Fitbit API Integration
Start Receiving Data From Fitbit In 5 Minutes
Fitbit
Enable Fitbit through your Terra dashboard
Select a Wearable
Fitbit
Connect users to your app
0. "active_durations_data":{...}
1. "calories_data": {...}
2. "device_data": {...}
3. "distance_data": {...}
4. "heart_rate_data": {...}
5. "movement_data": {...}
6. "position_data": {...}
Start receiving data from Fitbit on your app or website
How the Fitbit API integration works
- Data delivery
- Fitbit pushes to Terra on change
- Expected latency
- Seconds after the provider syncs
- Data types
- Activity, Body, Daily, Nutrition and Sleep, normalized by the Terra unified API
- Credentials
- Terra manages the Fitbit API credentials, so there is nothing to register
Two ways to receive Fitbit data
When data is available from a Fitbit device, Terra sends it straight to your webhook. During your Terra account setup you'll be prompted for a callback URL, and that is where Terra POSTs each payload.
Fitbit notifies Terra as soon as new data is available, and we POST the normalized payload straight to your webhook. No polling needed.
Fitbit's Activity
Payload Preview
Terra API preserves the raw data from the wearable, it just standardizes the units and the json format. It doesn’t augment or change the raw data in any way.
Data is organized into one of 6 data types.
Learn More
Activity Payload (JSON)
{
MET_data: { ... }
active_durations_data: { ... }
calories_data: { ... }
data_enrichment: { ... }
distance_data: { ... }
energy_data: { ... }
heart_rate_data: { ... }
metadata: { ... }
work_data: { ... }
}
Data can also be sent as a FIT File or with the FHIR Format.
Send Fitbit data to your backend
Terra forwards every normalized payload to the destination you already use.
Webhook
Firestore
SQL
Mongo DB
Google Cloud Storage Bucket
AWS S3 Bucket
AWS Simple Queue Service
Kafka
Supabase
Azure BlobPeople Often Ask Us...
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What devices are supported?
Terra API integrates with any Fitbit device currently supported by Fitbit.
Terra on Fitbit
Research and writing from the Terra team on Fitbit.
- Research27 Feb 2026
Can We Predict Cycle Phase Without Temperature?
Temperature has long been the go-to for tracking the menstrual cycle with wearables, but it turns out your body offers plenty of other clues. In this post, we show that heart rate, breathing rate, and HRV all follow a monthly rhythm driven by progesterone. Using a simple model trained on these signals alone, we can predict cycle phase with 89% accuracy, opening the door to temperature-free cycle tracking.
Alistair Brownlee
- Research22 Jan 2026
Sleep Metric Variation Across Wearables
We analyzed sleep data from 77,000+ nights across six major wearables, then zoomed in on same-person, same-night overlaps to isolate true device differences. The headline: most trackers converge on sleep duration — averages cluster around ~7 hours and median night-to-night differences are typically ~15–22 minutes. But once you break sleep into stages, agreement falls apart: REM, deep, and light can diverge by hours in direct comparisons, highlighting how much staging depends on each company’s sensors and algorithms. The takeaway is practical: trust trends in total sleep time, treat stage minutes as directional, and avoid over-optimizing for a “perfect” score when the underlying measurements don’t match.
Alistair Brownlee
- Blog20 May 2022
Behind the Scenes: The Data Standardisation Process
Behind the Scenes: The Standardisation Process POV: You're trying to integrate with Garmin, Google Fit, Oura, Wahoo and Fitbit for activities data to display your users'
Elliott Yu
- Research6 Jan 2026
Sleep Patterns Across the World
We analyzed 8,298 sleep records from October 2025 across wearable users in North America, Europe, and beyond, linking exercise locations to approximate where people slept. Ireland topped the charts at 7.31 hours with minimal variability, while Indonesia showed the shortest, most variable nights at 5.79 hours. The data reveals how geography, culture, and seasonality shape nightly rest—with Northern Hemisphere countries sleeping longer in autumn while Southern Hemisphere nations showed spring patterns pulling duration down. These population-level insights help refine health models and establish accurate baselines for personalized sleep tracking across borders.
Alistair Brownlee
- Research17 Dec 2025
How HRV Actually Works
We unpack what HRV really is and how it actually works to demystify the data from Apple Watch, Fitbit, and Garmin. Some devices deliver stronger signals for daily recovery (RMSSD), tightly linked to resting heart rate, while Apple produced significantly higher variability by measuring broader autonomic balance (SDNN). The results expose a fundamental metric divide: these devices are speaking different mathematical languages, meaning your choice of wearable determines whether you are tracking training readiness or overall stress.
Alistair Brownlee
- Research9 Dec 2025
How Do People Really Use Wearables?
We analyzed 93 days of continuous sleep data across Fitbit, Apple Watch, and Garmin users to answer a simple question: who actually wears their wearable device? While Garmin offers superior data fidelity, our analysis reveals it suffers from the lowest user adherence (69.3%).
Alistair Brownlee
- Research28 Nov 2025
Benchmarking Wearable Sleep Data Reliability
We analyzed sleep data from over 5,000 real-world nights across Apple Watch, Fitbit, and Garmin users. Garmin delivered the longest, most consistent sleep duration with the lowest variability and almost no outliers, while Apple consistently under-reported deep sleep and showed the highest error rates. The results reveal clear winners and losers in wearable sleep-tracking accuracy — and why device choice matters for long-term trends.
Halvard Ramstad
Fitbit API alternatives
These integrations also offer Activity, Body, Daily and Sleep data.
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