Changes In Step Time And Length Between Kilometer Eleven And Thirty-nine Of A Marathon
Bibliographic record
Abstract
PURPOSE: Marathon running invokes neuromuscular fatigue which has been shown to result in kinematic changes in a laboratory setting. However, there are limited studies on changes that take place during a race. The purpose of this study was three-fold: 1) to evaluate the step time and step length at an early and late time point in a full marathon and quantify the change between the two points; 2) to identify differences in step time and length associated with sex; and 3) to determine if step time and length are predictors of race finish time. METHODS: This is an observational study in which runners were filmed at two stations, at kilometer 11 (S1) and kilometer 39 (S2) of a full 42.2 kilometer marathon. Each station incorporated two cameras, one in the sagittal plane to assess kinematics and the second to identify the runners’ bib numbers. A 5-meter section of roadway was marked with chalk, delineating each meter, to allow for assessment of step length using Dartfish 5.5 Video Analysis software (Dartfish, Fribourg, Switzerland). RESULTS: Step time was slower at S2 compared to S1 with a mean difference ± SD of 0.290 ± 0.403 m/s (95%CI 0.246 – 0.334; p < 0.001). Step length was shorter at S2 compared to S1 with a difference of 0.098 ± 0.111 m (95%CI 0.086 – 0.110; p < 0.001). There was no interaction in step time for males or females between S1 and S2 (S1, Male 0.35 ± 0.02 s, Female 0.33 ± 0.02 s; S2, Male 0.35 ± 0.02 s, Female 0.34 ± 0.02 s; p = 0.099) however, an interaction was detected for step length (S1, Male 1.16 ± 0.13 m, Female 1.05 ± 0.11 m; S2, Male 1.05 ± 0.13 m, Female 0.98 ± 0.11 m; p = 0.01). A regression model to predict finish time found that step length at S1 accounted for 47% of the variability (F (1,323) = 283.7; p < 0.001), this increased to 68.3% when S1 step time was included (F (2,322) = 350.4; p < 0.001), S2 step length increased this to 75.2% (F(3,321)=328.4; p<0.001) while including S2 step time increased it to 76.7% (F(4,320)=268.4, p<0.001). CONCLUSION: Step time was slower and step length was shorter at the 39-kilometer point of the full-marathon compared to the 11-kilometer point. Step time did not differ at either time point in the race for males or females; however, they both demonstrated a reduction in step length. Step time and step length at both points in the race are able to account for a significant amount of finish-time variability.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".