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Fitbit User Activity and Health Habit Analysis
Data Science and Analytics
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About
Analysing consumer activity patterns through digital fitness trackers provides a window into daily health habits and exercise frequencies. By evaluating a month's worth of personal fitness data, businesses can uncover significant trends in physical exertion, such as step counts and distance covered. This information is vital for organisations to refine their marketing strategies and product features based on actual user behaviour, moving beyond simple tracking to actionable health insights regarding fitness levels.
Columns
- Id: A unique numerical identifier assigned to each user within the study.
- ActivityDay: The specific date on which the physical activity was recorded.
- Activity weekday: The specific day of the week when the activity occurred, such as Tuesday or Wednesday.
- TotalSteps: The total number of steps taken by the user over the course of the day.
- TotalDistance: The total distance covered by the user during the recorded period.
- TrackerDistance: The distance specifically recorded and calculated by the tracking device.
- LoggedActivitiesDistance: The distance for specific activities that were manually recorded by the user.
- VeryActiveDistance.x: The total distance covered during periods of high-intensity physical activity.
- ModeratelyActiveDistance.x: The total distance covered during periods of moderate-intensity activity.
- LightActiveDistance.x: The total distance covered during periods of low-intensity or light physical activity.
Distribution
The information is delivered in a CSV format titled
Final Analysis2.csv with a file size of 106.29 kB. The collection contains 713 valid records structured across 30 distinct columns. The data exhibits high integrity, with 100% validity for core activity metrics and no missing or mismatched entries. It holds a perfect usability score of 10.00 and is not scheduled for future updates.Usage
This collection is ideal for performing exploratory data analysis on personal health metrics and building predictive models for user activity levels. Analysts can use the weekday breakdown to identify peak exercise times or correlate step counts with different intensity levels. It also serves as a robust foundation for developing health-related visualisations, practice data cleaning, and refining marketing personas based on exercise habits.
Coverage
The temporal scope covers a one-month period ranging from 12 April 2016 to 12 May 2016. The demographic focus is centred on active users of digital fitness trackers. Data availability is consistent across the seven days of the week, with Tuesdays and Wednesdays being the most common days for recorded activity.
License
CC0: Public Domain
Who Can Use It
Product managers in the wearable technology sector can utilise these trends to enhance user engagement features and design more effective fitness applications. Health and wellness researchers can apply statistical methods to study the relationship between different activity intensities and daily routines. Additionally, data science students can use this well-structured record set to practice time-series analysis and statistical modelling.
Dataset Name Suggestions
- Bellabeat Fitness Tracker Activity Trends (April–May 2016)
- Consumer Physical Activity and Daily Step Records
- Monthly Fitness Tracker Performance and Distance Metrics
- Digital Health Trends: User Exercise and Intensity Data
- Fitbit User Activity and Health Habit Analysis
Attributes
Original Data Source: Fitbit User Activity and Health Habit Analysis
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