HRV across wearables: how to actually compare what your devices report
If you've worn two wearables on the same day and noticed they reported wildly different HRV values, you weren't imagining it. The signal is real but the number isn't standardized. Here's what each device is actually measuring and how to read across them.
Quick answer
There are two different HRV metrics in common use:
- RMSSD (root mean square of successive differences): the most common HRV unit, reported in milliseconds. Higher is generally better.
- SDNN (standard deviation of NN intervals): measures total variability, also in milliseconds. Reports a different number for the same heart, usually larger.
Most modern wearables report RMSSD. Apple Watch reports SDNN. So an Oura reading of 45 and an Apple Watch reading of 75 might be saying the same thing about your nervous system; they're just measured differently.
What each major wearable reports
| Wearable | HRV unit |
|---|---|
| Oura | ms_rmssd |
| WHOOP | ms_rmssd |
| Polar | ms_rmssd |
| Garmin | ms_rmssd |
| Withings | ms_rmssd |
| Apple Watch | ms_sdnn |
(ms_rmssd and ms_sdnn are how Freddy's data file labels them; the numeric values are in milliseconds in both cases.)
Why the same heart produces different numbers
Three reasons:
Sampling window. Oura averages overnight (six to eight hours of data, taken when you're asleep and motion is minimal). WHOOP samples during sleep too but uses a slightly different aggregation window. Polar's Vantage line samples nightly. Apple Watch samples opportunistically across the day. The signal you get is shaped by when and how it's pulled from the underlying inter-beat intervals.
Algorithm. RMSSD weights short-term variability heavily; SDNN weights overall variability. Same beats, different math. RMSSD numbers are typically smaller than SDNN numbers from the same data.
Sensor placement and noise. A finger ring has cleaner photoplethysmograph signal than a wrist watch. A chest strap (Polar H10) has cleaner electrocardiogram signal than either. The cleaner the signal, the more accurate the inter-beat-interval calculation, and the more stable the HRV reading.
So how do you compare across devices?
The honest answer is: don't, on a single-day basis. Cross-device comparison is unreliable on any given measurement. What works:
- Compare each device against itself over time. A 14-day rolling average of your Oura HRV is a meaningful baseline. So is a 14-day average from your WHOOP. Each is internally consistent.
- Compare directionally. If both devices show HRV trending down for a week, that's a real signal regardless of unit mismatch.
- Don't try to derive a "true HRV." No wearable gives you that. Polysomnography rooms in sleep labs do, with chest electrodes; consumer wearables approximate.
What this means if you connect HRV data to AI
When you ask an AI assistant like Claude or ChatGPT about your HRV across multiple wearables, the assistant should:
- Track each device's HRV trend separately, not pool them.
- Caveat the unit difference if you ask "is my Oura HRV high or low?" The answer depends on what's normal for you on Oura.
- Notice agreement: if Oura and WHOOP both show a downtrend, that's the most reliable signal you'll get.
Freddy reads HRV from each wearable in its native unit and exposes them to AI clients along with the unit field, so the AI knows what it's reading. If you've ever had an AI confidently tell you your HRV is "low" without specifying which device or unit, that's the kind of error this prevents.
Practical recommendation
If HRV is the metric you care most about:
- For sleep-context HRV: ring or chest strap. Oura (ms_rmssd) or Polar with H10 (ms_rmssd).
- For 24-hour HRV monitoring: WHOOP (ms_rmssd) or Garmin (ms_rmssd).
- For research-grade work: chest strap and a separate analysis pipeline. Don't rely on consumer-device-derived HRV for anything clinical.
Verified 2026-05-09. HRV measurement on consumer wearables is an active area; what's reported here will likely shift as algorithms update.