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Smart Device Daily Activity Metrics
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About
Explores fitness data derived from smart device usage to understand consumer habits. The data is valuable for revealing opportunities for growth and informing marketing strategies, particularly for companies in the wellness sector like Bellabeat, which focuses on health-focused smart products designed for women. The analysis centres on smart device usage data, including activity metrics, to gain insight into how users interact with their devices and how these trends can shape marketing efforts.
Columns
- Id: A unique identifier for each user.
- ActivityDate: The date on which the activity was logged.
- TotalSteps: The overall number of steps recorded for the day.
- TotalDistance: The overall physical distance covered.
- TrackerDistance: The distance tracked specifically using the device.
- LoggedActivitiesDistance: Distance accounted for by activities manually logged via the device.
- VeryActiveDistance: Distance covered during periods classified as very active movement.
- ModeratelyActiveDistance: Distance covered during periods of moderate movement.
- LightActiveDistance: Distance covered during periods of light movement.
- SedentaryActiveDistance: Distance logged during time when no active movement occurred.
- VeryActiveMinutes: The total minutes spent in very active movement.
- FairlyActiveMinutes: The total minutes spent in fairly active movement.
- LightlyActiveMinutes: The total minutes spent in lightly active movement.
- SedentaryMinutes: The total minutes spent being sedentary (up to 1440 minutes, which is a full day).
- Calories: The total calories burned.
Distribution
The primary data file is named
dailyActivity_merged.csv and is approximately 111.29 kB in size. The structure contains 15 distinct columns detailing daily activity metrics. There are 940 valid records in this dataset. The file is usually available in CSV format. The expected update frequency for this dataset is noted as never.Usage
This data is ideally suited for:
- Performing exploratory data analysis to find patterns in user fitness habits.
- Gaining strategic insights into consumer smart device usage to drive marketing decisions.
- Analysing the correlation between daily step counts, distances, and calorie expenditure.
- Studying the distribution of active versus sedentary minutes among users.
- Informing advertising campaigns and digital marketing efforts focused on health and wellness.
Coverage
The data covers daily activity metrics collected over a specific period, ranging from 12 April 2016 through to 12 May 2016. The context involves smart device users connected to a wellness company generally focused on women's health. Time range is explicit; specific geographic and detailed demographic scopes are not provided.
License
CC0: Public Domain
Who Can Use It
- Marketing Professionals: To better target consumers through digital channels (like Google Search, Facebook, and Instagram) by understanding usage patterns.
- Data Analysts: For analysing customer activity, segmenting users based on activity levels, and testing hypotheses about fitness behaviour.
- Product Developers: To understand which metrics are most recorded and valued by users of smart wearable devices.
- Students/Beginners: The data is suitable for introductory data analysis projects.
Dataset Name Suggestions
- Smart Device Daily Activity Metrics
- Bellabeat User Fitness Data Log
- Consumer Wearable Device Usage 2016
- Activity Tracking and Calorie Burn Data
Attributes
Original Data Source: Smart Device Daily Activity Metrics
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