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.

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.

Note:

Because this dataset has been used in a competition, we had to hide some of the data to prepare the test dataset for the competition. Thus, in the previous version of the dataset, only train.csv file is existed.

Content

This dataset represents 10 different physical poses that can be used to distinguish 5 exercises. The exercises are Push-up, Pull-up, Sit-up, Jumping Jack and Squat. For every exercise, 2 different classes have been used to represent the terminal positions of that exercise (e.g., “up” and “down” positions for push-ups).

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. From every video, at least 2 frames are manually extracted. The extracted frames represent the terminal positions of the exercise.

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.

https://mediapipe.dev/images/mobile/pose_tracking_full_body_landmarks.png" alt="33 pose landmarks">

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.

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.

Insulin resistance has wide-ranging effects on metabolism but there are knowledge gaps regarding the tissue origins of systemic metabolite patterns, and how patterns are altered by fitness and metabolic health. To address these questions, plasma metabolite patterns were determined every 5 min during exercise (30 min, ~45% of V̇O2peak, ~63 W) and recovery in overnight-fasted sedentary, obese, insulin resistant women under controlled conditions of diet and physical activity. We hypothesized that improved fitness and insulin sensitivity following a ~14 wk training and weight loss intervention would lead to fixed workload plasma metabolomics signatures reflective of metabolic health and muscle metabolism. Pattern analysis over the first 15 min of exercise—regardless of pre- vs. post-intervention status—highlighted anticipated increases in fatty acid tissue uptake and oxidation (e.g., reduced long-chain fatty acids), diminution of non-oxidative fates of glucose (e.g., lowered sorbitol-pathway metabolites and glycerol-3-galactoside [possible glycerolipid synthesis metabolite]), and enhanced tissue amino acid use (e.g., drops in amino acids; modest increase in urea). A novel observation was that exercise significantly increased several xenometabolites (“non-self” molecules, from microbes or foods), including benzoic acid/salicylic acid/salicylaldehyde, hexadecanol/octadecanol/dodecanol, and chlorogenic acid. In addition, many non-annotated metabolites changed with exercise. Although exercise itself strongly impacted the global metabolome, there were surprisingly few intervention-associated differences despite marked improvements in insulin sensitivity, fitness, and adiposity. These results, and previously-reported plasma acylcarnitine profiles, support the principle that most metabolic changes during sub-maximal aerobic exercise are closely tethered to absolute ATP turnover rate (workload), regardless of fitness or metabolic health status. Supporting Materials include graphs of blood patterns of metabolites in adult women during a sub-maximal exercise bout and recovery period, and primary data in spreadsheet format on model performance, exercise and recovery, and correlation statistics for metabolites. Journal information -- Am J Physiol, Endo & Metabolism, Exercise plasma metabolomics and xenometabolomics in obese, sedentary, insulin-resistant women: impact of a fitness and weight loss intervention. Resources in this dataset:Resource Title: Supporting Materials 1, exercise plasma metabolite excursions, annotated metabolites. File Name: Supporting Materials 1, exercise metabolite excursions, annotated metabolites, 7-23-19.pdfResource Description: Blood plasma concentrations of known, annotated metabolites in adult women during exercise at ~65W for 30 min, then 20 min cool-downResource Software Recommended: Adobe Acrobat,url: https://acrobat.adobe.com/us/en/acrobat/pdf-reader.html Resource Title: Supporting Materials 2, exercise plasma metabolite excursions, non-annotated (unknown identity) metabolites. File Name: Supporting Materials 2, exercise metabolite excursions, non-annotated (unknown identity) metabolites, 2-7-19.pdfResource Description: Blood plasma concentrations of non-annotated (as yet to be identified) metabolites in adult women during exercise at ~65W for 30 min, then 20 min cool-downResource Software Recommended: Adobe Acrobat,url: https://acrobat.adobe.com/us/en/acrobat/pdf-reader.html Resource Title: Supporting Materials 3, Correlation Stats, Pre & Post exercise plasma metabolite patterns in adults, All Timepoints. File Name: Supporting Materials 3, Correlation Stats, Pre & Post, All Timepoints, 2-16-19 FOR SUBMISSION xls.xlsResource Description: Correlation data for plasma metabolites using data across 30 min of sub-maximal exercise (~65W), then 20 min cool-down, in adult womenResource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Supporting Materials 4, CDS_SA0002 Analysis Results. File Name: Supporting Materials 4, CDS_SA0002 Analysis Results, 2-16-19 FOR SUBMISSION xls.xlsResource Description: Plasma metabolomics data from sub-maximal (~65W) exercise in adult womenResource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel

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.

Context

A team's mean seasons statistics can be used as predictors for their performance in future games. However, these statistics gain additional meaning when placed in the context of their opponents' (and opponents' opponents') performance. This dataset provides this context for each team. Furthermore, predicting games based on post-season stats causes data leakage, which from experience can be significant in this context (15-20% loss in accuracy). Thus, this dataset provides each of these statistics prior to each game of the regular season, preventing any source of data leakage.

Content

All data is derived from the March Madness competition data. Each original column was renamed to "A" and "B" instead of "W" and "L," and the mirrored to represent both orderings of opponents. Each team's mean stats are computed (both their stats, and the mean "allowed" or "forced" statistics by their opponents). To compute the mean opponents' stats, we analyze the games played by each opponent (excluding games played against the team in question), and compute the mean statistics for those games. We then compute the mean of these mean statistics, weighted by the number of times the team in question played each opponent. The opponents' opponent's stats are computed as a weighted average of the opponents' average. This results in statistics similar to those used to compute strength of schedule or RPI, just that they go beyond win percentages (See: https://en.wikipedia.org/wiki/Rating_percentage_index)

The per game statistics are computed by pretending we don't have any of the data on or after the day in question.

Next Steps

Currently, the data isn't computed particularly efficiently. Computing the per game averages for every day of the season is necessary to compute fully accurate opponents' opponents' average, but takes about 90 minutes to obtain. It is probably possible to parallelize this, and the per-game averages involve a lot of repeated computation (basically computing the final averages over and over again for each day). Speeding this up will make it more convenient to make changes to the dataset.

I would like to transform these statistics to be per-possession, add shooting percentages, pace, and number of games played (to give an idea of the amount uncertainty that exists in the per-game averages). Some of these can be approximated with the given data (but the results won't be exact), while others will need to be computed from scratch.

In 2021, around 70 percent of Millennial respondents from the United States stated that they participated in fitness sports, making them the generation with the highest participation rate. The generation with the lowest participation in fitness sports was Gen Z.

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).

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

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 ...).

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.

Dataset

This dataset was created by Jason Sim

Contents

The Molecular Transducers of Physical Activity Consortium (MoTrPAC) aims to elucidate how exercise improves health and ameliorates diseases by building a map of the molecular responses to endurance exercise. MoTrPAC is a multi-site collaboration across the US encompassing various scientific disciplines: preclinical animal study sites and human clinical exercise sites, which perform the exercise testing and biospecimen collection; a consortium coordinating center and biorepository, which manages sample collection, distribution of samples, and consortium logistics; chemical analysis sites, which are responsible for omics analysis from the samples collected; and a bioinformatics center to collaboratively analyze and map the data generated by the other sites along with data dissemination to make the data and other resources available to the public. The animal studies enable analysis of the effects of exercise on many different tissues that are not readily obtainable in humans, whereas the collection of accessible human tissues (muscle, blood, and adipose) will permit the analysis of the direct effect of exercise in humans. Additional information can be found at the main consortium page (https://motrpac.org) or at the data portal (https://motrpac-data.org).

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.

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.

Dataset

This dataset was created by Omar Pelcastre

Contents

BackgroundPhysical activity reduces the risk of noncommunicable diseases and is therefore an essential component of a healthy lifestyle. Regular engagement in physical activity can produce immediate and long term health benefits. However, physical activity levels are not as high as might be expected. For example, according to the global World Health Organization (WHO) 2017 statistics, more than 80% of the world’s adolescents are insufficiently physically active. In response to this problem, physical activity programs have become popular, with step counts commonly used to measure program performance. Analysing step count data and the statistical modeling of this data is therefore important for evaluating individual and program performance. This study reviews the statistical methods that are used to model and evaluate physical activity programs, using step counts.MethodsAdhering to PRISMA guidelines, this review systematically searched for relevant journal articles which were published between January 2000 and August 2017 in any of three databases (PubMed, PsycINFO and Web of Science). Only the journal articles which used a statistical model in analysing step counts for a healthy sample of participants, enrolled in an intervention involving physical exercise or a physical activity program, were included in this study. In these programs the activities considered were natural elements of everyday life rather than special activity interventions.ResultsThis systematic review was able to identify 78 unique articles describing statistical models for analysing step counts obtained through physical activity programs. General linear models and generalized linear models were the most popular methods used followed by multilevel models, while structural equation modeling was only used for measuring the personal and psychological factors related to step counts. Surprisingly no use was made of time series analysis for analysing step count data. The review also suggested several strategies for the personalisation of physical activity programs.ConclusionsOverall, it appears that the physical activity levels of people involved in such programs vary across individuals depending on psychosocial, demographic, weather and climatic factors. Statistical models can provide a better understanding of the impact of these factors, allowing for the provision of more personalised physical activity programs, which are expected to produce better immediate and long-term outcomes for participants. It is hoped that this review will identify the statistical methods which are most suitable for this purpose.

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.