Kinetic risk factors of running‐related injuries in female recreational runners
Bibliographic record
Abstract
Our objective was to prospectively investigate the association of kinetic variables with running-related injury (RRI) risk. Seventy-four healthy female recreational runners ran on an instrumented treadmill while 3D kinetic and kinematic data were collected. Kinetic outcomes were vertical impact transient, average vertical loading rate, instantaneous vertical loading rate, active peak, vertical impulse, and peak braking force (PBF). Participants followed a 15-week half-marathon training program. Exposure time (hours of running) was calculated from start of program until onset of injury, loss to follow-up, or end of program. After converting kinetic variables from continuous to ordinal variables based on tertiles, Cox proportional hazard models with competing risks were fit for each variable independently, before analysis in a forward stepwise multivariable model. Sixty-five participants were included in the final analysis, with a 33.8% injury rate. PBF was the only kinetic variable that was a significant predictor of RRI. Runners in the highest tertile (PBF < -0.27 BW) were injured at 5.08 times the rate of those in the middle tertile and 7.98 times the rate of those in the lowest tertile. When analyzed in the multivariable model, no kinetic variables made a significant contribution to predicting injury beyond what had already been accounted for by PBF alone. Findings from this study suggest PBF is associated with a significantly higher injury hazard ratio in female recreational runners and should be considered as a target for gait retraining interventions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 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".