Updated: 6 days ago
Measurement context and client characteristics are crucial to interpreting HRV time- and frequency-domain measurements. In this post, we review 24-hour and brief HRV norms and propose commonsense rules for using them. We caution our readers to compare artifacted client or personal data while breathing at normal rates with appropriate norms. These measurements should be true baselines, without feedback or animated pacing displays.
Contextual factors include:
1. monitoring period length (e.g., brief or 24-hour)
2. detection method (e.g., BVP or ECG) 3. artifacting
4. presence or absence of feedback and pacing
5. ambulatory or stationary monitoring
6. position (e.g., supine or sitting upright)
7. intensity of physical activity
8. tasks performed during the measurement
9. social demand characteristics within the monitoring situation
10. relationship with staff
Recording period length, detection method, feedback, breathing pacing, movement, position, the intensity of physical activity, tasks, demand characteristics, and relationship variables can affect measurements by changing ANS activation, breathing mechanics, and emotions.
Recording Period Length
The length of the recording period significantly affects both HRV time- and frequency-domain measurements. Resting values obtained from brief monitoring periods can dramatically underestimate HRV, correlating poorly with 24-hour indices. Dr. Inna Khazan (2022) invites us to consider the SDNN. The mean short-term value for healthy adults was 50 milliseconds (Nunan et al., 2010). For athletes, it was 70 milliseconds (Berkoff et al., 2007). However, for healthy 20-29-year-olds, it was 153 milliseconds (Umetani et al., 1998).
Twenty-Four-Hour Monitoring Detects Slower HRV Rhythms
Twenty-four-hour monitoring detects slower sources of variability over a wider range of activity than brief recording. Circadian rhythms, core body temperature, metabolism, the renin-angiotensin system, and parasympathetic and sympathetic activity may contribute to 24-hour HRV (Shaffer, McCraty, & Zerr, 2014).
Twenty-four-hour monitoring permits the calculation of ULF power and includes sleep data in calculating the power in all four frequency bands. Again, see the striking differences between day and night values in all the bands. Twenty-four-hour monitoring also provides a more complete assessment of frequency domain measures.
Services like the HeartMath Institute's Autonomic Assessment Report ® can artifact and analyze uploaded 24-hour data to provide a more comprehensive evaluation than is possible in a clinical setting. The following profile assesses a 51-year-old male at elevated risk for heart attack © Institute of HeartMath.
Caption: Note the dramatic difference of almost 26 milliseconds in SDNN between day and night values. Twenty-four-hour monitoring provides a more complete assessment of time-domain measures.
Under resting conditions, ECG and PPG methods disagreed by less than 6% for most HRV measures and 29.9% for pNN50 in one study (Jeyhani et al., 2015). However, the PPG method may inflate HRV values and be a poor surrogate for ECG when participants stand, perform slow-paced breathing, or have low HRV (Constant et al., 1999; Hemon & Phillips, 2016; Jan et al., 2019; Medeiros et al., 2011).
HRV recordings are vulnerable to diverse artifacts that can result in interbeat interval (IBI) values that are too long or too short. Artifacts are false values produced by the client’s body (ectopic beats) and actions (movement), the environment (line current), and hardware limitations (light leakage).
The normative values reported in this post are based on artifacted data. This is critical because a single artifactual IBI value in a 2-minute epoch can markedly distort time-domain indices like SDNN (Berntson & Stowell, 1998) and frequency-domain metrics like LF power.
Wearables and consumer-grade smartphone apps may perform limited or no artifacting before calculating HRV time- and frequency-domain metrics. Marco Altini (2021) showed the impact of a single uncorrected ectopic beat in his post, Resting Heart Rate and Heart Rate Variability (HRV): What’s the Difference? — Part 2. The single ectopic beat at the end of the recording raises RMSSD from 79 to 201 ms.
Where you can output the interbeat intervals to a data analysis program like Kubios, a best practice is to review the raw signal for its morphology and manually check its artifacting decisions, beat by beat.
Client characteristics include:
3. breathing rate
6. aerobic fitness
8. recent or immediate physical activity
9. cognitive and emotional activity (e.g., affect, expectancies, imagery, and self-statements)
HRV time-domain measurements decline with age (Nunan et al., 2010; Abhishekh et al., 2013) and decreased health (Agelink, Box, Ullrich, & Andrich, 2002; Bigger et al., 1995).
Almedia-Santos et al. (2016) obtained 24-hour ECG recordings of 1743 subjects 40-100 years of age. They found a linear decline in SDNN, SDANN, and SDNN index. However, they found a U-shaped pattern for RMSSD and pNN50 with aging, decreasing from 40-60 and then increasing after age 70.
Bonnemeier et al. (2003) obtained 24-hour recordings from 166 healthy volunteers (85 men and 81 women) ages 20-70. They found the most dramatic HRV parameter decrease between the second and third decades.
A meta-analysis of 296,247 healthy participants examined 50 HRV measures (Koenig & Thayer, 2016). Women had higher mean HR (smaller RR intervals) and lower SDNN and SDNN index values, especially in 24-hour studies, compared to men. They showed lower total, VLF, and LF power but greater HF power.
While women showed relative vagal dominance, despite higher mean HR, men showed relative SNS dominance, despite their lower HR.
Compared with breathing at typical rates, slow-paced breathing can increase time-domain measurements and render frequency-domain calculations meaningless (Gevirtz, 2020; Nunan et al., 2010).
Paced breathing in the LF range increases RSA, exercises the baroreflex, and maximizes time-domain and LF-band measurements (Shaffer & Meehan, 2020). Breathing at rates significantly above or below an individual's RF may diminish time-domain and LF band values. Overbreathing is often associated with shallow thoracic breathing at rates that are multiples of the RF (Khazan, 2019).
HRV measurements are state-dependent. HRV is lowered by stress, difficult emotions (e.g., anger and anxiety), and higher cognitive loads (McCraty, 2012).
Sympathetic nervous system activation may increase power in the ULF, VLF, and LF bands, resulting in a high LF/HF ratio (Lehrer, 2012).
Low HRV is a marker for cardiovascular disorders, including hypertension, especially with left ventricular hypertrophy; ventricular arrhythmia; chronic heart failure; and ischemic heart disease (Bigger et al., 1995; Casolo et al., 1989; Maver, Strucl, & Accetto, 2004; Nolan et al., 1992; Roach et al., 2004). Low HRV predicts sudden cardiac death, especially due to arrhythmia following myocardial infarction and post-heart attack survival (Bigger et al., 1993; Bigger et al., 1992; Kleiger et al., 1987).
Depression in myocardial infarction (MI) patients increases mortality. Depressed patients are twice as likely as non-depressed individuals to have lower HRV (16% vs. 7%). Lower HRV is a strong independent predictor of post-MI death (Carney et al., 2001). HRVB might reduce anxiety and depression, which are associated with low vagal activity, because it increases vagal tone. From Friedman’s (2007) perspective, the problem is not “a sticky accelerator.” HRVB may fix “bad brakes” (p. 186). Reduced HRV is also seen in disorders with autonomic dysregulation, including anxiety and depressive disorders, asthma, and vulnerability to sudden infant death (Agelink et al., 2002; Carney et al., 2001; Cohen & Benjamin, 2006; Giardino, Chan, & Borson, 2004; Kazuma, Otsuka, Matsuoka, & Murata, 1997). Lehrer (2007) believes that HRV indexes adaptability and marshals evidence that increased RSA represents more efficient regulation of BP, HR, and gas exchange by synergistic control systems.
Time-domain measurements rise with increased aerobic fitness (Aubert, Seps, & Beckers, 2003; De Meersman, 1993).
Medication can affect both time domain and frequency domain measurements. Reviewing a list of all the medications your client is currently taking is essential. While caffeine, calcium channel blockers, and SSRIs like Prozac have minimal effect on HRV measurements, drugs like bupropion (Wellbutrin) and tricyclics like Elavil can suppress SDNN.
Twenty-Four-Hour Measurement Norms
The Task Force report (1996) provided 24-hour norms for 144 healthy subjects and included cutoffs for increased mortality risk.
Umetani et al. (1998) published 24-hour norms for 260 healthy participants aged 10-99. They reported that several HRV time-domain indices declined with age. After age 65, subjects fell below cutoffs for increased threat of mortality. Before age 30, female subjects had lower HRV measurements than their male counterparts. This gender difference vanished after 50 years of age.
Brief Measurement Norms
We obtain brief measurements by recording ~ 5 minutes of HRV activity. Nunan et al. (2010) reviewed normative data from short-term HRV studies published after the Task Force report (1996). The 44 selected studies meeting their criteria involved 21,438 healthy adult participants. The authors reported HRV values according to whether breathing was free or paced, sex, and spectral power analysis (autoregression or Fast Fourier transformation).
Recall that LFnu and HFnu are normalized values calculated for brief measurements by dividing LF power or HF power by the sum of LF power + HF power.
Voss et al. (2015) reported HRV metrics from 5-minute recordings of 1906 healthy adults, 782 women and 1124 men, 25-49 and 50-74.
Urban et al. (2019) reported 5-minute baseline measurements on 85 undergraduates (59 women and 26 men), 18-28 years of age. Participants sat upright with eyes open, no feedback, and with instructions to breathe normally. HRV data were obtained using ECG. Data were detrended using a smoothness priors procedure. The frequency-domain analysis utilized Welch's periodogram (FFT) technique.
Optimal performance professionals should be interested in the Berkoff et al. (2007) short-term norms from 145 elite track-and-field athletes measured before the 2004 U.S.A. Olympic Trials. The investigators monitored the athletes in the supine position using ECG after up to 5 minutes of rest to stabilize their heart rates.
Children 6-8 Years of Age
Seppälä et al. (2014) reported HRV metrics from 1- and 5-minute ECG recordings from 465 children ages 6-8. The table reproduces the 5-minute percentiles for a majority of the parameters.
Heart rate variability norms can help you interpret your client's metrics when correctly used. Consider measurement context and client characteristics when interpreting HRV time- and frequency-domain measurements. The authors encourage professionals to follow these guidelines: 1. Compare 24-hour norms with 24-hour values 2. Use appropriate age, fitness, gender, and health norms 3. Compare identical brief measurement periods (e.g., 5-minute norms with 5-minute client values) 4. Compare ECG-derived norms with ECG measurements and BVP-derived norms with BVP measurements.
5. Obtain brief measurements while your client breathes at normal rates (e.g., 12-16 bpm)
6. Only compare artifacted values with norm tables.
absolute power: the magnitude of HRV within a frequency band measured in milliseconds squared divided by cycles per second (ms2/Hz).
approximate entropy (ApEn): a nonlinear index of HRV that measures the regularity and complexity of a time series.
baseline: measurement obtained while breathing at normal rates without feedback or animated pacing displays.
brief (short-term) measurements: values obtained from ~ 5 minutes of recording.
correlation dimension: nonlinear index of HRV that estimates the minimum number of variables required to construct a model of the studied system.
detrended fluctuation analysis (DFA): a nonlinear index of HRV that extracts the correlations between successive R-R intervals over different time scales and yields estimates of short-term (α1) and long-term (α2) fluctuations.
frequency-domain measures of HRV: the calculation of the absolute or relative power of the HRV signal within four frequency bands.
heart rate: 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 RR intervals between consecutive heartbeats.
HFnu: high-frequency band power expressed in normal units is a proxy for vagal tone when individuals breathe at normal rates.
high-frequency (HF) band: an HRV frequency range from 0.15-0.40 Hz that represents the inhibition and activation of the vagus nerve by breathing (respiratory sinus arrhythmia).
HR Max - HR Min: an index of heart rate variability that calculates the difference between the highest and lowest heart rates 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).
low-frequency (LF) band: an HRV frequency range of 0.04-0.15 Hz that may represent the influence of PNS and baroreflex activity (when breathing at the RF).
multiscale entropy (MSE): an extension of ApEn that uses resampling to increase the number of data points in a time series to detect slower fluctuations.
natural logarithm (Ln): the logarithm to the base e of a numeric value.
nonlinear measurements: indices that quantify the unpredictability of a time series, which results from the complexity of the mechanisms that regulate the measured variable.
normal units (nu): the division of the absolute power for a specific frequency band by the summed absolute power of the LF and HF bands.
pNN50: the percentage of adjacent NN intervals that differ by more than 50 milliseconds.
power: the signal energy found within a frequency band.
relative power: the percentage of total HRV.
RMSSD: the square root of the mean squared difference of adjacent NN intervals.
sample entropy (SampEn): a nonlinear index of HRV that was designed to provide a less-biased measure of signal regularity and complexity than ApEn.
SD1: the standard deviation of the distance of each point from the y = x-axis that measures short-term HRV.
SD2: the standard deviation of each point from the y = x + average RR interval that measures short- and long-term HRV.
SD1/SD2: a ratio that measures the unpredictability of the R-R time series and autonomic balance under appropriate monitoring conditions.
SDANN: the standard deviation of the average NN intervals (mean heart rate) for each of the 5-minute segments during a 24-hour recording.
SDNN: the standard deviation of the normal (NN) sinus-initiated IBI measured in milliseconds.
SDNN index: the mean of the standard deviations of all the NN intervals for each 5-minute segment of a 24-hour HRV recording.
time-domain measures of HRV: indices like SDNN that measure the degree to which the IBIs between successive heartbeats vary.
total power: the sum of power (ms2) in the ULF, VLF, LF, and HF bands for 24-hour recording and the VLF, LF, and HF bands for brief recording.
Triangular Interpolation of the NN Interval Histogram (TINN): the baseline width of a histogram displaying NN intervals.
ultra-low-frequency (ULF) band: an ECG frequency range below 0.003 Hz that may represent very slow biological processes that include circadian rhythms, core body temperature, metabolism, the renin-angiotensin system, and possible PNS and SNS contributions.
ultra-short-term HRV measurements: HRV metrics based on recording periods shorter than 5 minutes.
very-low-frequency (VLF) band: an HRV frequency range of 0.003-0.04 Hz that may represent temperature regulation, plasma renin fluctuations, endothelial and physical activity influences, and possible intrinsic cardiac, PNS, and SNS contributions.
Abhishekh, H. A., Nisarga, P., Kisan, R., Meghana, A., Chandran, S., Raju, T., & Satyaprabha, T. N. (2013). Influence of age and gender on autonomic regulation of heart. J Clin Monit Comput, 27, 259-264. https://doi.org/10.1007/s10877-012-9424-3