HealthLog/docs/integrations/data-import.md

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 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 for the details and the full type mapping.

2. Generic JSON import

POST /api/import accepts a single JSON object with two optional arrays:

{
  "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.
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:

{
  "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:

{
  "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.
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:

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.