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Data

SweatStack stores athletes' training data from wearables, devices, and other apps, and exposes it through a REST API and a Python SDK. Your app can read every activity an athlete has recorded, their daily body measurements, structured test results, and the metabolic profile derived from both.

Start with the Data model for the full schema and how the entities relate. Every other page in this section assumes you've read it.

The pages here cover concepts, common patterns, and curated response examples. For the full request and response schema of every endpoint, see the API reference; to call an endpoint live against your account, use the API playground.

Data types

  • Activities: training sessions. Per-activity timeseries (power, heart rate, speed, etc.) and longitudinal queries across a user's full history.
  • Dailies: once-per-day measurements. Body mass, resting heart rate, HRV, sleep, and a few others.
  • Tests: structured performance evaluations like lactate, VO2max, and FTP tests. Each produces a defined results schema.
  • Metabolic profile: thresholds, training zones, and intensity-duration models, computed from tests and activities.