Add a short-horizon OLS projection over the DAY-native per-day mean
series, extending the existing trendSlope. The fit, residual standard
error and prediction interval are computed at DAY granularity only — the
rollup invariant forbids composing a slope/sd from WEEK/MONTH buckets.
The projection is gated on fit quality (R squared), in-window history and
staleness: below any floor the engine returns insufficient rather than a
weak line, so noise is never extrapolated. The prediction-interval band
widens with the horizon distance from the data centre, so the fan
visibly communicates uncertainty.
Register TRAJECTORY in the derived registry and dispatch (additive,
iOS-forward-compatible; the capabilities endpoint and drift-guard pick it
up automatically). Surface it on the weight detail page as a forecast
band beneath the chart, labelled a projection with the cited method and
a conditional, range-only headline — never a prediction or a dated
event. i18n across all six locales.
The sleep midpoint was minutes-of-day in UTC, so a non-UTC sleeper's
consistency and timing sub-scores drifted with the offset (a 03:00-local
midpoint read as a different clock time than a UTC user's). Resolve the user's
stored zone in computeSleepScore and express the midpoint against it via the
existing tz helpers; reconstructNights takes an explicit tz (UTC default) so
the pure path stays test-pinnable.
Register four new derived metrics for the additive HealthKit signals:
- WRIST_TEMPERATURE_BASELINE, STAIR_ASCENT_SPEED_BASELINE,
STAIR_DESCENT_SPEED_BASELINE — any-user-baseline personal bands reusing
the type-generic median +/- k*MAD engine via a new BASELINE_CAPABLE_TYPES
allowlist. Wrist temperature is a personal-deviation band only (no illness
or cycle inference); stair speeds are personal trend bands (no population
cutoff, geometry-confounded).
- SIX_MINUTE_WALK_BAND — passthrough re-frame placing the device-estimated
distance against the Enright & Sherrill 1998 predicted 6MWD (ATS 2002
test standard), as percent-of-predicted with a green/yellow/red band;
band null when age/height/weight/sex are incomplete.
Fall count and the breathing-disturbance index stay trend-only by design,
documented in the registry: fall count is a zero-inflated discrete safety
event, the breathing index is a regulated screening signal where a derived
band would imply a diagnosis.
Note wrist temperature as a deferred correlation outcome channel held
pending a privacy review (cycle-phase inference risk), mirroring the
medication-compliance omission note.
Add provenance + standards entries, method/caveat copy across all six
locales, and tests for the engine routing, the Enright equation, and the
percent-of-predicted band.
Register RECOVERY_SCORE / STRESS_SCORE / STRAIN_SCORE as derived metrics that
read the nightly-persisted COMPUTED Measurement rows — a passthrough, never a
recompute. The dispatcher routes the three ids to a thin reader that returns
the latest score, a trailing-window trend, and a direction-aware band (stress
and strain invert, so a high stress reads red). This lets every surface — the
dashboard, the Coach snapshot, the doctor report — consume the scores through
the one Derived<T> route + hook rather than each growing its own read path.
Regenerate the OpenAPI enum, which picks up the three ids from the registry.
Resolve the overlap where the vitals tier and the composite tier both extend
the derived-metric registry, dispatch, query-key factory, i18n bundles and the
OpenAPI contract: every derived metric (vitals baseline, fitness/vascular age,
HRV balance, BMI, sleep score, readiness, coincident-deviation) is now wired and
implemented, the dispatch test asserts the full set, and the locale bundles carry
both the vitals and the score-anatomy/composite strings.
Build the composite tier of the derived-metrics layer on the shared seam:
- Sleep score — a transparent 0–100 composite from sufficiency, efficiency,
consistency, timing and composition sub-scores over the per-stage sleep
rows, reweighting around any sub-score the data cannot support (a legacy
asleep-only night drops composition gracefully rather than fabricating it).
- Wellness/readiness index — extends the health-score null-redistribution
blend with resting-HR, HRV (SDNN), sleep, respiratory-rate and mood
deviation-from-baseline components; returns insufficient below two present
components so it never headlines from one signal.
- Coincident-deviation flag — counts vitals outside their personal band on
the latest day and fires when two or more coincide, listing the
contributing vitals as possible factors, never a cause or a diagnosis.
Each composite cites its standard, carries coverage + confidence +
provenance, and reweights around missing inputs.
Add an FDR-controlled correlation discovery engine behind
GET /api/insights/correlations: it scans a curated behaviour x outcome
matrix, lag-joins each behaviour day to the next day's outcome, runs Pearson
with the exact Student-t p-value, and applies Benjamini-Hochberg control
across every tested pair so only statistically-defensible patterns surface —
each tagged with n, r, p and the adjusted q, framed descriptive not causal.
Add the score-anatomy detail view at /insights/scores/<metric>: a full
score ring, ranked contributor rows each showing its push/pull and weight
share, and the cited-standard provenance explainer — reused by every
composite, mobile-first and accessible.
Regenerate the OpenAPI contract for the new metric values + the correlation
discovery response, and add the i18n keys across all six locales.
Build the Apple-Health-Highlights grid of personal-vital tiles on the
Insights overview, backed by four new pure derived-metric compute
functions over the established seam:
- cardio-fitness band ("Fitness Age" frame) — VO2max placed against the
FRIEND-registry age/sex norm (Kaminsky 2015), surfaced + trended, never
re-derived.
- vascular-age delta — Withings vascular-age vs chronological age, a
relative wellness indicator (Vlachopoulos 2010 PWV framing), no medical
claim.
- HRV (SDNN) balance — reuses the typical-range engine for the band and
places the recent average as balanced / above / below; SDNN never
relabelled RMSSD; cadence-gated.
- BMI from weight + height — exact WHO classification, the fallback when
no device BMI exists.
Each metric carries coverage + confidence + provenance and cites its
standard through the provenance explainer. The dashboard renders one
SparklineDeltaTile per available vital: an absent vital does not render,
a still-building one shows the honest coverage meter rather than a
headline from too little history. Tiles read the generic
/api/insights/derived route through a TanStack Query hook keyed off the
centralised factory.
Regenerates the OpenAPI contract for the two new metric ids and adds the
vitals copy across all six locales.
Introduce src/lib/insights/derived/ — the shared, compute-once layer for
derived wellness metrics. Core pieces:
- types.ts: the Derived<T> discriminated union (ok carries value +
coverage + confidence + provenance; insufficient carries coverage +
provenance + reason). Client-safe — pure types, no server imports.
- coverage.ts: deriveCoverage() + the buildOk/buildInsufficient terminal
constructors. Confidence blends the history fraction and the
input-presence fraction so it drops monotonically as inputs go missing.
- registry.ts: the closed DERIVED_METRIC_ID enum + per-metric metadata
(inputs, archetype, min-history / min-inputs floors, implemented flag).
- norms.ts: age/sex reference-range tables (VO2max FRIEND registry,
resting-HR + respiratory-rate by age) behind lookupNormalRange().
Age + sex only — no ancestry/region ranges.
- baseline.ts: the flagship engine — computeVitalsBaseline() returns the
personal typical-range band (median +/- k*MAD) for one vital. Center
reads the composable DAY rollup mean; the spread is DAY-native only
(the sd/slope/r2-don't-compose invariant), with a per-type bounded
live-SQL fallback on a coverage miss.
- dispatch.ts: maps a DERIVED_METRIC_ID to its compute function; stubbed
metrics return an insufficient with reason not_implemented.
- index.ts: the append-only public barrel.
Unit tests cover the coverage model, the Derived<T> ok/insufficient
branching, the norms lookup, the baseline ok / insufficient /
coverage-miss-fallback paths + the composed-vs-raw-DAY band parity, and
the dispatch stub guard.
Marc-André Bombeck