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

As fitness studios and entire sports leagues have shuttered to limit the COVID-19 spread, home workouts have gained some popularity in recent weeks. From Instagram fitness challenges, YouTube video producers posting home workouts, to personal trainers taking their classes online, there has been a plethora of new online fitness content for consumers to try out. According to a March 2020 survey, 16 percent of adults in the United States had taken to using more online exercise videos due to social distancing and self-quarantining practices during the coronavirus pandemic.

For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

Introduction

Google Fit Statistics: Google Fit, since its launch in 2014, formed the major platform of fitness and health for Google, enabling users to track several health metrics and pool data from several fitness apps and devices. In its continued evolution were added unique features like Heart Points, developed under the auspices of WHO and AHA, aimed at inducing physical activity.

Changes of much significance are due in 2024, marking a change in Google's very own approach to health data-keeping. In this article, we will enclose the Google Fit statistics.

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.

Comprehensive dataset of 94 Physical fitness programs in Wyoming, United States as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

Comprehensive dataset of 309 Physical fitness programs in Arkansas, United States as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

As a consequence of the coronavirus outbreak, Danes have become less physically active. According to a survey from April 2020, ** percent of adult Danes who were exercising before the corona crisis quit all physical activities and sports during this period. On the other hand, ** percent continued staying active. In comparison, ** percent of the Danes, who did not exercise on a regular basis, started exercising in times of the corona pandemic. In March 2020, Denmark officially shut down and the possibilities of exercising and doing sports became limited. For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

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.

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

The relationship between physical activity and mental health, especially depression, is one of the most studied topics in the field of exercise science and kinesiology. Although there is strong consensus that regular physical activity improves mental health and reduces depressive symptoms, some debate the mechanisms involved in this relationship as well as the limitations and definitions used in such studies. Meta-analyses and systematic reviews continue to examine the strength of the association between physical activity and depressive symptoms for the purpose of improving exercise prescription as treatment or combined treatment for depression. This dataset covers 27 review articles (either systematic review, meta-analysis, or both) and 365 primary study articles addressing the relationship between physical activity and depressive symptoms. Primary study articles are manually extracted from the review articles. We used a custom-made workflow (Fu, Yuanxi. (2022). Scopus author info tool (1.0.1) [Python]. https://github.com/infoqualitylab/Scopus_author_info_collection that uses the Scopus API and manual work to extract and disambiguate authorship information for the 392 reports. The author information file (author_list.csv) is the product of this workflow and can be used to compute the co-author network of the 392 articles. This dataset can be used to construct the inclusion network and the co-author network of the 27 review articles and 365 primary study articles. A primary study article is "included" in a review article if it is considered in the review article's evidence synthesis. Each included primary study article is cited in the review article, but not all references cited in a review article are included in the evidence synthesis or primary study articles. The inclusion network is a bipartite network with two types of nodes: one type represents review articles, and the other represents primary study articles. In an inclusion network, if a review article includes a primary study article, there is a directed edge from the review article node to the primary study article node. The attribute file (article_list.csv) includes attributes of the 392 articles, and the edge list file (inclusion_net_edges.csv) contains the edge list of the inclusion network. Collectively, this dataset reflects the evidence production and use patterns within the exercise science and kinesiology scientific community, investigating the relationship between physical activity and depressive symptoms. FILE FORMATS 1. article_list.csv - Unicode CSV 2. author_list.csv - Unicode CSV 3. Chinese_author_name_reference.csv - Unicode CSV 4. inclusion_net_edges.csv - Unicode CSV 5. review_article_details.csv - Unicode CSV 6. supplementary_reference_list.pdf - PDF 7. README.txt - text file 8. systematic_review_inclusion_criteria.csv - Unicode CSV UPDATES IN THIS VERSION COMPARED TO V3 (Clarke, Caitlin; Lischwe Mueller, Natalie; Joshi, Manasi Ballal; Fu, Yuanxi; Schneider, Jodi (2023): The Inclusion Network of 27 Review Articles Published between 2013-2018 Investigating the Relationship Between Physical Activity and Depressive Symptoms. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-4614455_V3) - We added a new file systematic_review_inclusion_criteria.csv.

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).

Introduction

Health and Wellness Industry Statistics: Generally, healthy food, nutritional food, vitamins, and protein shakes are increasing rapidly in the health and wellness market. People around the world are not just concentrating on outdoor exercise, gyms, and activities, but also on food that includes nutrients, proteins, and many other things that benefit the after-workout body.

Considering today’s remote working era, it is essential to balance mental and physical health with a good diet. After the COVID-19 pandemic, people have become very health-conscious. The Health and Wellness Industry Statistics are an exciting sight that will grow the knowledge about the market from the corners of the globe.

Comprehensive dataset of 278 Physical fitness programs in Idaho, United States as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

The National Fitness 100 video provides information such as physical fitness certification and measurement methods, exercise prescription guides, exercise prescription videos, musculoskeletal exercise, standard exercise by life cycle, routine exercise by purpose, and exercise prescription guides for the National Fitness 100 video provided by the Seoul Olympic Memorial National Sports Promotion Foundation. It provides list search (search) information for the National Fitness 100 video using search conditions by criteria suitable for each exercise, such as age group, physical fitness factor, exercise location, exercise body part, exercise stage, exercise week, exercise order, and exercise purpose. This service can effectively support the promotion of physical fitness and health management of the nation by providing customized exercise information according to age, physical fitness level, exercise purpose, etc.

This dataset contains a list of 423 consumer-based wrist-worn activity trackers and smart watches, capable of collecting and estimating physical activity levels in individuals, using accelerometer and other sensors. Data were collected by automatic and manual searches through six online and offline databases, as well as manual collecting of data from company web sites. Data were collected in 2017, and contains all identified devices released between 2011 (earliest identified device) and July 2017. For each device, 12 attributes are included. See list in the ReadMe file.

During a 2024 survey, 86 percent of respondents from India and Indonesia stated they believed social media influencers were a useful source of information on travel topics. In Germany, the share stood at 46 percent.

Data of experiment 1. In experiment 1, the researchers invite subjects to participate in a fitness activity, which include a variety of high and low intensity stretching exercises. The data involves subjects’ participation outcome, perceived certainty and outcome salience, activity category selection in different situations of self-quantification and goal-setting. Data of experiment 2. In experiment 2, the researchers invite subjects to participate in a smart bracelet experience activity (the smart bracelet has step tracking and back-office footprints recording functions). The data involves subjects’ participation outcome, novelty-seeking in different situations of self-quantification and goal-setting. Data of experiment 3. In experiment 3, the researchers invite subjects to participate in a treadmill running. The data involves subjects’ activity participation experience, persistence intention, perceived enjoyment in different situations of self-quantification and goal-setting.

Abstract

This study aimed to assess the quality and reliability of the most-watched YouTube videos on Otago exercises. The keywords “Otago exercise” and “Otago exercise program” were searched between December 15-30, 2023. Sixty videos were selected for each keyword, sorted by number of views. Video metrics and upload sources were documented. The modified (m) DISCERN score and the Global Quality Score (GQS) were used to evaluate the reliability and quality of the videos, respectively. Out of the 34 videos reviewed, the majority (47.1%) were shared by physiotherapists. The median mDISCERN score was 2, indicating that a significant proportion (79.4%) of the videos exhibited low reliability (p<0.05). The median GQS score was 3, with 64.7% of videos classified as intermediate or high quality. However, no statistical differences in quality were observed (p>0.05). Although no statistical difference was noted, it was evident that physiotherapists uploaded a higher percentage of reliable and high-quality videos compared to other sources. Analysis of the video metrics among the quality groups revealed significant differences only in video duration (p<0.05). Positive correlations were found between certain video metrics and mDISCERN (video duration, number of comments) and GQS (video duration) scores. YouTube videos on OTAGO exercises demonstrate insufficient reliability and quality. Collaboration with professional organizations in geriatric rehabilitation is recommended for YouTube to produce high-quality and reliable videos aligned with their evolving health content policy.

Dataset

This dataset was created by Dhrumil Patel

Released under CC0: Public Domain

Contents

Fitness Nutrition Dataset

A processed and cleaned nutritional dataset optimized for fitness and health applications, derived from the USDA Food Data Central database.

  Dataset Description

This dataset contains nutritional information for branded food products with UPC codes, making it ideal for fitness apps, nutrition tracking, and health research. Each row represents a unique food product with comprehensive macronutrient and micronutrient data. Dataset Statistics:

📊 1… See the full description on the dataset page: https://huggingface.co/datasets/trentmkelly/US-food-nutrient-data.