Dataset Information

Interactions Between Running Volume and Running Pace and Injury Occurrence in Recreational Runners: A Secondary Analysis.

ABSTRACT:

Context

The combination of excessive increases in running pace and volume is essential to consider when investigating associations between running and running-related injury.

Objectives

To complete a secondary analysis, using a dataset from a randomized trial, to evaluate the interactions between relative or absolute weekly changes in running volume and running pace on the occurrence of running injuries among a cohort of injury-free recreational runners in Denmark.

Design

Prospective cohort study.

Setting

Running volume and pace were collected during a 24-week follow-up using global positioning systems data. Training data were used to calculate relative and absolute weekly changes in running volume and pace.

Patients or other participants

A total of 586 recreational runners were included in the analysis. All participants were injury free at baseline.

Main outcome measure(s)

Running-related injury was the outcome. Injury data were collected weekly using a modified version of the Oslo Sports Trauma Research Centre questionnaire. Risk difference (RD) was the measure of injury risk.

Results

A total of 133 runners sustained running-related injuries. A relative weekly change of progression >10% in running volume and progression in running pace (RD = 8.1%, 95% CI = -9.3%, 25.6%) and an absolute weekly change of progression >5 km in running volume and progression in running pace (RD = 5.2%, 95% CI = -12.0%, 22.5%) were not associated with a statistically significant positive interaction.

Conclusions

Given that coaches, clinicians, and athletes may agree that excessive increases in running pace and running volume are important contributors to injury development, we analyzed the interaction between them. Although we did not identify a statistically significant positive interaction on an additive scale in runners who progressed both running pace and running volume, readers should be aware that an interaction is an important analytical approach that could be applied to other datasets in future publications.

SUBMITTER: Ramskov D

PROVIDER: S-EPMC9387370 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Interactions Between Running Volume and Running Pace and Injury Occurrence in Recreational Runners: A Secondary Analysis.

Ramskov Daniel D Rasmussen Sten S Sørensen Henrik H Parner Erik Thorlund ET Lind Martin M Nielsen Rasmus R

Journal of athletic training 20220601 6

<h4>Context</h4>The combination of excessive increases in running pace and volume is essential to consider when investigating associations between running and running-related injury.<h4>Objectives</h4>To complete a secondary analysis, using a dataset from a randomized trial, to evaluate the interactions between relative or absolute weekly changes in running volume and running pace on the occurrence of running injuries among a cohort of injury-free recreational runners in Denmark.<h4>Design</h4>P ...[more]

PMID: 34543419

Similar Datasets

Project description:Cushioning systems in running shoes are used assuming that ground impact forces relate to injury risk and that cushioning materials reduce these impact forces. In our recent trial, the more cushioned shoe version was associated with lower injury risk. However, vertical impact peak force was higher in participants with the Soft shoe version. The primary objective of this study was to investigate the effect of shoe cushioning on the time, magnitude and frequency characteristics of peak forces using frequency-domain analysis by comparing the two study groups from our recent trial (Hard and Soft shoe group, respectively). The secondary objective was to investigate if force characteristics are prospectively associated with the risk of running-related injury. This is a secondary analysis of a double-blinded randomized trial on shoe cushioning with a biomechanical running analysis at baseline and a 6-month follow-up on running exposure and injury. Participants (n = 848) were tested on an instrumented treadmill at their preferred running speed in their randomly allocated shoe condition. The vertical ground reaction force signal for each stance phase was decomposed into the frequency domain using the discrete Fourier transform. Both components were recomposed into the time domain using the inverse Fourier transform. An analysis of variance was used to compare force characteristics between the two study groups. Cox regression analysis was used to investigate the association between force characteristics and injury risk. Participants using the Soft shoes displayed lower impact peak force (p < 0.001, d = 0.23), longer time to peak force (p < 0.001, d = 0.25), and lower average loading rate (p < 0.001, d = 0.18) of the high frequency signal compared to those using the Hard shoes. Participants with low average and instantaneous loading rate of the high frequency signal had lower injury risk [Sub hazard rate ratio (SHR) = 0.49 and 0.55; 95% Confidence Interval (CI) = 0.25-0.97 and 0.30-0.99, respectively], and those with early occurrence of impact peak force (high frequency signal) had greater injury risk (SHR = 1.60; 95% CI = 1.05-2.53). Our findings may explain the protective effect of the Soft shoe version previously observed. The present study also demonstrates that frequency-domain analyses may provide clinically relevant impact force characteristics. Clinical Trial Registration: https://clinicaltrials.gov/, identifier: 9NCT03115437.

Project description:Background: Fatigue is associated with increased injury risk along with changes in balance control and task performance. Musculoskeletal injury rates in runners are high and often result from an inability to adapt to the demands of exercise and a breakdown in the interaction among different biological systems. This study aimed to investigate whether changes in balance dynamics during a single-leg squat task following a high-intensity run could distinguish groups of recreational runners who did and did not sustain a running-related injury within 6 months. Methods: Thirty-one healthy recreational runners completed 60 s of single-leg squat before and after a high-intensity run. Six months after the assessment, this cohort was separated into two groups of 13 matched individuals with one group reporting injury within this period and the other not. Task performance was assessed by the number of repetitions, cycle time, amplitude, and speed. To evaluate balance dynamics, the regularity and temporal correlation structure of the center of mass (CoM) displacements in the transverse plane was analyzed. The interaction between groups (injury, non-injured) and time (pre, post) was assessed through a two-way ANOVA. Additionally, a one-way ANOVA investigated the percent change difference of each group across time. Results: The injured group presented more regular (reduced entropy; 15.6%) and diffusive (increased short-term persistence correlation; 5.6%) CoM displacements after a high-intensity run. No changes were observed in the non-injured group. The within-subject percent change was more sensitive in demonstrating the effects of fatigue and distinguishing the groups, compared to group absolute values. No differences were observed in task performance. Discussion: Runners who were injured in the future demonstrate changes in balance dynamics compared to runners who remain injury-free after fatigue. The single-leg squat test adopted appears to be a potential screening protocol that provides valuable information about balance dynamics for identifying a diminished ability to respond to training and exercise.

Dataset Information

Interactions Between Running Volume and Running Pace and Injury Occurrence in Recreational Runners: A Secondary Analysis.

Context

Objectives

Design

Setting

Patients or other participants

Main outcome measure(s)

Results

Conclusions

Publications

Interactions Between Running Volume and Running Pace and Injury Occurrence in Recreational Runners: A Secondary Analysis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets