Are All Running Workloads Created Equal?
Bibliographic record
Abstract
Training workload (WL) has become a more common monitoring approach in runners and is defined as the product of external and internal training loads. Although rating of perceived exertion (RPE) is a widely accepted measure of internal load, various types of external load metrics can be used in running. PURPOSE: The purpose of this study was to compare week-to-week changes among different training WL measures in high school runners. METHODS: 12 male cross-country runners from the same high school team participated in two consecutive weeks of training monitoring. Training minutes were prescribed by the team coach. Session internal load was collected after each run using RPE on a 1-10 visual analog scale. Session external loads included miles, minutes, IMU-based proprietary tibial load (Bone Stimulus, IMeasureU), and estimated cumulative peak vertical force obtained from wireless insole data (Force, loadsol, Novel). Different weekly WL measures were calculated from session RPE and external load measures. Paired t-tests and Cohen’s d effect sizes were used to compare between-week percent change (%Δ) among different WL measures and weekly minutes (p < 0.05). RESULTS: Different between-week %Δ were observed between RPExMinutes (p = 0.003; d = 1.83), RPExBone Stimulus (p < 0.001; d = 0.74), RPExForce (p = 0.002; d = 1.91), and weekly miles (p = 0.011; d = 0.71) compared to weekly minutes (Figure 1). We also observed greater individual variability in the between-week differences for all three WL measures compared to the volume only measures (Figure 1) due to high variability in the between-week %Δ in average sRPE (SD = 26%). CONCLUSION: These findings suggest that only monitoring a prescribed volume metric can greatly obscure week-to-week individual training responses of runners. The inclusion of an internal training load (i.e., session RPE) allows for the monitoring of the physiological response to training and explains the large variability of the three WL measures.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".