From raw signals to your why.
Four stages turn the noise from your wearables, CGM and calendar into one decision you can act on before noon — and we show our work the whole way down.
Every signal, one timeline.
LockIn90 ingests the streams you already generate — wearables, Apple Health, your CGM, your calendar, and the moments you log in three taps — and aligns them on a single, timezone-aware event timeline.
Wearables — Oura, WHOOP, Garmin & Polar push sleep, HRV (ms), RHR (bpm), strain & readiness.
Apple Health & Health Connect — a unified bridge across iOS and Android sources.
CGM — Dexcom & Freestyle Libre glucose curves feed the metabolic effect models.
Calendar & logged events — meetings, training, caffeine, meals, mood — each a timestamped node.
5 source categories · backfills 30d on connect.
Oura · Apple Health · Dexcom CGM · Calendar · Logged events → UNIFIED TIMELINE
Caffeine 180mg @ 16:00 (t½ ≈ 5h) · Glucose post-meal spike (peak +45m) · Sleep debt 6h vs 7.8h base (drag all-day)
Every event becomes a curve, not a flag.
A caffeine at 4pm isn't a yes/no — it's a rising-then-decaying influence on your alertness. LockIn90 runs each event through a family of effect generators built on pharmacokinetic (PK) half-life models, then projects them onto a 34-slot circadian curve.
PK / half-life models — caffeine ~5h, alcohol clearance, glucose rise & dip each decay on their own clock.
Effect generators — modular, versioned families (caffeine v2.3, sleep-debt v1.8) that compose per event.
34-slot circadian curve — your day is modeled in ~42-minute slots so peaks and dips land where they truly are.
Superposition — overlapping effects sum into a single net influence on each slot.
model v4.2 · 34 slots / day · 12 generator families.
One number, the whole curve, 31 days out.
The superposed effects collapse onto your personal baseline to produce your energy curve, a single 0–100 score, and a 31-day forecast from your scheduled events and habits.
Energy curve — the 34 slots rendered as the shape of your real high and low windows.
Score — HIGH ≥ 67, MODERATE 34–66, LOW < 34, zoned against your baseline.
31-day forecast — runs scheduled events & recurring habits through the same generators ahead of time.
SCORE 84 · 31-day horizon.
Energy 84 · 34-slot curve peaks late morning, dips mid-afternoon (3PM), recovers evening; dashed forecast projects tomorrow.
Morning training +6 · Late caffeine −9 · Short sleep (6h) −7 · Insight: move caffeine cutoff to 13:00 → est. +8 by 3PM.
The score moved. Here's exactly why.
A number you can't explain is just anxiety. LockIn90 traces every point up or down to a cause — then turns that attribution into insights and, if you choose, a privacy-gated coach view.
Attribution — late caffeine −9, short sleep −7, morning lift +6 — quantified, not guessed.
Insights — repeating loops surfaced: "afternoon dips follow late espresso 4 days in 5."
Coach — share a read-only energy view; coaches see trends, never your raw private logs.
show your work · privacy-gated coach.
A model that learns you.
You get a score on day one — but confidence grows as the model replaces population priors with your own physiology. No averages. Your baselines, your thresholds.
- Day 1: First score using population priors — 41% confidence.
- Day 7: Your sleep & HRV baselines forming — 63% confidence.
- Day 14: Effect sizes tuning to your responses — 79% confidence.
- Day 30: Fully personalized — attribution sharpens — 96% confidence.
Confidence over time
Model confidence climbs from 41% on day one to 96% by day 30 as more of your data calibrates it.
We don't claim it. We test it.
Before an effect generator ships, it's validated against real cohort outcomes with paired statistics — so the numbers you act on are signal, not story.
Paired t-tests
Each candidate generator is replayed against a held-constant cohort — version A vs version B on the same days — so we measure the effect, not the noise. Paired · within-subject · n = 1,284 days.
p-values & significance
We only promote a change when it clears a real threshold. The late-caffeine penalty: p = 0.003, well under 0.05. α = 0.05 · two-tailed.
Effect sizes
Significance isn't enough — we report how big the effect is. Cohen's d quantifies whether a driver actually matters to your day. Cohen's d = 0.62 · medium–large.
A/B replay · caffeine v2.3
Distribution of paired score deltas, late-caffeine days (μ ≈ −8.4) vs matched controls (μ ≈ 0). p = 0.003 · significant.
From raw signals to your why.
Connect a source, log a few moments, and get your first fully decoded day — score, curve and the why — by tomorrow morning.
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