Biological-age estimate
Metric’s biological-age feature compares a set of cardio-fitness, cardiovascular, body-composition, movement, and sleep signals with reference ranges. Most inputs use rolling 90-day data so that one unusual day has less influence.
Each available input produces an age-like contribution. Metric starts missing contributors at chronological age instead of redistributing their weight among the signals that happen to be present. This keeps sparse data from giving one measurement disproportionate influence.
| Signal | Weight | Role |
|---|---|---|
| VO₂ Max | 18% | Cardiorespiratory fitness |
| Resting heart rate | 14% | Baseline cardiovascular demand |
| Heart-rate variability (SDNN) | 10% | Autonomic and recovery context |
| Body-fat percentage | 10% | Preferred body-composition signal |
| Sleep duration | 10% | Typical nightly sleep duration |
| Daily steps | 9% | Everyday movement volume |
| Lean-mass ratio | 9% | Body-composition fallback when body-fat percentage is unavailable |
| Body mass index | 5% | Broad body-size context |
| Systolic blood pressure | 4% | Pressure when the heart contracts |
| Activity load | 4% | Active energy relative to basal energy |
| Sleep regularity | 4% | Night-to-night timing consistency |
| Diastolic blood pressure | 3% | Pressure between heartbeats |
Coverage, freshness, and fallbacks
- VO₂ Max, BMI, and body-composition measurements can contribute with fewer samples because they are often recorded less frequently.
- Resting heart rate, HRV, steps, activity load, and sleep generally require repeated samples before they contribute.
- Body-fat percentage is preferred for body composition. Lean-mass ratio can act as a fallback when body-fat percentage is unavailable and both lean mass and body mass are present.
- Activity load uses active energy in relation to basal energy when both are available.
- Older or missing data lowers the coverage shown in the app. Rolling-window values outside the model’s plausible ranges do not contribute.
Aging rate
Metric averages the most recent 90 generated daily biological-age entries and begins showing aging rate once at least seven entries are available. A daily entry can still be generated when source data is sparse because missing contributors default to chronological age; the separate coverage indicator shows how much eligible data supports the result. Aging rate divides the rolling estimate by chronological age and limits the displayed ratio to 0.10×–3.00×.
A result below 1.00× means the rolling estimate is younger than chronological age; a result above 1.00× means it is older. Because the value is derived from the same underlying inputs, a person should examine the contributors and data coverage rather than interpreting the ratio in isolation.
Workout strain and readiness context
Metric assigns workouts a training-load value using duration and heart-rate intensity, then tracks short-term and longer-term load with exponential decay. Acute load uses a roughly five-day decay on workout days and a faster roughly 2.5-day decay on rest days; chronic load uses about 32 days. The model is bootstrapped from up to 90 days of workouts.
The displayed 0–100 score is oriented as freshness: a higher score represents lower accumulated strain, while a lower score represents higher strain. The score considers short-term load relative to a longer-term baseline, absolute acute load, and today’s workout load. It is training context, not a directive to ignore fatigue, pain, illness, or professional guidance.
Model changes
Metric may adjust reference ranges, data-quality rules, weights, and presentation as the product improves. This page is updated when a material change affects how a public-facing result should be interpreted. The review date at the top identifies the current public methodology version.