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Smart Device Fitness Tracker Data
Public Health & Epidemiology
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About
This collection of data explores the influence of smart devices on fitness and sleep behaviours. The records detail the daily activities of women who utilised a Belladata smart device primarily to enhance their overall fitness levels. Key metrics include the number of steps taken, the total distance travelled, and the calculated caloric expenditure. The data also precisely captures time allocation across various activity intensities, such as minutes spent very active, fairly active, lightly active, and minutes dedicated to a sedentary lifestyle. Additionally, relevant information regarding sleep duration, such as total time spent asleep and total time in bed, is included within the merged records.
Columns (dailyActivity_merged)
- Id: Unique identifier assigned to each participant in the study.
- ActivityDate: The specific calendar date when the recorded activity took place.
- TotalSteps: The overall count of steps recorded for the day.
- TotalDistance: The aggregate distance covered by the participant.
- TrackerDistance: The distance specifically recorded by the device’s tracker.
- LoggedActivitiesDistance: Distance accounted for by activities logged manually or separately.
- VeryActiveDistance: Distance covered during periods of very intense activity.
- ModeratelyActiveDistance: Distance covered during moderate intensity activities.
- LightActiveDistance: Distance covered during light intensity activities.
- SedentaryActiveDistance: Minimal distance recorded while the user was sedentary.
- VeryActiveMinutes: Total time, in minutes, spent engaged in very active pursuits.
- FairlyActiveMinutes: Total time, in minutes, spent engaged in fairly active pursuits.
- LightlyActiveMinutes: Total time, in minutes, spent engaged in light activities.
- SedentaryMinutes: Total time, in minutes, spent in a sedentary state.
- Calories: The estimated total energy (calories) burned during the activity period.
Distribution
The primary file,
dailyActivity_merged.csv, is structured with 15 columns and contains 940 unique records. The file size is 111.29 kB. All fields within these records are validated, showing a 100% valid ratio and no missing entries reported in the sample metadata. The structure is tabular, typically found in CSV format. The expected schedule for updating this dataset is annually.Usage
This data is highly valuable for researchers studying quantified self and personal health trends. Ideal applications include modelling the correlation between activity patterns and caloric burn, assessing the impact of smart device usage on daily movement goals, and performing statistical analysis on sedentary versus active lifestyle minute allocation. It is suitable for projects aiming to understand user behaviour change spurred by wearable technology.
Coverage
The data captures daily activity metrics for women participants. The time scope ranges from April 12, 2016, through to May 12, 2016. Specific geographic information for the participants is not detailed in the available materials.
License
CC0: Public Domain
Who Can Use It
- Public Health Researchers: To study population-level fitness and activity statistics.
- Data Scientists: For developing predictive models related to user health outcomes based on movement data.
- Fitness App Developers: To benchmark activity logging against real-world user data and improve engagement features.
- Behavioural Economists: To analyse habits and adherence to fitness regimes facilitated by technology.
Dataset Name Suggestions
- Quantified Wellness Metrics
- Belladata Daily Activity Logs
- Smart Device Fitness Tracker Data
- Daily Health & Activity Metrics
Attributes
Original Data Source: Smart Device Fitness Tracker Data
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