analysis of cardiac vagal outflow: effects of various respiratory patterns. Clin Phys, 21, 365–376. https://doi.org/10.1046/j.1365-2281.2001.00337.x Salahuddin, L., Cho, J., Jeong, M. G., and 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 2007, Lyon, France, 4656–4659.

Schipke, J. D., Arnold, G., & Pelzer, M. (1999). Effect of respiration rate on short-term heart rate variability. J Clin Basic Cardiol 2, 92–95.

Shaffer, F., Meehan, Z. M., & Zerr, C. L. (2020). Frontiers in Neuroscience. A critical review of ultra-short-term heart rate variability norms research. https://doi.org/10.3389/fnins.2020.594880

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