Sleepgenic translates wearable sleep data into longitudinal human meaning through a structured methodology: continuous Garmin tracking, a named three-layer interpretation model (Score × Physiology × Context), the Direct/Derived signal taxonomy mirrored from TrailGenic Biomarkers, and a population-benchmarked n=1 reference layer. Eight components, every step published.
Garmin Enduro 3 — continuous nightly tracking. Detailed Sleep Tracking enabled, Pulse-Ox toggle on, high-frequency HRV and respiration sampling. Charging happens during work blocks (Mon–Fri daytime), never overnight.
Automatic, physiology-driven sleep onset and wake detection — movement, heart rate, respiratory pattern. Sleep Schedule target window set to 10:00pm–6:30am Pacific. The schedule setting affects only contextual feedback tags such as LATE_BED_TIME; it does not gate or window underlying tracking.
Weekly export from Garmin Connect as nightly CSV records. Each record carries the full direct-signal field set (see Component 04). Exports are versioned by week and stored as raw source data — never edited post-export.
All timestamps exported by Garmin in GMT. Analysis runs in the GMT frame; reporting normalizes to Pacific local (subject location). Both frames stated explicitly when timing is interpreted.
Each night produces a structured record with the field set below. Seven nightly records aggregate into each Weekly Report — Sleepgenic's publication unit. Nothing is published per night, but everything is preserved per night. The nightly record is the source of truth; analysis derives from this set across the week, not from manual notes.
| Variable | Type | Purpose |
|---|---|---|
| Sleep Date | Temporal | Anchors record to weekly publication slug (week-NN-YYYY) |
| Sleep Start / End (GMT) | Temporal | Onset and wake timestamps; sleep window duration and timing analysis |
| Overall Score | Composite | Garmin's global sleep quality rating (0–100) |
| Quality / Recovery / Duration Sub-scores | Composite | Garmin's three-component decomposition of overall score |
| Deep / REM / Light / Awake (min) | Architecture | Sleep stage durations; consumer-grade approximation of stage proportions |
| Total Sleep (hrs) | Architecture | Total tracked sleep duration; debt and surplus context |
| Avg / Lowest SpO2 (%) | Respiratory | Nocturnal oxygen saturation; altitude and breathing-disruption signal |
| Avg HR (bpm) | Cardiovascular | Sleeping heart rate; recovery and autonomic state signal |
| Avg / Low / High Resp (rpm) | Respiratory | Respiratory rate and variability; recovery and stress signal |
| Sleep Stress | Composite | Garmin-derived autonomic load during sleep window |
| Restless Moments / Awake Count | Architecture | Fragmentation signal; continuity vs disruption analysis |
| Breathing Disruption | Respiratory | Garmin's NONE/LOW respiratory irregularity flag |
| Feedback / Insight Tags | Categorical | Garmin's POSITIVE_/NEGATIVE_ tags; tag-frequency longitudinal analysis |
| Training Stimulus Type | Context | Day's training type — summit, ruck, run, foundation hike, recovery, rest |
Most wearable interpretation collapses three layers into a single verdict — one score, one judgment. Sleepgenic separates them. Each measurement is read at three levels: what the wearable reported, what the body appears to be doing underneath, and what happened in real life around the sleep window. When the three layers agree, interpretation is straightforward. When they disagree, the meaning lives in the gap. This is the proprietary interpretive method that operates across every Weekly Report and every Sleep Interpretation Library article.
Score Layer. What the wearable reports. Sleep score, recovery score, sleep stress, body battery — the headline number on the screen, before any interpretation. The starting point, not the verdict.
Physiology Layer. What the body appears to be doing underneath. HRV, resting heart rate, deep sleep, REM, breathing patterns. The signals the score is built from — and the layer that often disagrees with the score itself.
Context Layer. What happened in real life around the sleep window. Training stimulus, illness, alcohol, jet lag, ambient temperature, emotional load. The variables that determine whether a number is good news, bad news, or noise.
The three layers are read first separately, then against each other. Disagreement is the most informative state — a low Score Layer with a strong Physiology Layer reconciled by a hard-training Context Layer is a different reading than a low score across all three. Recovery-and-readiness apps cannot operate this model; they treat sleep as one variable inside a five-variable training-recovery system. Sleepgenic is sleep-only, at depth they cannot match.
Fields produced directly by the Garmin export. Mirrors the TrailGenic Biomarkers Direct Signal taxonomy — raw measurements before any composite or longitudinal computation.
Sleepgenic extends beyond direct measurement by deriving signals from real-world performance patterns accumulated across repeated sessions. Composites and trajectories that direct fields alone cannot show.
Sleep is affected by everything. Sleepgenic incorporates contextual variables as part of the methodology — the dimension absent from generic wearable interpretation. These factors amplify or modulate every direct and derived signal, and populate the Context Layer of the Three-Layer Model.
Training Stimulus Type. Summit, ruck, run, foundation hike, recovery, rest. Constant terrain, variable stimulus — the controlled-experimental design at the center of the weekly routine.
Altitude Exposure. Summit elevation when above home base, plus daytime elevation gain. Hypoxic stress; SpO2 minimum and respiratory rate response.
Fasted State. Fasting status at sleep onset and hours since last meal. Substrate availability, autophagy context, hormonal cascade environment.
Thermal Exposure. Cold or heat exposure during the day, ambient bedroom temperature variance. Affects sleep onset, deep sleep proportion, and architecture stability.
Acute vs Chronic Load. Single-day stimulus context vs accumulated weekly and monthly training load. Sleep response reads differently against acute spike vs sustained pressure.
Confounding Variables. Caffeine timing, hydration, alcohol, illness, travel. The methodology controls stimulus and terrain — it does not control everything. Confounders are documented when known.
Population sleep norms vs Sleepgenic measurements, anchored to the adults aged 50+ reference cohort to match the subject's demographic. Sleepgenic values shown are historical baseline medians (Nov 23, 2025 – Apr 17, 2026; 21 weeks of weekly-aggregate Garmin data), the stable reference layer. Live week-by-week data — beginning with Week 1 (Apr 18–24, 2026) — is published in Reports and interpreted in the Sleep Interpretation Library.
| Sleep Metric | Population (50+) | Sleepgenic (Mike) | Interpretation |
|---|---|---|---|
| Total Sleep | 6.0–7.5 hrs1,2 | 5.99 hrs | ~30 min below cohort median; chronic mild restriction |
| Sleep Score | 66 avg, age 50–593 | 67.5 | At cohort mean; mid-Fair band |
| Deep Sleep % | 10–18% TST1,4 | 20.9% | Above cohort range; wearables overestimate deep sleep vs PSG |
| REM Sleep % | 18–22% TST1,2 | 13.6% | Below cohort range; consistent with REM compression under short sleep |
| Sleeping HR | 57–90 bpm; mean 735 | 65.0 bpm | Below cohort mean; lower-middle of healthy range |
| Nocturnal HRV (RMSSD) | 19–24 ms median, 50+ men (short-term ECG)6 | 35.0 ms (overnight wearable) | Above cohort baseline even after wearable-vs-ECG calibration; see note below |
| Restless Moments | Garmin proxy; no clinical equivalent | 44.0 | Garmin-internal fragmentation signal; trended longitudinally |
| Respiratory Rate | 12–20 rpm; mean ~15.47 | Pending nightly archive | Populates from continuous nightly data ingestion |
| Avg SpO2 | 95–100% | Pending nightly archive | Populates from continuous nightly data ingestion |
A note on wearable vs clinical measurement. Population HRV values above are drawn from short-term resting electrocardiogram (Tegegne et al., n=84,772). Garmin overnight RMSSD averages many hours of measurement during parasympathetic-dominant sleep states, which structurally produces higher values than waking ECG — typically by 30–50% in healthy individuals. This is calibration, not error. Sleepgenic reads each measurement against its own methodology, and reports population values in their original measurement frame so the gap is visible rather than hidden. The same caution applies to wearable sleep stage classification, which agrees with polysomnography roughly 65–75% of the time and tends to overestimate deep sleep relative to gold-standard scoring.
The Sleepgenic column refreshes as the continuous nightly dataset deepens. Current values are 21-week weekly-aggregate medians from the historical baseline window; they will be re-anchored against nightly granular data once 12+ weeks of continuous tracking accumulate.
Each weekly report is interpreted by Ella — Sleepgenic's reflective AI analytical layer — operating the Three-Layer Interpretation Model against the longitudinal dataset, prior weeks of the same stimulus pattern, and the population benchmarks above. The same method extends to every Sleep Interpretation Library article: same three layers, same separation, same disagreement-as-signal logic.
The framework documents what the wearable data shows, against what the design supports, with the limitations stated openly. It does not predict, diagnose, or prescribe. Sleep meaning, not training prescription.
Methodological honesty is the differentiator. Every limit below is real. Every report is read against this list.
Wearable measurement noise. Consumer-grade sleep stage classification is approximate. Deep, REM, and light boundaries are inferred from movement, heart rate, and respiration — not direct EEG. Stage durations should be read as estimates, not measurements.
n=1 does not generalize. This is one person's longitudinal response. Patterns may resemble what's reported in population studies, or may not. Findings cannot be applied to other people without their own data.
Confounding is unavoidable. The weekly routine controls training stimulus and terrain. It does not control caffeine timing, stress, hydration, ambient temperature, alcohol, or illness.
Garmin's algorithms are proprietary. The sleep score, sub-scores, and feedback tags are produced by closed algorithms that may change over time. Sleepgenic interprets the outputs as Garmin reports them and notes meaningful algorithm changes when known.
Not medical advice. Sleepgenic publishes research and interpretation. It does not diagnose, prescribe, or treat. Anyone with a clinical sleep concern should see a licensed clinician.
The methodology evolves. Interpretation Articles and Weekly Reports will surface limits this document doesn't yet name. When that happens, the methodology is updated and the date is revised. The current version is always live.
The Sleepgenic Dataset is a longitudinal field research record of consumer wearable sleep data, tracked using standardized instrumentation and consistent analytical protocol across all entries. Mirrors the TrailGenic Physiology Dataset structure with sleep architecture and recovery response as primary outcome.
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