Childhood obesity has risen and is one of the most important global problems of our time, and school physical education programs are the key to ameliorating it. In American schools, physical fitness scores have declined; yet, global, national, state, and local concerns for the overall health, physical fitness, and wellbeing of children are at an all-time high. The lack of safe and affordable options for physical activity coupled with the significant decrease in physical activity rates among most American children underscores the need for programs, data, and research on physical fitness in schools, where children spend a significant amount of their time. The purpose of this brief research report is to call the federal government and states to mandate physical fitness programs and to increase data collection capacity on physical fitness in schools. Subsequently, this study asks researchers to study physical fitness in schools in the U.S. to increase its importance to policy makers and educational stakeholders and advance our understanding of educational inequities in school physical fitness. As an example, using descriptive analyses, we have provided policymakers, educational stakeholders, and researchers with a first look at California’s physical fitness data which shows how our findings complement prior literature as well as extend them. Implications for the research and practice are discussed.

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 dataset encompasses a comprehensive set of metrics aimed at evaluating the physical education performance of students. It includes various quantitative and qualitative measures that reflect students' physical fitness levels, engagement in class, and overall performance outcomes.

Key Features: ID: A unique identifier for each student participant. Age: The age of the student, which can be crucial for analyzing performance trends across different age groups. Gender: The gender of the student, allowing for comparisons in performance between different genders. Grade_Level: The current grade level of the student, providing context for developmental stage and age-related performance expectations. Strength_Score: A numerical score representing the student's strength, derived from specific physical assessments. Endurance_Score: A score reflecting the student's endurance capabilities based on fitness tests. Flexibility_Score: A score indicating the flexibility level of the student, measured through relevant exercises. Speed_Agility_Score: A score assessing the speed and agility of the student in various physical activities. BMI: The Body Mass Index of the student, calculated from height and weight, providing insights into physical health. Health_Fitness_Knowledge_Score: A score that measures the student's knowledge and understanding of health and fitness principles. Skills_Score: A score evaluating the student's physical skills related to sports and physical activities. Class_Participation_Level: A qualitative or quantitative measure of how actively the student participates in physical education classes. Attendance_Rate: The percentage of physical education classes attended by the student, an important factor in performance outcomes. Motivation_Level: A score that gauges the student's motivation towards engaging in physical education activities. Overall_PE_Performance_Score: A composite score representing the student's overall performance in physical education. Improvement_Rate: A measure of the student's improvement in physical education performance over time. Final_Grade: The final grade awarded to the student for their performance in the physical education course. Previous_Semester_PE_Grade: The grade received in the prior semester, useful for tracking performance trends. Hours_Physical_Activity_Per_Week: The average number of hours the student engages in physical activity outside of school, indicating lifestyle habits. Performance: The target variable that summarizes the overall performance outcome, which could be used for classification or regression tasks. Purpose: The dataset is designed to facilitate research and analysis in physical education, helping educators, researchers, and policymakers understand the factors influencing student performance in physical fitness. It can be utilized for statistical analysis, machine learning model development, and creating interventions aimed at enhancing physical education outcomes.

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 dataset includes data on weight status for children aged 3 months to 4 years old from Women, Infant, and Children Participant and Program Characteristics (WIC-PC). 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. For more information about WIC-PC visit https://www.fns.usda.gov/wic/national-survey-wic-participants.

The aim of this study was to investigate the development of concepts, levels of physical fitness related to health and physical activity patterns provided by the Physical Education (PE) classes. The sample consisted of 40 students (24 boys, 16 girls) high school students. Physical fitness was measured by the sit and reach test, running in 9 minutes and abdominal strength in 1 minute, belonging to the battery of tests PROESP. The knowledge was determined by theoretical test. The physical activity score was estimated by the International Physical Activity Questionnaire (IPAQ). In 30 PE classes the teaching contents were related to the concepts of stretching and gym exercises: Interval fitness and running. There were significant changes in abdominal strength, flexibility and knowledge to both sexes and aerobic endurance for girls, indicating that classes, worked through teaching procedures aimed at health promotion can modify the concepts and health-related physical fitness of high school students.

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

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.

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

This is a source dataset for a Let's Get Healthy California indicator at https://letsgethealthy.ca.gov/. Data originally from the California Department of Education Fitnessgram website at http://www.cde.ca.gov/ta/tg/pf/.

Descriptive statistics for age, physical activity, physical fitness and cardio-metabolic markers.

This is a new statistical bulletin which presents for the first time 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 obese hospital admissions and prescriptions dispensed related to obesity. The bulletin also summarises government plans and targets in this area, as well as providing sources of further information and links to relevant documents.

Persons achieving National Physical Activity Guidelines Details Download JSON-STAT Persons achieving National Physical Activity Guidelines Preview Download PX Persons achieving National Physical Activity Guidelines Details Download XLSX Persons achieving National Physical Activity Guidelines

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.

Tabular data includes:

• YearStart
• YearEnd
• LocationAbbr
• LocationDesc
• Datasource
• Class
• Topic
• Question
• Data_Value_Unit
• Data_Value_Type
• Data_Value
• Data_Value_Alt
• Data_Value_Footnote_Symbol
• Data_Value_Footnote
• Low_Confidence_Limit
• High_Confidence_Limit
• Sample_Size
• Total
• Age(years)
• Education
• Gender
• Income
• Race/Ethnicity
• GeoLocation
• ClassID
• TopicID
• QuestionID
• DataValueTypeID
• LocationID
• StratificationCategory1
• Stratification1
• StratificationCategoryID1
• StratificationID1

The HSCIC will be changing future publication dates for the four compendia reports which cover smoking, alcohol, drugs and obesity. The new dates for these reports will be approximately: Smoking - will move from end August to end May. Alcohol - will move from end May to end June. Drugs - will move from end November to end March. Obesity - will stay at end Feb (but 3rd March for 2015). One advantage of this change is that the Hospital Admissions data used in the Drugs compendia will now be able to use final data instead of provisional. A consequence is there will be no drugs compendia in 2015 with the next report being in March 2016. However, all the other data used in the report will be available from the sources where it is initially published. If you have any concerns over these changes then please send an email by 27 February 2015 to enquiries@hscic.gov.uk setting out your concerns. 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 this report 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). Previously unpublished figures on obesity-related Finished Hospital Episodes and Finished Consultant Episodes for 2012-13 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.

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.

Key Findings of Territory-wide Physical Fitness Survey for the Community The target sample size was categorized into the following age groups: 1. Children aged 7-11: 426 primary school children completed the physical fitness test (225 boys and 201 girls), and 409 primary school children completed the questionnaire (214 boys and 195 girls) 2. Adolescents aged 12-16: 180 boys and 170 girls 3. Adults aged 17-59: 2,026 men and 3,065 women 4. Elderly aged 60-79: 917 men and 1,635 women

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.

In April 2020, a survey carried out in the United Kingdom found that since the lockdown restrictions were imposed due to the coronavirus (COVID-19) pandemic, 28 percent of respondents aged between 18 and 24 years were engaging in a little more physical activity than usual, while a further 12 percent were doing a lot more physical activity than usual. On the other hand, 19 percent of people aged between 35 and 44 years said they are doing a lot less physical exercise than before, the highest share across all age groups. The latest number of cases in the UK can be found here. For further information about the coronavirus pandemic, please visit our dedicated Facts and Figures page.