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HealthLog/docs/integrations/data-import.md
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Marc-André Bombeck f75337e6f2 feat(measurements): record blood ketones and show the glucose-ketone index (#1367) 
Ketones had to live in a custom metric, with the glucose-ketone index typed
in by hand beside it, where it drifted from the readings it came from.

- Type: BLOOD_KETONES (beta-hydroxybutyrate, mmol/L, migration 0496),
  plausible from 0 to 10: the blood ketone meters read 0.0 to 8.0 and print
  HI above it, and a venous value off a lab sheet fits under 10. Zero is a
  real reading. No band, no direction, no personal record, off the Coach
  snapshot and the correlations; readable over MCP and owned by the glucose
  module like glucose.
- Entry: choosing blood ketones in the measurement form also takes the
  glucose from the same fingerstick, in the account's glucose unit. Both
  rows go through the array arm of POST /api/measurements with one
  timestamp, so they land in one transaction or not at all.
- Index: glucose (mg/dL / 18.0182, the tree's one factor) over ketones,
  computed at read time and never stored. Each ketone reading above zero
  pairs with the glucose reading at the same instant, else the nearest one
  within 15 minutes either side; a tie goes to the same source, then to the
  earlier reading. Nothing pairs across a wider gap.
- Surfaces: GET /api/measurements/series takes kind=ketones and
  kind=glucoseKetoneIndex (each point names the paired readings), and the
  day carries glucoseKetoneIndex when both reading types are visible. The
  new Ketones page charts the readings with the index beneath; the day view
  shows the index beside the value tiles; the doctor report, its PDF and the
  clinician share print it in the glucose group when both leaves are chosen,
  since beside the ketones it gives the glucose back.
- FHIR: ketone readings export under LOINC 104816-4 (Beta hydroxybutyrate
  [Moles/volume] in Blood). The index is left out: it has no LOINC term,
  and both readings it is built from are in the bundle.
- The measurement form's action row moved into its own component to keep
  the form inside its size budget.

OpenAPI regenerated; all ten locales; the import unit table lists the type.
2026-11-10 19:28:57 +01:00

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# Data import
HealthLog has four ways to bring data in, all reachable from
**Settings → Export & Import**:
1. An **Apple Health `export.zip`** upload — the full archive from the
iPhone Health app, parsed server-side. See
[`apple-health.md`](./apple-health.md) for the end-to-end walkthrough,
the HealthKit type mapping, and the streaming / idempotency model.
2. A **generic JSON import** — a small, explicit payload of measurements
and mood entries, useful for restoring a partial export or migrating
from another tracker. This page documents that format.
3. A **CSV import** — one measurement per row, for a spreadsheet or a
meter / sensor export. Documented in section 3 below.
4. A **medication dose-history import** — its own card in the same
panel, posting to `POST /api/medications/intake/dose-history-import`
with its own `?dryRun=1` preview.
All paths skip rows that already exist, so re-running an import is
safe — it merges rather than duplicating.
Documents (letters, lab reports, scans) from Paperless-ngx or Papra come in
through a document token and an import script instead; see
[`document-import.md`](./document-import.md).
Every import reports what it actually wrote. A file where nothing was
written is shown as a failure, a file where some rows landed and some
were refused is shown as a partial result, and the refused rows are
grouped by reason so one bad column reads as one line rather than a
scroll.
## 1. Apple Health `export.zip`
On the iPhone: **Health app → profile picture → Export All Health
Data**, then move the resulting `export.zip` somewhere you can reach
from a browser. In HealthLog open **Settings → Export & Import** and drop
the archive onto the Apple Health control (or click to pick it). The
upload streams to the server — a multi-gigabyte archive never buffers in
the browser — and a background job parses it while a progress indicator
polls for the imported / skipped counts.
Re-uploading the same archive resolves to the same job by content hash,
so it merges instead of creating duplicates. ECG CSVs and metric-typed
clinical records are imported too; see
[`apple-health.md`](./apple-health.md) for the details and the full
type mapping.
## 2. Generic JSON import
`POST /api/import` accepts a single JSON object with two optional
arrays:
```json
{
"measurements": [/* … */],
"moodEntries": [/* … */]
}
```
Each array holds at most **10 000** entries and the file at most 16 MB.
The endpoint is rate-limited to **5 imports per hour per account**.
Existing rows (matched on the type + timestamp uniqueness, or on
`externalId` for mood entries) are skipped and counted under `skipped` in
the response.
The app's own files go straight in: the JSON export (**Export → JSON**,
read inside its `data` envelope) and the portable full backup. Every field
either file writes for a reading or a mood entry comes back, apart from
the row ids, the source (every imported row is stored as `IMPORT`) and the
sync bookkeeping of the account the file came from. An empty field, `null`
in the backup and `""` in the export, reads as absent, a backup's deletion
records are skipped and counted, and a reading in a display unit (lb, °F,
mmol/L) is converted to the stored one.
A mood entry's tags and rated factors are linked by key. A full backup
also carries the account's own tags, rated factors and tag groups, and the
import brings them along: a tag the importing account already has is used
as it is, and one it lacks is created (within the account's limit of 50
custom tags and 12 groups). The account's own tag is never overwritten: when
the file names it differently, the account's name stays and the response
lists the key in `keptMoodTagKeys` (`keptMoodCategoryKeys` for a group).
Moving a file between two accounts of one server works too; the tags come
over under keys of their own. A key the account cannot resolve at all is
dropped from that entry and named in `droppedTagKeys` or
`droppedFactorKeys`, and the rest of the entry lands.
### Measurement entry
| Field | Required | Type | Notes |
| ---------------------- | -------- | ------ | --------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `type` | yes | enum | One of the `MeasurementType` values in the table below. |
| `value` | yes | number | Plausibility-checked per type; out-of-range values are rejected. |
| `unit` | yes | string | The canonical unit for the type (see the table). |
| `measuredAt` | yes | string | ISO-8601 datetime, e.g. `2026-05-01T08:00:00.000Z`. |
| `source` | no | string | Free-text origin label, not stored. Imported rows are tagged `IMPORT` server-side regardless. |
| `notes` | no | string | Optional free text. |
| `glucoseContext` | no | enum | Only for `BLOOD_GLUCOSE`. See [Glucose context](#glucose-context). |
| `valueMin` | no | number | Lower bound of a range reading, such as an hourly heart-rate statistic. Not above `valueMax`. |
| `valueMax` | no | number | Upper bound of a range reading. |
| `sleepStage` | no | enum | A sleep row's stage: `IN_BED`, `ASLEEP`, `AWAKE`, `CORE`, `DEEP` or `REM`. |
| `rhythmClassification` | no | enum | The device's verdict on an ECG, a rhythm alert or a steadiness reading. |
| `deviceType` | no | string | The writer of the reading (watch, phone), 1–32 chars. |
| `externalId` | no | string | A source-stable id (1–120 chars). When present, a re-import upserts on `(user, type, source, externalId)` so an edit upstream is reflected rather than skipped. |
### Glucose context
`glucoseContext` is one of `FASTING`, `POSTPRANDIAL`, `RANDOM`,
`BEDTIME`, and it is genuinely optional on a `BLOOD_GLUCOSE` row. Leave
it out (JSON) or leave the cell blank (CSV) and the reading is stored
with no context.
That is the normal case for a continuous sensor or meter export, which
records a value and a timestamp per reading and classifies nothing. A
reading with no context still counts everywhere a reading counts: the
chart, the all-time summary, and the trailing-30-day clinical panel
(time in range, GMI, CV%, estimated A1c) are all context-agnostic. It is
left out only of the per-context breakdowns — the doctor report's
fasting / post-meal rows, the matching FHIR observations, the per-context
targets — because no classification was recorded and none is invented.
Setting the column on any type other than `BLOOD_GLUCOSE` is rejected.
Setting it to a value outside the four above is rejected. Entering a
reading by hand in the app still asks for a context, because at that
moment you know it.
### Mood entry
| Field | Required | Type | Notes |
| ---------------- | -------- | ------- | --------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `date` | yes | string | `YYYY-MM-DD`. |
| `mood` | yes | enum | One of `SUPER_GUT`, `GUT`, `OKAY`, `SCHLECHT`, `LAUSIG`. |
| `score` | yes | integer | `1`–`5`. |
| `tags` | no | string | Comma-separated tags. |
| `note` | no | string | The entry's note, up to 500 chars. |
| `a1`–`a5` | no | integer | The five level-A values, `0`–`10`. Without `a1`, pleasantness is derived from `mood`. |
| `loggedAt` | no | string | ISO-8601 datetime; defaults to the start of the given `date` in the default timezone. |
| `tz` | no | string | The IANA zone `date` is anchored to. |
| `factors` | no | array | Rated factors, `[{ "key": "…", "rating": 1–5 }]`; replace the entry's rated links. |
| `structuredTags` | no | array | Ticked tags by key; replace the entry's tag links. |
| `context` | no | object | The day context (work, contacts, leisure, an event, a note), as the backup writes it; replaces the entry's context. |
| `externalId` | no | string | A source-stable id (1–120 chars). When present, a re-import upserts on `(user, source, externalId)` so an edit upstream is reflected rather than skipped. |
### `MeasurementType` values and canonical units
Storage is always in the canonical unit listed here; the UI converts for
display per the user's preference. Event-class types record a single
fired event (`value` is `1`) and are normally produced by a device, not
hand-authored.
| Type | Canonical unit |
| ------------------------------- | -------------------- |
| `WEIGHT` | kg |
| `BLOOD_PRESSURE_SYS` | mmHg |
| `BLOOD_PRESSURE_DIA` | mmHg |
| `PULSE` | bpm |
| `RESTING_HEART_RATE` | bpm |
| `WALKING_HEART_RATE_AVERAGE` | bpm |
| `AVERAGE_HEART_RATE` | bpm |
| `MAX_HEART_RATE` | bpm |
| `CARDIO_RECOVERY` | bpm |
| `HEART_RATE_VARIABILITY` | ms (SDNN) |
| `HRV_RMSSD` | ms (RMSSD) |
| `RESPIRATORY_RATE` | count/min |
| `OXYGEN_SATURATION` | percent (0–100) |
| `BLOOD_GLUCOSE` | mg/dL |
| `BLOOD_KETONES` | mmol/L |
| `BODY_FAT` | percent (0–100) |
| `FAT_MASS` | kg |
| `FAT_FREE_MASS` | kg |
| `LEAN_BODY_MASS` | kg |
| `MUSCLE_MASS` | kg |
| `BONE_MASS` | kg |
| `TOTAL_BODY_WATER` | kg |
| `VISCERAL_FAT` | rating |
| `BODY_MASS_INDEX` | kg/m² |
| `BODY_TEMPERATURE` | °C (core) |
| `SKIN_TEMPERATURE` | °C (dermal spot) |
| `WRIST_TEMPERATURE` | °C (overnight wrist) |
| `SLEEP_DURATION` | minutes |
| `SLEEP_NEED` | minutes |
| `SLEEP_PERFORMANCE` | percent (0–100) |
| `SLEEP_EFFICIENCY` | percent (0–100) |
| `SLEEP_CONSISTENCY` | percent (0–100) |
| `SLEEP_DISTURBANCE_COUNT` | count |
| `BREATHING_DISTURBANCES` | count |
| `ACTIVITY_STEPS` | count |
| `FLIGHTS_CLIMBED` | count |
| `WALKING_RUNNING_DISTANCE` | metres |
| `SIX_MINUTE_WALK_DISTANCE` | metres |
| `WALKING_STEP_LENGTH` | metres |
| `WALKING_SPEED` | m/s |
| `STAIR_ASCENT_SPEED` | m/s |
| `STAIR_DESCENT_SPEED` | m/s |
| `WALKING_STEADINESS` | percent (0–100) |
| `WALKING_ASYMMETRY` | percent (0–100) |
| `WALKING_DOUBLE_SUPPORT` | percent (0–100) |
| `ACTIVE_ENERGY_BURNED` | kcal |
| `BASAL_ENERGY_BURNED` | kcal |
| `ENERGY_EXPENDITURE_KJ` | kJ |
| `VO2_MAX` | mL/(kg·min) |
| `PULSE_WAVE_VELOCITY` | m/s |
| `VASCULAR_AGE` | years |
| `FALL_COUNT` | count |
| `AUDIO_EXPOSURE_ENV` | dBA |
| `AUDIO_EXPOSURE_HEADPHONE` | dBA |
| `TIME_IN_DAYLIGHT` | minutes |
| `RECOVERY_SCORE` | score (0–100) |
| `STRESS_SCORE` | score (0–100) |
| `STRAIN_SCORE` | score (0–100) |
| `DAY_STRAIN` | score (0–21) |
| `WORKOUT_STRAIN` | score (0–21) |
| `AUDIO_EXPOSURE_EVENT` | count (event) |
| `IRREGULAR_RHYTHM_NOTIFICATION` | event |
| `HIGH_HEART_RATE_EVENT` | event |
| `LOW_HEART_RATE_EVENT` | event |
| `WALKING_STEADINESS_EVENT` | event |
| `BREATHING_DISTURBANCE_EVENT` | event |
The table is a curated subset and lags the enum by a few
device-derived types. The live, machine-readable list of writable
types and their units is served by `GET /api/meta/capabilities` under
`ingest.quantityTypes` and is the authoritative source.
### Worked example
The **Download example** button in **Settings → Export & Import** mints
exactly this payload, which is a valid import body:
```json
{
"measurements": [
{
"type": "WEIGHT",
"value": 80.5,
"unit": "kg",
"measuredAt": "2026-05-01T08:00:00.000Z",
"source": "manual",
"notes": "morning"
},
{
"type": "BLOOD_PRESSURE_SYS",
"value": 120,
"unit": "mmHg",
"measuredAt": "2026-05-01T08:05:00.000Z"
},
{
"type": "BLOOD_PRESSURE_DIA",
"value": 80,
"unit": "mmHg",
"measuredAt": "2026-05-01T08:05:00.000Z"
}
],
"moodEntries": [
{
"date": "2026-05-01",
"mood": "GUT",
"score": 4,
"tags": "work,exercise"
}
]
}
```
The response is a count envelope:
```json
{
"data": { "measurements": 3, "moodEntries": 1, "skipped": 0 }
}
```
## 3. CSV import
`POST /api/import/csv` takes the raw file as `text/csv` — one measurement
per row, no JSON wrapper. The **CSV file** control in
**Settings → Export & Import** posts to it, and **Download example**
mints a file with the right header.
A header row is required; column order does not matter and header
matching is case-insensitive.
| Column | Required | Notes |
| ---------------- | -------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| `type` | yes | A `MeasurementType` from the table above. |
| `value` | yes | A number, in the row's `unit`. |
| `unit` | yes | The canonical unit, or a recognised alias: `mmol/L` → `mg/dL`, `lb` → `kg`, `in` → `cm`, `°F` → `°C`. |
| `measuredAt` | yes | ISO-8601 **with an explicit offset** (`Z` or `±HH:MM`, and `±HHMM` is accepted). No offset, no import. |
| `glucoseContext` | no | Only for `BLOOD_GLUCOSE`; blank means no context. See [Glucose context](#glucose-context). |
| `notes` | no | Free text, up to 200 characters. |
| `externalId` | no | A source-stable id. With it, a re-upload updates the same row even when the value changed; without it, an identical re-upload is skipped via the natural key `(user, type, measuredAt, source)`. |
A sensor export therefore needs no glucose column at all:
```csv
type,value,unit,measuredAt,glucoseContext,notes,externalId
BLOOD_GLUCOSE,5.3,mmol/L,2024-04-03T13:15:00+1100,,,sensor-1
BLOOD_GLUCOSE,5.9,mmol/L,2024-04-03T13:30:00+1100,,,sensor-2
```
Limits: 16 MB per upload, 10 000 valid rows per import, 5 imports per
hour (shared with the JSON import). `?dryRun=1` validates and previews
without writing anything — the **Preview** button uses it.
The response carries per-row outcomes alongside the counts, and the card
renders the counts, not the status code: rows written and none skipped is
a success, a mixed result is a warning, and nothing written is a failure
with the reasons grouped by count.
## Converting a CSV into the import JSON
The CSV import above covers measurements. Mood entries have no CSV
route, so a mood history still goes through the JSON payload.
A spreadsheet export is the most common starting point. The shape is
small enough to convert by hand for a few rows, or with a one-off script
for many. The steps are the same either way:
1. **One CSV per concept.** Keep measurements and mood in separate
sheets — they map to the two arrays. A blood-pressure reading is two
measurement rows (`BLOOD_PRESSURE_SYS` and `BLOOD_PRESSURE_DIA`) that
share a `measuredAt`.
2. **Pick the `type`.** Map each CSV column to a `MeasurementType` from
the table above. A "Weight (kg)" column becomes `type: "WEIGHT"`,
`unit: "kg"`.
3. **Normalise the unit.** Convert each value into the canonical unit
before writing it — e.g. pounds → kg, hours of sleep → minutes,
`mmol/L` glucose → `mg/dL`. The importer rejects values outside the
plausible range for the type, which catches most unit mistakes.
4. **Format the timestamp.** Turn the CSV date/time column into an
ISO-8601 `measuredAt` (mood uses a `YYYY-MM-DD` `date`). Include the
timezone offset (`Z` for UTC) so the reading lands on the right day.
5. **Emit the two arrays.** Collect the rows into `measurements` and
`moodEntries` as shown above and paste the result into the JSON
import control, or upload it as a `.json` file. Keep each array under
10 000 entries; split a larger history across runs (5 per hour).
Because the import is idempotent on the unique constraints, you can
re-run a corrected file over a partial one without creating duplicates.
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