Note: Athletes use the RMSSD to monitor workout intensity and their recovery. Graphic © Josep Suria/Shutterstock.com.
Click on our narrator icon to listen to this post.
What Does Heart Rate Variability (HRV) Mean?
HRV is the organized fluctuation of time intervals between successive heartbeats defined as interbeat intervals. HRV is associated with executive function, regulatory capacity, and health . . . Cardiac vagal control indexes how efficiently we mobilize and utilize limited self-regulatory resources during resting, reactivity, and recovery conditions (Shaffer, Meehan, & Zerr, 2020).
The oscillations of a healthy heart are complex. HRV indexes how efficiently we mobilize and utilize limited self-regulatory resources to maintain homeostasis. HRV plays a vital role in regulatory capacity, executive functions, health, and performance. A healthy heart can rapidly adjust to sudden challenges due to the cooperation of interlocking and better-calibrated control systems. HRV is crucial to health, performance, and resilience. Behavioral interventions like aerobic exercise, healthy breathing, compassion, and mindfulness meditation are powerful strategies for increasing HRV.
Dr. Gevirtz explains HRV © Association for Applied Psychophysiology and Biofeedback.
A healthy heart is not a metronome (Shaffer, McCraty, & Zerr, 2014). Metronome graphic © Axynia/shutterstock.com.
What Are HRV Time-Domain Metrics?
We can measure heart rate variability (HRV) using time- and frequency-domain metrics. Time-domain metrics quantify the amount of variability in measurements of the interbeat interval (IBI), which is the period between successive heartbeats. Frequency-domain values quantify absolute or relative power distribution into four frequency bands.
An IBI is also called an R-R interval because it is the time between adjacent R-spikes. Graphic © arka38/Shutterstock.com
We measure the time intervals between successive heartbeats in milliseconds (ms). The software starts counting after detecting the first beat and calculates the first IBI in ms after detecting the second beat. This process is repeated until the end of the epoch (data collection period). Graphic adapted from Dr. Richard Gevirtz.
Note: the numbers in boxes are IBIs measured in milliseconds.
A Few Cautions
We cannot compare HRV time-domain values (e.g., see RMSSD and SDNN below) obtained during slow-paced breathing to resting norms. Brief resting measurement periods underestimate 24-hour time-domain values. Nunan et al. (2010) found that published brief values for healthy adults were lower than Task Force (1996) norms. Although "gold standard" metrics like 24-hour SDNN predict cardiac risk, short-term SDNN does not. Finally, consumers should exercise caution in interpreting smartphone time-domain values because apps either do not artifact or perform limited data clean-up, inflating RMSSD and SDNN values.
To address this problem, export text file data from the device software to additional analysis software.
Kubios Standard is an HRV analysis suite for free non-commercial personal use that allows automatic and manual artifact correction and reports an extensive range of time- and frequency-domain metrics. Compare the difference in uncorrected and artifacted RMSSD values in the display below.
Overview
We will review the SDNN, SDRR, pNN50, NN50, HR Max - HR Min, RMSSD, and Triangular Interpolation of the NN Interval Histogram (TINN).
SDNN
The SDNN is the standard deviation of the interbeat interval of normal sinus beats measured in milliseconds (ms). "Normal" refers to the R-spike of healthy heartbeats and means that abnormal beats, like ectopic beats, have been removed. We calculate the SDNN after automatically or manually removing artifacts, including abnormal heartbeats. This means that real-time HRV displays do not show the SDNN.
In short-term (≤ 5 minutes) resting recordings, variability reflects multiple cyclic processes (Task Force, 1996). Parasympathetically mediated respiratory sinus arrhythmia (RSA) is the primary SDNN source, especially during slow-paced breathing protocols (Shaffer, McCraty, & Zerr, 2014). In 24-hour recordings, low-frequency (LF) band power contributes significantly to the SDNN (Kusela, 2013).
The SDNN is more accurate when calculated over 24 hours than during the shorter periods monitored during biofeedback sessions. More extended recording periods provide data about cardiac reactions to a greater range of environmental challenges and circadian processes (Lehrer, 2012).
Twenty-four-hour SDNN is the "gold standard" for predicting cardiac risk hours (Task Force, 1996). SDNN values predict both morbidity and mortality. Patients with SDNN values below 50 milliseconds are classified as unhealthy, 50-100 milliseconds have compromised health, and above 100 milliseconds are healthy.
Heart attack survivors with higher 24-hour measurements had a greater probability of living during a 31-month mean follow-up period. For example, patients with SDNN values over 100 milliseconds had 5.3 times lower mortality risk at follow-up than those under 50 milliseconds (Kleiger et al., 1987).
A Firstbeat Bodyguard 2, designed for ambulatory 24-hour HRV monitoring, is shown below.
How Can You Use SDNN in HRV Biofeedback Training?
Consider recording 24-hour SDNN in pre- and post-assessment for cardiovascular patients. Use short-term pre- and post-baseline measurements to evaluate client progress within and across training sessions.
SDRR
The SDRR is the standard deviation of the interbeat interval for all sinus beats (including abnormal or false beats) measured in milliseconds. Remember, the difference between RR and NN measurements is the removal of artifactual beats (hence "Normal to Normal"). Real-time displays typically show the SDRR only, which may contain abnormal beats or noise masquerading as HRV. Below is a BioGraph ® Infiniti heart rate variability recording. The roller coaster accelerates as SDRR increases.
pNN50
The pNN50 is the percentage of adjacent NN intervals that differ by more than 50 milliseconds. It correlates closely with parasympathetic nervous system activity (Umetani et al., 1998) and with the root mean square of successive differences (RMSSD) and high-frequency band power (Bigger et al., 1989). Short-term pNN50 may be more reliable than brief SDNN measurements. The RMSSD is superior to the pNN50 when assessing respiratory sinus arrhythmia (RSA), especially in older individuals. Most researchers prefer it to the pNN50 (Otzenberger et al., 1998).
HR Max-HR Min
HR Max – HR Min is the average difference between the highest and lowest heart rates during each respiratory cycle. This is the easiest time-domain metric for clients to understand. Where the RMSSD and SDNN are abstract, clients can easily visualize a wave's height (Moss, 2022).
At least a 2-minute sample is required to calculate HR Max – HR Min. Physically active individuals show larger peak-trough differences than those who are sedentary.
HR Max - HR Min reflects RSA instead of vagal tone. It is susceptible to the effects of respiration rate, independent of vagus nerve firing. Age and aerobic fitness affect it.
How Can You Use HR Max-HR Min in HRV Biofeedback Training?
Use HR Max - HR Min to explain HRV. Display instantaneous heart rate (red) and respirometer movement (blue) to train your clients to increase HRV. Using slow-paced breathing, teach them to align the two waveform peaks and valleys effortlessly.
RMSSD
The RMSSD is the root mean square of successive differences between normal heartbeats expressed in milliseconds. Let's unpack this. We obtain the RMSSD by 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. At least a 5-minute sample is required.
The RMSSD reflects rapid beat-to-beat variance in heart rate, is largely unaffected by respiration (Laborde et al., 2017), and better estimates vagal activity than SDNN or SDRR (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 the SDNN (Otzenberger et al., 1998).
HRV values are correlated with attentional capacity. Lower time-domain (e.g., RMSSD) and frequency-domain (e.g., HRV power) are correlated with inattention, higher anxiety, and diminished emotional regulation (Griffiths et al., 2017; Hsieh et al., 2010).
Individuals with prior depression and dysphoric mood have lower time-domain HRV and HF power. HRV is a promising predictor of depression risk. Not only do HRV values predict and correlate with depression and depression severity, but a meta-analysis demonstrated that HRV biofeedback improves depressive symptoms (Dar et al., 2016; Dell'Acqua et al., 2020; Hartmann et al., 2019; Pizzoli et al., 2021). Graphic © PKPix/Shutterstock.com.
Likewise, those diagnosed with stress-related conditions have lower time-domain measures than healthy controls (Cohen & Benjamin, 2006; Forte et al., 2021; Kim et al., 2018; Schneider & Schwerdtfeger, 2020).
A novel ratio of short-term RMSSD and C-reactive protein predicted survival in cancer patients and the general population (Jarczock et al., 2021).
Training individuals to master higher HRV under stress conditions is correlated with improved cognitive performance under such conditions (Hansen et al., 2009). Graphic © Maridav/Shutterstock.com.
How Can You Use the RMSSD in HRV Biofeedback Training?
Many consumer HRV apps (Apple Health, Elite HRV, Fitbit) use the RMSSD or the Ln RMSSD for feedback. Ln means the natural logarithm (i.e., log to the base e).
Optimal performance is more than excellent physical conditioning. The disciplined or trained capacity for heightened attention is essential to achieving optimal performance. HRV values are correlated with attentional capacity. Lower time-domain (e.g., RMSSD) and frequency-domain (e.g., HRV power) values are correlated with inattention, higher anxiety, and diminished emotional regulation (Griffiths et al., 2017; Hsieh et al., 2010).
Athletes use HRV measures to quantify and monitor cardiac modulation of the SNS and PNS components of the ANS. This tracks training adaptations to maximize training without inadvertently hindering performance by overtraining. Lower time-domain (e.g., RMSSD, SDNN) and frequency-domain (e.g., total, LF, and HF power) values can signal overtraining.
Coaches and elite athletes utilize HRV measures to select optimal training intensity, duration, and rest periods. HRV metrics can signal when an athlete achieves maximum recovery between workouts (Dong, 2016). Use the RMSSD during slow-paced breathing or muscle contraction exercises to assess athlete fatigue due to overtraining (it sharply declines). Measure pre- and post-baseline values to assess progress within and across training sessions.
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 RMSSD increased across the session.
HRV4Training Pro converts the RMSSD to its natural logarithm (Ln RMSSD), yielding values between 5 and 10. This app can detect acute HRV changes due to stressors like travel or overtraining and advise users to limit workout intensity. Graphic from Altini's The Ultimate Guide to Heart Rate Variability (HRV): Part 1.
Nunan et al. (2010) reported a mean RMSSD of 42 ms (SD = 15 ms, range = 19-75 ms) in their sample of 21,438 healthy adults.
The Triangular Interpolation of the NN Interval Histogram
The Triangular Interpolation of the NN Interval Histogram (TINN) is the baseline width of a histogram displaying NN intervals. To unpack this definition, visualize a histogram that plots the frequency of NN intervals. The X-axis represents interbeat interval length in milliseconds, and the Y-axis represents the number of intervals of identical length (Yilmaz et al., 2018). At least a 5-minute sample is required (Shaffer & Ginsberg, 2017). Five-minute ECG HRV triangular index (HTI) recordings predicted cardiovascular and all-cause death in atrial fibrillation patients with a mean follow-up from 1.6 to 3.6 years (Hämmerle et al., 2020).
Age and Circadian Effects on HRV
Age and time of day strongly influence HRV measurements.
Age
In a cross-sectional study of 8,203,261 participants using 24-hour Fitbit photoplethysmographic recordings, all HRV time- and frequency-domain metrics declined between ages 20 and 60 (Natarajan et al., 2020).
Almedia-Santos et al. (2016) obtained 24-hour ECG recordings of 1743 subjects 40-100 years of age. They found a linear decline in the SDNN, the standard deviation of the average NN intervals for each 5-minute segment (SDANN), and the standard deviation of NN intervals (SDNN index). However, they found a U-shaped pattern for the RMSSD and pNN50 with aging, decreasing from 40-60 and then increasing after age 70. The age groups were 1 (40–49 years), 2 (50–59 years), 3 (60–69 years), 4 (70–79 years), and 5 ( ≥ 80 years).
Time of Day
All HRV metrics reached their maximum values between 5 and 8 am, and minimum values between 7 and 10 pm.
Summary
HR Max - HR Min is invaluable in explaining the concept of HRV to clients. Short-term HR Max - HR Min reflects RSA (not vagal tone) and provides invaluable feedback for HRV training. The RMSSD, widely used by consumer apps to index HRV, is arguably the best overall measure of short-term HRV. You can use the RMSSD with the SDNN and HR Max - HR Min to assess client progress. Consider age and time of day when interpreting your clients' HRV values.
Glossary
heart rate (HR): the number of heartbeats per minute, also called stroke rate.
heart rate variability (HRV): the beat-to-beat changes in HR involving changes in the intervals between consecutive heartbeats.
HR Max – HR Min: an HRV index that calculates the average difference between the highest and lowest HRs during each respiratory cycle.
HRV triangular index (HTI): a geometric measure based on 24-hour recordings, which calculates the integral of the RR interval histogram's density divided by its height.
interbeat interval (IBI): the time interval between the peaks of successive R-spikes (initial upward deflections in the QRS complex).
NN interval: the normal-to-normal interval is an IBI after removing artifacts.
pNN50: the percentage of adjacent NN intervals that differ by more than 50 milliseconds.
respiratory sinus arrhythmia (RSA): the respiration-driven heart rhythm that contributes to the high frequency (HF) component of heart rate variability. Inhalation inhibits vagal nerve slowing of the heart (increasing HR), while exhalation restores vagal slowing (decreasing HR).
RMSSD: the square root of the mean squared difference of adjacent NN intervals.
SDNN: the standard deviation of the normal (NN) sinus-initiated IBI measured in milliseconds.
SDRR: the standard deviation of the interbeat interval for all sinus beats measured in milliseconds, which predicts morbidity and mortality.
Triangular Interpolation of the NN Interval Histogram (TINN): the baseline width of a histogram displaying NN intervals.
References
Almeida-Santos, M. A., Barreto-Filho, J. A., Oliveira, J. L., Reis, F. P., da Cunha Oliveira, C. C., & Sousa, A. C. (2016). Aging, heart rate variability and patterns of autonomic regulation of the heart. Archives of Gerontology and Geriatrics, 63, 1–8. https://doi.org/10.1016/j.archger.2015.11.011 Andreassi, J. L. (2000). Psychophysiology: Human behavior and physiological response. Lawrence Erlbaum and Associates, Inc.
Berntson, G. G., Quigley, K. S., & Lozano, D. (2007). Cardiovascular psychophysiology. In J. T. Cacioppo, L. G. Tassinary, & G. G. Berntson (Eds.). Handbook of psychophysiology (3rd ed.). Cambridge University Press. Bigger, J. T., Jr, Albrecht, P., Steinman, R. C., Rolnitzky, L. M., Fleiss, J. L., & Cohen, R. J. (1989). Comparison of time- and frequency domain-based measures of cardiac parasympathetic activity in Holter recordings after myocardial infarction. The American Journal of Cardiology, 64(8), 536–538. https://doi.org/10.1016/0002-9149(89)90436-0
Burr, R. L., Motzer, S. A., Chen, W., Cowan, M. J., Shulman, R. J., & Heitkemper, M. M. (2006). Heart rate variability and 24-hour minimum heart rate. Biol Res Nurs, 7(4), 256-267. https://doi.org/10.1177/1099800405285268 Cohen, H., & Benjamin, J. (2006). Power spectrum analysis and cardiovascular morbidity in anxiety disorders. Auton Neurosci., 128(1-2), 1-8. https://doi.org/10.1016/j.autneu.2005.06.007. PMID: 16731048. Dahr, A. K., Lambert, G. W., & Barton, D. A. (2016). Depression and cardiovascular disease: Psychobiological mechanisms. In M. E. Alvarenga, & D. Byrne (Eds.). Handbook of psychocardiology. Springer Singapore.
Dell'Acqua, C., Dal Bò, E., Benvenuti, S. M., & Palomba, D. (2020). Reduced heart rate variability is associated with vulnerability to depression. Journal of Affective Disorders Reports, 1, 100006. https://doi.org/10.1016/j.jadr.2020.100006 Dong J. G. (2016). The role of heart rate variability in sports physiology. Experimental and Therapeutic Medicine, 11(5), 1531–1536. https://doi.org/10.3892/etm.2016.3104 Forte, G., Favieri, F., & Casagrande, M. (2019). Heart rate variability and cognitive function: A systematic review. Frontiers in Neuroscience, 13, 710. https://doi.org/10.3389/fnins.2019.00710
Gevirtz, R. N. (2017). Cardio-respiratory psychophysiology: Gateway to mind-body medicine. Griffiths, K. R., Quintana, D. S., Hermens, D. F., Spooner, C., Tsang, T. W., Clarke, S., & Kohn, M. R. (2017). Sustained attention and heart rate variability in children and adolescents with ADHD. Biological psychology, 124, 11–20. https://doi.org/10.1016/j.biopsycho.2017.01.004
Hämmerle, P., Eick, C., Blum, S., Schlageter, V., Bauer, A., Rizas, K. D., Eken, C., Coslovsky, M., Aeschbacher, S., Krisai, P., Meyre, P., Vesin, J.-M., Rodondi, N., Moutzouri, E., Beer, J., Moschovitis, G., Kobza, R., Di Valentino, M., Corino, V. D. A., Laureanti, R., . . . Swiss‐AF Study Investigators (2020). Heart rate variability triangular index as a predictor of cardiovascular mortality in patients with atrial fibrillation. Journal of the American Heart Association, 9(15). https://doi.org/10.1161/JAHA.120.016075 Hartmann, R., Schmidt, F. M., Sander, C., & Hegerl, U. (2019). Heart rate variability as indicator of clinical state in depression. Frontiers in Psychiatry, 9, 735. https://doi.org/10.3389/fpsyt.2018.00735
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 Hsieh, T.-C., Huang, C.-J., & Hung, T.-M. (2010). Relationships between heart rate variability and athletic performance. International Journal of Sport and Exercise Psychology, 8(4), 473-475. https://doi.org/10.1080/1612197X.2010.9671964
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
Jovic, A., & Bogunovic, N. (2011). Electrocardiogram analysis using a combination of statistical, geometric, and nonlinear heart rate variability features. Artificial Intelligence in Medicine, 51, 175-186. https://doi.org/10.1016/j.artmed.2010.09.005 Kim, H. G., Cheon, E. J., Bai, D. S., Lee, Y. H., & Koo, B. H. (2018). Stress and Heart rate variability: A meta-analysis and review of the literature. Psychiatry Investigation, 15(3), 235–245. https://doi.org/10.30773/pi.2017.08.17
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 Laborde, S., Mosley, E., & Thayer, J. F. (2017). Heart rate variability and cardiac vagal tone in psychophysiological research - Recommendations for experiment planning, data analysis, and data reporting. Frontiers in Psychology, 8, 213. https://doi.org/10.3389/fpsyg.2017.00213
Lehrer, P. M. (2012). Personal communication regarding 24-hour HRV measurements.
Moss, D. (2022). HRV Biofeedback Bootcamp. Association for Applied Psychophysiology and Biofeedback. Natarajan, A., Pantelopoulos, A., Emir-Farinas, H., & Natarajan, P. (2020). Heart rate variability with photoplethysmography in 8 million individuals: a cross-sectional study. The Lancet. Digital Health, 2(12), e650–e657. https://doi.org/10.1016/S2589-7500(20)30246-6
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 Otzenberger, H., Gronfier, C., Simon, C., Charloux, A., Ehrhart, J., Piquard, F., & Brandenberger, G. (1998). Dynamic heart rate variability: A tool for exploring sympathovagal balance continuously during sleep in men. The American Journal of Physiology, 275(3), H946–H950. https://doi.org/10.1152/ajpheart.1998.275.3.H946
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 Pizzoli, S. F. M., Marzorati, C., Gatti, D., Monzani, D., Mazzocco, K., & Pravettoni, G. (2021). A meta-analysis on heart rate variability biofeedback and depressive symptoms. Sci Rep, 11, 6650. https://doi.org/10.1038/s41598-021-86149-7
Schipke, J. D., Arnold, G., and Pelzer, M. (1999). Effect of respiration rate on short-term heart rate variability. J. Clin Basic Cardiol. 2, 92–95. Schneider, M., & Schwerdtfeger, A. (2020). Autonomic dysfunction in posttraumatic stress disorder indexed by heart rate variability: A meta-analysis. Psychological Medicine, 50(12), 1937–1948. https://doi.org/10.1017/S003329172000207X
Shaffer, F., & Ginsberg, J. P. (2017). An overview of heart rate variability metrics and norms. Frontiers in Public Health. https://doi.org/10.3389/fpubh.2017.00258
Shaffer, F., McCraty, R., & Zerr, C. L. (2014). A healthy heart is not a metronome: An integrative review of the heart’s anatomy and heart rate variability. Frontiers in Psychology. https://doi.org/10.3389/fpsyg.2014.01040
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
Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology (1996). Heart rate variability: Standards of measurement, physiological interpretation, and clinical use. Circulation, 93, 1043-1065. PMID: 8598068
Tarvainen, M. P., & Niskanen, J.-P. (2022). Kubios HRV version 4.0 user's guide. University of Finland.
Umetani, K., Singer, D. H., McCraty, R., & Atkinson, M. (1998). Twenty-four hour time domain heart rate variability and heart rate: Relations to age and gender over nine decades. Journal of the American College of Cardiology, 31(2), 593-601. https://doi.org/10.1016/s0735-1097(97)00554-8
Feedback
We value your feedback because we produce these posts for you. Please complete our brief survey to help us improve this service.
Learn More
