Over ** million people in the United States went running or jogging at least once in 2024. This figure also represented an increase of roughly 5.7 percent over the previous year.

The revenue in the 'Gym & Training' segment of the footwear market in the United States was forecast to continuously increase between 2025 and 2030 by in total 0.9 billion U.S. dollars (+8.35 percent). After the 10th consecutive increasing year, the revenue is estimated to reach 11.63 billion U.S. dollars and therefore a new peak in 2030. Notably, the revenue of the 'Gym & Training' segment of the footwear market was continuously increasing over the past years.Find more key insights for the revenue in countries like revenue in Thailand and revenue growth in the United States.The Statista Market Insights cover a broad range of additional markets.

Between November 2023 and November 2024, over 6.5 million people in England regularly participated in running. This represented an increase over the previous study period's figure of 6.2 million.

The Running and Jogging market has experienced significant growth over the past decade, evolving into a dynamic segment of the global sports and fitness industry. As more individuals embrace healthy lifestyles, running and jogging have emerged as popular activities that not only enhance physical fitness but also pro

This dataset contains 10,703,690 records of running training during 2019 and 2020, from 36,412 athletes from around the world. The records were obtained through web scraping of a large social network for athletes on the internet.The data with the athletes' activities are contained in dataframe objects (tabular data) and saved in the Parquet file format using the Pandas library, part of the Python ecosystem for data science. Each Pandas dataframe contains the following data (as different columns) for each athlete (as different rows), the first word identifies the name of the column in the dataframe:- datetime: date of the running activity;- athlete: a computer-generated ID for the athlete (integer);- distance: distance of running (floating-point number, in kilometers);- duration: duration of running (floating-point number, in minutes);- gender: gender (string 'M' of 'F');- age_group: age interval (one of the strings '18 - 34', '35 - 54', or '55 +');- country: country of origin of the athlete (string);- major: marathon(s) and year(s) the athlete ran (comma-separated list of strings).For convenience, we created files with the athletes' activities data sampled at different frequencies: day 'd', week 'w', month 'm', and quarter 'q' (i.e., there are files with the distance and duration of running accumulated at each day, week, month, and quarter) for each year, 2019 and 2020. Accordingly, the files are named 'run_ww_yyyy_f.parquet', where 'yyyy' is '2019' or '2020' and 'f' is 'd', 'w', 'm' or 'q' (without quotes). The dataset also contains data with different government’s stringency indexes for the COVID-19 pandemic. These data are saved as text files and were obtained from https://ourworldindata.org/covid-stringency-index. The Jupyter notebooks that we created and made available in the https://github.com/BMClab/covid19 repository exemplify the use of the data.

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Trail running races are much like regular road running races except that they are off-road by definition, and generally longer than your typical road race. The premier global trail running race is the Ultra-Trail du Mont-Blanc, held in Chamonix-Mont-Blanc, France. It is a 174 km (108 mi) race with 10,000 m (33,000 ft) of elevation gain. This race is organized by the UTMB Group. In addition to the UTMB, they organize 40+ prominent races worldwide called the “UTMB World Series”. Their website, UTMB.world, contains the results of these events. But, to my surprise, it also contains the results of tens of thousands of other trail running races.

Data was scraped from UTMB.world in two steps: First, I had to find all unique race identifiers (race UIDs) since they are erratic, and then I had to scrape each year held. In total, I found 15,679 race UIDs ranging from 4 to 45,846. The total number of races held was 38,460. See the "Scripts used in data collection" notebook for all methods. Second I scraped the content of these all unique race UIDs.

This data collection contains raw scraped data and an easy-to-read CSV file. Please look at the raw file's documentation for more information. The CSV file is much smaller and easy to interpret because: categorical fields were summarized to integers and the array of result times was aggregated to First, Last and Mean finish times as well as the number of participants that did not finish (N DNF).

This statistic illustrates the share of Americans who went jogging or running as of 2021, by generation. In that year, ** percent of Gen Z respondents stated that they went long-distance jogging or running.

Data accompanying manuscript Data of 7 runners during a Marathon is provided to accompany the manuscript “Quantifying and correcting for speed and stride frequency effects on running mechanics in fatiguing outdoor running”. For each runner the following files are provided: - Global Navigation Satellite System (GNSS) running speed - Inertial Measurement unit (IMU) running speed - Acceleration of foot sensor - Acceleration of lower leg/tibia sensor - Knee angles GNSS running speed GNSS running speed during the full marathon was based on different sports watches. Sampling frequencies between sports watches differed but was on average 0.7 (0.4) Hz. In line with the manuscript, missing latitude-longitude data was linearly interpolated before speed was computed as the distance between two latitude-longitude coordinates based on the Haversine formula. GNSS speeds above 20 km/h were deemed extremely unlikely and replaced with spline interpolation. GNSS speed was then resampled to 240 Hz to match the sampling frequency of the IMUs. Note that GNSS and IMU data are not yet time synchronized! - Filename: SubXX_gnss_speed.csv - Size of matrix: [1xN] IMU running speed IMU running speed was solely used for time synchronization of the GNSS running speed with IMU data. The scaled biomechanical model (as described in the manuscript) provided the velocity of the pelvis segment at 240 Hz. Pelvis IMU speed was then computed as the resultant pelvis IMU velocity. - Filename: SubXX_imu_speed.csv - Size of matrix: [Nx1] Acceleration of foot sensor Accelerations of the right foot were used for initial contact detection in the manuscript. 3D accelerations of a sensor on the right foot are provided in a sensor-fixed coordinate system. The sensor was placed on the midfoot within the shoes, the sensor was aligned with the long axis of the foot. The positive axis of the first dimension points towards the center of the ankle joint. The positive axis of the second dimension points to the right. The positive axis of the third dimensions is directed approximately upwards. - Filename: SubXX_rfoot_acc.csv - Size of matrix: [Nx3] Acceleration of lower leg/tibia sensor Accelerations of the lower leg were one of the quantities of interest in the manuscript. 1D acceleration of a sensor on the right lower leg at 240 Hz is provided in a sensor-fixed coordinate system. The sensor was aligned with the axial direction of the tibia. - Filename: SubXX_rtibia_acc.csv - Size of matrix: [Nx1] Knee angles Knee flexion/extension angles were one of the quantities of interest in the manuscript. Knee flexion/extension angles of the right lower leg at 240 Hz are provided. Knee flexion angles were defined 0° when the leg was fully extended during neutral standing. Flexion resulted in positive knee flexion angles. - Filename: SubXX_rknee_angle.csv - Size of matrix: [Nx1]

The data set consists of a detailed training log from a Dutch high-level running team over a period of seven years (2012-2019). We included the middle and long distance runners of the team, that is, those competing on distances between the 800 meters and the marathon. This design decision is motivated by the fact that these groups have strong endurance based components in their training, making their training regimes comparable. The head coach of the team did not change during the years of data collection. The data set contains samples from 74 runners, of whom 27 are women and 47 are men. At the moment of data collection, they had been in the team for an average of 3.7 years. Most athletes competed on a national level, and some also on an international level. The study was conducted according to the requirements of the Declaration of Helsinki, and was approved by the ethics committee of the second author’s institution (research code: PSY-1920-S-0007).

AbstractThe aim of the present study was to verify the influence of the performance level in the strategy of running pacing from recreational runners. Additionally, it was aimed to describe the heart rate (HR) responses measured in a 10-km running performance in a track. Thirty-nine recreational runners took part (31.5 ± 6.7 years), who had experience in 10-km races and realized a 10-km running performance in a track (400 m). HR was constantly monitored (Polar RS800) and the time each 400 m was registered to determine MS, and then analyzed each 2 km. Participants were divided into two groups according to their MS attained in the tests: G1 - MS 10 km ≤ 11.81 (n = 20) and G2 - MS 10 km > 11.81 (n = 19). The comparison between MS and GR obtained in different moments of the performance for both groups was made by mixed Anova for repeated measures, adopting a significance level of p < 0.05. MS values were different between the groups in all moments analyzed with a significant increase in MS from moment 6-8 km to 8-10 km for the same group. HR values were different only within-groups. In G1, there was a significant increase in HR each 2 km. For G2, HR increased from the second to the forth km and remained stable up to the eighth km, increasing again in the last 2 km of the performance. We concluded that the performance level does not influence in the running strategy of recreational runners.

This statistic shows the number of participants in trail running in the United States from 2006 to 2017. In 2017, there were approximately **** million participants in trail running in the U.S., up from **** million the previous year.

Financial overview and grant giving statistics of Women Run Arkansas

Vacation Races is a company that specializes in organizing trail running events, with a focus on national parks and wild spaces. Founded on a passion for the outdoors, the company has expanded to include global adventures, ultramarathons, and trailfest events. With a strong commitment to sustainability, Vacation Races aims to minimize its environmental impact while promoting a sense of community among its runners.

Mice were housed under a 12:12 h light/dark (12:12 LD) cycle at 100 lux. Units of measurement and sample sizes are indicated in brackets. AU = arbitrary units. SEM = standard error of the mean.Descriptive statistics for selected rest-activity parameters derived from 14 consecutive days of wheel-running data.

The data captured came from mounting a single Shimmer3 IMU on the lumbar of 19 recreational runners. The participants were all regular runners and injury free. The study protocol was reviewed and approved by the human research ethics committee at University College Dublin.

The data was collected in three segments; in the first, the participant completed a 400m run at a comfortable pace; the second segment consisted of a beep test which acted as the fatiguing protocol for this study; and the last segment where the runner was required to complete the 400m run at their comfortable pace, this time in their fatigued state. The beep test requires the runner to continuously run between two points 20m apart following an audio which produces `beeps' indicating when the person should begin running from one end to the other. The test eventually requires the runner to increase their pace as the interval between the `beeps' reduces as the test progresses. The fatiguing protocol ends when the runner is unable to keep up the increase in pace. The runs were all done on an outdoor running track. The sensor captured acceleration, angular velocity and magnetometer data throughout the three stages of the trials at a sampling rate of 256Hz. The data included here are segmented strides from the two 400m runs of each of the 19 participants. The labels on the data represent the participant number and whether it was a fatigued stride ('F') or a not fatigued stride ('NF').
The data used from the sensors includes data from the accelerometer in three directions (X, Y, Z) and the gyroscope in three directions (X, Y, Z). The direction of each of the axis is relative to the sensor. Two extra signals, magnitude acceleration and magnitude gyroscope were derived from the component signals and included in the analysis.

Kindly cite one of the following papers when using this data:

B. Kathirgamanathan, B. Caulfield and P. Cunningham, "Towards Globalised Models for Exercise Classification using Inertial Measurement Units," 2023 IEEE 19th International Conference on Body Sensor Networks (BSN), Boston, MA, USA, 2023, pp. 1–4, doi: 10.1109/BSN58485.2023.10331612

B. Kathirgamanathan, T. Nguyen, G. Ifrim, B. Caulfield, P. Cunningham. Explaining Fatigue in Runners using Time Series Analysis on Wearable Sensor Data, XKDD 2023: 5th International Workshop on eXplainable Knowledge Discovery in Data Mining, ECML PKDD, 2023, http://xkdd2023.isti.cnr.it/papers/223.pdf

Competitive Timing is a leading provider of accurate results and timing services for various events, including road races, cycling, and triathlons. The company has a strong presence in the western United States, particularly in Montana, where it has established itself as a trusted partner for event organizers. With a focus on delivering exceptional timing and results, Competitive Timing has built a reputation for its attention to detail and commitment to accuracy.

The company has a diverse portfolio of events, ranging from small local runs to major endurance events. Its team of experienced professionals uses state-of-the-art equipment and software to ensure accurate and efficient timing services. Competitive Timing also offers a range of additional services, including online race registration, race day support, and post-event reporting. With its reputation for excellence and commitment to customer satisfaction, Competitive Timing has established itself as a go-to partner for event organizers in the western United States.

Dog runs are large, fenced-in areas for dogs to exercise unleashed during park hours. Each record in this dataset is an individual dog run. Planimetrics have ensured good GIS quality of the data. User guide: https://drive.google.com/open?id=1_LB-hb9eJ9Bph7K2Yf06_KOtra8qKppbPnfSyH1lRUg Data Dictionary: https://drive.google.com/open?id=1a3vdtIHUTUl2wokZ1r3fot8wm-lrnKGXYL-6xU5Fj2A

Subjects`anthropometric data, training history and weekly running mileage.

This statistic shows the number of youth participants in running in the United States from 2006 to 2020. According to the source, the number of youth participants (aged between six and 17 years) in running amounted to approximately 10.9 million in 2020.

Financial overview and grant giving statistics of Korean American Running Team