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Fitbit Daily Activity Logs
Patient Health Records & Digital Health
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About
Daily activity and sleep metrics from personal Fitbit trackers. It provides insights into the daily routines and habits of thirty Fitbit users, including details on physical activity, distance travelled, and sedentary periods. The data allows for exploration into user behaviour patterns related to health and fitness.
Columns
- Id: A unique identifier assigned to each individual user.
- ActivityDay: The specific date on which the activity and sleep data were recorded.
- TotalDistance: The entire distance, measured in kilometres, that a user travelled during a single day.
- TotalActiveHours: The total number of hours a user engaged in active pursuits throughout a day.
- SedentaryHours: The total number of hours a user spent in sedentary states during a day.
Distribution
The dataset is provided in a CSV format, specifically 'Activity (NR).csv', with a file size of 40.24 kB. It contains 940 records, representing daily logs from thirty unique Fitbit users over a period of 31 days in April 2016. The structure is tabular, with each row corresponding to a user's activity on a particular day.
Usage
This dataset is ideal for analysing personal fitness trends, understanding daily activity levels, and identifying correlations between different activity types. It can be used for developing predictive models for user engagement, creating visualisations of health patterns, or examining the impact of daily habits on overall well-being. Researchers can explore sedentary behaviour and its implications, while data scientists can segment users based on their activity profiles.
Coverage
The data encompasses the daily activities and sleep patterns of thirty Fitbit users. The time frame spans 31 days, from the 12th of April 2016, covering a month of user behaviour. Specific demographic or geographic information for these users is not detailed beyond their participation as Fitbit wearers.
License
CC0: Public Domain
Who Can Use It
- Data Analysts: To identify trends in physical activity and sedentary behaviour.
- Health Researchers: To study user habits and their potential impact on health.
- Fitness App Developers: To gain insights for feature development and user engagement strategies.
- Academics: For educational purposes, exploring real-world wearable device data.
- Individuals: Interested in comparing personal fitness data or understanding general activity patterns.
Dataset Name Suggestions
- Fitbit Daily Activity Logs
- Personal Fitness Tracker Data 2016
- User Activity and Sedentary Habits
- Wearable Device Health Data
- Fitbit User Behaviour Metrics
Attributes
Original Data Source: Fitbit Daily Activity Logs
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