Understanding Heart Rate Variability (HRV)

This page takes a closer, source-backed look at HRV (Heart Rate Variability), the starting point for Feelmo. It gets a little technical, but it's written for anyone who wants to understand the meaning of your Balance score more deeply. By the time you finish, it should click into place why Feelmo looks not at the heartbeat itself but at "the fluctuation between beats," and how to read your own numbers.

What is HRV?

Heart rate variability (HRV) is a family of measures that describe how the time between successive heartbeats varies. Cardiac autonomic regulation contributes to HRV, but HRV alone does not directly measure the whole autonomic nervous system, stress, fatigue, recovery, or illness.

Why is my HRV low?

A single low reading cannot identify a cause or a condition on its own. Sleep, exercise, posture, breathing, time of day, caffeine and body movement all affect HRV. Match your measurement conditions first, then compare against your own usual range rather than someone else's numbers.

What is a normal HRV?

There is no one normal value that applies to everyone. Measurement method, age, sex and daily conditions differ so widely between healthy people that population averages are only a rough reference. Age and individual variation are covered in Age, Sex, and Individual Differences.

How does average HRV change with age?

Across a population, HRV tends to decline with age. But values scatter widely within any single age band, so age alone cannot tell you about one person's state. See Age, Sex, and Individual Differences for how to read this.

Heartbeats and the "Interval Between Beats"

On an electrocardiogram, each beat produces a large, sharp peak (the R wave). The interval between one R wave and the next is called the R-R interval (the interval between normal beats — excluding arrhythmias and the like — is also called the N-N interval).

While heart rate describes the average number of beats per minute, HRV describes variation in the intervals between beats. Those intervals often change with breathing, but they are also affected by posture, activity, recording length, age, medication, illness, rhythm disturbances, and measurement error (Task Force, 1996; Shaffer & Ginsberg, 2017).

More variation is not automatically better. A value has meaning only in the context of the metric, recording method, conditions, and person. One reading cannot establish health, adaptability, stress, or overall autonomic balance.

Figure: The R waves on an electrocardiogram and the interval between consecutive R waves (R–R). The fact that the width is not constant but fluctuates — that is HRV.

The Three Domains of Analysis

HRV analysis methods are broadly organized into three "domains" (Task Force, 1996; Shaffer & Ginsberg, 2017). Think of them as "lenses" for looking at the same R-R interval data from different angles. This is general science, not a description of Feelmo's proprietary processing, whose details are not public.

1. Time Domain

This expresses the variability of R-R intervals as statistical measures along the time axis. It's the most intuitive lens, and the simplest to compute. Some views look at the overall spread of beat intervals; others look at short-term beat-to-beat changes.

Time-domain analysis includes several different metrics whose meaning depends on recording length, protocol, and population. Feelmo's proprietary processing details are not public.

2. Frequency Domain

This decomposes the fluctuation of R-R intervals into faster and slower components. In musical terms, it's like a lens that separates a chord into the individual notes that make it up. Breathing-related fast fluctuations and slower pressure-balance patterns can both be informative, but no single frequency component or ratio is "the answer."

Figure: A simplified example of respiratory sinus arrhythmia, in which heart rate can speed during inhalation and slow during exhalation. Under controlled conditions it can inform interpretation of cardiac vagal modulation, but it is also strongly affected by breathing rate and depth (Berntson et al., 1993). It does not guarantee an effect from a particular breathing technique.

3. Nonlinear

Beat-interval series can contain structures that are not described by a simple mean or variance. Nonlinear metrics quantify features such as fractal correlation or complexity (Shaffer & Ginsberg, 2017). Their values cannot be translated directly into “quality of balance,” health, or adaptability without validation for the particular protocol and population.

How It's Measured — ECG and Optical Sensors

• ECG (electrocardiogram) — The reference method, recording the heart's electrical activity directly through electrodes
• PPG (photoplethysmography) — The method used by Apple Watch and others, capturing the pulse wave of blood flow with LED light. The variability of the beat-to-beat intervals derived this way is, strictly speaking, called PRV (pulse rate variability)

Some studies report closer agreement between PRV and ECG-based HRV under resting conditions than during movement or exercise (Schäfer & Vagedes, 2013). Agreement depends on device, metric, and protocol. Feelmo's proprietary processing details are not public.

Measurement Length and Conditions

• A standard short-term recording has traditionally been set at 5 minutes (Task Force, 1996)
• The validity of shorter "ultra-short-term" recordings differs depending on which view of HRV is used (Munoz et al., 2015; Laborde et al., 2017)
• Every metric is affected by posture, breathing, time of day, caffeine, body movement, and more, so keeping measurement conditions reasonably consistent is important for interpretation (Laborde et al., 2017)

How Feelmo Uses This

Feelmo uses proprietary on-device processing to summarize available HRV-derived information and patterns in your own past as Balance (0–100). The processing details are not public. It is not a direct display of one public metric.

The principles for interpreting it are always the same.

• Do not use another person's value as a pass/fail line. Your own past trend can provide context, but detailed comparison and display rules are not public, and a change alone cannot identify your condition or its cause
• Keep measurement conditions comparable. Sitting or sleep may involve less large wrist movement, but neither eliminates error or missing data
• Do not treat the six companion faces as evidence. They are product interface characters whose display rules are not public; they do not infer emotion, fatigue, stress, or recovery

The detailed calculation is proprietary and not published. Balance does not infer emotion or directly measure autonomic state, fatigue, stress, recovery, or health. For background science, see The Science of Balance; for interpretation, see What the Balance score Is.

References

1. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. Heart rate variability: standards of measurement, physiological interpretation and clinical use. Circulation. 1996;93(5):1043–1065.
2. Shaffer F, Ginsberg JP. An Overview of Heart Rate Variability Metrics and Norms. Frontiers in Public Health. 2017;5:258.
3. Laborde S, Mosley E, Thayer JF. Heart Rate Variability and Cardiac Vagal Tone in Psychophysiological Research – Recommendations for Experiment Planning, Data Analysis, and Data Reporting. Frontiers in Psychology. 2017;8:213.
4. Billman GE. A frequency-domain ratio does not accurately measure cardiac sympatho-vagal balance. Frontiers in Physiology. 2013;4:26.
5. Schäfer A, Vagedes J. How accurate is pulse rate variability as an estimate of heart rate variability? A review on studies comparing photoplethysmographic technology with an electrocardiogram. International Journal of Cardiology. 2013;166(1):15–29.
6. Munoz ML, van Roon A, Riese H, et al. Validity of (Ultra-)Short Recordings for Heart Rate Variability Measurements. PLoS One. 2015;10(9):e0138921.
7. Berntson GG, Cacioppo JT, Quigley KS. Respiratory sinus arrhythmia: autonomic origins, physiological mechanisms, and psychophysiological implications. Psychophysiology. 1993;30(2):183–196.
8. Carter JR, et al. Guidelines for rigor and reproducibility of heart rate variability within human cardiovascular research. American Journal of Physiology-Heart and Circulatory Physiology. 2026. doi:10.1152/ajpheart.00041.2026.