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Overview

Readiness Scores give you a daily signal for how prepared a user is to take on load, based on their recent sleep and overnight physiology. This helps you track recovery trends, identify rising stress or strain, and understand whether sleep patterns are supporting day-to-day performance. The derived_readiness table returns one row per user and calendar day with:
  • sleep_score
  • recovery_score and recovery_zone
  • stress_score
  • strain_score and strain_zone
  • chronotype
Junction Sense computes these metrics for you, so you can query readiness directly without building custom scoring pipelines.

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What you can do

  • Pull one scored row per day for charting or downstream analytics.
  • Filter for specific cohorts (for example, low recovery or high strain days).
  • Group and aggregate scores like any other table.
DerivedReadiness.index() is a synonym of DerivedReadiness.col("date").

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How calculations work

Readiness Scores are computed from each user’s daily long sleep session using:
  • sleep duration
  • resting heart rate during sleep
  • HRV during sleep
  • Junction sleep score

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Baseline and warm-up

  • Recovery and stress are personalized against a rolling baseline from prior nights.
  • Baseline window is 21 days, with a minimum of 7 prior observations.
  • Before enough history exists, recovery_score, stress_score, and strain_score can be null.

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Recovery score (0-100)

Recovery combines three signals:
  • HRV relative to personal baseline (higher is better)
  • resting heart rate relative to baseline (lower is better)
  • sleep duration relative to an 8-hour target
The combined signal is mapped to a 0-100 scale with a logistic curve.
Higher recovery_score means better readiness.

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Stress score (0-100)

Stress is the inverse of recovery:
  • stress_score = 100 - recovery_score
Higher stress_score means greater physiological stress burden.

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Strain score (0-21)

Strain combines:
  • recovery load (how far recovery is below a neutral threshold), and
  • sleep load (sleep debt relative to 8 hours).
These are combined and exponentially scaled to a 0-21 score.
Higher strain_score means greater expected load/strain.

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Missing-data behavior

  • Missing HRV or resting heart rate: recovery_score and stress_score are null.
  • Missing sleep duration: strain_score is null.
  • sleep_score is returned when available.
This section describes the current Readiness model. If you need deeper methodology details for your use case, contact the Junction team.

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Available columns and meanings

ColumnMeaning
dateDaily index (YYYY-MM-DD).
chronotypeDaily chronotype category from sleep timing patterns.
sleep_scoreSleep quality score (0-100), where higher is better sleep quality.
recovery_scoreReadiness score (0-100), where higher means better recovery readiness.
recovery_zoneRecovery band: Red (0-33, low), Amber (34-66, moderate), Green (67-100, high).
stress_scoreStress score (0-100), where higher means higher physiological stress load.
strain_scoreDaily strain (0-21), where higher means greater combined sleep/recovery load.
strain_zoneStrain band: Low (0-6), Moderate (7-13), High (14-17), Very High (18-21).

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Example: Select daily scores

import vitalx.aggregation as va

va.select(
    va.DerivedReadiness.col("date"),
    va.DerivedReadiness.col("sleep_score"),
    va.DerivedReadiness.col("recovery_score"),
    va.DerivedReadiness.col("stress_score"),
    va.DerivedReadiness.col("strain_score"),
    va.DerivedReadiness.col("recovery_zone"),
    va.DerivedReadiness.col("strain_zone"),
)

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Example: Weekly aggregates

import vitalx.aggregation as va

va.select(
    va.group_key("*"),
    va.DerivedReadiness.col("recovery_score").mean(),
    va.DerivedReadiness.col("stress_score").mean(),
    va.DerivedReadiness.col("strain_score").max(),
).group_by(
    va.date_trunc(va.DerivedReadiness.index(), 1, "week")
)

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Example: Filter for high-strain days

import vitalx.aggregation as va

va.select(
    va.DerivedReadiness.col("date"),
    va.DerivedReadiness.col("strain_score"),
    va.DerivedReadiness.col("strain_zone"),
    va.DerivedReadiness.col("recovery_score"),
).where(
    "strain_score >= 14"
)