On the number of steps required to measure the mean and variability of spatio-temporal parameters at preferred running speed
Bibliographic record
Abstract
Preferred running speed is a relevant condition for measuring spatio-temporal parameters and their variability as it theoretically corresponds to an intensity where the energy cost, the biomechanical constraints and the pleasure are optimal. The objective of this study was to evaluate 1) the between-days reliability of spatio-temporal parameters measurements at preferred running speed and 2) the minimal number of steps required to obtain a representative measurement. Thirty-one recreational runners (age: 26 ± 5.5 years; running experience > 2 years) performed three sessions of treadmill running at preferred running speed separated by 24 h. Spatio-temporal parameters were stride, step, contact and flight times, cadence, step length, duty factor, vertical and leg stiffness. Mean, linear and non-linear measurements of spatio-temporal parameters were computed. Reliability was determined using intraclass coefficient correlation and his 95 % confidence interval lower band (95 %LB). Number of steps required for intra-session representativity were defined as the first number of steps from which all measurements are equal to the measurement at the longer time series length. For all spatio-temporal parameters, mean data showed excellent reliability (95 %LB > 0.90) but most spatio-temporal parameters linear and non-linear variability measurements showed questionable reliability (95 %LB < 0.79). The absolute speed variation at preferred running speed (minimal detectable change 0.8 km/h) may explain these results. Representative values required between 16 and 352 steps for mean value of spatio-temporal parameters, between 16 and 400 steps for linear measurements, and between 336 and 704 steps for non-linear measurements.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".