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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.
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. 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.
### 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. 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). |
| `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. |
| `loggedAt` | no | string | ISO-8601 datetime; defaults to the start of the given `date` in the default timezone. |
| `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 |
| `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 |
| `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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