Reliability of Running Economy Measurements: Influence of Shoe Familiarisation
Bibliographic record
Abstract
The purpose was to investigate differences in reliability of running economy measurements between familiar and unfamiliar shoes. Thirty-seven runners were included who all ran in familiar and unfamiliar running shoes while running economy was measured at steady state using a treadmill. Each participant was tested on two different visits (three sessions in total), with two trials in each of the three shoe conditions completed at each visit. Coefficient of variation, standard deviation of differences, and limits of agreement of running economy were used to quantify the repeatability (within-visit variation) and reproducibility (between-visit variation). The coefficient of variation showed a marginal difference in reproducibility across shoe conditions, whereas no differences were seen in coefficient of variation, standard deviation of the differences, or limits of agreement for repeatability across shoes. All three shoe conditions showed greater repeatability than reproducibility for running economy, and enhanced repeatability at visit 3 compared to visit 2. Our results indicate that familiarisation to shoes might not be needed for reliable measurements of running economy. Based on our results, when evaluating benefits in running shoes we suggest that running economy be assessed within the same day. Further, our data suggest a beneficial effect of using multiple familiarisation sessions if small differences between shoe conditions are expected.
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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 | 0.010 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".