External Training Demands in Women's Varsity Rugby Union Players Quantified by Wearable Microtechnology With Individualized Speed Thresholds
Bibliographic record
Abstract
ABSTRACT: Nyman, DLE and Spriet, LL. External training demands in women's varsity rugby union players quantified by wearable microtechnology with individualized speed thresholds. J Strength Cond Res 36(11): 3151-3158, 2022-This study used wearable Global Positioning System (GPS) microtechnology with individualized speed thresholds to examine external training demands in 29 female varsity rugby union athletes during competitive season practices. Players were categorized as forwards or backs and observed during fitness, game-based, or skill training days (FT/GT/ST). Global Positioning System-derived variables included distances, high-intensity running (HIR), work-to-rest ratio, power plays, and PlayerLoad. Five speed zones categorized athlete movements and were customized according to each individual's overall maximum velocity (V̇max). Compared with backs, forwards had lower overall V̇max (6.62 ± 0.66 m·s -1 ; 7.38 ± 0.61 m·s -1 , p = 0.003) and HIR thresholds (3.97 ± 0.40 m·s -1 ; 4.43 ± 0.37 m·s -1 , p = 0.003). There were no interaction effects between position and training day for any GPS-derived variables. However, there were several effects of position ( p ≤ 0.05), with forwards being lower than backs in mean V̇max, zone 1 distance, and power plays, and greater than backs in zone 3, zone 4, and HIR distances. Effects of the training day ( p ≤ 0.05) were also observed in zone 1 distance, with FT being lower than GT and ST, and in power plays, with FT being greater than ST. In female varsity rugby union athletes, there were significant positional differences in individualized speed thresholds and external training demands. However, there were few marked differences between fitness, game-based, and skill training days.
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 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 | 0.001 | 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) | 0.002 | 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".