garmin-health-data 2.6.0

Extract your Garmin Connect health data to a local SQLite database

Extract your complete Garmin Connect health and activity data to a local SQLite database.

Adapted from the Garmin pipeline in OpenETL, a comprehensive ETL framework with Apache Airflow and PostgreSQL/TimescaleDB. This standalone version of the OpenETL Garmin data pipeline provides the same data extraction and modeling scheme without requiring Airflow or PostgreSQL infrastructure. Ships a self-contained Garmin Connect client (garmin_health_data/garmin_client/) that handles SSO authentication and API access with no dependency on python-garminconnect.

Features

• ⚡ Zero Configuration: Single command to get started.
• 🖥️ Cross-Platform: Works on macOS, Linux, Windows.
• 💾 Local Storage: SQLite database - your data stays on your machine.
• 🏥 Comprehensive Health Data: Sleep, HRV, stress, body battery, heart rate, respiration, VO2 max, training metrics.
• 🏃 Activity Data: FIT files with detailed time-series metrics, lap data, split data.
• 👥 Multi-Account: Extract data from multiple Garmin Connect accounts (e.g., family members) into a single database. Run garmin auth once per account — accounts are discovered and extracted automatically.
• 🔄 Auto-Resume: Automatically detects last update and syncs new data.

Requirements

• Python 3.10 or higher
• Garmin Connect account
• Internet connection for data extraction

Quick Start

Installation

pip install garmin-health-data

First-Time Setup

# Authenticate with Garmin Connect (one-time setup)
garmin auth

You'll be prompted for your Garmin Connect email and password. Your credentials are used only to obtain OAuth tokens. After login, your Garmin user ID is auto-detected and tokens are stored in ~/.garminconnect/<user_id>/.

Extract Your Data

# Extract all available data
garmin extract

# View database statistics
garmin info

That's it! Your data is now in a local SQLite database (garmin_data.db).

Usage

Authentication

# Interactive authentication (one-time setup, run once per account)
garmin auth

# If you have MFA enabled, you'll be prompted for your code

• garmin auth always performs a fresh login and refreshes your OAuth tokens, even if valid tokens already exist.
• After login, your Garmin user ID is auto-detected and tokens are stored in ~/.garminconnect/<user_id>/.
• Access tokens (~18h) are auto-refreshed transparently using the refresh token (30 days, rotates on each use). As long as you extract at least once within 30 days, tokens stay valid indefinitely.
• You typically only need to run garmin auth once per account initially, or after 30+ days of inactivity.
• garmin extract automatically checks for existing tokens and only prompts for authentication if they're missing.
• Recommendation: Run garmin auth once per account for initial setup, then just use garmin extract for regular data extraction.

How Authentication Works

garmin auth uses a self-contained SSO client (garmin_health_data/garmin_client/) that tries five login strategies in order until one succeeds:

1. Portal web login via curl_cffi (TLS browser fingerprint impersonation, 30-45s pre-submit delay)
2. Portal web login via requests (30-45s pre-submit delay)
3. Mobile portal login via curl_cffi (mobile TLS impersonation, 30-45s pre-submit delay)
4. Mobile login via requests (30-45s pre-submit delay)
5. Widget login via curl_cffi (last resort — 429s reliably under current Cloudflare config, kept for future use)

If you see a 30-45 second pause during garmin auth, this is normal. The delay is a deliberate Cloudflare WAF countermeasure — submitting credentials too quickly triggers a 429 rate limit. Tokens obtained are DI OAuth2 Bearer tokens; no session cookies or password are stored after the initial login.

If all five strategies are exhausted without success (uncommon — typically only during Garmin-side outages), garmin auth will exit with an error. Wait a few minutes and retry.

Multi-Account Support

You can extract data from multiple Garmin Connect accounts (e.g., family members) into the same database. Run garmin auth once for each account:

# Authenticate first account
garmin auth --email [e-mail] --password pass1

# Authenticate second account
garmin auth --email [e-mail] --password pass2

Tokens are stored in per-account subdirectories:

~/.garminconnect/
├── 12345678/              # Account 1 tokens
│   └── garmin_tokens.json
└── 87654321/              # Account 2 tokens
    └── garmin_tokens.json

Accounts are discovered automatically by scanning for numeric subdirectories. All discovered accounts are extracted sequentially when running garmin extract, with per-account error isolation (one failing account doesn't block others).

Data Extraction

# Auto-detect date range (extracts from last update to today)
garmin extract

# Specify custom date range
garmin extract --start-date 2024-01-01 --end-date 2024-12-31

# Extract specific data types only
garmin extract --data-types SLEEP --data-types HEART_RATE --data-types ACTIVITY

# Use custom database location
garmin extract --db-path ~/my-garmin-data.db

# Extract only specific accounts (multi-account setup)
garmin extract --accounts 12345678
garmin extract --accounts 12345678,87654321

Date Range Behavior

The date range parameters --start-date and --end-date define the period for data extraction:

• --start-date: Inclusive, data from this date is included.
• --end-date: Exclusive, data from this date is NOT included (except when start and end dates are the same, then inclusive).
• Example: --start-date 2024-01-01 --end-date 2024-01-31 extracts Jan 1-30 (31st excluded).
• Example: --start-date 2024-01-15 --end-date 2024-01-15 extracts Jan 15 only (same-day inclusive).

Automatic Date Detection

One of the key features of garmin-health-data is that you can run garmin extract anytime without specifying dates, and it automatically continues from where it left off:

1. First Run (Empty Database)

   • Extracts the last 30 days of data.
   • Creates your initial database.

2. Subsequent Runs (Existing Data)

   • Queries 10 core time-series tables (sleep, heart_rate, activity, stress, body_battery, steps, respiration, floors, intensity_minutes, training_readiness).
   • Finds the most recent (maximum) date across these tables.
   • Automatically starts from the day after this maximum date.
   • Extracts up to today.

This approach assumes that each automatic extraction covers all data types up to the maximum date, even if some specific data types have no data for certain days (e.g., no activities recorded, no training readiness calculated). Using the maximum date ensures:

• Only new data is extracted (efficient, no redundant API calls).
• Gaps in specific data types are automatically filled when available.
• Simple, predictable behavior for users.

Example:

If your database has sleep data through Dec 20th but activities only through Dec 18th (you didn't exercise on Dec 19-20), the next extraction starts from Dec 21st. This is correct because:

• Sleep data for Dec 19-20 was already extracted.
• No activity data exists for Dec 19-20 (you didn't exercise).
• The Dec 21st extraction will get all available data for that day.

Duplicate Prevention & Reprocessing

This package prevents duplicates through a three-tier approach:

1. FIT Activity Metrics: Uses delete+insert pattern for time-series, lap, and split metrics. Existing rows are deleted and fresh data re-inserted in the same transaction, handling added/removed laps or records between reprocesses. The ts_data_available flag tracks whether time-series data exists.
2. JSON Wellness Time-Series: Uses INSERT...ON CONFLICT DO NOTHING for idempotent upserts. Reprocessing the same date won't create duplicates.
3. Main Records (activities, sleep): Uses INSERT...ON CONFLICT DO UPDATE to update existing records with new data.

This means you can safely:

• Reprocess dates without creating duplicate time-series points
• Backfill missing data by re-extracting date ranges
• Retry failed extractions without manual cleanup

GarminDB comparison: GarminDB uses SQLAlchemy session.merge() operations (via insert_or_update() methods) that handle duplicates at the ORM level. However, this behavior is not explicitly documented. garmin-health-data uses explicit SQL-level ON CONFLICT clauses that make idempotency guarantees clear and verifiable at the database level.

Data Types

You can limit extraction to specific data types using the --data-types parameter. If omitted, all data types are extracted. The --data-types parameter accepts the exact values from the "Data Type" column in the Data Types table below (e.g., SLEEP, HEART_RATE, ACTIVITY, STRESS, etc.).

View Database Info

# Show statistics and last update dates
garmin info

Last Update Dates:
   • Activity: 2024-12-18          # Haven't exercised in 2 days
   • Body Battery: 2024-12-20       # Up to date
   • Floors: 2024-12-20             # Up to date
   • Heart Rate: 2024-12-20         # Up to date
   • Sleep: 2024-12-20              # Up to date
   • Steps: 2024-12-20              # Up to date
   • Stress: 2024-12-20             # Up to date
   ...

# Check specific database
garmin info --db-path ~/my-garmin-data.db

Next garmin extract will start from 2024-12-21 (the day after the maximum date, 2024-12-20), ensuring all data types are updated.

Example Workflow

# Week 1: Initial extraction
$ garmin extract
📅 Using default start date: 2024-11-20 (30 days ago)
📆 Date range: 2024-11-20 to 2024-12-20
✅ Extracted 1,234 files

# Week 2: Automatic resume (just run the same command!)
$ garmin extract
📅 Auto-detected start date: 2024-12-21 (day after last update)
📆 Date range: 2024-12-21 to 2024-12-27
✅ Extracted 87 files  # Only new data!

# Week 3: Missed a few days? No problem!
$ garmin extract
📅 Auto-detected start date: 2024-12-28 (day after last update)
📆 Date range: 2024-12-28 to 2025-01-10
✅ Extracted 156 files  # Automatically fills the gap

Data Types

Data Type	Description	Frequency
SLEEP	Sleep stages, HRV, SpO2, restlessness, scores	Per session
HEART_RATE	Continuous heart rate measurements	2-min intervals
STRESS	Stress levels throughout the day	3-min intervals
RESPIRATION	Breathing rate measurements	2-min intervals
TRAINING_READINESS	Readiness scores and factors	Daily
TRAINING_STATUS	VO2 max, load balance, ACWR	Daily
STEPS	Step counts and activity levels	15-min intervals
FLOORS	Floors climbed and descended	15-min intervals
INTENSITY_MINUTES	Moderate/vigorous activity minutes	15-min intervals
ACTIVITIES_LIST	Detailed activity summaries	Per activity
EXERCISE_SETS	Per-set strength training data: reps, weight, ML-classified exercise name	Per activity
PERSONAL_RECORDS	All-time bests across sports	As achieved
RACE_PREDICTIONS	Predicted race times	Periodic updates
USER_PROFILE	Demographics, fitness metrics	Periodic updates
ACTIVITY	Binary FIT files with detailed time-series sensor data	Per activity

Database Schema

The SQLite database contains 33 tables organized by category. The complete schema is defined in garmin_health_data/tables.ddl following the same pattern as the openetl project. The schema includes inline documentation comments for all tables and columns, which are preserved in the SQLite database.

Viewing inline documentation:

# View schema for a specific table
sqlite3 ~/garmin_data.db "SELECT sql FROM sqlite_master WHERE type='table' AND name='personal_record';"

# View all table schemas
sqlite3 ~/garmin_data.db "SELECT sql FROM sqlite_master WHERE type='table';"

The schema is automatically created when you initialize the database.

SQLite Adaptations

The database schema has been adapted from the original PostgreSQL/TimescaleDB schema in OpenETL to be fully compatible with SQLite, while preserving all relationships and data integrity. Key adaptations include:

• Removed PostgreSQL schemas - SQLite doesn't support schemas, all tables are in the default namespace.

• Converted SERIAL to AUTOINCREMENT - PostgreSQL SERIAL types converted to SQLite INTEGER PRIMARY KEY AUTOINCREMENT.

• Replaced TimescaleDB hypertables - Time-series tables use regular SQLite tables with indexes on timestamp columns for efficient queries.

• SQLite-compatible upsert syntax - Uses SQLite's INSERT ... ON CONFLICT for handling duplicate records.

• JSON over JSONB - PostgreSQL JSONB columns (e.g., activity_path.path_json) are stored in SQLite as JSON/TEXT. CHECK constraints rely on SQLite JSON functions (json_valid, json_type, json_array_length), which are commonly available in SQLite 3.9+ but depend on the SQLite library bundled with your Python/runtime environment. If CREATE TABLE fails with errors about missing json_valid or json_type, verify JSON support first:

  python - <<'PY'
  import sqlite3
  print("SQLite version:", sqlite3.sqlite_version)
  with sqlite3.connect(":memory:") as conn:
      print("json_valid available:", conn.execute("SELECT json_valid('[]')").fetchone()[0] == 1)
  PY
  

• Preserved all relationships - All foreign key relationships and table structures maintained.

These adaptations ensure the standalone application maintains complete feature parity with the OpenETL Garmin pipeline while using a zero-configuration SQLite database.

Table Structure

User & Profile (2 tables)

user (root table)
└── user_profile (fitness profile, physical characteristics)

Foreign keys: user_profile → user.user_id

Activities (11 tables)

activity (main activity records)
├── activity_lap_metric (lap-by-lap metrics)
├── activity_path (eagerly materialized GPS path as JSON array)
├── activity_split_metric (split data)
├── activity_ts_metric (time-series sensor data)
├── cycling_agg_metrics (cycling-specific aggregates)
├── running_agg_metrics (running-specific aggregates)
├── strength_exercise (per-exercise aggregates: sets, reps, volume, duration)
├── strength_set (per-set data: reps, weight, ML-classified exercise name)
├── swimming_agg_metrics (swimming-specific aggregates)
└── supplemental_activity_metric (additional activity metrics)

Foreign keys: activity → user.user_id; all child tables → activity.activity_id

Sleep Metrics (7 tables)

sleep (main sleep sessions)
├── sleep_level (discrete sleep stage intervals)
├── sleep_movement (movement during sleep)
├── sleep_restless_moment (restless periods)
├── spo2 (blood oxygen saturation)
├── hrv (heart rate variability)
└── breathing_disruption (breathing events)

Foreign keys: sleep → user.user_id; all child tables → sleep.sleep_id

Health Time-Series (7 tables)

heart_rate (continuous heart rate measurements)
stress (stress level readings)
body_battery (energy level tracking)
respiration (breathing rate data)
steps (step counts and activity levels)
floors (floors climbed/descended)
intensity_minutes (activity intensity tracking)

Foreign keys: all tables → user.user_id

Training Metrics (4 tables)

vo2_max (VO2 max estimates)
acclimation (heat/altitude acclimation)
training_load (training load metrics)
training_readiness (daily readiness scores)

Foreign keys: all tables → user.user_id

Records & Predictions (2 tables)

personal_record (personal bests)
race_predictions (predicted race times)

Foreign keys: all tables → user.user_id. Note: personal_record.activity_id column exists but has no FK constraint (allows processing PRs before the linked activity is extracted).

Privacy & Security

• Your credentials never leave your machine: they're only used to obtain OAuth tokens, stored locally in ~/.garminconnect/<user_id>/. On Unix-like systems, token directories and files are locked to owner-only access (0o700 directories, 0o600 files); on Windows, standard user-profile permissions apply.
• All data stays on your machine: no cloud services involved.
• No analytics or tracking: this tool doesn't send any data anywhere except querying the Garmin Connect API directly.

Comparison With Other Tools

garmin-health-data is designed for comprehensive data extraction with a well-structured relational schema that supports both human-powered analytics and LLM-powered analysis via agents querying the locally created SQLite file. It extracts complete FIT file data with per-second activity metrics, 1-minute sleep intervals, and sport-specific tables for detailed analysis. The normalized 33-table schema with explicit SQL constraints ensures data integrity and makes it easy to understand relationships for complex queries, power zone analysis, running dynamics, and long-term trend studies.

garmy is optimized for programmatic access to the Garmin Connect API, particularly useful for AI assistant integration via its built-in MCP (Model Context Protocol) server. It enables real-time interaction with Claude Desktop or custom chatbots for quick daily insights and summaries. However, it's limited to API-provided metrics (daily aggregates only, no FIT file access), making deep analytics or granular time-series analysis impossible. Best suited for lightweight health monitoring apps that prioritize AI integration over comprehensive data collection.

garmindb is a mature and well-documented tool, but has been functionally superseded by garmin-health-data. While it pioneered local Garmin data extraction, it offers less comprehensive schemas (missing power meter data, limited FIT metrics) and uses implicit duplicate handling at the ORM level rather than explicit database constraints. For new projects requiring detailed data extraction and analysis, garmin-health-data is the recommended choice.

Want the full data pipeline with Airflow, scheduled updates, and TimescaleDB? Check out OpenETL's Garmin pipeline.

Feature	garmin-health-data	garmindb	garmy	garminexport	garmin-fetch
Interface	CLI	CLI	CLI + Python API + MCP	CLI	GUI
Setup complexity	✅ Single command	⚠️ Config file + 2 commands	✅ Single command	✅ Single command	⚠️ Manual setup
Storage	SQLite database	SQLite database	SQLite (optional)	File export	Excel export
Cross-platform	✅	✅	✅	✅	✅
Health metrics (sleep, HRV, stress)	✅ Comprehensive	⚠️ Basic coverage	⚠️ Basic coverage	❌ Activities only	❌ Activities only
Sleep data granularity	✅ 7 tables, 1-min intervals	⚠️ 2 tables, less granular	⚠️ 1 table, daily aggregate	❌	❌
FIT file time-series data	✅ All metrics (EAV schema)	⚠️ Limited (~10 core fields)	❌ API-only (no FIT files)	❌	❌
Power meter & advanced metrics	✅ Full support	❌ Not captured	❌ API limitations	❌	❌
Database schema quality	✅ Normalized, 33 tables	⚠️ ~31 tables, mixed normalization	❌ Very simple	N/A	N/A
Duplicate prevention	✅ Explicit SQL ON CONFLICT	⚠️ ORM merge (undocumented)	✅ ORM merge + sync tracking	N/A	N/A
Auto-resume	✅	✅	✅	✅	❌
Active maintenance	✅	✅	✅	✅	⚠️ Limited

Schema Comparison: garmin-health-data vs garmindb vs garmy

Activity Time-Series Data

garmin-health-data uses a flexible EAV (Entity-Attribute-Value) schema in the activity_ts_metric table:

• Schema: (activity_id, timestamp, name, value, units).
• Captures ALL FIT file metrics: heart rate, power, cadence, GPS coordinates, advanced running dynamics (ground contact time, vertical oscillation, stride length), cycling power metrics (left/right balance, pedal smoothness), swimming metrics, and more.
• Future-proof: Automatically handles any new metrics Garmin adds without requiring schema changes.
• Example: A cycling activity with a power meter captures power, left_right_balance, left_pedal_smoothness, right_pedal_smoothness, left_torque_effectiveness, right_torque_effectiveness, etc.

garmindb uses a fixed column schema in the ActivityRecords table:

• Only ~10 predefined columns: hr, cadence, speed, distance, altitude, temperature, position_lat, position_long, rr.
• Missing critical data: No power data, no advanced running/cycling dynamics, no device-specific metrics.
• Limited extensibility: Requires schema changes and code updates to add new metrics.

garmy (API-only approach):

• No per-second activity data: API provides only aggregated summaries (avg/max HR, duration, training load).
• No FIT file access: Cannot capture detailed time-series metrics that exist only in device files.

Sport-Specific Metrics

garmin-health-data provides dedicated tables for each sport:

• running_agg_metrics: Running cadence, vertical oscillation, ground contact time, stride length, VO2 max.
• cycling_agg_metrics: Power metrics (avg/max/normalized), cadence, pedal dynamics, FTP.
• swimming_agg_metrics: Stroke count, SWOLF, pool length, stroke type.
• strength_exercise: Per-exercise aggregates (sets, reps, volume, duration, max weight) from the activities list.
• strength_set: Per-set granular data (set type, duration, reps, weight, ML-classified exercise name/category) from the exercise sets API endpoint.

garmindb uses activity-type tables:

• StepsActivities, PaddleActivities, CycleActivities, ClimbingActivities
• Less comprehensive sport-specific metrics

garmy uses basic activity records:

• activities: Simple table with activity name, duration, avg HR, training load.
• No sport-specific metrics: API doesn't provide detailed power/cadence/dynamics data.

Sleep Data Granularity

garmin-health-data provides comprehensive sleep tracking with 7 tables:

• sleep: Main sleep session with scores and metadata.
• sleep_level: Variable-length intervals classifying each segment of the night as Deep, Light, REM, or Awake.
• sleep_movement: 1-minute interval movement data throughout sleep.
• hrv: 5-minute interval heart rate variability measurements.
• spo2: 1-minute interval blood oxygen saturation.
• breathing_disruption: Event-based breathing disruption timestamps.
• sleep_restless_moment: Event-based restless moment timestamps.

garmindb uses only 2 tables:

• Sleep: Main sleep session data.
• SleepEvents: Sleep events (less granular than garmin-health-data's separate time-series tables).

garmy uses 1 table with daily aggregates:

• daily_health_metrics: Single row per day with summary columns (total hours, deep/light/REM percentages).
• No per-minute data: Cannot analyze sleep cycles, movement patterns, or SpO2 fluctuations throughout the night.

Health Time-Series Organization

garmin-health-data uses separate normalized tables for each metric type:

• Each metric type (heart_rate, stress, body_battery, respiration, steps, floors, intensity_minutes) has its own table.
• Consistent schema: (user_id, timestamp, value) plus metric-specific fields.
• Optimized for time-series queries and analysis.

garmindb uses a mixed approach:

• Some monitoring tables for specific metrics.
• Wide DailySummary table containing many aggregated metrics in a single row.
• Less optimized for granular time-series analysis.

garmy uses normalized tables optimized for API sync:

• daily_health_metrics: Wide table (~50 columns) for daily summaries.
• timeseries: High-frequency data when available from API (heart rate, stress, body battery).
• sync_status: Tracks which metrics have been synced for each date.

Update Strategy & Data Integrity

garmin-health-data uses explicit conflict resolution for idempotent reprocessing:

• Updatable data (activities, user profile, training status): Uses ON CONFLICT UPDATE to refresh data when reprocessing.
• Immutable time-series (heart rate, sleep movement, stress): Uses ON CONFLICT DO NOTHING to prevent duplicates.
• FIT activity metrics (time-series, laps, splits): Uses delete+insert for idempotent reprocessing. The ts_data_available flag tracks time-series data availability.
• Latest flags: Manages latest=True flags for user_profile, personal_record, race_predictions to track most recent values.
• Referential integrity: Explicit foreign key relationships with cascade deletes.
• Fully idempotent: Safe to reprocess the same date range multiple times without creating duplicate data.

garmindb update strategy:

• Uses SQLAlchemy session.merge() operations via insert_or_update() and s_insert_or_update() methods.
• Handles duplicates at the ORM level rather than explicit SQL constraints.
• Implementation detail not documented in README or schema documentation.
• Idempotency behavior exists but is implicit rather than guaranteed at database level.

garmy update strategy:

• Uses SQLAlchemy session.merge() for upserts + sync_status table for tracking.
• Sync-aware: Tracks which metrics have been synced for each date to avoid redundant API calls.
• Status tracking: Records pending, completed, failed, or skipped status per metric/date.

Contributing

Contributions are welcome! Please note:

• Data extraction and processing logic is synchronized with the openetl Garmin pipeline
• For changes to extraction/processing logic, please contribute to openetl first, as this application is a wrapper that provides a standalone CLI
• For CLI-specific features, documentation, or packaging improvements, feel free to contribute directly here

Please feel free to submit a Pull Request.

Support

• Issues: GitHub Issues
• Discussions: GitHub Discussions