Record discrete biomarker readings (HbA1c, LDL, ferritin, TSH, …) with
their value, unit, reference range and sample date. The store pairs with
the annual blood-panel reminder, which until now had nowhere to record
the result it reminds the user to obtain.
The list/create/update/delete endpoints narrow userId from the session,
build the write data field-by-field, soft-delete via deletedAt, and
encrypt the optional note at rest. A neutral three-state reference-range
classifier (in-range / below / above / unknown) is computed
server-side so every surface renders the same calm verdict.
findLatestLabResultForAnalytes exposes the read point the blood-panel
reminder consults to mark itself satisfied, without coupling the two.
Register the /api/measurement-reminders surface in the OpenAPI table and
regenerate the committed spec so the iOS client consumes the
MeasurementReminderDTO contract from a single source.
Add the Vorsorge page and section: an upcoming-reminders list sorted by
server-computed next-due, with a create form (measurement-linked or
free-text, cadence, notify hour, location) and a per-card Erledigt and
delete action. Cards stay neutral-coloured regardless of due state — the
status reads through a discreet badge only, never an alarming tint. Copy
ships in all six locales.
Register the MEASUREMENT_REMINDER event type (default on at the channel
layer; the real gate is the per-reminder enabled flag) and add the
notificationPrefs.measurementReminder.clientManaged sub-object plus its
cron-side resolver, mirroring the medication and cycle client-managed
precedent. The event rides the generic APNs category path and stays
non-time-sensitive, so it delivers as a plain banner with no client
change.
Add the measurement-reminder cron, mirroring the mood-reminder shape: a
pure due-predicate (past nextDueAt AND inside the reminder's local
notify-hour) and a tick runner that dispatches through the existing
notification cascade. Auto-resolve runs in the cron, not on the ingest
hot path: a matching reading of the reminder's type advances
lastSatisfiedAt and skips the nudge (BP on the SYS sentinel; free-text
reminders resolve only on a manual satisfy). Dedup is the nextDueAt
advance past now, so no ledger table is needed. The queue is registered in
the all-queues list, scheduled, and bound to its worker; a registration
guard test pins that wiring.
Add /api/measurement-reminders (list, create) and /{id} (get, patch,
delete) plus /{id}/satisfy. Every route wraps in apiHandler, parses with
Zod, narrows userId from the session, and builds the data object
field-by-field. Create and patch recompute the server-authoritative
nextDueAt; satisfy stamps lastSatisfiedAt and re-anchors the next-due.
Delete soft-deletes for tombstone parity.
Add the Vorsorge (measurement) reminder cadence layer: a thin adapter that
maps a reminder onto the canonical recurrence engine so a rolling
"every N days" or an RFC-5545 rrule cadence is driven by the same code the
medication next-due line uses. nextDueAt is computed and stored on the
server, so web and iPhone read identical numbers. Covers the DTO serializer
and the create/update validation schemas (exactly one of intervalDays or
rrule; BP auto-resolves on the SYS sentinel).
The add form already defaults the timestamp to now and accepts any earlier
instant — the plausibility bound and the picker's max both block the future —
but many users never notice they can change it. Add a one-line hint under the
date/time field so logging a past reading is discoverable, and pin the
manual-create contract with a guard asserting the single POST schema strips an
externalId (the stats:-prefix overwrite stays batch-scoped).
A self-hoster migrating from a spreadsheet or a meter export overwhelmingly
has CSV, which neither the JSON nor the Apple Health import covers. Add
`POST /api/import/csv` reusing the existing write loop and rollup re-fold.
The documented header is `type,value,unit,measuredAt[,glucoseContext,notes,
externalId]`, order-independent. measuredAt must carry an explicit ISO-8601
offset — a row without one is skipped rather than guessed — and is bounded by
the shared entry-instant validator. Glucose accepts mmol/L (converted to
canonical mg/dL) and weight accepts lb (converted to kg); any other
non-canonical unit is skipped, never mis-stored. An optional externalId
column makes a re-upload idempotent; without it a re-upload duplicates.
The response carries a per-row status envelope so a 2000-row file with three
bad rows imports 1997 and names the three. `?dryRun=1` validates and previews
the outcome without writing. The parse, validation, and unit conversion live
in a pure module so they are unit-tested without a database.
Settings -> Export & Import gains a third CSV card mirroring the JSON and
Apple Health cards, with upload, paste, preview, and a downloadable example.
The new route is documented in the OpenAPI contract.
The generic JSON import validated measurement `measuredAt` with a bare
`z.string().datetime()` — no ceiling — so a crafted payload could forward-date
a reading, the gap the single-POST plausibility bound already closed. Route
the measurement and mood instant fields through the shared
`validateEntryInstant` (no future beyond a 5-minute skew, no instant before
1900).
Measurement rows were also always created with a NULL externalId, which is
distinct in the `(userId, type, source, externalId)` unique key, so a
re-import duplicated the whole set. Give measurements the same optional
externalId upsert mood already has: present -> idempotent upsert, absent ->
first-write-wins create.
A collapsible "About your health" card on the baseline step captures
the genuinely-new baseline the app can act on — chronic conditions and
allergies — without re-asking the height/sex/birthdate already gathered
above. Both write through the existing encrypted self-context path
(PUT /api/coach/about-me), so there is no new model and no new
contract; the form preserves any existing free-text about-me on save
and only sends a field the user actually typed.
The card is collapsed by default with a clear optional framing, so the
step stays clear and the progress bar keeps its five dots; every field
is editable later in Settings. Copy is warm and medically grounded —
context, not a clinical form — with keys across all six locales. Also
re-truths the goals body copy now that the selection genuinely surfaces
on the dashboard.
Wire the goals step to actually keep its promise. The picker now posts
the chosen slugs on the step-2 submit; the step endpoint validates them
against the closed slug enum (422 on an unknown slug), persists them to
User.onboardingGoals field-by-field, and on completion seeds
dashboardWidgetsJson from the selection — promoting the goal-mapped
tiles to the top and forcing them visible.
The seed is one-time, gated on a null dashboardWidgetsJson via the
updateMany WHERE clause, so a user who already arranged tiles is never
clobbered. An empty or general-wellness-only selection leaves the
column untouched (default layout). The resulting layout is what both
the web dashboard and the iOS widgets contract read — no client
recompute. Skip posts an explicit empty array (deliberate no
preference).
Polar and Oura resolved their OAuth client id/secret from the server
environment only, forcing every user on a deploy to share one app. Both
now read per-user BYO keys first, falling back to the shared env app when
unset, mirroring the WHOOP and Fitbit model.
- Add DB-first-then-env resolvers in src/lib/{polar,oura}/credentials.ts,
encrypting the client id/secret at rest on User.
- Repoint the connect, callback, status routes and the Oura refresh path
to the user-scoped resolver.
- Add /api/{polar,oura}/credentials (GET/PUT/DELETE) with Zod-validated
input and userId narrowed from the session.
- Render an optional 'your OAuth app credentials' form on the Polar and
Oura cards; the status read exposes hasOwnCredentials for the saved
placeholder. Existing env-only deploys keep working unchanged.
- Cover both resolvers (DB hit, env fallback, no creds) and the card form
with unit tests; add the new strings across all six locales.
The WHOOP timeline reconstruction and the Withings END-stamp change the
stored measuredAt (and, for WHOOP, the externalId), so existing rows
keep the old broken shape until re-synced. An in-place update collides
with the (userId, type, measuredAt, source, sleepStage) unique index, so
the backfill deletes the affected SLEEP_DURATION rows for the source and
re-syncs from scratch; the re-sync re-folds the rollup tier in its tail.
Add a boot-time pg-boss one-shot per connection, modelled on the WHOOP /
Fitbit backfills: discovery enqueues one job per (user, provider) where
sleep_timeline_backfill_at is null, the per-user pass deletes + re-syncs
and stamps the marker, and the pass is idempotent across reboots. The
queue is registered in allQueues, wired to a handler, and enqueued at
boot.
Withings sleep sync stamped each segment's measuredAt with its startdate,
but every reader treats measuredAt as the segment END and resolves the
span as start = end − duration. Each night was therefore rendered one
segment-length earlier than reality. Stamp the enddate so the span lands
at its true clock time.
The Google sleep payload carries a real per-stage segment series, but the
mapper summed each stage's segments into one row stamped on the stage's
last end. The hypnogram then reconstructed all of a stage's time as a
single block ending at the night's right edge — the same stacked-bar
artefact as WHOOP, despite the source carrying true timing.
Emit one row per segment with measuredAt at that segment's end and an
indexed externalId so the several segments of one stage stay distinct
under the dedup key. The timeline is measured (real onsets), so these
rows are not flagged reconstructed.
WHOOP's v2 API returns only per-stage sleep duration totals — no onset
timestamps, no hypnogram endpoint. The mapper stamped all five stage
totals on the one sleep-END instant, so the hypnogram reconstructed
every stage as a span touching the night's right edge: bars stacked
right-aligned with no clock times whenever WHOOP was the sole source.
Lay the asleep stages back-to-back from sleep onset in a fixed
physiological order (AWAKE lead, then CORE → DEEP → REM), emitting one
timed row per segment with measuredAt at that segment's end and a
distinct indexed externalId. IN_BED stays a single envelope row over the
whole window. The existing reader and renderer are untouched — they now
see distinct per-segment instants and place each block at its own time.
The order is synthesised, not measured, so every reconstructed row is
flagged and the night DTO carries a per-session `reconstructed` boolean
(GET /api/sleep/night, regenerated OpenAPI). The client renders the
hypnogram but labels it an approximate layout and never recomputes;
real-series sources (Apple Health, Withings, Fitbit) stay false.
When both the sleep-debt and chronotype cards carry a settled readout, render
them two-up on large screens instead of stacked; they are compact and read
better paired. A lone data-bearing card — or either card in its learning /
partial state — keeps full width so it never leaves a half-width orphan. The
grid collapses to one column below the lg breakpoint and uses items-start so a
toggled-open chronotype card does not stretch its neighbour.
The mobile settings test asserted three integration pills; Polar, Oura and
Nightscout bring the list to six. Remove the unused Oura and Polar field-map
documentation constants and the unused advanced-indices interface the dead-code
gate flagged.
Surface WHOOP's native day strain to the coach on its own 0-21 scale, distinct
from the computed strain score, and instruct the model to prefer the device
number — the gold-standard signal a WHOOP user connected was previously invisible
to the coach. Document the glucose clinical block in the prompt so the model reads
time-in-range, GMI, estimated A1C and variability correctly and never asserts them
while still learning or off spot readings, and extend the non-English unit
vocabulary to cover glucose. Centralize the glucose panel window and run the
independent derived reads concurrently.
Add WHOOP-native DAY_STRAIN (0-21) to the Coach snapshot as a dedicated
dayStrain block, kept distinct from the computed STRAIN_SCORE (0-100) and
preferred native-over-computed, mirroring recovery resolution. Document the
glucose.clinical panel (TIR/GMI/eA1C/CV) plus the stillLearning and
isSpotEstimate gates in the system prompt, and the native dayStrain scale in
the derived paragraph. Extend the DE and FR/ES/IT/PL evidence-block unit lists
with mg/dL, mmol/L and min so a non-English coach can cite glucose and duration
values. Export GLUCOSE_PANEL_WINDOW_DAYS as the single source for the 30-day
clinical-panel window and import it across the analytics route, dashboard
snapshot, and Coach snapshot. Compute the derived metrics concurrently.
Bring the source step up to date — present the six shipped integrations instead
of a single vendor and a stale coming-soon badge, and signpost the import path
for existing history. Stop promising that the goals and source choices configure
the dashboard, which they do not yet; frame them honestly as helping the user
decide what to track. Set the calibrating expectation on the done screen so empty
tiles read as learning, not broken, and lead forward with connect and log CTAs.
Count any connected integration toward the setup checklist, not Withings alone.
Rewrite the source step to list the live integrations (Apple Health,
WHOOP, Oura, Polar, Nightscout, Fitbit) alongside Withings and manual
entry, each linking to where it is connected; drop the stale Apple
Health coming-soon badge. Add an import affordance for existing history.
Reword the goals and source copy so it no longer promises automatic
dashboard or reminder configuration that does not happen yet. Add a
calibration line to the done screen setting the expectation that some
insights sharpen over the first weeks, and lead the success screen
forward with connect-a-device and log-first-measurement actions.
Generalize the dashboard checklist data-source item so any connected
integration satisfies it, not Withings alone.
Derive the recovery source rank from the one priority ladder instead of a second
hand-kept map, re-point the WHOOP and Fitbit HTTP classifiers at the shared
status classifier, and lift the near-identical poll-cohort job handlers onto one
factory. Drop the Polar refresh-token column, which was only ever written null
since Polar tokens do not expire. Behaviour is unchanged across the recovery,
classifier, queue and route suites.
Give the Polar, Oura and Nightscout cards the same parked-state resume banner
and test-connection button the other integrations carry, distinct brand icons,
a connected-state link to where their data appears, and the correct status-key
invalidation so the pill refreshes after a disconnect. Fix the sleep empty-state
link that pointed at a non-existent settings slug.
Lift the byte-identical Polar and Oura response classifiers onto one shared
classifyHttpStatus over the integration classification union, give the vendor
API errors a common base carrying the vendor label, and funnel the duplicated
failure-kind adapters through a single toFailureKind. Behaviour is unchanged —
the per-vendor names, signatures and the Oura invalid-grant lift are preserved.
Drop the bespoke vertical-rhythm override on the sleep-debt and chronotype
cards so they inherit the standard card spacing the glucose panel uses, give the
sleep-rhythm section a quiet error notice instead of an endless skeleton on a
failed read, and move the card accents onto the semantic info token.
Polar and Oura response classifiers were byte-identical copies of the
status-driven verdict, the typed API error, and the invalid_grant probe.
Extract classifyHttpStatus, an IntegrationApiError base carrying the vendor
label, and a vendor-scoped isOAuthInvalidGrant into a shared module; Polar
and Oura now re-export under their existing names and types so call sites
and instanceof checks are unchanged.
Collapse the triplicated classifyXFailure mapping (Polar, Oura, Nightscout)
onto one toFailureKind helper. Behaviour is identical: same classifications,
same failure kinds, same error types.
Drop the compact gap/padding override on the sleep-debt and chronotype
cards so they inherit the standard Card rhythm, matching the glucose
panel. Switch the card icons to the semantic text-info token. Branch
SleepRhythmSection on a query error to a quiet inline notice instead of
an endless skeleton, keeping loading and settled states distinct.
Every other briefing destination links to its dedicated insight sub-page; the
step row alone pointed at the generic readings view. Route it to /insights/steps
so the briefing, the tab strip and the dashboard all open the same step page.
Only the Nightscout sync queue was asserted in allQueues; the two OAuth-
wearable polls shipped alongside it carry the same v1.4.37 dead-queue risk.
Assert POLAR_SYNC_QUEUE and OURA_SYNC_QUEUE membership, cron, and handler
wiring so a future refactor can't silently drop one.
Both disconnect routes ran unbounded, unlike nightscout/disconnect and the
connect routes. Add the same per-user 20/60s checkRateLimit guard after
requireAuth so the surface returns 429 on bucket exhaustion.
The entries URL carries the access token as ?token=<secret>; a SafeFetchError
embeds the full target URL in its message, and the sync feeds err.message into
recordSyncFailure, which the status endpoint returns as lastError. Strip token
and api-secret query values to =REDACTED before the message reaches the ledger,
so the secret can never surface in the status response.
buildExtras emits bpInTargetCount90 + bpInTargetSpanDays90, but the
registered extras schema (additionalProperties: false) omitted them, so a
strict iOS decoder would reject the dashboard snapshot. Declare both as
nullable integers to match the runtime shape and regenerate the spec.
Hard-coding isSpotEstimate: true mislabels a dense Nightscout CGM stream
(~288 readings/day) as a spot estimate and undersells the data in the
clinical panel and coach. Compute readings-per-day over the covered span
and treat the series as continuous below the spot caveat once it clears a
~hourly bar (24 readings/day); sparse spot fingersticks keep the caveat.
The panel only renders the spot-reading line when isSpotEstimate, and the
OpenAPI field relaxes from a literal-true to a boolean.
Move the low-stock trigger from a bare runway threshold to the larger of that
threshold and the reorder lead time plus one dose interval, so a weekly
medication is flagged before its last dose rather than after — early enough to
reorder. The change only moves the alert earlier; no one's threshold shrinks.
Surface concrete dates (runs out on, reorder by) and a running-low vs last-dose
state through the existing badge and channel, never the card colour. A reorder
lead time is configurable globally with a per-medication override, and the glp1
card flag now runs the same runway decision as the daily check instead of a
stale dose count.
Marc-André Bombeck