One row per day joining readiness, sleep, activity, and recovery signals.

THIS IS THE CROSS-SOURCE JOIN TABLE. Use it first for almost any question about how training/activity relates to recovery — it lets you join Oura against a Strava connector on date in one step. Reach for the detail tools only to drill into a specific signal.

CSV columns (units in the name): date, readiness_score(0-100), sleep_score(0-100), activity_score(0-100), total_sleep_h, bedtime (HH:MM local, when sleep started), resting_hr_bpm (lowest nightly HR, a resting-HR proxy), avg_hrv_ms, resp_rate_brpm (overnight respiratory rate), temp_deviation_c (body temp vs baseline), breathing_disturbance_idx, steps, active_cal, stress_high_min (stressful daytime minutes), day_summary

Defaults to the last 30 days. Higher readiness/sleep/activity scores are better; a rising resting_hr or positive temp_deviation often signals incomplete recovery. resp_rate_brpm and breathing_disturbance_idx are leading illness/strain flags — a sustained rise in either often precedes a subjective sense of getting sick. bedtime is included because sleep timing (not just duration) drives next-day recovery, and it anchors any analysis of late meals/alcohol relative to sleep.

Per-night sleep architecture for drilling into specific nights.

Use after oura_get_daily_summary when you need stage breakdown or sleep timing. One row per sleep period (a day may have a nap plus the main sleep).

Concise CSV columns: date, type, bedtime_start, bedtime_end, total_sleep_h, time_in_bed_h, deep_h, rem_h, light_h, awake_h, efficiency_pct, latency_min, avg_hr_bpm, lowest_hr_bpm, avg_hrv_ms, resp_rate_brpm

Defaults to the last 14 days. Use 'detailed' for raw fields and 5-min hypnogram.

Oura's own bedtime guidance: the optimal bedtime window and what it recommends.

Use to answer "am I going to bed at the right time?" — distinct from oura_get_daily_summary's bedtime, which reports when you ACTUALLY slept. This is what Oura thinks you SHOULD do, derived from your circadian/temperature data.

CSV columns: date, optimal_bedtime_start, optimal_bedtime_end (local HH:MM; blank when Oura couldn't compute a window), recommendation, status

recommendation is one of: follow_optimal_bedtime — you're aligned; keep the current window earlier_bedtime — go to bed earlier later_bedtime — go to bed later earlier_wake_up_time / later_wake_up_time — adjust the wake side instead

status is one of: optimal_found — a concrete window was computed (times populated) only_recommended_found — Oura has a direction but NOT enough data for a window, so the time columns are blank. This is normal, not an error. not_enough_nights / no_data — insufficient history in the range.

Note: Oura returns these times as raw second-offsets from local midnight; this tool decodes them to HH:MM. A run of identical earlier_bedtime rows means Oura has been persistently flagging late sleep timing — worth reading as a trend, not a single day.

Daily readiness with every contributor broken out — explains WHY readiness moved.

Use when oura_get_daily_summary shows a readiness change and you want the driver. Each contributor is a 0-100 sub-score (higher = better). A low hrv_balance or resting_heart_rate contributor the morning after a hard session points to load.

CSV columns: date, score, temp_deviation_c, activity_balance, body_temperature, hrv_balance, previous_day_activity, previous_night, recovery_index, resting_heart_rate, sleep_balance

Defaults to the last 30 days.

Daytime stress load plus long-term resilience — the daytime side of recovery.

Combines the daily_stress and daily_resilience collections into one table so you can see whether high-load days accumulate physiological stress and erode resilience.

CSV columns: date, stress_high_min (stressful daytime minutes), recovery_high_min (restorative minutes), day_summary (restored|normal|stressful), resilience_level (limited|adequate|solid|strong|exceptional), sleep_recovery, daytime_recovery, stress (the last three are 0-100 resilience contributors).

Defaults to the last 30 days. Resilience needs ~weeks of data to populate.

Workouts as logged by Oura — cross-check or supplement Strava activities.

Oura auto-detects and lets you tag workouts. Use this to reconcile against a Strava connector (match on date/time), catch sessions Strava missed, or see Oura's intensity label. source shows how the workout was recorded (e.g. auto_detected, manual, confirmed).

CSV columns: date, activity, intensity (easy|moderate|hard), start_time, end_time, duration_min, distance_km, calories, source, label

Defaults to the last 30 days.

Slow-moving health baselines: SpO2, breathing, arterial stiffness, VO2 max.

These trend over weeks/months, so the default window is 90 days. Use to track long-term vascular and aerobic health alongside training history.

CSV columns: date, spo2_avg_pct (overnight blood-oxygen %), breathing_disturbance_index, pulse_wave_velocity_m_s, vascular_age_years, vo2_max_ml_kg_min

pulse_wave_velocity_m_s (meters/second) is the RAW arterial-stiffness measurement and the most clinically meaningful vascular metric here — lower is better/more elastic; it is the same class of measure (PWV) used in hypertension research. vascular_age_years is a derived presentation of it, so prefer PWV when tracking real change.

IMPORTANT — sparsity: vo2_max is a measurement EVENT, not a daily value (often only a handful of readings per quarter), so most rows will be blank for it. Blank does NOT mean "no VO2 data" — read the footer, which reports the most recent reading in the window. cardiovascular_age/VO2 also require a compatible ring/firmware.

Fine-grained heart-rate timeseries (one sample every few minutes).

This is high-volume data, so default to a SHORT window (<= ~2 days) and the 'summary' format. Oura tags each sample with a source: awake, rest, sleep, workout, etc. — useful for isolating workout HR or overnight resting HR around hard training days.

summary -> CSV with one row per source: source, samples, min_bpm, avg_bpm, max_bpm, plus a final 'all' row. raw -> CSV: timestamp, bpm, source (capped at limit; narrow the window for more).