> ## Documentation Index
> Fetch the complete documentation index at: https://openwearables.io/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Resilience Score

> A HRV-CV computed by Open Wearables that measures the stability of the body's autonomic nervous system.

## 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 \[1].

| Property   | Value                                                     |
| ---------- | --------------------------------------------------------- |
| Range      | 0-100                                                     |
| Frequency  | Weekly rolling window, updated daily                      |
| Daily view | Per-night HRV value shown alongside the weekly score      |
| Component  | Heart Rate Variability (overnight, sleep-filtered)        |
| Source     | Computed by Open Wearables - not a provider-sourced score |

***

## Requirements

### Data syncing

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

### Minimum data threshold

A Resilience Score is only computed when there is sufficient data to produce a meaningful result:

| Requirement                  | Value      |
| ---------------------------- | ---------- |
| Lookback window              | 7 nights   |
| Minimum nights with HRV data | 5 nights   |
| Minimum HR samples per night | 20 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.

***

## How It Works

### HRV metric

The score uses **RMSSD** (Root Mean Square of Successive Differences) as the HRV metric - the most widely available measure of short-term HRV, reported by most consumer wearables. If no RMSSD data survives the sleep-window filter, the score cannot be computed.

The metric type used (RMSSD) is returned in the score response so it can be surfaced in the UI where appropriate.

### 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.

### 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.

### Daily view

Each score response includes a day-by-day breakdown of overnight HRV values for the lookback window:

```json theme={null}
{
  "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`.

\[1] Grosicki GJ, Carter JR, Laursen PB, et al. Heart rate variability coefficient of variation during sleep as a digital biomarker that reflects behavior and varies by age and sex. Am J Physiol Heart Circ Physiol. 2026;330(1):H187-H199. doi:10.1152/ajpheart.00738.2025