A Guide to Selecting HRV Norms

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.

Click on our narrator icon to listen to this post.

Measurement Context

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.

Detection Method

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).

Artifacting

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

Client characteristics include:

1. age

2. sex

3. breathing rate

4. emotions

5. health

6. aerobic fitness

7. medication

8. recent or immediate physical activity

9. cognitive and emotional activity (e.g., affect, expectancies, imagery, and self-statements)

Age