In 2023, it was found that 22.4 percent of men in the United States participated in sports, exercise, and recreational activities daily, compared to only 19.9 percent of women. These statistics highlight a notable difference in the daily engagement of different genders in sporting activities. Other factors influencing this participation include socioeconomic status, age, disability, ethnicity, geography, personal interests, and societal expectations. These barriers can prevent individuals from having equal access to, and opportunities for, sport participation. What role does gender play in sports participation? Historically, many sports have been segregated by gender, with men and women participating in separate leagues and competitions. This segregation has led to a lack of opportunities for women and girls to participate in sports at the same level as men and boys. Additionally, societal attitudes and stereotypes about gender can discourage women and girls from participating in sports or limit their access to resources and support for their athletic pursuits. This often results in fewer women and girls participating in sports and a lack of representation of women and girls in leadership roles within the sports industry. However, in recent years, there has been an increased focus on promoting gender equality in sports and providing equal opportunities for men and women to participate in sports. This includes initiatives to increase funding and support for women's sports, as well as efforts to challenge gender stereotypes and discrimination in the athletic world. Impact of the COVID-19 pandemic on sports participation The COVID-19 pandemic led to many people spending more time at home due to lockdowns, remote work, and school closures. This resulted in many people having more time to engage in sports and other physical activities, as seen in the share of the U.S. population engaged in sports and exercise peaking in 2020. With gyms and sports facilities closed or with limited access, many people turned to home-based workouts and other activities. This included activities such as running, cycling, and strength training that could all be done at home with minimal equipment. Online classes and streaming services also saw an increase in usage during the pandemic, providing people with access to a wide range of workout options and fitness programs.

When surveyed in 2023, it was found that the civilian population of the United States spent an average of 0.31 hours per day on sports, exercise, and recreation. In total, this was a slight increase on the previous year's figure, with a higher increase seen among men than women. Overall, the year with the highest average number of hours spent on sports, exercise, and recreation was 2020. Share of people participating in sports, exercise, and recreation in the U.S. In 2023, U.S. participation in sports, exercise, and recreational activities was skewed slightly in favor of men. This highlights a notable difference in the daily engagement of different genders in sporting activities. Other factors that can influence sports participation include socioeconomic status, age, disability, ethnicity, geography, personal interests, and societal expectations. Such barriers can prevent individuals from having equal access to, and opportunities for, sport participation. What are the most popular outdoor activities in the U.S.? Some of the more common outdoor activities in the U.S. include hiking, fishing, cycling, and jogging. In 2023, hiking was the most popular outdoor activity in the U.S., with 20 percent of surveyed Americans having participated in the outdoor pastime. Meanwhile, around 18 percent of Americans engaged in recreational fishing and 17.9 percent engaged in running, jogging, and trail running.

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.

Content

This dataset represents sequential poses that can be used to distinguish 5 physical exercises. The exercises are Push-up, Pull-up, Sit-up, Jumping Jack and Squat. The dataset consists of 33 landmarks that represents several important body parts' positions. Using these landmarks, the angles and the distances between several landmarks are calculated and included in the dataset. The sequence of the poses is provided by preserving the frame order in every record.

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Collection Process

About 500 videos of people doing the exercises have been used in order to collect this data. The videos are from Countix Dataset that contain the YouTube links of several human activity videos. Using a simple Python script, the videos of 5 different physical exercises are downloaded. All the frames of the videos are extracted, processed and included in the dataset.

Processing Data

For every frame, MediaPipe framework is used for applying pose estimation, which detects the human skeleton of the person in the frame. The landmark model in MediaPipe Pose predicts the location of 33 pose landmarks (see figure below). Visit Mediapipe Pose Classification page for more details. Using these landmarks, the angles and the distances between several landmarks are calculated.

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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 dataset is a comprehensive list of gym exercises that can be used to improve your fitness. It includes exercises for all levels of fitness, from beginners to advanced. The dataset also includes information on the muscles worked by each exercise, the equipment needed, and how to do the exercise safely.

This dataset can be used to create a personalized workout routine that meets your individual fitness goals. You can use the information in the dataset to choose exercises that target the muscles you want to strengthen or tone. You can also use the information to find exercises that are safe for your fitness level.

The dataset is a valuable resource for anyone who wants to improve their fitness. It can be used by beginners to learn the basics of gym exercises, by intermediate exercisers to find new and challenging exercises, and by advanced exercisers to fine-tune their workouts.

Here are some additional tips for using the dataset:

Start with a few exercises and gradually add more as you get stronger. Listen to your body and don't push yourself too hard. Warm up before you start your workout and cool down afterwards. Stay hydrated by drinking plenty of water. Eat a healthy diet to support your fitness goals.

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

High-income countries in the Asia Pacific region had the highest prevalence of sufficient physical inactivity in 2022, at ** percent. By 2030, this figure was expected to climb to almost ** percent.

This table contains 1260 series, with data for years 1990 - 1998 (not all combinations necessarily have data for all years), and was last released on 2007-01-29. This table contains data described by the following dimensions (Not all combinations are available): Geography (30 items: Austria; Belgium (Flemish speaking); Belgium; Belgium (French speaking) ...), Sex (2 items: Males; Females ...), Age group (3 items: 11 years;13 years;15 years ...), Frequency of exercise (7 items: Everyday; Once a week;2 to 3 times a week;4 to 6 times a week ...).

The raw data on behavior and physical fitness. The behavior for sampling worker before joining WE is on sheet behavior 31 and 62 Then, we show all data for behavior and physical fitness.

A September 2023 survey on exercise habits in the United States revealed that around 65 percent of male respondents took part in strength training. Meanwhile, just under one quarter of female respondents participated in yoga.

Includes 24 hour recall data that children were instructed to fill-out describing the previous day’s activities at baseline, weeks 2 and 4 of the intervention, after the intervention (6 weeks), and after washout (10 weeks). Includes accelerometer data using an ActiGraph to assess usual physical and sedentary activity at baseline, 6 weeks, and 10 weeks. Includes demographic data such as weight, height, gender, race, ethnicity, and birth year. Includes relative reinforcing value data showing how children rated how much they would want to perform both physical and sedentary activities on a scale of 1-10 at baseline, week 6, and week 10. Includes questionnaire data regarding exercise self-efficacy using the Children’s Self-Perceptions of Adequacy in and Predilection of Physical Activity Scale (CSAPPA), motivation for physical activity using the Behavioral Regulations in Exercise Questionnaire, 2nd edition (BREQ-2), motivation for active video games using modified questions from the BREQ-2 so that the question refers to motivation towards active video games rather than physical activity, motivation for sedentary video games using modified questions from the BREQ-2 so that the question refers to motivation towards sedentary video games behavior rather than physical activity, and physical activity-related parenting behaviors using The Activity Support Scale for Multiple Groups (ACTS-MG). Resources in this dataset:Resource Title: 24 Hour Recall Data. File Name: 24 hour recalldata.xlsxResource Description: Children were instructed to fill out questions describing the previous day's activities at baseline, week 2, and week 4 of the intervention, after the intervention (6 weeks), and after washout (10 weeks).Resource Title: Actigraph activity data. File Name: actigraph activity data.xlsxResource Description: Accelerometer data using an ActiGraph to assess usual physical and sedentary activity at baseline, 6 weeks, and 10 weeks.Resource Title: Liking Data. File Name: liking data.xlsxResource Description: Relative reinforcing value data showing how children rated how much they would want to perform both physical and sedentary activities on a scale of 1-10 at baseline, week 6, and week 10.Resource Title: Demographics. File Name: Demographics (Birthdate-Year).xlsxResource Description: Includes demographic data such as weight, height, gender, race, ethnicity, and year of birth.Resource Title: Questionnaires. File Name: questionnaires.xlsxResource Description: Questionnaire data regarding exercise self-efficacy using the Children's Self-Perceptions of Adequacy in and Predilection of Physical Activity Scale (CSAPPA), motivation for physical activity using the Behavioral Regulations in Exercise Questionnaire, 2nd edition (BREQ-2), motivation for active video games using modified questions from the BREQ-2 so that the question refers to motivation towards active video games rather than physical activity, motivation for sedentary video games using modified questions from the BREQ-2 so that the question refers to motivation towards sedentary video games behavior rather than physical activity, and physical activity-related parenting behaviors using The Activity Support Scale for Multiple Groups (ACTS-MG).

**Description of the three datasets **

1) PremierLeaguePlayersDataset: This dataset includes statistics ranging from general information such as the goals and assists in a season, to more precise statistics like key passes and dribble attempts. It also includes the player of the year for a given season. Interesting predictive analysis could be done with this attribute. This dataset ranges from the 02/03 season, to the 20/21 season.

2) League Standings: This dataset includes the final standings of a given season. The data ranges from the 10/11 season, to the 20/21. The attributes are the same you may find on the official Premier League site or Sky Sports site (where the data actually comes from)

3) Full Dataset: This dataset merges the two datasets described above. For a given player and season, you have the final ranking of his team. An interesting analysis would be to see the players involvement in the teams goals.

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.

This data set is an example data set for the data set used in the experiment of the paper "A Multilevel Analysis and Hybrid Forecasting Algorithm for Long Short-term Step Data". It contains two parts of hourly step data and daily step data

Created for Exercise 3 in Digital Preservation Exercises (Course at Vienna University of Technology).

Experiment that generates statistics from open data on sporting institutes in Vienna. The script is written in Python, the data comes as plain text as well as graphs in .eps format. See README.md for details.

Results of the experiment are written in German.

This research examines the relationship between physical exercise and subjective well-being via the mediation of body image and self-esteem, thereby providing some suggestions on the improvement of subjective well-being in college students. A total of 671 college students from three universities of science and engineering in Sichuan, China voluntarily participated in the survey. Descriptive statistics, Pearson’s product-moment correlation, and mediation model analysis were conducted using the SPSS statistics 19.0. The results showed that (1) the physical exercise level was positively and significantly correlated with the subjective well-being level in each dimension (R = 0.12–0.64, p < 0.01) (2) college students with the medium and high level of exercise have higher subjective well-being than those with the low level of exercise, and (3) body image and self-esteem played a complete mediation role between physical exercise and subjective well-being. The mediation analysis revealed two paths: first, the single mediating path via self-esteem [indirect effect = 0.087, 95% CI: (0.037, 0.141)] and second, the serial mediating path via body image and self-esteem [indirect effect = 0.038, 95% CI: (0.021, 0.158)]. Some practical implications have been discussed on the physical exercise intervention for promoting the subjective well-being level in college students.

Physical fitness is a key element of a healthy life, and being overweight or lacking physical exercise will lead to health problems. Therefore, assessing an individual’s physical health status from a non-medical, cost-effective perspective is essential. This paper aimed to evaluate the national physical health status through national physical examination data, selecting 12 indicators to divide the physical health status into four levels: excellent, good, pass, and fail. The existing challenge lies in the fact that most literature on physical fitness assessment mainly focuses on the two major groups of sports athletes and school students. Unfortunately, there is no reasonable index system has been constructed. The evaluation method has limitations and cannot be applied to other groups. This paper builds a reasonable health indicator system based on national physical examination data, breaks group restrictions, studies national groups, and hopes to use machine learning models to provide helpful health suggestions for citizens to measure their physical status. We analyzed the significance of the selected indicators through nonparametric tests and exploratory statistical analysis. We used seven machine learning models to obtain the best multi-classification model for the physical fitness test level. Comprehensive research showed that MLP has the best classification effect, with macro-precision reaching 74.4% and micro-precision reaching 72.8%. Furthermore, the recall rates are also above 70%, and the Hamming loss is the smallest, i.e., 0.272. The practical implications of these findings are significant. Individuals can use the classification model to understand their physical fitness level and status, exercise appropriately according to the measurement indicators, and adjust their lifestyle, which is an important aspect of health management.

During a survey in the United States in 2023, around 58 percent of respondents stated that they exercised at least three times a week. In the same survey, some of the most popular physical activities in the U.S. were hiking, biking, and running.

This report presents information on obesity, physical activity and diet drawn together from a variety of sources for England. More information can be found in the source publications which contain a wider range of data and analysis. Each section provides an overview of key findings, as well as providing links to relevant documents and sources. Some of the data have been published previously by NHS Digital. A data visualisation tool (link provided within the key facts) allows users to select obesity related hospital admissions data for any Local Authority (as contained in the data tables), along with time series data from 2013/14. Regional and national comparisons are also provided. The report includes information on: Obesity related hospital admissions, including obesity related bariatric surgery. Obesity prevalence. Physical activity levels. Walking and cycling rates. Prescriptions items for the treatment of obesity. Perception of weight and weight management. Food and drink purchases and expenditure. Fruit and vegetable consumption. Key facts cover the latest year of data available: Hospital admissions: 2018/19 Adult obesity: 2018 Childhood obesity: 2018/19 Adult physical activity: 12 months to November 2019 Children and young people's physical activity: 2018/19 academic year