Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Distributed Loads01:19

Distributed Loads

843
Distributed loads are a common type of load that engineers and scientists encounter in various practical situations. Distributed loads often refer to a type of load spread over a surface or a structure and can be modeled as continuous force per unit area.
For example, consider a bookshelf filled with books stacked vertically adjacent to each other. The weight of the books is evenly distributed over the length of the shelf. As a result, the pressure at different locations on the surface of the...
843
Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

981
Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
981
Relation Between the Distributed Load and Shear01:23

Relation Between the Distributed Load and Shear

1.0K
Understanding the relationship between the distributed load and shear force in structural analysis is crucial for analyzing beams subjected to various loading conditions. Consider the case of a beam experiencing a distributed load, two concentrated loads, and a couple moment.
1.0K
Maximum Power Flow and Line Loadability01:23

Maximum Power Flow and Line Loadability

504
The maximum power flow for lossy transmission lines is derived using ABCD parameters in phasor form. These parameters create a matrix relationship between the sending-end and receiving-end voltages and currents, allowing the determination of the receiving-end current. This relationship facilitates calculating the complex power delivered to the receiving end, from which real and reactive power components are derived.
504
Elastic Curve from the Load Distribution01:16

Elastic Curve from the Load Distribution

409
The structural behavior of beams under distributed loads is critical for engineering analysis, which focuses on predicting how beams bend and react under such conditions. Different types of beams (e.g., cantilever, supported, or overhanging) behave differently under distributed load conditions.
For all beams, the analysis of the beam's reaction to distributed loads begins by understanding the relationship between a beam's load and the resulting shear forces and bending moments. Initially, this...
409
Run Charts01:12

Run Charts

201
Run charts serve as an essential instrument for visualizing the performance of various processes over time, enabling the identification of trends and patterns crucial for quality improvement. These charts map out a series of data points chronologically, offering insights into the stability and efficiency of a process. A run chart's creation involves plotting data points on a graph, with the time intervals on the horizontal axis and the specific measurements on the vertical axis. For...
201

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

More time spent in moderate-to-vigorous physical activity and sleep is associated with a lower incidence of hip or knee osteoarthritis.

BMJ open sport & exercise medicine·2026
Same author

Why do we run? A cross-sectional analysis of motivation profiles and training characteristics in the Garmin-RUNSAFE Running Health Cohort Study.

Journal of science and medicine in sport·2026
Same author

Comparison of Dried Blood Spot Sampling Methods for RNA-Based Biomarker Measurement in Anti-Doping.

Drug testing and analysis·2026
Same author

Mineralocorticoid Receptor Antagonists With an Acylurea as a Key Polar Interaction Motif.

ChemMedChem·2026
Same author

Mapping Digital Nudges and Recommender Systems for Obesity Prevention: Scoping Review.

Interactive journal of medical research·2026
Same author

Perspectives of stakeholders in Danish voluntary sports clubs on engaging in potential social prescribing programmes: a qualitative case study.

BMC primary care·2026

Related Experiment Video

Updated: Dec 12, 2025

Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior
10:52

Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior

Published on: April 13, 2016

9.0K

Predicting cumulative load during running using field-based measures.

Anne Backes1, Sebastian Deisting Skejø2, Paul Gette3

  • 1Physical Activity, Sport and Health Research Group, Department of Population Health, Luxembourg Institute of Health, Luxembourg City, Luxembourg.

Scandinavian Journal of Medicine & Science in Sports
|August 9, 2020
PubMed
Summary

Wearable devices can predict lower limb cumulative load in runners using spatiotemporal data like step rate. This finding offers insights into running biomechanics and injury prevention strategies.

Keywords:
biomechanicsinjury preventionrunningsports injurywearables

More Related Videos

A Protocol for Conducting Rainfall Simulation to Study Soil Runoff
10:35

A Protocol for Conducting Rainfall Simulation to Study Soil Runoff

Published on: April 3, 2014

21.3K
Continuous Venous-Arterial Doppler Ultrasound During a Preload Challenge
09:32

Continuous Venous-Arterial Doppler Ultrasound During a Preload Challenge

Published on: January 20, 2023

3.9K

Related Experiment Videos

Last Updated: Dec 12, 2025

Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior
10:52

Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior

Published on: April 13, 2016

9.0K
A Protocol for Conducting Rainfall Simulation to Study Soil Runoff
10:35

A Protocol for Conducting Rainfall Simulation to Study Soil Runoff

Published on: April 3, 2014

21.3K
Continuous Venous-Arterial Doppler Ultrasound During a Preload Challenge
09:32

Continuous Venous-Arterial Doppler Ultrasound During a Preload Challenge

Published on: January 20, 2023

3.9K

Area of Science:

  • Biomechanics
  • Sports Science
  • Wearable Technology

Background:

  • Assessing cumulative lower limb load in running is crucial for understanding injury risk.
  • Traditional methods for measuring load require specialized laboratory equipment.
  • Commercially available wearable devices offer a potential solution for accessible load monitoring.

Purpose of the Study:

  • To determine if spatiotemporal variables from wearable devices can predict lower limb cumulative load during running.
  • To evaluate the predictive performance of specific spatiotemporal variables for various cumulative load metrics.

Main Methods:

  • Thirty-nine runners completed running tests at 10 and 12 km/h.
  • Spatiotemporal data (step rate, ground contact time, vertical oscillation) were collected using a wearable device.
  • Kinetic variables (peak vertical ground reaction force, vertical instantaneous loading rate, impulses, joint moments) were measured using gold-standard equipment.

Main Results:

  • Predictive models showed varying precision (R²m: .11–.66; R²c: .22–.98).
  • Spatiotemporal variables accurately predicted cumulative peak vertical ground reaction force (R²m = .66, R²c = .97).
  • Good prediction was also observed for vertical instantaneous loading rate, braking impulse, and peak hip extension moment.

Conclusions:

  • Spatiotemporal data from wearable devices can effectively predict certain measures of lower limb cumulative load in runners.
  • This approach may enable non-invasive, real-world monitoring of running load.
  • Findings support the use of wearable technology for assessing biomechanical load and potentially preventing running-related injuries.