The “impacts cause injury” hypothesis: Running in circles or making new strides?

Gruber, Allison H.

doi:10.1016/j.jbiomech.2023.111694

Cited by 4 publications

(1 citation statement)

References 138 publications

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“…This study ultimately evaluated the relationship between landing position and braking during running, with the assumption that braking is relevant to injury 35 and therefore landing position (and degree of overstriding) is also relevant to injury. Recent systematic reviews show a lack of consensus on the relationship between braking force and injury [36][37][38] , but given that bone is susceptible to failure under shear stress 39 , monitoring braking force is likely still relevant to injury 40 . In this study, we measured peak braking force, but other metrics exist, i.e.…”

Section: Discussionmentioning

confidence: 99%

Predicting overstriding with wearable IMUs during treadmill and overground running

Baker,

Yawar,

Lieberman

et al. 2024

Sci Rep

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Running injuries are prevalent, but their exact mechanisms remain unknown largely due to limited real-world biomechanical analysis. Reducing overstriding, the horizontal distance that the foot lands ahead of the body, may be relevant to reducing injury risk. Here, we leverage the geometric relationship between overstriding and lower extremity sagittal segment angles to demonstrate that wearable inertial measurement units (IMUs) can predict overstriding during treadmill and overground running in the laboratory. Ten recreational runners matched their strides to a metronome to systematically vary overstriding during constant-speed treadmill running and showed similar overstriding variation during comfortable-speed overground running. Linear mixed models were used to analyze repeated measures of overstriding and sagittal segment angles measured with motion capture and IMUs. Sagittal segment angles measured with IMUs explained 95% and 98% of the variance in overstriding during treadmill and overground running, respectively. We also found that sagittal segment angles measured with IMUs correlated with peak braking force and explained 88% and 80% of the variance during treadmill and overground running, respectively. This study highlights the potential for IMUs to provide insights into landing and loading patterns over time in real-world running environments, and motivates future research on feedback to modify form and prevent injury.

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Section: Discussionmentioning

confidence: 99%

Predicting overstriding with wearable IMUs during treadmill and overground running

Baker,

Yawar,

Lieberman

et al. 2024

Sci Rep

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The influence of midsole horizontal and vertical deformation on soft tissue vibrations and bone acceleration during running

Trama,

Wannop,

Smith

et al. 2023

Journal of Sports Sciences

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Use of subject-specific models to detect fatigue-related changes in running biomechanics: a random forest approach

Dimmick,

van Rassel,

MacInnis

et al. 2023

Front. Sports Act. Living

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Running biomechanics are affected by fatiguing or prolonged runs. However, no evidence to date has conclusively linked this effect to running-related injury (RRI) development or performance implications. Previous investigations using subject-specific models in running have demonstrated higher accuracy than group-based models, however, this has been infrequently applied to fatigue. In this study, two experiments were conducted to determine whether subject-specific models outperformed group-based models to classify running biomechanics during non-fatigued and fatigued conditions. In the first experiment, 16 participants performed four treadmill runs at or around the maximal lactate steady state. In the second experiment, nine participants performed five prolonged runs using commercial wearable devices. For each experiment, two segments were extracted from each trial from early and late in the run. For each participant, a random forest model was applied with a leave-one-run-out cross-validation to classify between the early (non-fatigued) and late (fatigued) segments. Additionally, group-based classifiers with a leave-one-subject-out cross validation were constructed. For experiment 1, mean classification accuracies for the single-subject and group-based classifiers were 68.2 ± 8.2% and 57.0 ± 8.9%, respectively. For experiment 2, mean classification accuracies for the single-subject and group-based classifiers were 68.9 ± 17.1% and 61.5 ± 11.7%, respectively. Variable importance rankings were consistent within participants, but these rankings differed from each participant to those of the group. Although the classification accuracies were relatively low, these findings highlight the advantage of subject-specific classifiers to detect changes in running biomechanics with fatigue and indicate the potential of using big data and wearable technology approaches in future research to determine possible connections between biomechanics and RRI.

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The “impacts cause injury” hypothesis: Running in circles or making new strides?

Cited by 4 publications

References 138 publications

Predicting overstriding with wearable IMUs during treadmill and overground running

Predicting overstriding with wearable IMUs during treadmill and overground running

The influence of midsole horizontal and vertical deformation on soft tissue vibrations and bone acceleration during running

Use of subject-specific models to detect fatigue-related changes in running biomechanics: a random forest approach

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