The Science of Regulation

The science behind Feelmo, with sources. The algorithm that composes the score is not public, but the science it rests on stands on published, peer-reviewed research.

The heart is not a metronome

A healthy heart does not beat at a fixed, clock-like rhythm. The interval between beats (the R-R interval) fluctuates continuously with breathing, posture, stress, and recovery. This fluctuation is HRV (heart rate variability).

Since a 1996 joint Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology standardized its measurement and interpretation (Task Force, 1996), HRV has been used in thousands of studies as a non-invasive window onto autonomic function (Shaffer & Ginsberg, 2017).

RMSSD, the measuring stick

Among the many HRV indices, Feelmo uses RMSSD (the root mean square of successive differences between R-R intervals) as its standard, because:

• It is an established index of parasympathetic (vagal) activity (Task Force, 1996; Shaffer & Ginsberg, 2017)
• It is relatively stable over short recordings and less affected by breathing pattern, which is why it is recommended in psychophysiological research (Laborde et al., 2017)

Why we look at the night

Feelmo's Regulation Score is born mainly from heart data during sleep, for good reasons:

• During sleep, the external factors that perturb HRV — exercise, food, posture, caffeine — are at their lowest, so each day can be compared under the same conditions
• Deep non-REM sleep shows parasympathetic dominance (Trinder et al., 2001), making recovery easier to observe
• The relationship between sleep and HRV has been organized in a systematic review (Stein & Pu, 2012)

Stress and regulation

The association between psychological stress and reduced HRV has been reported repeatedly in meta-analysis (Kim et al., 2018). A meta-analysis of neuroimaging studies further proposes a model in which the vagal pathway linking the prefrontal cortex and the heart is what makes HRV a marker of stress and health (Thayer et al., 2012).

Why slow breathing works

Feelmo's breathing sessions guide you toward slow breathing (around six breaths per minute) because breathing at this rate is known to resonate with the baroreflex (the blood-pressure reflex) and transiently raise HRV markedly (Lehrer & Gevirtz, 2014).

The psychophysiological effects of slow breathing have also been linked, in systematic reviews, to increased parasympathetic activity and feelings of relaxation (Zaccaro et al., 2018; Russo et al., 2017).

Why compare with "your own baseline"

Absolute HRV values vary widely with age, sex, and constitution. Large-scale data across nine decades show HRV declining with age (Umetani et al., 1998).

So Feelmo looks not at comparison with others, but at change from your own baseline. Not whether you are higher or lower than the person next to you, but how you compare with your usual self — that is the meaningful question.

Measuring at the wrist

Apple Watch reads a pulse wave with an optical sensor and estimates R-R intervals. Under resting conditions, Apple Watch HRV has been validated as agreeing well with ECG-based measurement (Hernando et al., 2018). Accuracy drops during vigorous movement, which is another reason Feelmo emphasizes data at rest and during sleep.

On light and movement

The behavior log handles sunlight and activity for a reason, too:

• Light is among the strongest environmental factors shaping the human circadian rhythm, sleep, and mood (Blume et al., 2019)
• Regular exercise has been associated with higher HRV in meta-analysis (Sandercock et al., 2005)

What we can say about Lumo Core

The Regulation Score takes the established HRV indices above and integrates them — through the Lumo Core algorithm developed by the research-led studio Lumo — into a single 0–100 score.

• The specific composition and weighting of that integration are not disclosed, as a trade secret
• The validity of the analysis is continuously examined within joint research with Keio University

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. Trinder J, Kleiman J, Carrington M, et al. Autonomic activity during human sleep as a function of time and sleep stage. Journal of Sleep Research. 2001;10(4):253–264.
5. Stein PK, Pu Y. Heart rate variability, sleep and sleep disorders. Sleep Medicine Reviews. 2012;16(1):47–66.
6. Kim HG, Cheon EJ, Bai DS, Lee YH, Koo BH. Stress and Heart Rate Variability: A Meta-Analysis and Review of the Literature. Psychiatry Investigation. 2018;15(3):235–245.
7. Thayer JF, Åhs F, Fredrikson M, Sollers JJ, Wager TD. A meta-analysis of heart rate variability and neuroimaging studies. Neuroscience & Biobehavioral Reviews. 2012;36(2):747–756.
8. Lehrer PM, Gevirtz R. Heart rate variability biofeedback: how and why does it work? Frontiers in Psychology. 2014;5:756.
9. Zaccaro A, Piarulli A, Laurino M, et al. How Breath-Control Can Change Your Life: A Systematic Review on Psycho-Physiological Correlates of Slow Breathing. Frontiers in Human Neuroscience. 2018;12:353.
10. Russo MA, Santarelli DM, O'Rourke D. The physiological effects of slow breathing in the healthy human. Breathe (Sheffield). 2017;13(4):298–309.
11. Umetani K, Singer DH, McCraty R, Atkinson M. 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. 1998;31(3):593–601.
12. Hernando D, Roca S, Sancho J, Alesanco Á, Bailón R. Validation of the Apple Watch for Heart Rate Variability Measurements during Relax and Mental Stress in Healthy Subjects. Sensors (Basel). 2018;18(8):2619.
13. Blume C, Garbazza C, Spitschan M. Effects of light on human circadian rhythms, sleep and mood. Somnologie. 2019;23(3):147–156.
14. Sandercock GR, Bromley PD, Brodie DA. Effects of exercise on heart rate variability: inferences from meta-analysis. Medicine & Science in Sports & Exercise. 2005;37(3):433–439.