In 2024, it was found that 23.6 percent of men in the United States participated in sports, exercise, and recreational activities daily, compared to only 19.4 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.

Dataset Overview:

This dataset contains simulated (hypothetical) but almost realistic (based on AI) data related to sleep, heart rate, and exercise habits of 500 individuals. It includes both pre-exercise and post-exercise resting heart rates, allowing for analyses such as a dependent t-test (Paired Sample t-test) to observe changes in heart rate after an exercise program. The dataset also includes additional health-related variables, such as age, hours of sleep per night, and exercise frequency.

The data is designed for tasks involving hypothesis testing, health analytics, or even machine learning applications that predict changes in heart rate based on personal attributes and exercise behavior. It can be used to understand the relationships between exercise frequency, sleep, and changes in heart rate.

File: Filename: heart_rate_data.csv File Format: CSV

- Features (Columns):

Age: Description: The age of the individual. Type: Integer Range: 18-60 years Relevance: Age is an important factor in determining heart rate and the effects of exercise.

Sleep Hours: Description: The average number of hours the individual sleeps per night. Type: Float Range: 3.0 - 10.0 hours Relevance: Sleep is a crucial health metric that can impact heart rate and exercise recovery.

Exercise Frequency (Days/Week): Description: The number of days per week the individual engages in physical exercise. Type: Integer Range: 1-7 days/week Relevance: More frequent exercise may lead to greater heart rate improvements and better cardiovascular health.

Resting Heart Rate Before: Description: The individual’s resting heart rate measured before beginning a 6-week exercise program. Type: Integer Range: 50 - 100 bpm (beats per minute) Relevance: This is a key health indicator, providing a baseline measurement for the individual’s heart rate.

Resting Heart Rate After: Description: The individual’s resting heart rate measured after completing the 6-week exercise program. Type: Integer Range: 45 - 95 bpm (lower than the "Resting Heart Rate Before" due to the effects of exercise). Relevance: This variable is essential for understanding how exercise affects heart rate over time, and it can be used to perform a dependent t-test analysis.

Max Heart Rate During Exercise: Description: The maximum heart rate the individual reached during exercise sessions. Type: Integer Range: 120 - 190 bpm Relevance: This metric helps in understanding cardiovascular strain during exercise and can be linked to exercise frequency or fitness levels.

Potential Uses: Dependent T-Test Analysis: The dataset is particularly suited for a dependent (paired) t-test where you compare the resting heart rate before and after the exercise program for each individual.

Exploratory Data Analysis (EDA):Investigate relationships between sleep, exercise frequency, and changes in heart rate. Potential analyses include correlations between sleep hours and resting heart rate improvement, or regression analyses to predict heart rate after exercise.

Machine Learning: Use the dataset for predictive modeling, and build a beginner regression model to predict post-exercise heart rate using age, sleep, and exercise frequency as features.

Health and Fitness Insights: This dataset can be useful for studying how different factors like sleep and age influence heart rate changes and overall cardiovascular health.

License: Choose an appropriate open license, such as:

CC BY 4.0 (Attribution 4.0 International).

Inspiration for Kaggle Users: How does exercise frequency influence the reduction in resting heart rate? Is there a relationship between sleep and heart rate improvements post-exercise? Can we predict the post-exercise heart rate using other health variables? How do age and exercise frequency interact to affect heart rate?

Acknowledgments: This is a simulated dataset for educational purposes, generated to demonstrate statistical and machine learning applications in the field of health analytics.

It sounds like you have a substantial amount of personal exercise and health data accumulated over 150 days. This data can provide valuable insights into your fitness journey and overall well-being. Here are some suggestions on how you can analyze and make the most of this information:

Exercise Types:

Identify the types of exercises you've been engaging in. Categorize them into cardiovascular, strength training, flexibility, and other categories. Note the frequency and duration of each type of exercise.

Intensity Levels: Assess the intensity of your workouts. This can be measured in terms of heart rate, perceived exertion, or weight lifted. Determine if there are patterns in intensity levels over time.

Progress and Setbacks: Look for trends in your progress. Are you consistently improving, or have you encountered any setbacks? Identify factors that contribute to your success or challenges.

Rest and Recovery: Analyze your rest days and recovery strategies. Ensure that you're allowing your body enough time to recover between intense workouts. Look for patterns in your energy levels and performance related to rest.

Nutrition and Hydration: Correlate your exercise data with your nutrition and hydration habits. Consider whether certain eating patterns impact your workouts positively or negatively.

Sleep Patterns: Examine your sleep data if available. Adequate sleep is crucial for recovery and overall health. Identify any correlations between your sleep patterns and exercise performance.

Mood and Stress Levels: Reflect on your mood and stress levels on different days. Exercise can have a significant impact on mental well-being. Consider whether there are connections between your exercise routine and your emotional state.

Injury Analysis: If you've experienced any injuries during this period, analyze the circumstances surrounding them. This can help in understanding potential risk factors.

Goal Alignment: Evaluate whether your exercise routine aligns with your initial goals. Are you progressing toward your desired outcomes?

Adjustment of Exercise Routine: Based on the analysis, consider adjustments to your exercise routine. This might involve modifying the types of exercises, intensity, or frequency.

Remember, the goal of analyzing this data is to make informed decisions about your fitness routine, identify areas of improvement, and celebrate your successes. If you have specific questions about the data or need guidance on certain aspects, feel free to provide more details for personalized advice.

The statistic depicts the participation rate in physical activity in the United States in 2021, by type. During the survey, 67.3 percent of respondents stated that they participated in fitness sports in 2021.

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

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 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.

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.

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.

This statistical report presents information on obesity, physical activity and diet, drawn together from a variety of sources. The topics covered include: Obesity related hospital admissions. Prescription items for the treatment of obesity. Adult obesity prevalence. Childhood obesity prevalence. Physical activity levels among adults and children. Diet among adults and children, including trends in purchases, and consumption of food and drink and energy intake. Each section provides an overview of the key findings from these sources, as well as providing sources of further information and links to relevant documents and sources. Some of the data have been published previously by NHS Digital. A data visualisation tool at the link below allows users to select obesity related hospital admissions data for any Local Authority (as contained in Excel tables 3, 7 and 11 of this publication), along with time series data from 2013/14. Regional and national comparisons are also provided.

This statistic presents the health benefits of regular physical activity in the United Kingdom (UK) in 2017. Regular physical exercise reduces an individual's risk of hip fractures by ** percent, followed by getting type 2 diabetes by ** percent.

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This dataset provides detailed information on 50 diverse exercises designed to promote overall health and fitness. It includes a wide range of activities suitable for beginners to advanced fitness enthusiasts, targeting various muscle groups and fitness goals. The data can be used for personal fitness planning, workout app development, or data analysis projects in health and sports science.

Column Descriptions

1. Name of Exercise: The common name of the exercise. Type: String Description: Unique identifier for each exercise in the dataset.

2. Sets: The recommended number of sets for the exercise. Type: Integer Description: Indicates how many times the group of repetitions should be performed.

3. Reps: The recommended number of repetitions per set. Type: Integer Description: Specifies how many times the exercise should be performed in each set.

4. Benefit: The primary health or fitness benefit of the exercise. Type: String Description: Briefly explains the main advantage or target of the exercise.

5. Burns Calories (per 30 min): Estimated calorie burn for a 30-minute session. Type: Integer Description: Approximates the number of calories burned by an average person (155 lbs/70 kg) performing the exercise for 30 minutes.

6. Target Muscle Group: The main muscles or muscle groups engaged during the exercise. Type: String Description: Lists the primary muscles worked, helping users target specific areas.

7. Equipment Needed: Any equipment required to perform the exercise. Type: String Description: Specifies necessary equipment, or "None" if the exercise can be performed without equipment.

8. Difficulty Level: The relative challenge level of the exercise. Type: String Description: Categorizes exercises as "Beginner," "Intermediate," or "Advanced" to guide appropriate selection based on fitness level.

During a survey in the United States in 2023, around ** 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.

1) Data Introduction • The Gym Members Exercise Dataset is a dataset built to systematically collect gym members' exercise routines, body information, exercise habits, and fitness indicators to analyze individual exercise patterns and health conditions.

2) Data Utilization (1) Gym Members Exercise Dataset has characteristics that: • This dataset contains various body and exercise related numerical and categorical variables such as age, gender, weight, height, body fat percentage, BMI, exercise type (e.g., aerobic, muscular, yoga, HIIT), exercise frequency, session time, heart rate (maximum, average, rest), calorie consumption, water intake, and experience level. (2) Gym Members Exercise Dataset can be used to: • Exercise effect analysis and customized fitness strategy: Various variables such as exercise type, frequency, session time and heart rate, calorie burn, body fat percentage, etc. can be analyzed and used to establish customized exercise plans for each member and optimize exercise effectiveness. • Healthcare and Member Characteristics Based Marketing: Based on demographics and exercise habit data such as age, gender, and experience level, it can be used to develop healthcare programs, segment members, and establish targeted marketing strategies.

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 Tanya Sharma04

Released under Apache 2.0

Contents

ABSTRACT Introduction Outdoor sports can help people develop good living habits and improve people’s physical fitness. For this reason, it is very important to cultivate sports hobbies and analyze the factors of healthy sports. Objective To understand the factors that affect the healthy sports behavior of college students, we provide a reference for the relevant departments of the school and physical education teachers. Methods The thesis uses literature data method, questionnaire survey method and mathematical statistics method to analyze sports influencing factors with college students as the research object. Results The physical education method and the completeness of the facilities will affect the students’ interest in sports. Students from different family backgrounds have very different preferences for healthy sports. Conclusions The school environment and sports atmosphere are the main factors that constitute the school sports environment. College students’ cognition and understanding of healthy sports will affect their own sports situation. Level of evidence II; Therapeutic studies - investigation of treatment results.

Increase the percentage of the population that have participated in any physical activity in the last 30 days from 71.7% in 2012 to 79.2% by 2017.

Note 09/05/2013 A presentation error has been identified in the data in tables 7.1 and 7.2 originally included in this publication. The tables have been republished with corrected figures. The accompanying errata note provides more detail. The Health and Social Care Information Centre apologise for any inconvenience this may have caused. Summary: This statistical report presents a range of information on obesity, physical activity and diet, drawn together from a variety of sources. The topics covered include: Overweight and obesity prevalence among adults and children Physical activity levels among adults and children Trends in purchases and consumption of food and drink and energy intake Health outcomes of being overweight or obese. This report contains seven chapters which consist of the following: Chapter 1: Introduction; this summarises government policies, targets and outcome indicators in this area, as well as providing sources of further information and links to relevant documents. Chapters 2 to 6 cover obesity, physical activity and diet and provides an overview of the key findings from these sources, whilst maintaining useful links to each section of these reports. Chapter 7: Health Outcomes; presents a range of information about the health outcomes of being obese or overweight which includes information on health risks, hospital admissions and prescription drugs used for treatment of obesity. Figures presented in Chapter 7 have been obtained from a number of sources and presented in a user-friendly format. Some of the data contained in the chapter have been published previously by the Health and Social Care Information Centre (HSCIC) or the National Audit Office. Previously unpublished figures on obesity-related Finished Hospital Episodes and Finished Consultant Episodes for 2011/12 are presented using data from the HSCIC's Hospital Episode Statistics as well as data from the Prescribing Unit at the HSCIC on prescription items dispensed for treatment of obesity.