Table of INEBase Regular physical exercise and sedentary lifestyle in the free time by type of household. Annual. National. Quality of Life Indicators

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Connected Gym Equipment Market Size and Forecast 2025-2029

The connected gym equipment market size estimates the market to reach USD 10.16 billion, at a CAGR of 42.4% between 2024 and 2029. North America is expected to account for 39% of the growth contribution to the global market during this period. In 2019, the CTE segment was valued at USD 531.90 billion and has demonstrated steady growth since then.

The market is experiencing significant growth, driven by the increasing penetration of smartphones and the rising demand for connected gym services. Consumers are seeking convenience and personalized fitness experiences, leading to a surge in demand for technology-enabled gym equipment. However, this market faces challenges as well. Compatibility with various mobile operating systems is essential to cater to a diverse user base, making it crucial for manufacturers to ensure their equipment is adaptable. Another obstacle is the lack of awareness regarding gym-related technology and connected equipment among potential customers, necessitating marketing efforts to educate and engage consumers.
Companies in this market must navigate these challenges while capitalizing on the growing demand for connected fitness solutions to remain competitive and thrive in the evolving landscape.

What will be the Size of the Connected Gym Equipment Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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The market continues to evolve, integrating advanced technologies to enhance user experiences and optimize fitness outcomes. Strength training metrics are no longer limited to manual tracking; IoT fitness ecosystems now enable real-time workout feedback through exercise video streaming and API integration. Home gym connectivity, workout scheduling systems, and wearable device sync facilitate convenience and consistency. Body composition analysis, data encryption protocols, fitness app integration, sleep tracking integration, and user activity dashboards offer comprehensive insights into overall health and progress. Virtual fitness classes, personalized training plans, and augmented reality training cater to diverse fitness goals. Machine learning algorithms and biometric data capture enable AI-powered fitness guidance, while cloud data storage ensures accessibility.

One notable example of market innovation is a fitness platform that experienced a 50% increase in user engagement through the integration of real-time workout feedback and customized workout routines. Industry growth is expected to reach double-digit percentages as the market unfolds, incorporating features like community fitness features, virtual reality fitness, gamified fitness programs, secure user authentication, remote fitness coaching, equipment maintenance alerts, and cardio performance analysis.

How is this Connected Gym Equipment Industry segmented?

The connected gym equipment industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Product

  CTE
  STE


End-user

  Residential
  Commercial


Distribution Channel

  Online
  Offline


Type

  Cardio
  Strength Training


Technology Specificity

  IoT
  AI
  Bluetooth


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    Spain
    UK


  Middle East and Africa

    UAE


  APAC

    China
    India
    Japan
    South Korea


  South America

    Brazil


  Rest of World (ROW)

By Product Insights

The CTE segment is estimated to witness significant growth during the forecast period.

The market is witnessing significant growth due to the fusion of technology and fitness. Strength training metrics and cardio performance analysis enable users to track their progress and optimize workouts. Exercise video streaming and virtual fitness classes offer immersive and personalized training experiences. Home gym connectivity and workout scheduling systems ensure harmonious integration of equipment and routines. API integration, fitness app integration, and wearable device sync facilitate seamless data transfer and analysis. Body composition analysis, sleep tracking integration, and user activity dashboards provide holistic health insights. Real-time workout feedback, progress visualization tools, and personalized training plans cater to individual fitness goals.

Exercise equipment sensors, customized workout routines, and augmented reality training offer engaging and effective workouts. Digital fitness subscription models provide affordable access to a wide range of features. Community fitness features foster a supportive and motivating environment. Virtual reali

With Versium REACH Demographic Append you will have access to many different attributes for enriching your data.

Basic, Household and Financial, Lifestyle and Interests, Political and Donor.

Here is a list of what sorts of attributes are available for each output type listed above:

Basic: - Senior in Household - Young Adult in Household - Small Office or Home Office - Online Purchasing Indicator
- Language - Marital Status - Working Woman in Household - Single Parent - Online Education - Occupation - Gender - DOB (MM/YY) - Age Range - Religion - Ethnic Group - Presence of Children - Education Level - Number of Children

Household, Financial and Auto: - Household Income - Dwelling Type - Credit Card Holder Bank - Upscale Card Holder - Estimated Net Worth - Length of Residence - Credit Rating - Home Own or Rent - Home Value - Home Year Built - Number of Credit Lines - Auto Year - Auto Make - Auto Model - Home Purchase Date - Refinance Date - Refinance Amount - Loan to Value - Refinance Loan Type - Home Purchase Price - Mortgage Purchase Amount - Mortgage Purchase Loan Type - Mortgage Purchase Date - 2nd Most Recent Mortgage Amount - 2nd Most Recent Mortgage Loan Type - 2nd Most Recent Mortgage Date - 2nd Most Recent Mortgage Interest Rate Type - Refinance Rate Type - Mortgage Purchase Interest Rate Type - Home Pool

Lifestyle and Interests: - Mail Order Buyer - Pets - Magazines - Reading
- Current Affairs and Politics
- Dieting and Weight Loss - Travel - Music - Consumer Electronics - Arts
- Antiques - Home Improvement - Gardening - Cooking - Exercise
- Sports - Outdoors - Womens Apparel
- Mens Apparel - Investing - Health and Beauty - Decorating and Furnishing

Political and Donor: - Donor Environmental - Donor Animal Welfare - Donor Arts and Culture - Donor Childrens Causes - Donor Environmental or Wildlife - Donor Health - Donor International Aid - Donor Political - Donor Conservative Politics - Donor Liberal Politics - Donor Religious - Donor Veterans - Donor Unspecified - Donor Community - Party Affiliation

This data has seasonal stats which can all be easily calculated to per game and other various labels and stats. I used nba_api to get all this data. You can check that out at: https://github.com/Tman1351/NBA-API-Data-Getter. Feel free to use it on whatever you want.

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Gym Management Software Market Size 2025-2029

The gym management software market size is forecast to increase by USD 201.5 million, at a CAGR of 12.5% between 2024 and 2029.

The market is experiencing significant growth, driven by the increasing number of fitness centers and health clubs worldwide. This expansion is fueled by the rising demand for efficient and streamlined gym operations, as well as the growing trend towards digitalization in the fitness industry. However, this market also faces challenges, with data privacy emerging as a major concern. With the increasing use of technology in gym management, ensuring the security and protection of members' personal information is crucial. Navigating this data privacy landscape requires a robust and transparent approach from gym management software providers.
As the market continues to evolve, companies must prioritize data security while also offering innovative features to differentiate themselves and meet the evolving needs of fitness businesses.

What will be the Size of the Gym Management Software Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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The market continues to evolve, with dynamic market activities unfolding across various sectors. Seamlessly integrated solutions enable attendance tracking, appointment booking, studio management, progress monitoring, gym analytics, global deployment, class scheduling, personal training management, user experience (UX), subscription management, data encryption, social media integration, pricing models, inventory management, and more. Scheduling optimization and multi-location support are crucial features for gym operators managing multiple facilities. Group class management, data visualization, and training and onboarding ensure effective workouts and member engagement. Support services, wearable device integration, and biometric integration offer enhanced functionality and convenience. Maintenance and support, fitness assessments, security features, API integrations, payment processing, data backup, and membership tracking are essential components for gym management software.

HIPAA compliance, user interface (UI), payroll integration, cross-platform compatibility, performance benchmarking, and cloud-based solutions cater to the evolving needs of the industry. Real-time data, reporting and analytics, member management, access control, nutrition tracking, software updates, and marketing automation are features that help gym operators make data-driven decisions and improve overall performance. Compliance with data privacy regulations such as GDPR and HIPAA, staff management, lead generation, equipment tracking, resource allocation, and customer feedback are essential for maintaining a successful gym business.

How is this Gym Management Software Industry segmented?

The gym management software industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Application

  Gyms and health clubs
  Sports clubs


Deployment

  Cloud-based
  On-premises


Functionality

  Membership Management
  Scheduling and Booking
  Billing and Payments


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Spain
    UK


  APAC

    China
    India
    Japan


  South America

    Brazil


  Rest of World (ROW)

By Application Insights

The gyms and health clubs segment is estimated to witness significant growth during the forecast period.

In the dynamic fitness industry, gym management software has emerged as a crucial tool for gyms and health clubs to streamline their operations and enhance member experiences. This software facilitates scheduling optimization, ensuring efficient use of resources and reducing wait times. Multi-location support caters to gym chains, enabling seamless management across multiple facilities. Group class management simplifies the process of organizing and tracking classes, while data visualization offers valuable insights into gym analytics. Training and onboarding tools help new members get acclimated, and support services ensure that any issues are promptly addressed. Integration with wearable devices and biometric systems allows for advanced fitness assessments and personalized workouts.

Maintenance and support features keep equipment in optimal condition, and security measures protect sensitive member data. API integrations enable seamless data exchange with third-party applications, while payment processing and data backup ensure smooth financial transactions and data security. Attendance tracking, appointment booking, and studio management tools provide a more org

Context

This dataset was collected to work on NBA games data. I used the nba stats website to create this dataset.

You can find more details about data collection in my GitHub repo here : nba predictor repo.

If you want more informations about this api endpoint feel free to go on the nba_api GitHub repo that documentate each endpoint : link here

Content

You can find 5 datasets :

• games.csv : all games from 2004 season to last update with the date, teams and some details like number of points, etc.
• games_details.csv : details of games dataset, all statistics of players for a given game
• players.csv : players details (name)
• ranking.csv : ranking of NBA given a day (split into west and east on CONFERENCE column
• teams.csv : all teams of NBA

Acknowledgements

I would like to thanks nba stats website which allows all NBA data freely open to everyone and with a great api endpoint.

Inspiration

• Predict NBA games winner

Enjoy it ! Nathan

Yahoo Knowledge Graph COVID-19 Datasets

Background

The Yahoo Knowledge Graph team at Verizon Media is responsible for providing critical COVID-19 data that feeds into Yahoo properties like Yahoo News, Yahoo Finance, and Yahoo Weather. The COVID-19 datasets include country, state, and county level information updated on a rolling basis, with updates occuring approximately hourly.

The COVID-19 datasets are constructed entirely from primary (government and public agency) sources with a clear attribution of the primary sources used for each geographical region. While other aggregations of COVID-19 data are already available, we believe ours to be the only open source COVID-19 dataset that is constructed entirely from primary sources with clear attribution back to those sources. Our hope is that additional transparency will enable more accurate analysis, aiding researchers who seek to understand and prevent further spread of the disease.

Released together with the COVID-19 dataset are two other open source projects:

• An API powered by Elide, which provides JSON-API and GraphQL interfaces to the COVID-19 public data
• A dashboard to explore the data

Datasets

The data is logically organized by region and time. Time is further organized into a snapshot of the latest updates received for all regions and the updates reported by regions for a given date. As the COVID-19 pandemic develops and local governments and agencies improve their ability to collect and present their data to the public, the schema will evolve. Please check back as sources frequently evolve.

We welcome data feeds or links to web pages that you would like us to crawl, extract, and merge into the overall stats. Feel free to submit an issue.

region-metadata

Provides general information about the regions covered in the dataset, such as geographical location and links to other public data sources.

by-region-[DATE]

Provides detailed case counts of COVID-19 in each region on [DATE] in local time for that region. Each entry (row) in the daily file represents a single region.

Please be aware that different sources release data at different and often unpredictable frequencies. The by-region-[DATE] numbers will be updated as sources release data for the given date for their region. In some cases, data for a given region is not released until many days after that calendar date has elapsed everywhere in the world. As a result, the same by-region-[DATE] file may show different aggregate statistics for the same date depending on when the by-region-[DATE] is accessed. Generally speaking, by-region-[DATE] data more than one week old is stable.

Content

This table represents details of CHP (Child Health Plus) insurance. Child Health Plus provides free or low-cost health insurance for children under the age of 19 who are not eligible for Medicaid, coverage for children. All children receive their health care through a managed care plan. There are no immigration requirements for Child Health Plus.

Context

This is a dataset hosted by the City of New York. The city has an open data platform found here and they update their information according the amount of data that is brought in. Explore New York City using Kaggle and all of the data sources available through the City of New York organization page!

• Update Frequency: This dataset is updated monthly.

Acknowledgements

This dataset is maintained using Socrata's API and Kaggle's API. Socrata has assisted countless organizations with hosting their open data and has been an integral part of the process of bringing more data to the public.

Context: COVID-19 Cases are on the rise in India. This dataset allows one to model the spread of COVID-19 in India.

Content: The data shows the number of total confirmed covid-19 cases per day in India.

Acknowledgement: This data comes from the Free COVID-19 API and can be found at https://documenter.getpostman.com/view/10808728/SzS8rjbc.

Context

This dataset contains the lap time information for the FIA World Endurance Championship (WEC) for the 2012 to 2022 seasons. Included in the dataset is the: lap times, the driver who set the lap times, the car they set the lap time with, the car number of the car the lap time was set with, the class they were in, the team they were in, the track they set the time at, what round of the championship they set the time at, and which year of the championship they set the lap time at. Data was scraped from the FIA WEC timing, hosted at http://fiawec.alkamelsystems.com/index.php.

Content

number: The car number that completed the lap driver_number: The driver of the car number that completed the lap lap_number: The lap number of the race the lap was completed at lap_time: The lap time recorded as they crossed the timing beam lap_improvement: Haven't looked into this, but my guess would a variable showing if the driver made improvement vs previous lap_times. Likely 0 is no improvement, 1 is green (personal best), 2 is purple (race best), and 3 is a WR? 99% of the laps have 0 improvement, so further research probably needed. crossing_finish_line_in_pit: boolean for if they crossed the finish line, B if they did, nan else s1, s2, s3 The sector times recorded as they crossed the timing beam (recorded in ss.mss) s1/s2/s3_improvement similar to lap_improvement s1/s2/s3_large: how they crossed the timing beams similar to lap_time kph: the average kph of the lap top_speed: the fastest recorded time of the lap driver_name: the driver that recorded the lap pit_time: the recorded time that was spent in the pitlane (typically followed by "B" in crossing_finish_line_in_pit) class: the class of the car that set the lap time group: the group of the car that set the lap time, only applicable to LMP1s and LMP2 Pro/Am (2021 season?) team: the team of the car that set the lap manufacturer: the manufacturer of the car that set the lap season: the WEC season the lap was set at circuit: the circuit the lap was set at round: the round (race number in the championship) the lap was set at vehicle: the car the lap was set with flag_at_fl: the flag status at the timing beam (only for 2022) lap_time_ms: The lap time recorded in milliseconds (seconds*1000) lap_time_s: the lap time recorded in seconds team_no: A combination of team and the team's number e.g Toyota Gazoo Racing #7 engine: The engine of the car the lap was set with. driver_stint_no: Labeling the driver stint. A stint changes when the driver pits and either a. stays in the car, or b. swaps into the car. driver_stint: A combination of driver_name and the driver_stint_no, e.g. Mike CONWAY Stint #1 team_stint_no: Labeling the team stint. A stint changes when the driver pits. team_stint: A combination of team_no and the team_stint_no, e.g. Toyota Gazoo Racing #7 Stint #1 position: The position of the car at the time of the lap. class_position: The position of the car in class at the time of the lap. interval_ms: The interval (gap to the car in front for position) in ms interval: The interval (gap to the car in front for position) in s gap: The total time to the leader (time to 1st position overall) in s class_interval: The interval (gap to the car in front for position in class) in s class_gap The total time to the leader (time to 1st position in class) in s

If I had a better way of organizing it, I probably would, in multiple databases that contain circuit information, driver information, class information, etc. instead of one singular almost 200mb sheet. I don't think I'll be adding any more columns to this, I've added too many so far. Feel free to comment with any questions! Cheers.

Updated as of 23rd June 2022, with Lemans race data.