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Overview

The Resilience Score is an Open Wearables native score that measures how stable a user’s heart rate variability (HRV) is. It is computed from raw overnight biometric data - not sourced from or influenced by any manufacturer’s proprietary score. HRV-CV during sleep is an established marker of autonomic nervous system resilience: the more stable the overnight HRV, the better the body is recovering and adapting to physiological stress.
PropertyValue
Range0-100
FrequencyWeekly rolling window, updated daily
Daily viewPer-night HRV value shown alongside the weekly score
ComponentHeart Rate Variability (overnight, sleep-filtered)
SourceComputed by Open Wearables - not a provider-sourced score

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Requirements

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Data syncing

  • The user must have at least one connected provider that syncs heart rate data and sleep data.
  • The device must be worn during sleep - overnight HR samples outside of detected sleep windows are excluded from the calculation.

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Minimum data threshold

A Resilience Score is only computed when there is sufficient data to produce a statistically meaningful result:
RequirementValue
Lookback window7 nights
Minimum nights with HRV data5 nights
Minimum HR samples per night20 samples
If fewer than 5 of the 7 nights have valid overnight HRV recordings, the score is returned as null. The daily breakdown is still returned so you can show users how many nights have been counted and encourage consistent device wear.

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How It Works

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HRV metric selection

The score prefers RMSSD (Root Mean Square of Successive Differences) as the HRV metric, since it is the most widely available measure of short-term HRV and is reported by most consumer wearables. If no RMSSD data survives the sleep-window filter, the algorithm automatically falls back to SDNN (Standard Deviation of Normal-to-Normal intervals). The metric type used (RMSSD or SDNN) is returned in the score response so it can be surfaced in the UI where appropriate.

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Sleep-window filtering

Only HR samples recorded during confirmed sleep are used. The algorithm:
  1. Fetches all sleep sessions in the lookback window.
  2. Extracts stage-level windows where the user was in any asleep stage (Light, Deep, REM, or generic Sleeping).
  3. Filters HR data points to those timestamps - excluding awake periods and sleep latency.
If a sleep session has no stage-level data, the full session window is used as a conservative fallback.

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Coefficient of variation (HRV-CV)

The weekly score is the coefficient of variation of nightly average HRV values across the lookback window: 
HRV-CV = standard deviation of nightly HRV averages / mean of nightly HRV averages
A lower HRV-CV means more consistent overnight HRV - the body is recovering steadily night to night. A higher HRV-CV indicates more variability, which can reflect stress, illness, overtraining, or lifestyle disruption.

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Daily view

Each score response includes a day-by-day breakdown of overnight HRV values for the lookback window: 
{
  "hrv_cv": 0.087,
  "metric_type": "RMSSD",
  "days_counted": 6,
  "lookback_days": 7,
  "daily_scores": [
    { "date": "2025-04-09", "hrv_value_ms": 42.1, "has_data": true },
    { "date": "2025-04-10", "hrv_value_ms": null, "has_data": false },
    { "date": "2025-04-11", "hrv_value_ms": 39.8, "has_data": true },
    { "date": "2025-04-12", "hrv_value_ms": 44.3, "has_data": true },
    { "date": "2025-04-13", "hrv_value_ms": 40.5, "has_data": true },
    { "date": "2025-04-14", "hrv_value_ms": 38.9, "has_data": true },
    { "date": "2025-04-15", "hrv_value_ms": 43.7, "has_data": true }
  ]
}
Days where the device was not worn during sleep or where insufficient samples were recorded will have has_data: false.