The Influence of Running Technique Modifications on Vertical Tibial Load Estimates: A Combined Experimental and Machine Learning Approach in the Context of Medial Tibial Stress Syndrome
<b>Background/Objectives</b>: Currently, there is no strong evidence to support interventions for medial tibial stress syndrome (MTSS), a common running injury associated with tibial loading. Vertical ground reaction force (vGRF) and axial tibial acceleration (TA) are the most common met...
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MDPI AG
2025-04-01
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| Series: | Biomechanics |
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| Online Access: | https://www.mdpi.com/2673-7078/5/2/22 |
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| author | Taylor Miners Jeremy Witchalls Jaquelin A. Bousie Ceridwen R. Radcliffe Phillip Newman |
| author_facet | Taylor Miners Jeremy Witchalls Jaquelin A. Bousie Ceridwen R. Radcliffe Phillip Newman |
| author_sort | Taylor Miners |
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| description | <b>Background/Objectives</b>: Currently, there is no strong evidence to support interventions for medial tibial stress syndrome (MTSS), a common running injury associated with tibial loading. Vertical ground reaction force (vGRF) and axial tibial acceleration (TA) are the most common methods of estimating tibial loads, yet clinical recommendations for technique modification to reduce these metrics are not well documented. This study investigated whether changes to speed, cadence, stride length, and foot-strike pattern influence vGRF and TA. Additionally, machine-learning models were evaluated for their ability to estimate vGRF metrics. <b>Methods</b>: Sixteen runners completed seven 1 min trials consisting of preferred technique, ±10% speed, ±10% cadence, forefoot, and rearfoot strike. <b>Results</b>: A 10% speed reduction decreased peak tibial acceleration (PTA), vertical average loading rate (VALR), vertical instantaneous loading rate (VILR), and vertical impulse by 13%, 10.9%, 9.3%, and 3.2%, respectively. A 10% cadence increase significantly reduced PTA (11.5%), VALR (15.6%), VILR (13.5%), and impulse (3.5%). Forefoot striking produced significantly lower PTA (26.6%), VALR (68.3%), and VILR (68.9%). Habitual forefoot strikers had lower VALR (58.1%) and VILR (47.6%) compared to rearfoot strikers. Machine-learning models predicted all four vGRF metrics with mean average errors of 9.5%, 10%, 10.9%, and 3.4%, respectively. <b>Conclusions</b>: This study demonstrates that small-scale modifications to running technique effectively reduce tibial load estimates. Machine-learning models offer an accessible, affordable tool for gait retraining by predicting vGRF metrics without reliance on IMU data. The findings support practical strategies for reducing MTSS risk. |
| format | Article |
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| institution | Kabale University |
| issn | 2673-7078 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
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| series | Biomechanics |
| spelling | doaj-art-9a39ff926abf4f32895c7ecb89d1793f2025-08-20T03:32:28ZengMDPI AGBiomechanics2673-70782025-04-01522210.3390/biomechanics5020022The Influence of Running Technique Modifications on Vertical Tibial Load Estimates: A Combined Experimental and Machine Learning Approach in the Context of Medial Tibial Stress SyndromeTaylor Miners0Jeremy Witchalls1Jaquelin A. Bousie2Ceridwen R. Radcliffe3Phillip Newman4Department of Physiotherapy, University of Canberra, Canberra, ACT 2617, AustraliaDepartment of Physiotherapy, University of Canberra, Canberra, ACT 2617, AustraliaDepartment of Physiotherapy, University of Canberra, Canberra, ACT 2617, AustraliaResearch Institute for Sport and Exercise, University of Canberra, Canberra, ACT 2617, AustraliaDepartment of Physiotherapy, University of Canberra, Canberra, ACT 2617, Australia<b>Background/Objectives</b>: Currently, there is no strong evidence to support interventions for medial tibial stress syndrome (MTSS), a common running injury associated with tibial loading. Vertical ground reaction force (vGRF) and axial tibial acceleration (TA) are the most common methods of estimating tibial loads, yet clinical recommendations for technique modification to reduce these metrics are not well documented. This study investigated whether changes to speed, cadence, stride length, and foot-strike pattern influence vGRF and TA. Additionally, machine-learning models were evaluated for their ability to estimate vGRF metrics. <b>Methods</b>: Sixteen runners completed seven 1 min trials consisting of preferred technique, ±10% speed, ±10% cadence, forefoot, and rearfoot strike. <b>Results</b>: A 10% speed reduction decreased peak tibial acceleration (PTA), vertical average loading rate (VALR), vertical instantaneous loading rate (VILR), and vertical impulse by 13%, 10.9%, 9.3%, and 3.2%, respectively. A 10% cadence increase significantly reduced PTA (11.5%), VALR (15.6%), VILR (13.5%), and impulse (3.5%). Forefoot striking produced significantly lower PTA (26.6%), VALR (68.3%), and VILR (68.9%). Habitual forefoot strikers had lower VALR (58.1%) and VILR (47.6%) compared to rearfoot strikers. Machine-learning models predicted all four vGRF metrics with mean average errors of 9.5%, 10%, 10.9%, and 3.4%, respectively. <b>Conclusions</b>: This study demonstrates that small-scale modifications to running technique effectively reduce tibial load estimates. Machine-learning models offer an accessible, affordable tool for gait retraining by predicting vGRF metrics without reliance on IMU data. The findings support practical strategies for reducing MTSS risk.https://www.mdpi.com/2673-7078/5/2/22musculoskeletal injuriesmedial tibial stress syndromeestimated tibial loadvertical ground reaction forcetibial accelerationmachine-learning analysis |
| spellingShingle | Taylor Miners Jeremy Witchalls Jaquelin A. Bousie Ceridwen R. Radcliffe Phillip Newman The Influence of Running Technique Modifications on Vertical Tibial Load Estimates: A Combined Experimental and Machine Learning Approach in the Context of Medial Tibial Stress Syndrome Biomechanics musculoskeletal injuries medial tibial stress syndrome estimated tibial load vertical ground reaction force tibial acceleration machine-learning analysis |
| title | The Influence of Running Technique Modifications on Vertical Tibial Load Estimates: A Combined Experimental and Machine Learning Approach in the Context of Medial Tibial Stress Syndrome |
| title_full | The Influence of Running Technique Modifications on Vertical Tibial Load Estimates: A Combined Experimental and Machine Learning Approach in the Context of Medial Tibial Stress Syndrome |
| title_fullStr | The Influence of Running Technique Modifications on Vertical Tibial Load Estimates: A Combined Experimental and Machine Learning Approach in the Context of Medial Tibial Stress Syndrome |
| title_full_unstemmed | The Influence of Running Technique Modifications on Vertical Tibial Load Estimates: A Combined Experimental and Machine Learning Approach in the Context of Medial Tibial Stress Syndrome |
| title_short | The Influence of Running Technique Modifications on Vertical Tibial Load Estimates: A Combined Experimental and Machine Learning Approach in the Context of Medial Tibial Stress Syndrome |
| title_sort | influence of running technique modifications on vertical tibial load estimates a combined experimental and machine learning approach in the context of medial tibial stress syndrome |
| topic | musculoskeletal injuries medial tibial stress syndrome estimated tibial load vertical ground reaction force tibial acceleration machine-learning analysis |
| url | https://www.mdpi.com/2673-7078/5/2/22 |
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