Session effort
Hard minutes count differently.
Workout load weights duration and heart-rate intensity, so a short hard session can affect strain differently from an easy session of the same length.
Apple Watch readiness
Know how much training strain you are carrying.
Metric turns recent Apple Watch workout duration and heart-rate intensity into a strain and readiness view. It compares fast-changing short-term load with your slower-moving baseline, so you can see whether training is light, balanced, or accumulating. Treat the score as context for your own judgment, not an instruction.
- Duration and heart-rate intensity
- Short- versus long-term load
- 0–100 strain and readiness view
The useful answer
Readiness should explain its inputs.
Metric assigns workout effort from duration and heart-rate intensity, then lets recent sessions fade at different rates. Short-term load reacts quickly; the longer baseline changes more slowly. The relationship between them helps show whether strain is building or settling.
The number is most useful beside the workout history that created it. Open the sessions, check your own energy and soreness, and use the score as one part of the day’s decision.
Inside Metric
Designed to keep the context attached.
Every summary should lead back to the signal, date range, or source that shaped it.
Personal baseline
Recent work meets your longer pattern.
Exponential decay lets new workouts move short-term load quickly while the chronic baseline responds more gradually.
Explainable ranges
See low, moderate, or high strain.
The 0–100 view translates the load relationship into readable ranges and leaves the underlying workout list close at hand.
How it works
Three steps from data to a better question.
Record or import workouts
Let Apple Watch or another approved Health source contribute workout and heart-rate samples.
Build a baseline
Consistent history gives short- and long-term load something meaningful to compare.
Check context
Review readiness with how you feel, the sessions behind it, and the plan you intended to follow.
What to know first
Useful, with clear limits.
- Watch fit, heart-rate gaps, and workouts without usable intensity data can change the estimate.
- The score models training load; it does not measure every source of fatigue, stress, illness, soreness, or recovery.
- Readiness is not an injury prediction and should not override symptoms or professional guidance.
A good fit if
- You train with Apple Watch and want a simple view of accumulated workout strain.
- You want to see why a readiness number changed.
- You prefer a personal baseline over a generic population target.
Probably not for you if
- You have little or no recent workout and heart-rate history.
- You want an app to make an automatic go-or-stop decision for you.
- You need return-to-play, injury, or rehabilitation clearance.
Common questions
The short version.
What does Metric use for Apple Watch readiness?
Metric uses available workout duration and heart-rate intensity to estimate session load, then compares fast-changing recent load with a slower baseline.
Is a higher readiness number always better?
It means accumulated training strain is lower in Metric’s model. It does not capture every physical or life factor, so the number still needs your context.
How long does a baseline take?
The view can respond as workouts arrive, but a longer stretch of consistent data gives the chronic baseline more personal history.
Sources and context
Read the primary material.
Official platform documentation and original research are linked for context, not used to overstate what one app view can conclude.
Apple Support
Monitor your heart rate with Apple Watch
Apple describes how Watch measures heart rate and HRV, along with fit, sampling, and reliability considerations.
Apple Developer
Authorizing access to health data
Official documentation for HealthKit’s category-by-category read and write permissions, including limited-history access.
Journal of Athletic Training / PubMed
Workload and noncontact musculoskeletal injury in collegiate swimmers
A prospective cohort study of one athlete population. It is useful context for load monitoring, not a universal injury rule or individual prediction.
Metric for iPhone
Make your health history easier to use.
Connect only the categories you choose. Start with the dashboard, then go deeper when a trend earns your attention.
Download on theApp Store