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Daily Fitness Tracker User Activity Logs
Data Science and Analytics
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About
Daily physical movement metrics tracked by fitness devices are presented in this collection of records. The data captures key behavioural metrics from thirty-three unique users over a one-month period in 2016. It details daily step counts, total distance travelled, calorie output, and the minutes spent in various intensity levels, ranging from very active to sedentary. The records were originally gathered via a distributed survey conducted through Amazon Mechanical Turk and have been cleaned and prepared for immediate use in data analysis and statistical modelling projects.
Columns
- Id: A unique identifier assigned to each of the 33 study participants.
- ActivityDate: The specific date when the daily activity was logged (April to May 2016).
- TotalSteps: The total number of steps recorded for the day.
- TotalDistance: The aggregate distance travelled by the user.
- TrackerDistance: The distance specifically measured by the fitness tracking device itself.
- LoggedActivitiesDistance: Any distance manually entered by the user.
- VeryActiveDistance: The distance covered during very intense activity.
- ModeratelyActiveDistance: The distance covered during medium-intensity activity.
- LightActiveDistance: The distance covered during low-intensity activity.
- SedentaryActiveDistance: Distance recorded while the user was sitting or lying down.
- VeryActiveMinutes: The total minutes spent in states of high activity.
- FairlyActiveMinutes: The total minutes spent in moderate activity.
- LightlyActiveMinutes: The total minutes spent in low-intensity activity.
- SedentaryMinutes: The total minutes spent sitting or lying down.
- Calories: The total number of calories burned by the user for that day.
Distribution
The dataset is provided as a CSV file, named
dailyActivity_merged_cleaned.csv. The file size is approximately 111.55 kB. It contains 15 distinct columns and 940 daily activity records. The data is validated and shows a 100% validity rate, with zero missing or mismatched values across all fields, ensuring immediate utility for analysis.Usage
This information is highly suitable for statistical analysis focusing on public health trends and individual fitness behaviour. It is frequently used in data analytics professional projects to demonstrate proficiency in data cleaning, SQL analysis, and data visualisation using tools like Tableau. Researchers can utilise it to understand correlations between various activity levels, sedentary time, and calorie expenditure patterns.
Coverage
The data covers 33 unique eligible users of fitness trackers who responded to a survey. The collection period is limited to a single month, running from the 12th of April, 2016, through to the 12th of May, 2016. The respondents were sourced via the Amazon Mechanical Turk platform.
License
CC0: Public Domain
Who Can Use It
- Data Analysts: For practicing data metric calculation, cleaning, and transformation processes.
- Academics/Researchers: To study short-term daily activity patterns, step goal attainment, and time use habits.
- Business Strategists: To gain insight into user engagement and the types of activity metrics tracked by consumers of wearable technology.
- Students: Ideal for capstone projects requiring a clean, structured dataset for data analytics certifications.
Dataset Name Suggestions
- Daily Fitness Tracker User Activity Logs
- Wearable Device Step and Calorie Metrics (2016)
- 33 User Daily Activity Summary
- Fitbit Survey Activity Data
Attributes
Original Data Source: Daily Fitness Tracker User Activity Logs
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