Comparison of different measures to monitor week-to-week changes in training load in high school runners
Bibliographic record
Abstract
Training load is commonly used to monitor training stress and is the product of external and internal physiological loads experienced by an athlete. With emerging wearable technology, it is possible to evolve existing external load measurement from duration or distance to runner-specific biomechanical data, which when combined with existing measures of internal load such as session rating of perceived exertion (sRPE), may improve the quantification of training stress. This study compared week-to-week changes in training duration with different training loads obtained from common and more individualized measures of external load. The training of nine male high school cross-country runners from the same team undertaking the same training program was monitored for two consecutive weeks. This two-week cycle included a “coach-prescribed” low and high training load week. Training loads were calculated with sRPE and external load measures including: duration (minutes), Step Count, “Bone Stimulus” (IMeasureU), and cumulative vertical force. Weekly distances (in miles) were also measured. Between-week percent change (%Δ) was compared among training loads and minutes using paired t-tests and Cohen’s d effect size. Different %Δ were found between sRPExMinutes (%Δ=65 ± 25%; p = 0.002, d = 1.83), sRPExStep Count (%Δ=66 ± 31%; p = 0.006, d = 2.06), sRPExForce (%Δ=66 ± 29%; p = 0.002, d = 1.91), and miles (%Δ=28 ± 13%; p = 0.019, d = 0.71) compared to minutes (%Δ=20 ± 8%). These findings highlight that only using weekly volume can greatly misrepresent changes in training stress in runners. We therefore encourage coaches and practitioners to consider training monitoring approaches beyond just weekly distance or duration. Simple measures of training load that include duration and sRPE might be sufficient.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".