Fitbit Wellness Tracker Data
Public Health & Epidemiology
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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:
Dataset Features:
-
Daily Activity:
- Aggregated metrics for steps, calories, and intensity.
- Files:
dailyActivity_merged.csv
,dailyCalories_merged.csv
,dailyIntensities_merged.csv
,dailySteps_merged.csv
.
-
Hourly Data:
- Hourly breakdowns of activity and calorie burn.
- Files:
hourlyCalories_merged.csv
,hourlyIntensities_merged.csv
,hourlySteps_merged.csv
.
-
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
.
- Narrow:
-
Heart Rate:
- Second-by-second heart rate data for precise analysis.
- File:
heartrate_seconds_merged.csv
.
-
Sleep Data:
- Insights into sleep quality, duration, and patterns.
- Files:
minuteSleep_merged.csv
,sleepDay_merged.csv
.
-
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:
- Fitness and Wellness Trends: Uncover patterns in activity, sleep, and heart rate data.
- Temporal Analysis: Study how routines and behaviors change over time.
- Predictive Analytics: Build models to predict fatigue or health risks using granular data.
- 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
Robert Furberg, Julia Brinton, Michael Keating, and Alexa Ortiz
Original Source:
Contributors to related analyses: