Reducing flight time during running decreases tibial-fibular strains in male runners: a finite element analysis
Bibliographic record
Abstract
Reducing strains within the tibia and fibula during running may reduce the risk of stress fractures. We examined the effect of reduced flight time during running (i.e., grounded running) on finite-element predicted bone strains within the tibia-fibula complex. Nine physically active males ran on an instrumented treadmill at 2.2 m/s using a preferred and reduced flight time technique in a randomized order. Three-dimensional force and motion capture data were recorded during running and a computed tomography image was subsequently acquired for the participant's left leg. An inverse-dynamics-based musculoskeletal modeling workflow was used to calculate bone-on-bone contact and muscle forces during the stance phase of running. These forces served as inputs to a participant-specific finite-element model to estimate peak bone strains and strained volume (i.e., the volume of bone experiencing strains above a specific threshold) within the tibia-fibula complex. Guided attempts to reduce flight time was successful with an 18 ms (95 % CI: 12 ms, 25 ms; p < 0.001) reduction in flight time. Reducing flight time was associated with significant reductions in peak tibial/fibular strains (17 % lower; 95 % CI: -7.1 %, -25.0 %; p = 0.002) and strained volume (35 % lower; 95 % CI: -13.57 %, -50.87 %; p = 0.007). We conclude that guided attempts to reduce flight time significantly reduces strains in the tibia and fibula during treadmill running at a fixed speed. These results suggest that grounded running may be a viable technique to reduce musculoskeletal loading and stress fracture risk, particularly in slow runners and those runners coming back from injury.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".