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.

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 (beats per minute)" describes your average speed, HRV describes how much those intervals fluctuate. Even at the same "60 bpm," there is a heart that ticks as regularly as a metronome, and a heart that supplely stretches and contracts in time with your breath. The latter is generally considered a state in which the autonomic nervous system switches quickly to match the situation — in other words, a state of high adaptability (Task Force, 1996; Shaffer & Ginsberg, 2017).

Here's the key point. Fluctuation isn't a sign of trouble — if anything, it can be a sign of being well-balanced. The heart is not a machine that keeps a fixed rhythm; it is a living tuner, finely changing tempo with every inhale and exhale, every moment of tension and release. What Feelmo looks at is exactly this "suppleness."

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. Feelmo uses these domains as scientific background, but it does not disclose the exact recipe used to translate signals into the Balance score.

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.

Feelmo treats these as ingredients, not as public score components. The app combines HRV-derived clues with measurement quality and your own baseline rather than exposing a single metric as the score.

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: When you breathe in, the heart rate speeds up slightly; when you breathe out, it slows slightly (respiratory sinus arrhythmia, RSA). This respiratory fluctuation is an important clue to parasympathetic activity (Berntson et al., 1993). That's exactly why slow, long exhalation breathing techniques can gently influence heartbeat variability in the moment.

3. Nonlinear

Heart rhythm is not a simple periodicity; it has a fractal, complex structure. Shape-based and complexity-based analyses have been studied as ways to capture the "quality" of balance (Shaffer & Ginsberg, 2017). The view is that a healthy heart rhythm is "moderately complex" — too monotonous or too disordered, and adaptability declines. This nonlinear perspective is an idea we hold dear in the background.

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)

Systematic reviews report that at rest, PRV agrees well with ECG-based HRV (Schäfer & Vagedes, 2013). On the other hand, because agreement declines during body movement or exercise, data taken at rest and during sleep is given more weight. The Apple Watch measures at the arm — a place that moves easily — so noise creeps in when the wrist is in motion. This is also one reason Feelmo places special value on data from "when you're sitting and settled" or "while you sleep."

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 consistent is the most important thing of all (Laborde et al., 2017)

How Feelmo Uses This

Feelmo combines multiple HRV-derived clues, measurement quality, and movement from your own baseline to build your Balance score (0–100) from Apple Watch-derived HRV. It is not a direct display of one public metric.

The principles for interpreting it are always the same.

• Read it as the difference from your own baseline, not against others. Rather than "higher/lower than average," we look at "how you're fluctuating today compared with your usual self"
• Give weight to measurements taken under conditions with few external factors — especially during sleep, or when sitting and settled. Because the wrist is vulnerable to body movement, data from stable situations is more trustworthy
• Receive it through the expressions of your six companions placed beside it, rather than through the score itself. We translate the number not into a "grade" but into gentle words of observation for you today — we never make definitive judgments

Exactly how multiple clues are integrated and converted into the 0–100 Balance score — the precise composition and weighting — is Feelmo's proprietary analysis logic and is not disclosed publicly. What matters here is the principle that Feelmo does not "guess your emotions" but gently watches over the balance of your autonomic nervous system. For the background science, please also see The Science of Balance, and for how to read the score, 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.