What caffeine_intake measures
caffeine_intake is the total caffeine logged as consumed during the local day, in milligrams.
| Field | caffeine_intake |
|---|---|
| Unit | Milligrams |
| Reported | Daily total, on days with at least one log |
| Window | Midnight to midnight, profile local time |
| Wearable required | No |
| API | GET /api/v1/profile/biomarker/{externalId}?categories=nutrition&types=caffeine_intake plus a date range |
| Webhook | BiomarkerCreatedIntegrationEvent. Entries update in place, upsert on id. |
Data sources
| Platform | Record | Notes |
|---|---|---|
| iOS, HealthKit | dietaryCaffeine | Food loggers with caffeine in the entry, dedicated caffeine trackers, manual entry |
| Android, Health Connect | NutritionRecord caffeine field | Same kinds of apps |
| Direct integrations | None | |
| Phone only | Manual entry, or entries your own app writes |
How Sahha calculates caffeine_intake
- Pick one source for the day, for caffeine. The source with the highest daily caffeine total wins, independently of other nutrients.
- Sum the winning source’s entries with an end time inside the local day.
Absence means nothing was logged that day.
Interpreting the value
A cup of coffee is roughly 80 to 100 mg [1], so a total of 300 is three or four coffees. Users think in cups; show cups.
Timing matters more than total for sleep, and the biomarker has no timing. For the late-afternoon question, use the timestamped entries in data logs.
Related scores and insights
Caffeine intake is not a score factor and has no trend or comparison insight.
Use cases
A caffeine-and-sleep correlation card. Over 30 days, compare sleep latency and sleep start time on days above and below the user’s median caffeine total. Show the difference only when it is large enough to be worth a sentence.
GET /api/v1/profile/biomarker/{externalId}?categories=nutrition&types=caffeine_intake&startDateTime=2026-08-04&endDateTime=2026-09-03An afternoon cut-off nudge, from data logs. Subscribe to caffeine data logs and prompt when an entry lands after the user’s chosen cut-off time.
Limitations
- Logged only, and often incomplete, since many food loggers omit caffeine.
- No timing in the biomarker.
- Absence is not zero.
Related guides
- Energy intake for how nutrition sources and per-nutrient selection work
- Sleep latency for the sleep field caffeine affects most directly
- Biomarkers vs data logs for timestamped entries
- Data dictionary in the developer docs
References
- EFSA Panel on Dietetic Products, Nutrition and Allergies. Scientific opinion on the safety of caffeine. EFSA Journal. 2015;13(5):4102. https://doi.org/10.2903/j.efsa.2015.4102