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.

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.

This statistic illustrates the share of Americans who went jogging or running as of 2021, by generation. In that year, 10 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]

Centre of mass acceleration data was collected while runners ran a long distance overground run. This was for a PhD project investigating running bomechanics related to running performance and runnng injuries.

Centre of mass acceleration data was captured from a tri-axial accelerometer attached to the lower back of runners while they ran for 8 km around an outdoor athletics track.

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.

This statistic shows the number of participants in trail running in the United States from 2006 to 2017. In 2017, there were approximately 9.15 million participants in trail running in the U.S., up from 8.58 million the previous year.

Context

Every Marathoner has a time goal in mind, and this is the result of all the training done in months of exercises. Long runs, Strides, Kilometers and phisical exercise, all add improvement to the result. Marathon time prediction is an art, generally guided by expert physiologists that prescribe the weekly exercises and the milestones to the marathon.
Unfortunately, Runners have a lot of distractions while preparing the marathon, work, family, illnes, and therefore each one of us arrives to the marathon with his own story. The "simple" approach is to look at data after the competition, the Leaderboard.

But what if we could link the Marathon result to the training history of the Athlete? Could we find that "non orthodox" training plans give good results?

The Athlete Training History

As a start, I'll take just two data from the Athlete History, easy to extract. Two meaningful data, the average km run during the 4 weeks before the marathon, and the average speed that the athlete has run these km.

Meaningful, because in the last month of the training I have the recap of all the previous months that brought me to the marathon.

Easy to extract, because I can go to Strava and I have a "side-by-side" comparison, myself and the reference athlete. I said easy, well, that's not so easy, since I have to search every athlete and write down those numbers, the exact day the marathon happened, otherwise I will put in the average the rest days after the marathon.

I've set my future work in extracting more data and build better algorithms. Thank you for helping me to understand or suggest.

Content

id:
simple counter

Marathon:
the Marathon name where the data were extracted. I use the data coming out from Strava "Side by side comparison" and the data coming from the final marathon result

Name:
The athlete's name, still some problems with UTF-8, I'll fix that soon

Category:
the sex and age group of a runner - MAM Male Athletes under 40 years - WAM Women under 40 Years - M40 Male Athletes between 40 and 45 years

km4week
This is the total number of kilometers run in the last 4 weeks before the marathon, marathon included. If, for example, the km4week is 100, the athlete has run 400 km in the four weeks before the marathon

sp4week
This is the average speed of the athlete in the last 4 training weeks. The average counts all the kilometers done, included the slow kilometers done before and after the training. A typic running session can be of 2km of slow running, then 12-14km of fast running, and finally other 2km of slow running. The average of the speed is this number, and with time this is one of the numbers that has to be refined

cross training:
If the runner is also a cyclist, or a triathlete, does it counts? Use this parameter to see if the athlete is also a cross trainer in other disciplines

Wall21: In decimal. The tricky field. To acknowledge a good performance, as a marathoner, I have to run the first half marathon with the same split of the second half. If, for example, I run the first half marathon in 1h30m, I must finish the marathon in 3h (for doing a good job). If I finish in 3h20m, I started too fast and I hit "the wall". My training history is, therefore, less valid, since I was not estimating my result

Marathon time:
In decimal. This is the final result. Based on my training history, I must predict my expected Marathon time

Category:
This is an ancillary field. It gives some direction, so feel free to use or discard it. It groups in:
- A results under 3h
- B results between 3h and 3h20m
- C results between 3h20m and 3h40m
- D results between 3h40 and 4h

Acknowledgements

Thank you to the main Athletes data sources, GARMIN and STRAVA

The Goal of this Competition:

Based on my training history, I must predict my expected Marathon time. Which other relevant data could help me to be more precise? Heart rate, cadence, speed training, what else? And how could I get those data?

The data set comprises raw and processed lower extremity gait kinematics and kinetics signals of 39 subjects in different file formats (c3d and txt). A file of metadata (in txt and xls formats), including demographics, running characteristics, foot-strike patterns, and muscle strength and flexibility measurements is provided. In addition, a model file (mdh) and a pipeline file (v3s) for the Visual 3D software program are also provided. The data were collected using a three-dimensional (3D) motion-capture system and an instrumented treadmill while the subjects ran at 2.5 m/s, 3.5 m/s, and 4.5 m/s wearing standard neutral shoes.

Supplementary files for the "Running a Confirmatory Factor Analysis in R: a step-by-step tutorial" consist of an R script and data needed to run the analysis.

Financial overview and grant giving statistics of Running Water Club Inc

This .zip contains the raw accelerometry data used to develop SEAR (speed estimation algorithm for running), the results of which are described in the following paper:

Davis, J., Oeding, B., and Gruber, A., 2022. Estimating Running Speed From Wrist- or Waist-Worn Wearable Accelerometer Data: A Machine Learning Approach. Journal for the Measurement of Physical Behaviour. DOI: 10.1123/jmpb.2022-0011

For a detailed description of the data, see the Data README.md file.

Section 1: Introduction

Brief overview of dataset contents:

Current database contains anonymised data collected during exercise testing services performed on male and female participants (cycling, rowing, kayaking and running) provided by the Human Performance Laboratory, School of Medicine, Trinity College Dublin, Dublin 2, Ireland.

835 graded incremental exercise test files (285 cycling, 266 rowing / kayaking, 284 running)

Description file with each row representing a test file - COLUMNS: file name (AXXX), sport (cycling, running, rowing or kayaking)

Anthropometric data of participants by sport (age, gender, height, body mass, BMI, skinfold thickness,% body fat, lean body mass and haematological data; namely, haemoglobin concentration (Hb), haematocrit (Hct), red blood cell (RBC) count and white blood cell (WBC) count )

Test data (HR, VO2 and lactate data) at rest and across a range of exercise intensities

Derived physiological indices quantifying each individual’s endurance profile

Following a request from athletes seeking assessment by phone or e-mail the test protocol, risks, benefits and test and medical requirements, were explained verbally or by return e-mail. Subsequently, an appointment for an exercise assessment was arranged following the regulatory reflection period (7 days). Following this regulatory period each participant’s verbal consent was obtained pre-test, for participants under 18 years of age parent / guardian consent was obtained in writing. Ethics approval was obtained from the Faculty of Health Sciences ethics committee and all testing procedures were performed in compliance with Declaration of Helsinki guidelines.

All consenting participants were required to attend the laboratory on one occasion in a rested, carbohydrate loaded and well-hydrated state, and for male participants’ clean shaven in the facial region. All participants underwent a pre-test medical examination, including assessment of resting blood pressure, pulmonary function testing and haematological (Coulter Counter Act Diff, Beckmann Coulter, CA,US) review performed by a qualified medical doctor prior to exercise testing. Any person presenting with any cardiac abnormalities, respiratory difficulties, symptoms of cold or influenza, musculoskeletal injury that could impair performance, diabetes, hypertension, metabolic disorders, or any other contra-indicatory symptoms were excluded. In addition, participants completed a medical questionnaire detailing training history, previous personal and family health abnormalities, recent illness or injury, menstrual status for female participants, as well as details of recent travel and current vaccination status, and current medications, supplements and allergies. Barefoot height in metre (Holtain, Crymych, UK), body mass (counter balanced scales) in kilogram (Seca, Hamburg, Germany) and skinfold thickness in millimetre using a Harpenden skinfold caliper (Bath International, West Sussex, UK) were recorded pre-exercise.

Section 2: Testing protocols

2.1: Cycling

A continuous graded incremental exercise test (GxT) to volitional exhaustion was performed on an electromagnetically braked cycle ergometer (Lode Excalibur Sport, Groningen, The Netherlands). Participants initially identified a cycling position in which they were most comfortable by adjusting saddle height, saddle fore-aft position relative to the crank axis, saddle to handlebar distance and handlebar height. Participant’s feet were secured to the ergometer using their own cycling shoes with cleats and accompanying pedals. The protocol commenced with a 15-min warm-up at a workload of 120 Watt (W), followed by a 10-min rest. The GxT began with a 3-min stationary phase for resting data collection, followed by an active phase commencing at a workload of 100 or 120 W for female and male participants, respectively, and subsequently increasing by a 20, 30 or 40 W incremental increase every 3-min depending on gender and current competition category. During assessment participants maintained a constant self-selected cadence chosen during their warm-up (permitted window was 5 rev.min−1 within a permitted absolute range of 75 to 95 rev.min−1) and the test was terminated when a participant was no longer able to maintain a constant cadence.

Heart rate (HR) data were recorded continuously by radio-telemetry using a Cosmed HR monitor (Cosmed, Rome, Italy). During the test, blood samples were collected from the middle finger of the right hand at the end of the second minute of each 3-min interval. The fingertip was cleaned to remove any sweat or blood and lanced using a long point sterile lancet (Braun, Melsungen, Germany). The blood sample was collected into a heparinised capillary tube (Brand, Wertheim, Germany) by holding the tube horizontal to the droplet and allowing transfer by capillary action. Subsequently, a 25μL aliquot of whole blood was drawn from the capillary tube using a YSI syringepet (YSI, OH, USA) and added into the chamber of a YSI 1500 Sport lactate analyser (YSI, OH, USA) for determination of non-lysed [Lac] in mmol.L−1. The lactate analyser was calibrated to the manufacturer’s requirements (± 0.05 mmol.L−1) before each test using a standard solution (YSI, OH, USA) of known concentration (5 mmol.L−1) and analyser linearity was confirmed using either a 15 or 30 mmol.L-1 standard solution (YSI, OH, USA).

Gas exchange variables including respiration rate (Rf in breaths.min-1), minute ventilation (VE in L.min-1), oxygen consumption (VO2 in L.min-1 and in mL.kg-1.min-1) and carbon dioxide production (VCO2 in L.min-1), were measured on a breath-by-breath basis throughout the test, using a cardiopulmonary exercise testing unit (CPET) and an associated software package (Cosmed, Rome, Italy). Participants wore a face mask (Hans Rudolf, KA, USA) which was connected to the CPET unit. The metabolic unit was calibrated prior to each test using ambient air and an alpha certified gas mixture containing 16% O2, 5% CO2 and 79% N2 (Cosmed, Rome, Italy). Volume calibration was performed using a 3L gas calibration syringe (Cosmed, Rome, Italy). Barometric pressure recorded by the CPET was confirmed by recording barometric pressure using a laboratory grade barometer.

Following testing mean HR and mean VO2 data at rest and during each exercise increment were computed and tabulated over the final minute of each 3-min interval. A graphical plot of [Lac], mean VO2 and mean HR versus cycling workload was constructed and analysed to quantify physiological endurance indices, see Data Analysis section. Data for VO2 peak in L.min-1 (absolute) and in mL.kg-1.min-1 (relative) and VE peak in L.min-1 were reported as the peak data recorded over any 10 consecutive breaths recorded during the last minute of the final exercise increment.

2.2: Running protocol

A continuous graded incremental exercise test (GxT) to volitional exhaustion was performed on a motorised treadmill (Powerjog, Birmingham, UK). The running protocol, performed at a gradient of 0%, commenced with a 15-min warm-up at a velocity (km.h-1) which was lower than the participant’s reported typical weekly long run (>60 min) on-road training velocity. Subsequently, the warm-up was followed by a 10 minute rest / dynamic stretching phase. From a safety perspective during all running GxT participants wore a suspended lightweight safety harness to minimise any potential falls risk. The GxT began with a 3-min stationary phase for resting data collection, followed by an active phase commencing at a sub-maximal running velocity which was lower than the participant’s reported typical weekly long run (>60 min) on-road training velocity, and subsequently increased by ≥ 1 km.h-1 every 3-min depending on gender and current competition category. The test was terminated when a participant was no longer able to maintain the imposed treadmill.

Measurement variables, equipment and pre-test calibration procedures, timing and procedure for measurement of selected variables and subsequent data analysis were as outlined in Section 2.1.

2.3: Rowing / kayaking protocol

A discontinuous graded incremental exercise test (GxT) to volitional exhaustion was performed on a Concept 2C rowing ergometer (Concept, VA, US) in rowers or a Dansprint kayak ergometer (Dansprint, Hvidovre, Denmark) in flat-water kayakers. The protocol commenced with a 15-min low-intensity warm-up at a workload (W) dependent on gender, sport and competition category, followed by a 10-min rest. For rowing the flywheel damping (120, 125 or 130W) was set dependent on gender and competition category. For kayaking the bungee cord tension was adjusted by individual participants to suit their requirements. A discontinuous protocol of 3-min exercise at a targeted load followed by a 1-min rest phase to facilitate stationary earlobe capillary blood sample collection and resetting of ergometer display (Dansprint ergometer) was used. The GxT began with a 3-min stationary phase for resting data collection, followed by an active phase commencing at a sub-maximal load 80 to 120 W for rowing, 50 to 90 W for kayaking and subsequently increased by 20,30 or 40 W every 3-min depending on gender, sport and current competition category. The test was terminated when a participant was no longer able to maintain the targeted workload.

Measurement variables, equipment and pre-test calibration procedures, timing and procedure for measurement of selected variables and subsequent data analysis were as outlined in Section 2.1.

3.1: Data analysis

Constructed graphical plots (HR, VO2 and [Lac] versus load / velocity) were analysed to quantify the following; load / velocity at TLac, HR at TLac, [Lac] at TLac, % of VO2 peak at TLac, % of HRmax at TLac, load / velocity and HR at a nominal [Lac] of 2 mmol.L-1, load / velocity, VO2 and [Lac} at a nominal HR of

The statistic shows the primary motivation to continue running according to a survey in 2017. 53 percent of the survey respondents said that they continued to run in order to control their weight.

Financial overview and grant giving statistics of Running Without the Devil

Includes the raw IMU data for 20 participants performing seven different running exercises each.

20 participants: 16 m, 4 f, 16 to 31 yo, healthy, do sports regularly Seven exercises: Carioca left, carioca right, heel-to-butt, high-knee running, sideskips left, sideskips right, regular running Four IMUs: accelerometer + gyroscope each, two at wrists, two at ankles Ten seconds per recording under supervision One .json file per recording with sensor values and timestamps

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

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

Financial overview and grant giving statistics of Minnesota Distance Running Association

R Scripts that generates the data in https://doi.org/10.17632/5rmrxkybbz.1