Dataset Information

Using stiffness to assess injury risk: comparison of methods for quantifying stiffness and their reliability in triathletes.

ABSTRACT:

Background

A review of the literature has indicated that lower body stiffness, defined as the extent to which the lower extremity joints resists deformation upon contact with the ground, may be a useful measure for assessing Achilles injury risk in triathletes. The nature of overuse injuries suggests that a variety of different movement patterns could conceivably contribute to the final injury outcome, any number and combination of which might be observed in a single individual. Measurements which incorporate both kinetics and kinematics (such as stiffness) of a movement may be better able to shed light on individuals at risk of injury, with further analysis then providing the exact mechanism of injury for the individual. Stiffness can be measured as vertical, leg or joint stiffness to model how the individual interacts with the environment upon landing. However, several issues with stiffness assessments limit the effectiveness of these measures to monitor athletes' performance and/or injury risk. This may reflect the variety of common biomechanical stiffness calculations (dynamic, time, true leg and joint) that have been used to examine these three stiffness levels (vertical, leg and joint) across a variety of human movements (i.e. running or hopping) as well as potential issues with the reliability of these measures, especially joint stiffness. Therefore, the aims of this study were to provide a comparison of the various methods for measuring stiffness during two forms of human bouncing locomotion (running and hopping) along with the measurement reliability to determine the best methods to assess links with injury risk in triathletes.

Methods

Vertical, leg and joint stiffness were estimated in 12 healthy male competitive triathletes on two occasions, 7 days apart, using both running at 5.0 ms^-1 and hopping (2.2 Hz) tasks.

Results

Inter-day reliability was good for vertical (ICC = 0.85) and leg (ICC = 0.98) stiffness using the time method. Joint stiffness reliability was poor when assessed individually. Reliability was improved when taken as the sum of the hip, knee and ankle (ICC = 0.86). The knee and ankle combination provided the best correlation with leg stiffness during running (Pearson's Correlation = 0.82).

Discussion

The dynamic and time methods of calculating leg stiffness had better reliability than the "true" method. The time and dynamic methods had the best correlation with the different combinations of joint stiffness, which suggests that they should be considered for biomechanical screening of triathletes. The knee and ankle combination had the best correlation with leg stiffness and is therefore proposed to provide the most information regarding lower limb mechanics during gait in triathletes.

SUBMITTER: Lorimer AV

PROVIDER: S-EPMC6214235 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Publications

Using stiffness to assess injury risk: comparison of methods for quantifying stiffness and their reliability in triathletes.

Lorimer Anna V AV Keogh Justin W L JWL Hume Patria A PA

PeerJ 20181030

<h4>Background</h4>A review of the literature has indicated that lower body stiffness, defined as the extent to which the lower extremity joints resists deformation upon contact with the ground, may be a useful measure for assessing Achilles injury risk in triathletes. The nature of overuse injuries suggests that a variety of different movement patterns could conceivably contribute to the final injury outcome, any number and combination of which might be observed in a single individual. Measurem ...[more]

PMID: 30397548

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Dataset Information

Using stiffness to assess injury risk: comparison of methods for quantifying stiffness and their reliability in triathletes.

Background

Methods

Results

Discussion

Publications

Using stiffness to assess injury risk: comparison of methods for quantifying stiffness and their reliability in triathletes.

Similar Datasets

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