If you wear an Apple Watch and an Oura ring on the same night, the two devices will report different HRV numbers, sometimes far apart. Neither device is wrong. They’re computing different metrics from the same underlying heartbeat data, and the two metrics are correctly producing different values.
This page is the disambiguation. If you’re here because your numbers don’t match across devices, this is why.
What each metric is
Both metrics start with the same input: the time interval between consecutive heartbeats, measured in milliseconds, across a window of anything from under a minute to a whole night. They then summarize that interval series in different ways.
SDNN — Standard Deviation of Normal-to-Normal intervals. The classical statistical spread. Take all the intervals in the window, compute their standard deviation, report the result. It captures the total variation across the whole window, and what drives that variation depends on the window.Shaffer and Ginsberg (2017) note that in short resting recordings the main source is respiratory sinus arrhythmia, while over 24 hours slower, low-frequency oscillations make a significant contribution.
RMSSD — Root Mean Square of Successive Differences. Take each pair of adjacent intervals, compute their difference, square it, average all the squares, take the square root. This emphasizes short-term beat-to-beat variation, which is why researchers use it to track the vagus nerve’s influence on the heart.
Their review describes RMSSD as the primary time-domain measure used to estimate vagally mediated changes in HRV, and as more influenced by the parasympathetic nervous system than SDNN.
Why the numbers differ
How far apart the two numbers sit depends on the recording window. In five-minute resting ECGs from 1,906 healthy adults,Voss et al. (2015)found SDNN only modestly above RMSSD: 50.0 against 39.7 ms for men aged 25–34, and 29.6 against 19.1 ms for men aged 65–74.
Over longer windows SDNN takes in slower oscillations as well, and Shaffer and Ginsberg note that recording length strongly influences time-domain values. So there is no fixed conversion factor between the two, and we don’t offer one. A device integrating a whole night and a watch sampling a minute during the day are not measuring the same window, whatever the metric is called.
Which device reports which
| Device | Metric | Measurement window |
|---|---|---|
| Apple Watch | SDNN | 30–60s during stillness |
| Apple Health (clinical) | SDNN | Imported from supported devices |
| Oura Ring | RMSSD | Whole-night integration |
| Whoop | RMSSD | Last sleep cycle of the night |
| Garmin | RMSSD | Overnight + on-demand |
| Polar H10 (chest strap) | Both | User-configurable window |
| Welltory (camera PPG) | Both | 60s seated measurement |
Which one should you track?
Whichever your primary device reports natively. Don’t try to translate between them. The right approach is to pick a device, learn what its number looks like for you on good days and bad days, and watch your own seven-day rolling average.
RMSSD tracks short-term, vagally mediated change, which suits short measurements. SDNN’s clinical weight comes from 24-hour recordings: Shaffer and Ginsberg call 24-hour SDNN the “gold standard” for medical stratification of cardiac risk, and Kleiger et al. (1987)used it in their study linking low HRV to mortality after heart attack. That weight belongs to the 24-hour measure; the same review notes that five-minute SDNN does not predict risk the way 24-hour SDNN does.
Neither metric is the right one in the abstract. The one that matters is the one your device reports consistently, read against its own history. Pick one, ignore the other.
Why HRV Breathe uses SDNN
We read HRV from Apple Health. Apple Health stores HRV asHKQuantityTypeIdentifierHeartRateVariabilitySDNN — that’s the only HRV type Apple exposes through its HealthKit API. Apple Watch samples it natively in SDNN. Third-party devices that sync HRV into Apple Health have to write into that same SDNN type. Whether each vendor converts its native RMSSD first or writes it into the SDNN slot as is, we haven’t been able to confirm. If you mix sources in Apple Health, check which app wrote each reading.
For our purposes, the choice was made by the platform. SDNN is the metric Apple gives us. The trend chart in HRV Breathe is an SDNN trend, and the occasional reading on the completion screen is an SDNN reading. We’d use RMSSD if HealthKit offered it as a ready-made type; it doesn’t, so we report what the platform gives us and say so.
Higher-order metrics — pNN50, LF/HF, SD1/SD2
The HRV literature has half a dozen more metrics — pNN50, SDANN, frequency-domain LF and HF power, the SD1/SD2 ratio from Poincaré analysis. They’re all related; they all measure slightly different things; and for non-clinical practical use they add complexity without adding insight beyond SDNN and RMSSD.
If you’re a researcher or a clinician working with clinical-grade data and a specific hypothesis, those metrics matter. If you’re an adult with a wearable trying to get your numbers up through behavior change, they don’t. Pick the metric your device already reports, follow your trend, and ignore the rest.