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.

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.

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).

The seven most prestigious marathons in the world are known as the World Marathon Majors, with thousands taking part in the events each year. In 2024, the New York City Marathon was completed by over 55,000 runners, representing the highest number out of the leading marathons.

Each individual result graph is associated with 4 different comma-separated files: (i) Raw—the (anonymised) raw data behind the means and standard deviations used for a particular result graph; (ii) Paired—the paired statistical significance results; (iii) Successive Male—the statistical significance results to compare successive groups (age and ability) for male runners; and (iv) Successive Female—the corresponding results for the statistical significance tests to compare successive groups (age and ability) of female runners. (ZIP)

A summary of the repeaters dataset by city/race showing the city name and years for which there are data, the number of runners (#R), the percentage of female runners (%F), the average finish-time (FT) and the percentage of runners hitting the wall (%HTW) based on the operational defintion adopted here.

The aim of the analysis was to evaluate how single or combinations of weather parameters (temperature, humidity, wind speed, solar load) affect peak performance during endurance running events and identify which events are most vulnerable to varying weather conditions.

Results for the marathon, 50 km race-walk, 20 km race-walk, 10,000 m, 5,000 m and 3,000 m-steeplechase were obtained from the official websites of the largest competitions in the world. Finish times for all races were collected from the first year of each competition for which data were available online until the end of 2019. The collection of these data was completed between February 2016 and September 2020

We obtained the date, time, and location for each race from its official website while the relevant longitude and latitude were obtained from www.locationiq.com. Weather data (air temperature, dew point, wind speed, and cloud coverage) corresponding to the time at half-way in each race were obtained from the closest meteorological station using the official dataset of the National Oceanic and Atmospheric Administration (www.ncei.noaa.gov/data/global-hourly). In cases where these data were not available, we retrieved the information from widely-used meteorology websites (www.wunderground.com and www.weatherspark.com). Wind speed was adjusted for height above the ground and air friction coefficient (i.e., large city with tall buildings). Dew point data were converted to relative humidity. For cases where cloud coverage was not available in the National Oceanic and Atmospheric Administration datasets, the cloud coverage (in okta) was computed using relative humidity data based on previous methodology and applying coefficients of 0.25 for low and high as well as 0.5 for middle clouds, as previously suggested. Solar radiation was calculated using the date, time, and coordinates of each race, while accounting for cloud coverage. Thereafter, the Heat Index, Simplified WBGT and WBGT, were calculated using previous methodology.

Financial overview and grant giving statistics of Road Runners Club Of America

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What is The Bull Runner (TBR) Dream Marathon ?

The Bull Runner (TBR) Dream Marathon is the first and only marathon in the world that caters to first- or second-time marathoners. In 2012 and 2013, Runners World Philippines recognized it as among the Top 10 Marathons in the Philippines.

From https://www.tbrdream.com/about/what-is-tbr-dream-marathon/

Data Source

• https://www.tbrdream.com/race-info/race-results/
• Thanks to u/kumonpeople of Reddit for providing 2011, 2012, 2016, and 2019 results!

Unavailable Years

The following years' data are not available anymore - 2017

Important notes

• I included the splits as is, as per year the distance on where the splits are measured are different.
• I also didnt group splits that are close to each other as one (ex: 21.1 kms vs 21kms). Up to the users of the dataset to do their interpolation of the splits
• For 2011 results, I dont know what the distance for Splits 1 and 2 are, so im leaving the values as is
• For 2016, there was no Gender field, so i manually annotated the runners' genders based on their names.

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Financial overview and grant giving statistics of Road Runners Club Of America Inc

This is the dataset for runners' step frequency (SF), heart rate (HR) and rating of perceived exertion (RPE) collected in laboratory, which was used in the study on Furukawa et al. (2023) 'Auditory Interaction Between Runners: Does Footstep Sound Affect Step Frequency of Neighboring Runners?'.

Abstract This study aimed to investigate the effect of footsteps of a neighboring runner (NR) on the main runner's SF, HR, and RPE. The participants were male long-distance runners belonging to a university track and field team. Two experiments were conducted in which the main runner (participant) and NR (examiner) ran with the same running speed on two adjacent treadmills separated by a thin wall. The participants were instructed that the experimental purpose was to investigate the HR when running with others and running alone. In Experiment 1, NR performed three trials of changing the footstep tempo in 5 bpm (beat per minute) faster (+5bpmFS), 5 bpm slower (-5bpmFS), or no footsteps (NF) conditions. The results showed that the footstep condition affected the variability of the SF but not the mean SF. Next, Experiment 2 was conducted by increasing the footstep tempo condition. NR performed seven trials of changing the footstep tempo by ±3 bpm, ±5 bpm, ±10 bpm, or no footstep. The results showed that the footstep condition affected the mean SF and the SF decreased at -10bpmFS compared to NF. There were no differences in the HR and RPE between conditions. These results indicated that the footsteps of NR could influence the SF, although it was unclear whether footsteps were involved in the synchronization between runners. Overall, our findings emphasize the environmental factors that influence running behavior, including the NR's footsteps.

For more details on how to view the data sets

SF, HR, and RPE data are in .xlsx format. Experiment 1 or Experiment 2 is indicated in the file name. p1-p16 represent participant number.

The name of each sheet is the name of the condition.

Also, the running speed data is in .docx format.

In Experiment 2, the all data of participant p12 (equal to #18 in running speed data) was excluded from the analysis of this study because he had a within-participant standard deviation of SF greater than 3 SDs from the mean across participants.

Dataset

This dataset was created by lazyJun01

Released under CC0: Public Domain

Contents

Current profile data from Downward-looking ADCP #02 on FPF Front Runner, 2.763000e+01N, 9.044000e+01W, 2008/01/02 00:01 through 2008/12/28 23:53

In 2024, around 5.53 million people in Germany aged 14 years or older often went jogging in their spare time. This was an increase compared to the year before. The statistic shows the results of a survey on the number of people who went on runs in their spare time in Germany from 2019 to 2024.The Allensbach Market and Advertising Media Analysis (Allensbacher Markt- und Werbeträgeranalyse or AWA in German) determines attitudes, consumer habits and media usage of the population in Germany on a broad statistical basis.

In 2024, around **** million people in Germany aged 20-29 years old stated that they, either frequently or sometimes, went jogging, trail or cross-country running. The group with the fewest people who went running was the 70 years and older group. This survey asked people in Germany whether they go running.

Financial overview and grant giving statistics of Road Runners Club Of America Ashburn Area Running Club

ABSTRACT Running shoes are an essential element for sports practice. Evidence on the effect of the shoe characteristics and prescription in running injuries are scarce. Thus, this study aimed to investigate runners's process of choosing running shoes and to verify whether the variables related to running shoes and their selection process are associated with the presence and recurrence of injuries in the previous year. An observational study was conducted with 254 runners who answered a self-reported questionnaire about demographics, sports practice, shoe characteristics and selection criteria, and injuries in the last 12 months. The chi-square test evaluated whether there was a difference in the distribution of answers to each question. The logistic regression evaluated whether the variables related to shoes and selection criteria predicted injury’s presence and recurrence in the previous year. The results showed that most runners had specific shoes for sports practice and considered some characteristics of the shoes to choose them, including intermediate cushioning and a difference in the heel-to-toe drop of approximately 10mm. Most respondents indicated knowing their foot type but not considering it when choosing shoes. Besides, most individuals did not use foot orthotics and did not receive guidance to select their shoes. The model obtained with the regression was not significant. Therefore, despite considering shoe characteristics when choosing it, these features and the selection criteria were not associated with the presence and recurrence of injuries in the previous 12 months.

This dataset provides information about the number of properties, residents, and average property values for Runners Place cross streets in Loomis, CA.

Current profile data from Downward-looking ADCP #00 on MODU Noble Amos Runner, 2.611000e+01N, 9.205000e+01W, 2010/04/27 23:50 through 2010/05/21 09:35