The Effect of Contextual Factors on Physiological and Activity Profiles in International Women’s Rugby Sevens
Bibliographic record
Abstract
PURPOSE: To evaluate the effects of contextual game factors on activity and physiological profiles of international-level women's rugby sevens players. METHODS: Twenty international-level female rugby sevens players from the same national team participated in this study. Global positioning system and heart-rate data were collected at 5 World Rugby Women's Sevens Series events (2013-14 season). RESULTS: Total, moderate-speed (0.2-3.5 m/s), and high-speed running (3.5-5.0 m/s) distances were significantly greater in the first half (20.1% ± 4.1%, 17.6% ± 6.9%, 24.5% ± 7.8%), during losses (11.4% ± 6.1%, 6.1% ± 6.4%, 26.9% ± 9.8%), during losses of large magnitudes (≥2 tries) (12.9% ± 8.8%, 6.8% ± 10.0%, 31.2% ± 14.9%), and against top-4 opponents (12.6% ± 8.7%, 11.3% ± 8.5%, 15.5% ± 13.9%). In addition, total distance increased (5.0% ± 5.5%) significantly from day 1 to day 2 of tournaments, and very-high-speed (5.0-6.5 m/s) running distance increased significantly (26.0% ± 14.2%) during losses. Time spent between 90% and 100% of maximum heart rate (16.4% ± 14.5%) and player load (19.0% ± 5.1%) were significantly greater in the second half. No significant differences in physiological or activity profiles were observed between forwards and backs. CONCLUSIONS: Game half, game outcome, tournament day, opponent rank, and margin of outcome all affected activity profiles, whereas game half affected physiological profiles. No differences in activity or physiological profiles were found between playing positions. Practitioners are advised to develop high-speed running ability in women's rugby sevens players to prepare them to tolerate the varying factors that affect activity profiles.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".