Content This dataset generated by respondents to a distributed survey via Amazon Mechanical Turk between 03.12.2016-05.12.2016. Thirty eligible Fitbit users consented to the submission of personal tracker data, including minute-level output for physical activity, heart rate, and sleep monitoring. Individual reports can be parsed by export session ID (column A) or timestamp (column B). Variation between output represents use of different types of Fitbit trackers and individual tracking behaviors / preferences.

301 Global import shipment records of Fitbit Fitness Watch with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

Explore Fitness, Health, and Wellness Through Comprehensive Tracker Data

This dataset contains 29 merged files covering minute-level, hourly, and daily tracking across multiple health and wellness metrics. The data is split into two distinct time periods:

• Export 1: March 12, 2016 - April 11, 2016
• Export 2: April 12, 2016 - May 12, 2016

These exports provide detailed insights into user behavior patterns using Fitbit devices, allowing for robust analyses in health and fitness trends.
Dataset Features: 1. Daily Activity:
- Aggregated metrics for steps, calories, and intensity.
- Files: dailyActivity_merged.csv, dailyCalories_merged.csv, dailyIntensities_merged.csv, dailySteps_merged.csv.

1. Hourly Data:

   • Hourly breakdowns of activity and calorie burn.
   • Files: hourlyCalories_merged.csv, hourlyIntensities_merged.csv, hourlySteps_merged.csv.

2. Minute-Level Data:

   • High-resolution data in narrow and wide formats for calories, steps, intensity, and METs.
   • Files:
     • Narrow: minuteCaloriesNarrow_merged.csv, minuteIntensitiesNarrow_merged.csv, minuteStepsNarrow_merged.csv, minuteMETsNarrow_merged.csv.
     • Wide: minuteCaloriesWide_merged.csv, minuteIntensitiesWide_merged.csv, minuteStepsWide_merged.csv.

3. Heart Rate:

   • Second-by-second heart rate data for precise analysis.
   • File: heartrate_seconds_merged.csv.

4. Sleep Data:

   • Insights into sleep quality, duration, and patterns.
   • Files: minuteSleep_merged.csv, sleepDay_merged.csv.

5. Weight Logs:

   • Tracking user weight and trends over time.
   • File: weightLogInfo_merged.csv.

Who can use it:

• Health Behavior Analysis: Study routines, anomalies, and behavioral trends in activity, sleep, and heart rate.
• Machine Learning Applications: Develop predictive models for fatigue, health risks, or fitness improvements.
• Wearable Technology Research: Evaluate user engagement with fitness trackers and related behavioral insights.
• Personalized Wellness Studies: Correlate heart rate, activity levels, and sleep to derive personalized health strategies.

Usage:

1. Fitness and Wellness Trends: Uncover patterns in activity, sleep, and heart rate data.
2. Temporal Analysis: Study how routines and behaviors change over time.
3. Predictive Analytics: Build models to predict fatigue or health risks using granular data.
4. Wearable Insights: Enhance the understanding of Fitbit devices and their impact on user health.

License

Free for public use.

📌 Acknowledgment

This dataset was collected and shared by:
Robert Furberg, Julia Brinton, Michael Keating, and Alexa Ortiz

Original Source:

• Zenodo: Fitbit Data Archive

Contributors to related analyses:
- Julen Aranguren
- Anastasiia Chebotina

LifeSnaps Dataset Documentation Ubiquitous self-tracking technologies have penetrated various aspects of our lives, from physical and mental health monitoring to fitness and entertainment. Yet, limited data exist on the association between in the wild large-scale physical activity patterns, sleep, stress, and overall health, and behavioral patterns and psychological measurements due to challenges in collecting and releasing such datasets, such as waning user engagement, privacy considerations, and diversity in data modalities. In this paper, we present the LifeSnaps dataset, a multi-modal, longitudinal, and geographically-distributed dataset, containing a plethora of anthropological data, collected unobtrusively for the total course of more than 4 months by n=71 participants, under the European H2020 RAIS project. LifeSnaps contains more than 35 different data types from second to daily granularity, totaling more than 71M rows of data. The participants contributed their data through numerous validated surveys, real-time ecological momentary assessments, and a Fitbit Sense smartwatch, and consented to make these data available openly to empower future research. We envision that releasing this large-scale dataset of multi-modal real-world data, will open novel research opportunities and potential applications in the fields of medical digital innovations, data privacy and valorization, mental and physical well-being, psychology and behavioral sciences, machine learning, and human-computer interaction. The following instructions will get you started with the LifeSnaps dataset and are complementary to the original publication. Data Import: Reading CSV For ease of use, we provide CSV files containing Fitbit, SEMA, and survey data at daily and/or hourly granularity. You can read the files via any programming language. For example, in Python, you can read the files into a Pandas DataFrame with the pandas.read_csv() command. Data Import: Setting up a MongoDB (Recommended) To take full advantage of the LifeSnaps dataset, we recommend that you use the raw, complete data via importing the LifeSnaps MongoDB database. To do so, open the terminal/command prompt and run the following command for each collection in the DB. Ensure you have MongoDB Database Tools installed from here. For the Fitbit data, run the following: mongorestore --host localhost:27017 -d rais_anonymized -c fitbit

Taken verbatim from the source: Ubiquitous self-tracking technologies have penetrated various aspects of our lives, from physical and mental health monitoring to fitness and entertainment. Yet, limited data exist on the association between in the wild large-scale physical activity patterns, sleep, stress, and overall health, and behavioral patterns and psychological measurements due to challenges in collecting and releasing such datasets, such as waning user engagement, privacy considerations, and diversity in data modalities. In this paper, we present the LifeSnaps dataset, a multi-modal, longitudinal, and geographically-distributed dataset, containing a plethora of anthropological data, collected unobtrusively for the total course of more than 4 months by n=71 participants, under the European H2020 RAIS project. LifeSnaps contains more than 35 different data types from second to daily granularity, totaling more than 71M rows of data. The participants contributed their data through numerous validated surveys, real-time ecological momentary assessments, and a Fitbit Sense smartwatch, and consented to make these data available openly to empower future research. We envision that releasing this large-scale dataset of multi-modal real-world data, will open novel research opportunities and potential applications in the fields of medical digital innovations, data privacy and valorization, mental and physical well-being, psychology and behavioral sciences, machine learning, and human-computer interaction.

3114 Global export shipment records of Fitbit with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

28 Global import shipment records of Fitbit with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

Objectives There is considerable promise for using commercial wearable devices for measuring physical activity at the population level. The objective of this study was to examine whether commercial wearable devices could accurately predict lying, sitting, and different physical activity intensity in a lab based protocol. Methods We recruited a convenience sample of 46 participants (26 women) to wear three devices, a GENEActiv, and Apple Watch Series 2, a Fitbit Charge HR2. Participants completed a 65-minute protocol with 40-minutes of total treadmill time and 25-minutes of sitting or lying time. Indirect calorimetry was used to measure energy expenditure. The outcome variable for the study was the activity class; lying, sitting, walking self-paced, 3 METS, 5 METS, and 7 METS. Minute-by-minute heart rate, steps, distance, and calories from Apple Watch and Fitbit were included in four different machine learning models. Results Our analysis dataset included 3656 and 2608 minutes of Apple Watch and Fitbit data, respectively. We test decision trees, support vector machines, random forest, and rotation forest models. Rotation forest models had the highest classification accuracies at 82.6% for Apple Watch and 89.3% for Fitbit. Classification accuracies for Apple Watch data ranged from 72.5% for sitting to 89.0% for 7 METS. For Fitbit, accuracies varied between 86.2 for sitting to 92.6% for 7 METS. Conclusion This study demonstrated that commercial wearable devices, Apple Watch and Fitbit, were able to predict physical activity type with a reasonable accuracy. The results support the use of minute by minute data from Apple Watch and Fitbit combined machine learning approaches for scalable physical activity type classification at the population level.

Dataset

This dataset was created by Chuks Chinedu

Contents

The data is composed of different files, each one containing measurements for different physiological monitoring variables collected from Fitbit and Withings devices collected for the H2020 VITALISE project. The next list explains what each file contains.

• heart_rate_2023-04-23.json and heart_rate_2023-04-24.json: Contains heart rate measurements (beats/min) in the Fitbit source format taken for one subject during one day (each file represents a day) and samples every 5 minutes.
• fat_free_mass-2023-04-21-2023-10-20-37e23d5a-1901-4ce3-bcac-f4ea36bc0b1d.json: Contains fat free mass (kg) mesurements taken form Withings Device and transformed to the VITALISE data model for a subject during 6 months. There are 69 measurements in total.
• fat_mass_weight-2023-04-21-2023-10-20-37e23d5a-1901-4ce3-bcac-f4ea36bc0b1d.json: Contains fat mass weight (kg) mesurements taken form Withings Device and transformed to the VITALISE data model for a subject during 6 months. There are 69 measurements in total.
• fat_ratio-2023-04-21-2023-10-20-37e23d5a-1901-4ce3-bcac-f4ea36bc0b1d.json: Contains fat ratio (%) mesurements taken form Withings Device and transformed to the VITALISE data model for a subject during 6 months. There are 69 measurements in total.
• hydration-2023-04-21-2023-10-20-37e23d5a-1901-4ce3-bcac-f4ea36bc0b1d.json: Contains hydration (kg) mesurements taken form Withings Device and transformed to the VITALISE data model for a subject during 6 months. There are 69 measurements in total.
• muscle_mass-2023-04-21-2023-10-20-37e23d5a-1901-4ce3-bcac-f4ea36bc0b1d.json: Contains muscle mass (kg) mesurements taken form Withings Device and transformed to the VITALISE data model for a subject during 6 months. There are 69 measurements in total.
• weight-2023-04-21-2023-10-20-37e23d5a-1901-4ce3-bcac-f4ea36bc0b1dd.json: Contains body weight (kg) mesurements taken form Withings Device and transformed to the VITALISE data model for a subject during 6 months. There are 69 measurements in total.

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Fitbit Sleep Score Data

  About the Dataset





  Description

The Fitbit Sleep Score dataset, available on Kaggle, comprises detailed sleep data sourced from an individual's Fitbit device. It includes metrics such as overall sleep score, revitalization score, deep sleep duration, resting heart rate, and restlessness, each timestamped for in-depth analysis.

  Data Fields

timestamp: The specific date and time the sleep data was recorded. overall_score: An… See the full description on the dataset page: https://huggingface.co/datasets/aai530-group6/sleep-score-fitbit.

Fitbit and BEVO Beacon data. All participants do not have both Fitbit and BEVO Beacon data available.

Introduction: Consumer-wearable activity trackers are small electronic devices engineered to monitor and record fitness and health-related measures. The purpose of this systematic review is to examine the validity and reliability of commercial wearables in measuring step count, heart rate, and energy expenditure. Method: We extracted information about commercial wearable devices (e.g., price, size, battery life, sensors, measurements, algorithms) using an Internet search conducted from November 2016- January 2017. From this search we identified devices to be included in the review. Database searches were conducted in PubMed, Embase, and SPORTDiscus, and only included articles published in the English language up to May 2019. Studies were excluded if they did not identify the device used and if they did not examine the validity and/or reliability of a device. Studies including the general population and all special populations were included. We operationalized validity as criterion (as compared to other measures) and construct (degree to which device is measuring what it purports) validity. Reliability measures focused on intradevice and interdevice reliability. Results: We included 158 publications examining 9 different commercial wearable device brands. Fitbit was by far the most studied brand. In lab-based settings Fitbit, Apple, and Samsung appeared to measure steps accurately. Heart rate was more variable with Apple Watch, Garmin was the most accurate and Fitbit tended towards underestimation. For energy expenditure, no brand was accurate. We also examined validity between devices within a specific brand. Conclusion: Activity trackers are still an emerging market and the devices are constantly being upgraded and redesigned to new models, suggesting the need for more current reviews and research.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

These datasets were generated by respondents to a distributed survey via Amazon Mechanical Turk between 03.12.2016-05.12.2016. Thirty eligible Fitbit users consented to the submission of personal tracker data, including minute-level output for physical activity, heart rate, and sleep monitoring. Individual reports can be parsed by export session ID (column A) or timestamp (column B). Variation between output represents use of different types of Fitbit trackers and individual tracking behaviors / preferences.

Introduction

Fitbit statistics: In today's health-conscious society, monitoring personal wellness metrics has become increasingly important. Fitbit, a leader in wearable technology, offers users detailed insights into their daily activities, sleep patterns, and heart health. On average, Fitbit users take between 10,000 to 18,000 steps per day, aligning with general fitness recommendations.

Sleep tracking data reveals that users typically achieve about 6.5 hours of sleep each night, accompanied by an average Sleep Score of 77. Regarding cardiovascular health, the average resting heart rate among Fitbit users is approximately 65 beats per minute, with variations influenced by factors such as age and gender. These statistics underscore Fitbit's role in providing users with actionable data to support their health and wellness goals.

Let's delve into the fascinating insights through Fitbit statistics and explore what they can tell us about the brand’s performance in 2025.

CSV files after merging of tables of Fitbit data shared at fitbit-dataset

Meta-data file: Link This dataset is created for analysis for the google capstone project for quicker loading of combined useful data

Major changes from the source dataset:

Sleep and Heart Rate Variability (HRV) data is extracted from the heartrate_seconds file in the source data-set combined with the Daily_merged file. Hourly_merged also contains combined data. Heartrate data resampled at 1 min & 1 hour added to the dataset.

FitBit Fitness Tracker Data (CC0: Public Domain, dataset made available through Mobius): This Kaggle data set contains personal fitness tracker from thirty fitbit users. Thirty eligible Fitbit users consented to the submission of personal tracker data, including minute-level output for physical activity, heart rate, and sleep monitoring. It includes information about daily activity, steps, and heart rate that can be used to explore users’ habits.

Data were collected from 113 participants, who shared their physical activity (PA) data using privately owned smart watches and activity trackers from Garmin and Fitbit. This data set consists of two data files: "data.csv" and "data raw.csv": The first file ("data.csv") contains daily averages for steps, total energy expenditure (TEE), activity energy expenditure (AEE), moderate-to-vigorous physical activity (MVPA), light PA (LPA), moderate PA (MPA), vigorous PA (VPA), and sedentary time, grouped by month. In addition, daily averages for the whole year of 2019 and 2020 are included. Finally, separate variables for the first and second half of March 2020 (pre- and post COVID-19 lockdown in Norway) are included. The second file ("data raw.csv") contains raw daily values for steps, TEE, AEE, MVPA, LPA, MPA, VPA, sedentary time, and non-wear time.