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Data Types

ROOK organizes health data into a consistent structure based on three core health pillars: Physical Health, Body Health, and Sleep Health. This document outlines the datasets provided by ROOK, including summaries and events, and explains their standardized datetime format.

Core Data Structure​

ROOK’s data is organized into health pillars to simplify integration and ensure consistency across all data sources:

  1. Physical Health: Metrics related to activity, stress, and cardiovascular performance.
  2. Body Health: Data on vital signs, hydration, nutrition, and other physiological metrics.
  3. Sleep Health: Insights into sleep duration, stages, and quality.

Data Types​

  • Summaries: Daily aggregated metrics, such as total steps or sleep duration.
  • Events: Detailed records of specific activities or measurements, like a workout session or blood pressure reading.

Complete schema details are available in the API Reference or on ROOK GitHub Datasets.

User Information​

Each dataset includes essential user information for context and personalization:

  • Name: Full name of the user.
  • Date of Birth: Used for age-based calculations.
  • Gender: Utilized for tailoring insights and metrics.
  • Height & Weight: Applied to metrics like BMI or activity tracking.

Physical Health Data​

The Physical Health pillar captures metrics related to physical activity, stress, and cardiovascular health.

Key Data Types​

Physical Summary​

  • Aggregates daily physical activity metrics:
    • Total steps.
    • Active minutes.
    • Calories burned.

Activity Event​

  • Records specific activities:
    • Type (e.g., running, walking).
    • Duration and intensity.

Heart Rate Event​

  • Tracks heart rate metrics:
    • Minimum and maximum heart rate.
    • Time spent in heart rate zones.

Oxygenation Event​

  • Captures oxygen saturation and respiratory rate.

Stress Event​

  • Measures stress levels and identifies potential stressors.

Sleep Health Data​

The Sleep Health pillar provides insights into sleep quality and patterns.

Sleep Summary​

  • Aggregates nightly sleep data:
    • Total sleep duration.
    • Time spent in each sleep stage (e.g., REM, light, deep sleep).

Body Health Data​

The Body Health pillar captures vital signs and other physiological metrics.

Key Data Types​

Body Summary​

  • Aggregates daily body health metrics:
    • Blood glucose and blood pressure.
    • Hydration levels.
    • Calorie intake and macronutrient breakdown.

Event Types​

  1. Blood Glucose Event:
    • Includes glucose level and type of test.
  2. Blood Pressure Event:
    • Contains systolic and diastolic readings.
  3. Heart Rate Event:
    • Tracks minimum and maximum heart rate during the day.
  4. Hydration Event:
    • Measures daily water intake.
  5. Mood Event:
    • Provides mood ratings and associated timestamps.
  6. Nutrition Event:
    • Breaks down protein, carbohydrates, and calorie intake.
  7. Oxygenation Event:
    • Captures oxygen saturation and respiratory rate.
  8. Temperature Event:
    • Includes body temperature readings.

Datetimes​

ROOK ensures consistent datetime formats for seamless integration and interpretation.

Standard Format​

  • Format: YYYY-MM-DDTHH:MM:SS.MS+-TZ
  • Example: '2023-08-09T15:30:50.456700Z'

Key Notes​

  • Time Zone: All timestamps are aligned to UTC (Z) unless otherwise specified.
  • Microseconds: Rounded to six digits for precision.
  • ISO 8601 Compliance: Ensures compatibility with industry standards.

Examples of Standardized Conversion​

  1. '2023-08-09' → '2023-08-09T00:00:00.000000Z'
  2. '2023-08-09T15:30:50.4567' → '2023-08-09T15:30:50.456700Z'
  3. '2023-08-09T15:30:50.4567+02:00' → '2023-08-09T15:30:50.456700+02:00'
note
  • Timestamps without a timezone are assumed to be UTC.
  • Consistent datetime formatting simplifies data integration across sources. :::

Conclusion​

Why It Matters​

ROOK’s structured data approach ensures clarity and consistency across all datasets. By organizing health data into pillars and maintaining standardized formats, ROOK simplifies integration and enables actionable insights for clients.

  • Simplicity: Unified schemas reduce development complexity.
  • Consistency: Data is presented in the same structure, regardless of the source.

For more information, refer to the API Reference or explore ROOK GitHub Datasets.