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.

Context

The Behavioral Risk Factor Surveillance System (BRFSS) is the Unites States’s premier system of health-related telephone surveys that collect state data about U.S. residents regarding their health-related risk behaviors, chronic health conditions, and use of preventive services. Established in 1984 with 15 states, BRFSS now collects data in all 50 states as well as the District of Columbia and three U.S. territories. BRFSS completes more than 400,000 adult interviews each year, making it the largest continuously conducted health survey system in the world.

By collecting behavioral health risk data at the state and local level, BRFSS has become a powerful tool for targeting and building health promotion activities.

This is a subest of "Behavioral Risk Factor Surveillance System (BRFSS) Prevalence Data (2011 to present)." It is organized by state and year, goes from 2015 to 2021, and contains responses to the following physical activity questions:

• During the past month, did you participate in any physical activities? (variable calculated from one or more BRFSS questions) BRFSS QuestionID: _TOTINDA
• Participated in 150 minutes or more of Aerobic Physical Activity per week (variable calculated from one or more BRFSS questions) BRFSS QuestionID: _PAINDX2
• Participated in enough Aerobic and Muscle Strengthening exercises to meet guidelines (variable calculated from one or more BRFSS questions) BRFSS QuestionID: _PASTAE2
• Participated in muscle strengthening exercises two or more times per week (variable calculated from one or more BRFSS questions) BRFSS QuestionID: _PASTRNG

Datasets

BRFSS Physical Activity.csv: Contains responses to the mental health questions listed above. 385 rows and 18 columns. BRFSS Physical Activity column KEY.csv: Contains a key to help understand the column names in the dataset. BRFSS location_id KEY.csv: Contains a key to help understand the location ids used in the dataset.

Columns

There is a column for state, year, and then column pairs for responses the above questions. The column pairs generally come in the following format: respXXX_value - which is the percentage of respondants who gave that response for that question for that location/year. respXXX_sample - which is the number of respondants who gave that response for that question for that location/year.

Source

https://chronicdata.cdc.gov/Behavioral-Risk-Factors/Behavioral-Risk-Factor-Surveillance-System-BRFSS-P/dttw-5yxu

This data comes under public domain licensing. Please use it responsibly and ethically. Thank you :)

Thumbnail image thanks to: https://unsplash.com/photos/Lx_GDv7VA9M?utm_source=unsplash&utm_medium=referral&utm_content=creditShareLink

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.

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

According to recent survey data, ** percent of adults in the United States said that sharing fitness tracker users’ data with medical researchers seeking to better understand the link between exercise and heart disease was acceptable. However, ** percent of adults in the United States believed that this was unacceptable. Interestingly, fitness trackers users were far more supportive of sharing data from their devices than those who didn't use them, with ** percent saying that this was acceptable.

This shows the market potential that an adult follows a regular exercise routine in the U.S. in 2022 in a multiscale map (by country, state, county, ZIP Code, tract, and block group). The pop-up is configured to include the following information for each geography level:Market Potential Index and count of adults expected to follow a regular exercise routineMarket Potential Index and count of adults expected to have participated in various forms of exercise in the last 12 monthsMarket Potential Index and count of adults expected to eat healthyEsri's 2022 Market Potential (MPI) data measures the likely demand for a product or service in an area. The database includes an expected number of consumers and a Market Potential Index (MPI) for each product or service. An MPI compares the demand for a specific product or service in an area with the national demand for that product or service. The MPI values at the US level are 100, representing average demand for the country. A value of more than 100 represents higher demand than the national average, and a value of less than 100 represents lower demand than the national average. For example, an index of 120 implies that demand in the area is 20 percent higher than the US average; an index of 80 implies that demand is 20 percent lower than the US average. See Market Potential database to view the methodology statement and complete variable list.Esri's Shopping Data Collection measures the likely demand to shop at specific stores, such as clothing, office, and convenience stores, as well as shopping habits like coupon usage and online buying. The database includes an expected number of consumers and a Market Potential Index (MPI) for each product, activity, or service. See the United States Data Browser to view complete variable lists for each Esri demographics collection.Additional Esri Resources:U.S. 2022/2027 Esri Updated DemographicsEssential demographic vocabularyEsri's arcgis.com demographic mapsThis item is for visualization purposes only and cannot be exported or used in analysis.Permitted use of this data is covered in the DATA section of the Esri Master Agreement (E204CW) and these supplemental terms.

The number of members of fitness centers and health clubs within the United States has experienced a near continual increase between 2000 and 2024. In 2024, there were found to be around ** million members of fitness centers and health clubs within the U.S., the greatest number during the period of observation.

Keeping track of your health is, for many people, a continuous task. Monitoring what you eat, how often you exercise and how much water you drink can be time-consuming, fortunately there are tens of...

This dataset comprises a comprehensive collection of 1.5-mile field test times recorded over a span of 50 years, from 1973 to 2023, involving more than twelve thousand college-age students at a private evangelical university in the South Central United States. The dataset, which includes 12,937 individual records, was meticulously curated from required health and physical exercise classes, offering a unique longitudinal perspective on physical fitness trends among college-age adults.Each entry in the dataset represents a completed 1.5-mile run, detailing the year of the test, the completion time in seconds, and the sex of the participant. This longitudinal data provides an invaluable resource for researchers, policymakers, and educators interested in understanding and addressing the declining trends in physical fitness over several decades.The data were digitized from original paper records and later electronic submissions, ensuring a comprehensive historical record. The dataset is presented in a de-identified format, with all personal information removed to uphold participant privacy and confidentiality. The records are further classified by sex and include an indication of whether the test was completed through running or an alternative mode permitted due to specific circumstances, although the primary focus of analysis is on running times due to their prevalence and comparability over the study period.

Data for cities, communities, and City of Los Angeles Council Districts were generated using a small area estimation method which combined the survey data with population benchmark data (2022 population estimates for Los Angeles County) and neighborhood characteristics data (e.g., U.S. Census Bureau, 2017-2021 American Community Survey 5-Year Estimates). The current Physical Activity Guidelines for Americans is issued by the US Department of Health and Human Services. To meet physical activity guidelines, adults must meet aerobic physical activity guidelines (vigorous activity for at least 75 minutes a week, or moderate activity for at least 150 minutes a week, or a combination of vigorous and moderate activity for at least 150 minutes a week) and muscle-strengthening physical activity guidelines (exercise all major muscle groups on 2 or more days a week).Physical inactivity contributes to our current obesity epidemic and is a major risk factor for heart disease, diabetes, cancer, and many other chronic health conditions. It can be difficult for people to be physically active if their communities do not have available and safe places for recreation.For more information about the Community Health Profiles Data Initiative, please see the initiative homepage.

Publicly accessible open spaces provide valuable opportunities for people to exercise, play, socialize, and build community. People are more likely to use public open spaces that are close (ideally within walking distance) to their homes, and larger open spaces often provide more amenities. To assess the potential benefit of creating new open space in the southeast US, we identified areas without access to open space within a certain distance category (in this case, 3 miles). Then, for each 30-meter pixel in the study area, we then totaled the number of people within 3 miles who do not currently have access to open space within that distance. This represents the number of people who would benefit from new open space created on that pixel.

The Lahman Baseball Database

2012 Version Release Date: December 31, 2012

0.1 Copyright Notice & Limited Use License

This database is copyright 1996-2013 by Sean Lahman.

This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 Unported License. For details see: http://creativecommons.org/licenses/by-sa/3.0/

For licensing information or further information, contact Sean Lahman

at: seanlahman@gmail.com

0.2 Contact Information

Web site: http://www.baseball1.com E-Mail : seanlahman@gmail.com

If you're interested in contributing to the maintenance of this database or making suggestions for improvement, please consider joining our mailinglist at:

http://groups.yahoo.com/group/baseball-databank/

If you are interested in similar databases for other sports, please vist the Open Source Sports website at http://OpenSourceSports.com

1.1 Introduction

This database contains pitching, hitting, and fielding statistics for Major League Baseball from 1871 through 2012. It includes data from the two current leagues (American and National), the four other "major" leagues (American Association, Union Association, Players League, and Federal League), and the National Association of 1871-1875.

This database was created by Sean Lahman, who pioneered the effort to make baseball statistics freely available to the general public. What started as a one man effort in 1994 has grown tremendously, and now a team of researchers have collected their efforts to make this the largest and most accurate source for baseball statistics available anywhere. (See Acknowledgements below for a list of the key contributors to this project.)

None of what we have done would have been possible without the pioneering work of Hy Turkin, S.C. Thompson, David Neft, and Pete Palmer (among others). All baseball fans owe a debt of gratitude to the people who have worked so hard to build the tremendous set of data that we have today. Our thanks also to the many members of the Society for American Baseball Research who have helped us over the years. We strongly urge you to support and join their efforts. Please vist their website (www.sabr.org).

This database can never take the place of a good reference book like The Baseball Encyclopedia. But it will enable people do to the kind of queries and analysis that those traditional sources don't allow.

If you have any problems or find any errors, please let us know. Any feedback is appreciated

1.2 What's New in 2012

There has been significant cleanup in the master file

MLB's addition of wildcard games in 2012 adds two new types of records to the post-season files. The abbreviations ALWC and NLWC are used to denote each league's wild card game.

Added the MLB "Comeback Player of the Year" award to the awards table

Florida Marlins changed their name to the Miami Marlins, new team abbr is MIA

1.3 Acknowledgements

Much of the raw data contained in this database comes from the work of Pete Palmer, the legendary statistician, who has had a hand in most of the baseball encylopedias published since 1974. He is largely responsible for bringing the batting, pitching, and fielding data out of the dark ages and into the computer era. Without him, none of this would be possible. For more on Pete's work, please read his own account at: http://sabr.org/cmsfiles/PalmerDatabaseHistory.pdf

Two people have been key contributors to the work that followed, first by taking the raw data and creating a relational database, and later by extending the database to make it more accesible to researchers.

Sean Lahman launched the Baseball Archive's website back before most people had heard of the world wide web. Frustrated by the lack of sports data available, he led the effort to build a baseball database that everyone could use. Baseball researchers everywhere owe him a debt of gratitude. Lahman served as an associate editor for three editions of Total Baseball and contributed to five editions of The ESPN Baseball Encyclopedia. He has also been active in developing databases for other sports.

The work of Sean Forman to create and maintain an online encyclopedia at "baseball-reference.com" has been remarkable. Recognized as the premier online reference source, Forman's site provides an oustanding interface to the raw data. His efforts to help streamline the database have been extremely helpful. Most importantly, Forman has spearheaded the effort to provide standards that enable several different baseball databases to be used together. He was also instrumental in launching the Baseba...

These data are from the 2013 California Dietary Practices Surveys (CDPS), 2012 California Teen Eating, Exercise and Nutrition Survey (CalTEENS), and 2013 California Children’s Healthy Eating and Exercise Practices Surveys (CalCHEEPS). These surveys have been discontinued. Adults, adolescents, and children (with parental assistance) were asked for their current height and weight, from which, body mass index (BMI) was calculated. For adults, a BMI of 30.0 and above is considered obese. For adolescents and children, obesity is defined as having a BMI at or above the 95th percentile, according to CDC growth charts.

The California Dietary Practices Surveys (CDPS), the California Teen Eating, Exercise and Nutrition Survey (CalTEENS), and the California Children’s Healthy Eating and Exercise Practices Surveys (CalCHEEPS) (now discontinued) were the most extensive dietary and physical activity assessments of adults 18 years and older, adolescents 12 to 17, and children 6 to 11, respectively, in the state of California. CDPS and CalCHEEPS were administered biennially in odd years up through 2013 and CalTEENS was administered biennially in even years through 2014. The surveys were designed to monitor dietary trends, especially fruit and vegetable consumption, among Californias for evaluating their progress toward meeting the Dietary Guidelines for Americans and the Healthy People 2020 Objectives. All three surveys were conducted via telephone. Adult and adolescent data were collected using a list of participating CalFresh households and random digit dial, and child data were collected using only the list of CalFresh households. Older children (9-11) were the primary respondents with some parental assistance. For younger children (6-8), the primary respondent was parents. Data were oversampled for low-income and African American to provide greater sensitivity for analyzing trends among the target population. Wording of the question used for these analyses varied by survey (age group). The questions were worded are as follows: Adult:1) How tall are you without shoes?2) How much do you weigh?Adolescent:1) About how much do you weigh without shoes?2) About how tall are you without shoes? Child:1) How tall is [child's name] now without shoes on?2) How much does [child's name] weigh now without shoes on?

Context

NYC Open Data is an opportunity to engage New Yorkers in the information that is produced and used by City government. We believe that every New Yorker can benefit from Open Data, and Open Data can benefit from every New Yorker. Source: https://opendata.cityofnewyork.us/overview/

Content

Thanks to NYC Open Data, which makes public data generated by city agencies available for public use, and Citi Bike, we've incorporated over 150 GB of data in 5 open datasets into Google BigQuery Public Datasets, including:

• Over 8 million 311 service requests from 2012-2016

• More than 1 million motor vehicle collisions 2012-present

• Citi Bike stations and 30 million Citi Bike trips 2013-present

• Over 1 billion Yellow and Green Taxi rides from 2009-present

• Over 500,000 sidewalk trees surveyed decennially in 1995, 2005, and 2015

This dataset is deprecated and not being updated.

Fork this kernel to get started with this dataset.

Acknowledgements

https://opendata.cityofnewyork.us/

https://cloud.google.com/blog/big-data/2017/01/new-york-city-public-datasets-now-available-on-google-bigquery

This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - https://data.cityofnewyork.us/ - and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

By accessing datasets and feeds available through NYC Open Data, the user agrees to all of the Terms of Use of NYC.gov as well as the Privacy Policy for NYC.gov. The user also agrees to any additional terms of use defined by the agencies, bureaus, and offices providing data. Public data sets made available on NYC Open Data are provided for informational purposes. The City does not warranty the completeness, accuracy, content, or fitness for any particular purpose or use of any public data set made available on NYC Open Data, nor are any such warranties to be implied or inferred with respect to the public data sets furnished therein.

The City is not liable for any deficiencies in the completeness, accuracy, content, or fitness for any particular purpose or use of any public data set, or application utilizing such data set, provided by any third party.

Banner Photo by @bicadmedia from Unplash.

Inspiration

On which New York City streets are you most likely to find a loud party?

Can you find the Virginia Pines in New York City?

Where was the only collision caused by an animal that injured a cyclist?

What’s the Citi Bike record for the Longest Distance in the Shortest Time (on a route with at least 100 rides)?

https://cloud.google.com/blog/big-data/2017/01/images/148467900588042/nyc-dataset-6.png" alt="enter image description here"> https://cloud.google.com/blog/big-data/2017/01/images/148467900588042/nyc-dataset-6.png

Snapshot img

Fitness Tracker Market Size 2024-2028

The fitness tracker market size is valued to increase by USD 67.81 billion, at a CAGR of 19.95% from 2023 to 2028. Growing adoption of fitness tracker in emerging countries will drive the fitness tracker market.

Market Insights

North America dominated the market and accounted for a 37% growth during the 2024-2028.
By Application - Running segment was valued at USD 8.94 billion in 2022
By Distribution Channel - Online segment accounted for the largest market revenue share in 2022

Market Size & Forecast

Market Opportunities: USD 355.99 million 
Market Future Opportunities 2023: USD 67806.40 million
CAGR from 2023 to 2028 : 19.95%

Market Summary

The market is experiencing significant growth, driven by the increasing awareness of health and wellness, as well as the proliferation of technology in daily life. Fitness trackers, which monitor various health metrics such as heart rate, sleep patterns, and physical activity, have gained popularity among consumers seeking to improve their overall fitness and well-being. Emerging economies are also embracing fitness trackers, with rising disposable income and a growing health consciousness driving demand. In fact, according to market research, the Asia Pacific region is expected to witness the fastest growth in the market in the coming years. Despite this optimistic outlook, challenges persist. One major concern is data privacy and security, as fitness trackers collect and store sensitive health information. Ensuring the protection of this data is crucial for both consumers and businesses, and has become a top priority for fitness tracker manufacturers and health technology companies. For instance, a large retailer may use fitness tracker data to optimize its supply chain by forecasting demand for certain health and wellness products based on consumer trends. By analyzing data from fitness trackers, the retailer can gain insights into consumer behavior and preferences, enabling it to stock the right products in the right quantities and locations. This not only improves operational efficiency but also enhances the customer experience. In conclusion, the market is poised for continued growth, fueled by increasing consumer adoption and the integration of fitness trackers into various industries. However, addressing concerns related to data privacy and security will be essential for the market's long-term success.

What will be the size of the Fitness Tracker Market during the forecast period?

Get Key Insights on Market Forecast (PDF) Request Free SampleThe market continues to evolve, driven by advancements in data analytics platforms and progress tracking tools. Heart rate variability, a critical health metric trend, is now accurately measured in real-time by these devices. Sensor accuracy and algorithm accuracy are paramount to delivering personalized insights, exercise recommendations, and workout intensity data. Device durability is also a significant concern, with companies focusing on enhancing battery life through power management and data synchronization. Sleep cycle analysis has emerged as a key feature, offering users data on sleep duration and quality. Data encryption ensures user privacy, while health coaching tools and notification systems promote fitness program adherence. Motion sensor fusion enables step detection and workout progress monitoring, with distance calculation providing a comprehensive daily activity summary. User experience is a top priority, with user engagement metrics and data storage methods designed to keep users motivated and invested. A notable trend in the market is the integration of advanced data analytics capabilities. This enables users to gain personalized insights, enhancing their overall fitness journey. For instance, companies have reported a 30% increase in user engagement due to these features. As businesses strive to meet the evolving needs of consumers, the focus on data-driven insights and real-time feedback will continue to shape the market.

Unpacking the Fitness Tracker Market Landscape

In the dynamic realm of wearable technology, the market showcases significant advancements, driven by the integration of cutting-edge sensors and data transmission protocols. Accelerometer data and movement detection enable precise activity tracking metrics, leading to a 30% increase in user engagement and a 25% improvement in physical activity levels. Sleep apnea detection, facilitated by wearable sensors, aligns with compliance initiatives, reducing healthcare costs by 15%. Biometric data, including heart rate sensor and skin temperature sensor readings, power the creation of personalized workout plans and fitness goals setting, enhancing user experience and ROI. Data privacy remains a priority, with robust user data security measures ensuring compliance with industry standar

The National Health and Nutrition Examination Survey (NHANES) is designed to assess the health and nutritional status of adults and children in the United States. The survey is unique in that it combines interviews with standardized physical examinations and laboratory tests.
NHANES was conducted on a periodic basis from 1971 to 1994, including NHANES I (1971-1975), NHANES II (1976-1980), NHANES III (1988-1994), and a Hispanic Health and Nutrition Examination Survey (HHANES, 1982-1984). In 1999, NHANES became continuous and has been collecting data annually ever since.
All of the NHANES programs utilized a stratified, multistage probability cluster design to provide a nationally representative sample of the U.S. civilian, noninstitutionalized population. The NHANES interview includes demographic, socioeconomic, dietary, and health-related questions. The examination component conducted in a mobile examination center consists of medical, dental, and physiological measurements, as well as the collection of biospecimens, such as blood and urine for laboratory testing.

This set of restricted data contains indirect identifying and/or sensitive information collected in NHANES prior to 1999. Please refer to the links below for additional data available from NHANES:

• NHANES Public Use Data at: https://wwwn.cdc.gov/nchs/nhanes/Default.aspx
• NHANES Restricted Data: 1999 - present at: https://data.cdc.gov/dataset/National-Health-and-Nutrition-Examination-Survey-N/9ixb-fwvy
• NHANES Genetic Restricted Data at: https://data.cdc.gov/dataset/National-Health-and-Nutrition-Examination-Survey-N/hdx4-e4uu
• NHANES National Youth Fitness Survey (NNYFS) Restricted Data at: https://data.cdc.gov/dataset/3-NHANES-National-Youth-Fitness-Survey-NNYFS-Restr/5u84-m4rs

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

The purpose of the National Health Interview Survey (NHIS) is to obtain information about the amount and distribution of illness, its effects in terms of disability and chronic impairments, and the kinds of health services people receive. This supplement includes variables from the core Person File (see NATIONAL HEALTH INTERVIEW SURVEY, 1991 [ICPSR 6049]), including sex, age, race, marital status, veteran status, education, income, industry and occupation codes, and limits on activity. The variables unique to this supplement cover 12 topic areas that relate to the United States Department of Health and Human Services' "Healthy Year 2000" objectives: environmental health (radon, smoking in the home, and lead paint), tobacco (smoking history, use of tobacco, and health), nutrition (weight control and exercise), immunization and infectious disease (vaccinations and foreign travel), occupational safety and health (seat-belt use, smoking in the workplace, and wellness programs), heart disease and stroke (blood pressure and cholesterol concerns), other chronic and disabling conditions (diabetes, glaucoma, asthma, and mobility problems), clinical and preventive services (seat-belt usage and complete physical exam), physical activity and fitness (types and frequency of physical activity), alcohol (drinking history in past year), mental health (feelings of anger, depression, and boredom recently), and oral health (dental visits in past year).

Publicly accessible open spaces provide valuable opportunities for people to exercise, play, socialize, and build community. People are more likely to use public open spaces that are close (ideally within walking distance) to their homes. To assess the spatial distribution of access to open space for recreation in the southeastern United States, we constructed an index of open space access based on the size of the largest publicly accessible open space within 10 miles of each point on the landscape, using three distance categories to represent whether people can reach the open spaces by walking (within 0.5 mile), via a short drive (within 3 miles), or via a longer drive (within 10 miles). Using the open space access index, we identified regional priority areas at the county scale based on the number of people who would have increased access to open space (within the three distance categories) if new open space were created within those areas.

Publicly accessible open spaces provide valuable opportunities for people to exercise, play, socialize, and build community. People are more likely to use public open spaces that are close (ideally within walking distance) to their homes, and larger open spaces often provide more amenities. To assess the spatial distribution of access to open space for recreation in the southeastern United States, we constructed an index of open space access based on the size of the largest publicly accessible open space of at least 10 acres within 10 miles of each point on the landscape, using three distance categories to represent whether people can reach the open spaces by walking (within 0.5 mile), via a short drive (within 3 miles), or via a longer drive (within 10 miles).