In 2019, there were 68.7 million smartphone owners in the United States who used at least one health or fitness app at last once per month. It is forecasted that in 2022, there will be 86.3 million users of health or fitness apps in the United States.

During of the first quarter of 2020, health and fitness apps were downloaded 593 million times. It is projected that by the end of the second quarter of 2020, health and fitness apps will have generated 656 million downloads. In the same quarter of the previous year, health and fitness apps were only downloaded 446 million times. This increase is largely due to the global coronavirus pandemic which has caused consumers to stay at home and restructure their exercise regimen and general lifestyle practices.

ABSTRACT Introduction According to the 2015 National Physical Health Monitoring Report, most of the national physical health indicators have begun to rebound, but some people’s physical health is still declining. Object The thesis studies the problems existing in people’s physical exercise and guides the development of these people’s habits. Methods Our mathematical statistics and other research methods investigate the current situation of people’s physical exercise habits, and explore the factors that restrict habits from the factors that affect the formation of sports and fitness concepts. Result The proportion of people developing physical exercise habits is low. People invest less time and energy in physical exercise. Conclusion The less time and energy that people invest in physical exercise is the main reason that affects their belief in exercise and fitness and physical exercise habits. Level of evidence II; Therapeutic studies - investigation of treatment results.

The National Health and Nutrition Examination Survey’s (NHANES) National Youth Fitness Survey (NNYFS) was conducted in 2012 to collect nationally representative data on physical activity and fitness levels for U.S. children and adolescents aged 3-15 years, through household interviews and fitness tests conducted in mobile examination centers.
The NNYFS interview includes demographic, socioeconomic, dietary, and health-related questions. The fitness tests included standardized measurements of core, upper, and lower body muscle strength, and gross motor skills, as well as a measurement of cardiovascular fitness by walking and running on a treadmill. A total of 1,640 children and adolescents aged 3-15 were interviewed and 1,576 were examined.
This set of restricted data files contains indirect identifying and/or sensitive information collected in NNYFS. For NNYFS public use files, please visit NNYFS 2012 at: https://wwwn.cdc.gov/nchs/nhanes/search/nnyfs12.aspx. For more information on the survey design, implementation, and data analysis, see the NNYFS Analytic Guidelines at: https://www.cdc.gov/nchs/nnyfs/analytic_guidelines.htm. For more information on NHANES, visit the NHANES - National Health and Nutrition Examination Survey Homepage at: https://www.cdc.gov/nchs/nhanes/index.htm.

A meticulously compiled dataset providing deep insights into the global fitness industry in 2025. This dataset covers high-demand topics such as the exponential growth of fitness clubs, emerging trends in boutique fitness studios, skyrocketing online fitness training statistics, the flourishing fitness equipment market, and changing consumer behavior and expenditure patterns in the fitness sector.

This survey charted Finnish citizens' as well as social and healthcare service professionals' attitudes and views concerning secondary use of health and social care data in research and development of services. The study contained two target groups: (1) persons who suffered or had a close relative or acquaintance who suffered from one or more chronic conditions, diseases or disorders, and (2) social and healthcare service professionals. First, the respondents' opinions on the reliability of a variety of authorities and organisations were examined (e.g. the police, Kela, register and statistics authorities, universities) as well as trust in appropriate handling of personal data. They were also asked which type of information they deemed personal or not (e.g. bank account number and balance, purchase history at a grocery store, web browsing history, patient records, genetic information, social security number, phone number). They were asked to evaluate which principles they considered important in handling personal health data (e.g. being able to access one's personal data and to have inaccurate data rectified, and being able to restrict data processing), and the study also surveyed how interested the respondents were in keeping track of the use of their health data, and how willing they would be to permit the use of anonymous health data and genetic information for a variety of purposes (e.g. medicine and treatment development, development of equipment and services, and operations of insurance companies). Next, it was examined whether the respondents kept track of their physical activity with a smartphone or a fitness tracker, for instance, and if they would be willing to permit the use of anonymous data concerning physical activity for a variety of purposes. In addition, the respondents' attitudes were charted with regard to developing medicine research by combining anonymous health data and patient records with other data on, for instance, physical activity, alcohol use, grocery store purchase history, web browsing history, and social media use. The study also examined the willingness to permit access to personal health data for social and healthcare service professionals in a service situation, as well as for social and healthcare authorities and other authorities outside of a service situation. Finally, it was charted how important the respondents deemed different factors relating to data collection (e.g. being able to decide for which purposes personal data, or even anonymous data, can be used, and increasing awareness on how health data can be utilised in scientific research). The reliability of a variety of authorities and organisations, such as social welfare/healthcare organisations, academic researchers and pharmaceutical companies, was also examined in terms of data security and purposes for using data. Background variables included, among others, mother tongue, marital status, household composition, housing tenure, socioeconomic class, political party preference, left-right political self-placement, gross income, economic activity and occupational status, and respondent group (citizen/healthcare service professional/social service professional).

Dataset

This dataset was created by Moath Mohamed

Contents

This Synthetic Gym Members Exercise Dataset is created for educational and research purposes in fitness, public health, and data science. It provides detailed demographic, physiological, and workout-related information about gym members, enabling analysis of exercise patterns, health metrics, and fitness progress. The dataset can be utilized for building predictive models and exploring personalized workout and fitness management strategies.

Dataset Features

• Age: Age of the gym member in years.
• Gender: Biological sex of the gym member (Male/Female).
• Weight (kg): Weight of the individual in kilograms.
• Height (m): Height of the individual in meters.
• Max_BPM: Maximum heartbeats per minute during exercise.
• Avg_BPM: Average heartbeats per minute during exercise.
• Resting_BPM: Resting heartbeats per minute.
• Session_Duration (hours): Duration of the exercise session in hours.
• Calories_Burned: Total calories burned during the workout session.
• Workout_Type: Type of workout performed (e.g., HIIT, Yoga, Cardio).
• Fat_Percentage: Body fat percentage of the individual.
• Water_Intake (liters): Water intake during the workout session in liters.
• Workout_Frequency (days/week): Number of workout days per week.
• Experience_Level: Experience level of the gym member (1 = Beginner, 2 = Intermediate, 3 = Advanced).
• BMI: Body Mass Index, calculated as weight (kg) / (height (m))².

Distribution

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Usage

This dataset is suited for the following applications:

• Health and Fitness Insights: Analyze relationships between BMI, workout types, and health metrics like fat percentage or heart rate.
• Personalized Exercise Plans: Develop algorithms to recommend tailored workout routines based on individual fitness levels and goals.
• Calorie Burn Prediction: Build predictive models to estimate calories burned during workout sessions based on key features.
• Public Health Research: Study exercise trends and their impact on health outcomes.
• Fitness Tracking: Use data to monitor individual or group fitness progress over time. ### Coverage This synthetic dataset is anonymized and adheres to data privacy standards. It is designed for research and learning purposes, with diverse cases representing various fitness levels, workout types, and health metrics.

License

CC0 (Public Domain)

Who Can Use It

• Data Science Practitioners: For practicing data preprocessing, regression, and classification tasks related to fitness and health.
• Fitness Professionals and Researchers: To explore trends and patterns in gym members' workout habits and health outcomes.
• Public Health Analysts: To design effective strategies promoting physical activity and healthy lifestyles.
• Policy Makers and Regulators: For data-driven decision-making to promote fitness and public health initiatives.

The Fitness Tracker Dataset contains detailed information about individuals' fitness metrics, exercise routines, and health parameters. This dataset is designed to provide insights into fitness trends, workout habits, and overall health patterns. It is ideal for exploratory data analysis (EDA), machine learning applications, and health analytics. The dataset can help identify relationships between physical activity, body metrics, and health outcomes.

Features: Age: Age of the individual in years. Gender:Gender of the individual (e.g., Male, Female). Weight (kg): Weight of the individual in kilograms. Height (m): Height of the individual in meters. Max_BPM:Maximum heartbeats per minute recorded during exercise. Avg_BPM: Average heartbeats per minute during a workout session. Resting_BPM:Resting heartbeats per minute. Session_Duration (hours):Duration of the workout session in hours. Calories_Burned:Total calories burned during a workout session. Workout_Type:Type of workout performed (e.g., Cardio, Strength, Yoga). Fat_Percentage:Percentage of body fat. Water_Intake (liters):Water intake in liters during or after the workout. Workout_Frequency (days/week): Number of days per week the individual exercises. Experience_Level:Level of fitness experience (e.g., Beginner, Intermediate, Advanced). BMI:Body Mass Index, calculated as weight (kg) / height (m)^2.

##Usage: This dataset is suitable for: - Analyzing the impact of fitness routines on health metrics. Exploring trends in heart rate, calorie burn, and workout habits. Correlating body metrics like BMI and fat percentage with exercise patterns. Building predictive models for fitness and health analytics. This is a synthetic dataset created for educational and analytical purposes and does not represent real-world data.

Market Size statistics on the Gym, Health & Fitness Clubs industry in United States

In 2019, mobile health and fitness apps were used by ** percent of consumers in the United States. In comparison, health apps, workout apps, and meditation apps reached approximately ** percent of U.S. consumers. The adoption of health and fitness apps experienced an acceleration in 2020 and 2021, due to the consequences of the COVID-19 pandemic global outbreak.

Dataset

This dataset was created by Selçuk Yılmaz

Contents

Keeping track of your health is, for many people, a continuous task. Monitoring what you eat, how often you exercise and how much water you drink can be time-consuming, fortunately there are tens of...

Gym, health and fitness clubs stand at a dynamic crossroads, shaped by both impressive resilience and evolving consumer expectations. Despite economic headwinds—including persistent inflation, rising membership fees and supply chain disruptions—Americans’ appetite for fitness hasn’t waned. While higher prices and tariff-driven equipment costs have prompted some concerns around affordability and retention, leading operators have kept pace by doubling down on transparency, technological innovation and community-driven experiences, keeping the industry remarkably buoyant, even as members become more discerning and hybrid workout habits take root. Revenue has expanded at a CAGR of 7.1% to $45.7 billion in 2025, including an uptick of 2.0% that year. Home workouts and digital fitness surged in recent years, with brands like Peloton, Apple Fitness and countless app-based platforms filling the void. Still, the desire for social connection, accountability and access to specialized classes supported attendance at gyms and fitness centers, with group classes, boutique experiences and sports leagues (like the nation’s pickleball boom) fueling a new wave of growth. Technological integration has become standard, as fitness centers capitalized on mobile booking, wearables, hybrid class offerings and personalized digital experiences to boost retention. Gyms have also responded to sticky inflation and financial uncertainty by offering more flexible, tiered memberships and novel pay-per-visit plans, making fitness accessible across a wider range of budgets and life stages, boosting profit. Gym, health and fitness clubs will deepen their shift into a wellness-centric, tech-enabled ecosystem, with opportunities and challenges in equal measure. Demographic tailwinds will prove significant: as the population ages and healthcare costs climb, older adults will turn to gyms for exercise as well as holistic health management. Gyms, health and fitness centers are shifting toward integrated, medically informed offerings, blending classes with diagnostics, tracking devices and partnerships with healthcare providers. Affordability, digital convenience and privacy will be crucial considerations as gyms race to balance premium health solutions with accessibility. Gyms and fitness centers that innovate around flexibility and evidence-based care will sustain growth. Revenue is expected to grow at a CAGR of 1.4% to reach an estimated $49.1 billion by 2030.

Note 08/07/13: Errata for regarding two variables incorrectly labelled with the same description in the Data Archive for the Health Survey for England - 2008 dataset deposited in the UK Data Archive Author: Health and Social Care Information Centre, Lifestyle Statistics Responsible Statistician: Paul Eastwood, Lifestyles Section Head Version: 1 Original date of publication: 17th December 2009 Date of errata: 11th June 2013 · Two physical activity variables (NSWA201 and WEPWA201) in the Health Survey for England - 2008 dataset deposited in the Data Archive had the same description of 'on weekdays in the last week have you done any cycling (not to school)?'. This is correct for NSWA201, but incorrect for WEPWA201 · The correct descriptions are: · NSWA201 - 'on weekdays in the last week have you done any cycling (not to school)?' · WEPWA201 - 'on weekends in the last week have you done any cycling (not to school)?' · This has been corrected and the amended dataset has been deposited in the UK Data Archive. NatCen Social Research and the Health and Social Care Information Centre apologise for any inconvenience this may have caused. Note 18/12/09: Please note that a slightly amended version of the Health Survey for England 2008 report, Volume 1, has been made available on this page on 18 December 2009. This was in order to correct the legend and title of figure 13G on page 321 of this volume. The NHS IC apologises for any inconvenience caused. The Health Survey for England is a series of annual surveys designed to measure health and health-related behaviours in adults and children living in private households in England. The survey was commissioned originally by the Department of Health and, from April 2005 by The NHS Information Centre for health and social care. The Health Survey for England has been designed and carried out since 1994 by the Joint Health Surveys Unit of the National Centre for Social Research (NatCen) and the Department of Epidemiology and Public Health at the University College London Medical School (UCL). The 2008 Health Survey for England focused on physical activity and fitness. Adults and children were asked to recall their physical activity over recent weeks, and objective measures of physical activity and fitness were also obtained. A secondary objective was to examine results on childhood obesity and other factors affecting health, including fruit and vegetable consumption, drinking and smoking.

Dataset

This dataset was created by Arun Kumar

Contents

Introduction

Google Fit Statistics: Google Fit, since its launch in 2014, formed the major platform of fitness and health for Google, enabling users to track several health metrics and pool data from several fitness apps and devices. In its continued evolution were added unique features like Heart Points, developed under the auspices of WHO and AHA, aimed at inducing physical activity.

Changes of much significance are due in 2024, marking a change in Google's very own approach to health data-keeping. In this article, we will enclose the Google Fit statistics.

Abstract Introduction: This study is relevant insofar since it provides information on the elements that interfere in the level of physical activity of the elderly that attend the Fitness zone. Objective: Identifying the level of physical activity and its associated factors related to the elderly who usually go to third age fitness centers in Maringá/PR. Method: A cross-sectional study was carried out with 970 elderly users of the Fitness zone of the municipality of Maringá, state of Paraná, Brazil. A sociodemographic questionnaire and the International Physical Activity Questionnaire (IPAQ) was used. The data were analyzed by the Pearson’s Chi-squared, the Binary Logistic Regression and the Hosmer-Lemeshow test (p < 0,05). Results: The data indicates that being married (p = 0,047) and having completed higher education (p = 0,001) is significantly associated with higher physical activity level. The lower use of medication (p = 0,008), the excellent health perception (p = 0,037), and no history of near-falls (p = 0,038) were associated with the physical activity practice. The subjects who had no history of near-falls in the last six months and who did not have osteoporosis were 1.671 [95% CI = 1.009-2.613] and 1.891 [95% CI = 1.008-2.915] times more likely to be active/very active when compared to the elderly who had near-falls in the last semester and who had osteoporosis. Conclusion: It was concluded that sociodemographic variables and health conditions are associated to the physical activity level in the elderly. Further, elderly who reported the absence of a history of near falls and osteoporosis have more chance to be physically active.

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.

Distribution of the household population by physical fitness classification, by sex and age group.