Effects of Load Carriage and Step Length Manipulation on Achilles Tendon and Knee Loads
Bibliographic record
Abstract
INTRODUCTION: Longer steps with load carriage is common in shorter Soldiers when matching pace with taller Soldiers whereas shorter steps are hypothesized to reduce risk of injury with load carriage. The effects of load carriage with and without step length manipulation on loading patterns of three commonly injured structures were determined: Achilles tendon, patellofemoral joint (PFJ) and medial tibiofemoral joint (mTFJ). MATERIALS AND METHODS: ROTC Cadets (n = 16; 20.1 years ± 2.5) walked with and without load carriage (20-kg). Cadets then altered preferred step lengths ±7.5% with load carriage. Achilles tendon, PFJ and mTFJ loads were estimated via musculoskeletal modeling. RESULTS: Large increases in peak Achilles tendon load (p < 0.001, d = 1.93), Achilles tendon impulse per 1-km (p < 0.001, d = 0.91), peak mTFJ load (p < 0.001, d = 1.33), and mTFJ impulse per 1-km (p < 0.001, d = 1.49) were noted with load carriage while moderate increases were observed for the PFJ (peak: p < 0.001, d = 0.69; impulse per 1-km: p < 0.001, d = 0.69). Shortened steps with load carriage only reduced peak Achilles tendon load (p < 0.001, d = -0.44) but did not reduce Achilles impulse per km due to the resulting extra steps and also did not reduce peak or cumulative PFJ and mTFJ loads (p > 0.05). Longer steps with load carriage increased PFJ loads the most (p < 0.001, d = 0.68-0.75) with moderate increases in mTFJ forces (p < 0.001, d = 0.48-0.63) with no changes in Achilles tendon loads (p = 0.11-0.20). CONCLUSION: A preferred step length is the safest strategy when walking with load carriage. Taking a shorter step is not an effective strategy to reduce loading on the Achilles tendon, PFJ, and mTFJ.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".