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.

The statistic depicts the share of participants in physical activity in the United States in 2018, by age group. During the survey, 42 percent of Millennial respondents in 2018 stated that they actively engaged in physical activities.

This is a source dataset for a Let's Get Healthy California indicator at https://letsgethealthy.ca.gov/. This table displays the percentage of adults meeting Aerobic Physical Activity guidelines in California. It contains data for California only. The data are from the California Behavioral Risk Factor Surveillance Survey (BRFSS). The California BRFSS is an annual cross-sectional health-related telephone survey that collects data about California residents regarding their health-related risk behaviors, chronic health conditions, and use of preventive services. The BRFSS is conducted by the Public Health Survey Research Program of California State University, Sacramento under contract from CDPH. The column percentages are weighted to the 2010 California Department of Finance (DOF) population statistics. Population estimates were obtained from the CA DOF for age, race/ethnicity, and sex. Values may therefore differ from what has been published in the national BRFSS data tables by the Centers for Disease Control and Prevention (CDC) or other federal agencies.

Number and percentage of adults being moderately active or active during leisure time, by age group and sex.

According to a study conducted at the end of 2023, China reported the highest physical activity participation among 22 countries studied worldwide. At that time, nearly ***** out of ten Chinese respondents said that they engaged in at least 150 minutes per week of moderate exercise.

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.

Sponsored by the Robert Wood Johnson Foundation, the Active for Life (AFL) initiative investigated how two physical activity programs for adults aged 50 and older, Active Choices (AC) and Active Living Every Day (ALED), worked in community settings. Created by researchers at Stanford University, Active Choices used lifestyle counseling and personalized telephone support to encourage older adults to be physically active. In AFL, this was a 6-month program delivered through one face-to-face meeting followed by up to eight one-on-one telephone counseling calls. Active Living Every Day, which was created by the Cooper Institute and Human Kinetics Inc., also provided lifestyle counseling to promote physical activity, but in a classroom and workbook format. During the first three years of the four-year AFL initiative, ALED was delivered as a 20-week program where participants attended weekly small group meetings, but in the last year it was shortened to 12 weekly meetings. Nine organizations received AFL grants to implement the programs during 2003-2006. Four grantees implemented the one-on-one AC model, while five implemented the group-based ALED model. Data were collected from the AC and ALED sites for both a process and outcomes evaluation. The primary aims of the process evaluation were to (1) monitor the extent to which the grantees demonstrated fidelity to the AC and ALED models in their program implementation, (2) assess staff experiences implementing the programs, and (3) assess participants' impressions of the programs. A quasi-experimental, pre-post study design was used to assess outcomes. Primary aims of the outcomes evaluation were to evaluate the impact of AC and ALED on self-reported physical activity, and to evaluate the impact of the programs on self-reported stress, depressive symptoms, and satisfaction with body function and appearance. Secondary aims of the outcome evaluation were to (1) evaluate the impact of the programs on measures of functional fitness, (2) examine whether changes in self-reported physical activity and functional fitness were moderated by participant characteristics, including age, gender, race, baseline physical activity self-efficacy, and baseline physical activity social support, and (3) examine whether changes in self-reported physical activity were consistent with a mediation model for physical activity self-efficacy and physical activity social support. The collection has 14 data files (datasets). Datasets 1-7 constitute the process evaluation data, and Datasets 8-14 the outcomes evaluation data: Dataset 1 (AC Initial Face-to-Face Sessions Data) contains information about the initial face-to-face AC session: the format, date, and length of the session, whether the 8 steps required in the face-to-face session were completed, what was discussed between the health educator and the participant related to physical activity plans, interests, benefits, and barriers, and the health educator's progress notes. The file contains one record for each AC participant. Dataset 2 (AC Completed Calls Data) comprises information about the completed AC calls, but does not cover the topics discussed on the calls. Recorded information about each call includes the date and length of the call, the health educator's progress notes, and whether the participant was assessed for injury, light activity, moderate activity, exercise goals, or exercise intentions. Each call is represented by a separate record in the data file and, typically, there are multiple records per participant. Dataset 3 (AC Topics Discussed on Completed Calls ) contains information about the topics discussed on each completed AC call, e.g., exercise barriers/benefits, previous exercise experiences, goal setting, long term goals, injury prevention, rewards/reinforcement, social support, progress tracking, and relapse prevention. Each record in the file represents one topic and there are often multiple records per call for each participant. Dataset 4 (AC Aggregate Call Data) aggregates the call data across calls for each AC participant. For example, for a given participant, this dataset shows the total number of calls completed, the number of calls where injury/health problems were assessed, etc. The file contains one record per participant. Dataset 5 (ALED Sessions Data) contains information about each class session for e

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.

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.

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">

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).

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.

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 dataset is from the 2013 California Dietary Practices Survey of Adults. This survey has been discontinued. Adults were asked a series of eight questions about their physical activity practices in the last month. These questions were borrowed from the Behavior Risk Factor Surveillance System. Data displayed in this table represent California adults who met the aerobic recommendation for physical activity, as defined by the 2008 U.S. Department of Health and Human Services Physical Activity Guidelines for Americans and Objectives 2.1 and 2.2 of Healthy People 2020.

The California Dietary Practices Surveys (CDPS) (now discontinued) was the most extensive dietary and physical activity assessment of adults 18 years and older in the state of California. CDPS was designed in 1989 and was administered biennially in odd years up through 2013. The CDPS was designed to monitor dietary trends, especially fruit and vegetable consumption, among California adults for evaluating their progress toward meeting the 2010 Dietary Guidelines for Americans and the Healthy People 2020 Objectives. For the data in this table, adults were asked a series of eight questions about their physical activity practices in the last month. Questions included: 1) During the past month, other than your regular job, did you participate in any physical activities or exercise such as running, calisthenics, golf, gardening or walking for exercise? 2) What type of physical activity or exercise did you spend the most time doing during the past month? 3) How many times per week or per month did you take part n this activity during the past month? 4) And when you took part in this activity, for how many minutes or hours did you usually keep at it? 5) During the past month, how many times per week or per month did you do physical activities or exercises to strengthen your muscles? Questions 2, 3, and 4 were repeated to collect a second activity. Data were collected using a list of participating CalFresh households and random digit dial, approximately 1,400-1,500 adults (ages 18 and over) were interviewed via phone survey between the months of June and October. Demographic data included gender, age, ethnicity, education level, income, physical activity level, overweight status, and food stamp eligibility status. Data were oversampled for low-income adults to provide greater sensitivity for analyzing trends among our target population.

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.

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).

This dataset includes data on adult's diet, physical activity, and weight status from Behavioral Risk Factor Surveillance System. This data is used for DNPAO's Data, Trends, and Maps database, which provides national and state specific data on obesity, nutrition, physical activity, and breastfeeding.