HRV: RMSSD vs SDNN, and why your wearables disagree
Updated
Short answer
RMSSD and SDNN are two different summaries of heart rate variability (HRV). RMSSD looks at differences between consecutive heartbeats and mostly reflects fast, beat-to-beat changes. SDNN is the standard deviation of all the beat-to-beat intervals in a recording. The two are not interchangeable, so compare like with like.
What HRV measures
Your heart does not beat at a perfectly steady pace. The time between one beat and the next (the inter-beat interval) varies from beat to beat, and heart rate variability is the name for that variation. It is summarised with a number, and there are several ways to calculate that number, which is where the confusion starts.
The standard reference for how HRV is measured and reported is the 1996 Task Force report of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. It separates time-domain measures, which work on the intervals themselves, from frequency-domain measures. RMSSD and SDNN are both time-domain measures.
RMSSD
RMSSD is the root mean square of successive differences: the typical size of the change from one inter-beat interval to the next. Because it looks only at neighbouring beats, it mostly reflects fast changes and is less affected by slow drifts over the recording. That makes it practical for short recordings, which is why many wearables report it from a night's sleep.
SDNN
SDNN is the standard deviation of the NN intervals, the times between successive heartbeats once irregular beats and artefacts have been removed: how spread out all the intervals in a recording are. It takes in slow changes as well as fast ones, so it depends strongly on how long the recording is. An SDNN from a five-minute recording and one from a full day are different quantities, and published reference values for 24-hour, short-term and ultra-short-term recordings are not interchangeable.
Which wearables report which
| Source | HRV metric | Notes |
|---|---|---|
| Oura | RMSSD | An average from sleep |
| Whoop | RMSSD | Its developer data names the field hrv_rmssd_milli |
| Apple Health | SDNN | Stored as heart rate variability SDNN, in milliseconds |
Devices also differ in the sensor they use, when they measure (overnight or in short spot checks), and how they turn many readings into one daily number (an average, a value from one stretch of sleep, a maximum). Two devices worn at the same time can report different HRV for the same night without either being wrong.
Compare like with like
- Stay with one device and one metric when you look at your own trend. A change of device is a change of method.
- Do not convert RMSSD to SDNN or back. They are calculated differently and depend on the recording, so there is no reliable conversion.
- Look at your own baseline. HRV depends on age, sex, fitness, time of day, sleep, alcohol and illness, so published ranges describe particular groups measured in a particular way, not you.
Frequently asked questions
Is RMSSD or SDNN better?
Neither is better in general. They answer different questions and suit different recordings. Use the metric your device reports and keep using it, so your trend compares like with like.
What is a typical HRV number?
There is no single value that applies to everyone. HRV depends on age, sex, fitness, the device, the measuring window and the metric. Published ranges apply to specific methods and populations. BiomarkerHQ does not label HRV values. It shows your own trend.
Can I convert RMSSD to SDNN?
Not reliably. They are computed differently and SDNN depends on the length of the recording. BiomarkerHQ does not convert between them.
Why does my HRV differ between two devices?
The devices may use different metrics (RMSSD or SDNN), different sensors, different measuring windows and different ways of averaging. Compare each device's trend with its own history.
Sources
- 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. (opens in a new tab)
- Shaffer F, Ginsberg JP. An overview of heart rate variability metrics and norms. Frontiers in Public Health 2017;5:258. (opens in a new tab)
- Oura. Heart rate variability. Oura support article: nighttime HRV is calculated as rMSSD, as the mean of five-minute samples taken during sleep. (opens in a new tab)
- WHOOP Developer Platform. API reference, recovery score: hrv_rmssd_milli, in milliseconds. (opens in a new tab)
- Apple. HealthKit: heartRateVariabilitySDNN, the standard deviation of heartbeat intervals. (opens in a new tab)
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