The Relationship Between Trunk Muscle Endurance and Three-dimensional Knee Kinematics During Jump-Landing Among Military Personnel
Bibliographic record
Abstract
Introduction: Musculoskeletal injuries are prevalent among active individuals, such as military personnel, which may lead to high health costs. The injuries, specifically in lower extremity joints, are mostly taking place during movements such as jumping. This study investigates the correlation between trunk muscle endurance and three-dimensional knee kinematics during jump-landing among military personnel. Materials and Methods: Thirty commandoes (Mean±SD age: 29.45±4.76) participated in the study. The researchers performed McGill standardized testing battery (core muscle endurance tests). Our subjects underwent jump-landing maneuver and 3D motion analysis of knee joint kinematics. Then the investigators analyzed the obtained data using the Pearson correlation test (P<0.05). The main outcome measures were trunk muscles’ endurance and knee joint angles of flexion-extension, abduction-adduction, and internal rotation-external rotation estimated using MATLAB scripts for three trials during peak angle of knee flexion just before the rebound. We also calculated the average value for the aforementioned motions from initial contact to the take-off phase. Results: The Pearson correlation test indicated a statistically significant correlation between the peak and average value of knee flexion angle and right (rpeak=0.511, raverage=0.505/ Ppeak=0.021, Paverage=0.023) and left (rpeak=0.486, raverage=0.445/ Ppeak=0.030, Paverage=0.049) side bridge tests. Moreover, we observed a significant negative correlation between average knee internal rotation and trunk flexors’ endurance test value (r=-0.496, P=0.026). Discussion: The trunk muscles provide knee stability in dynamic tasks. Particularly, strengthening abdominal and lateral muscles of the trunk high shown to decrease knee rotation and flexion, respectively, during landing from a jump. Consequently, the authorities should emphasize this fact while designing conditioning or rehabilitation programs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".