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Fitbit Wellness Tracker Data

Public Health & Epidemiology

Related Searches

Fitness Tracker

Health Monitoring

Wearable Data

Behavioral Analytics

Heart Rate Monitoring

Sleep and Weight Analysis

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Fitbit Wellness Tracker Data Dataset on Opendatabay data marketplace

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Free

About

Explore Fitness, Health, and Wellness Through Comprehensive Tracker Data

This dataset contains 29 merged files covering minute-level, hourly, and daily tracking across multiple health and wellness metrics. The data is split into two distinct time periods:
  • Export 1: March 12, 2016 - April 11, 2016
  • Export 2: April 12, 2016 - May 12, 2016
These exports provide detailed insights into user behavior patterns using Fitbit devices, allowing for robust analyses in health and fitness trends.
Dataset Features:
  1. Daily Activity:
    • Aggregated metrics for steps, calories, and intensity.
    • Files: dailyActivity_merged.csv, dailyCalories_merged.csv, dailyIntensities_merged.csv, dailySteps_merged.csv.
  2. Hourly Data:
    • Hourly breakdowns of activity and calorie burn.
    • Files: hourlyCalories_merged.csv, hourlyIntensities_merged.csv, hourlySteps_merged.csv.
  3. Minute-Level Data:
    • High-resolution data in narrow and wide formats for calories, steps, intensity, and METs.
    • Files:
      • Narrow: minuteCaloriesNarrow_merged.csv, minuteIntensitiesNarrow_merged.csv, minuteStepsNarrow_merged.csv, minuteMETsNarrow_merged.csv.
      • Wide: minuteCaloriesWide_merged.csv, minuteIntensitiesWide_merged.csv, minuteStepsWide_merged.csv.
  4. Heart Rate:
    • Second-by-second heart rate data for precise analysis.
    • File: heartrate_seconds_merged.csv.
  5. Sleep Data:
    • Insights into sleep quality, duration, and patterns.
    • Files: minuteSleep_merged.csv, sleepDay_merged.csv.
  6. Weight Logs:
    • Tracking user weight and trends over time.
    • File: weightLogInfo_merged.csv.

Who can use it:

  • Health Behavior Analysis: Study routines, anomalies, and behavioral trends in activity, sleep, and heart rate.
  • Machine Learning Applications: Develop predictive models for fatigue, health risks, or fitness improvements.
  • Wearable Technology Research: Evaluate user engagement with fitness trackers and related behavioral insights.
  • Personalized Wellness Studies: Correlate heart rate, activity levels, and sleep to derive personalized health strategies.

Usage:

  1. Fitness and Wellness Trends: Uncover patterns in activity, sleep, and heart rate data.
  2. Temporal Analysis: Study how routines and behaviors change over time.
  3. Predictive Analytics: Build models to predict fatigue or health risks using granular data.
  4. Wearable Insights: Enhance the understanding of Fitbit devices and their impact on user health.

License

Free for public use.

📌 Acknowledgment

This dataset was collected and shared by:
Robert Furberg, Julia Brinton, Michael Keating, and Alexa Ortiz

Original Source:

Contributors to related analyses:

Dataset Information

VIEWS

18

DOWNLOADS

1

LICENSE

CC0

REGION

GLOBAL

UDQSSQUALITY

5 / 5

VERSION

1

Free