115 Relationship between readiness indicators, training load and fatigue in collegiate female volleyball athletes
Bibliographic record
Abstract
Background Proper load monitoring can help to determine if athletes are adjusting properly to training loads, minimizing the risk of developing illnesses and injuries. Objective The main objective of this study was to find relationships between internal and external load variables, and fatigue to enable a better understanding of specific adaptations. Design An 8-week prospective observational cohort study with 213 observations. Setting U Sports Canadian volleyball athletes. Patients (or Participants) Six female volleyball athletes (21±2 years, 179.8±6.1 cm, 72±9.5 kg) with competitive experience of at least three years and able to participate without any physical limitation. Interventions (or Assessment of Risk Factors) Pre-practice heart rate variability (HRV), energy level, level of soreness, and hours of sleep were recorded before every practice. The number of jumps, the activity minutes, post-practice rating of perceived exertion (RPE), and HRV value the morning after were also collected. Day of the week, previous strength and conditioning practice, quality of sleep, and medical/physio attention were additional factors included in the analyses. Main Outcome Measurements Fatigue expressed as the percentage of jump-loss (10%) was the dependent binary variable. A stepwise logistic regression analysis was used to analyze the relationship between fatigue, covariates, and factors. Results Previous soreness and the number of jumps performed in practice or competition were the only factors found to be related to a significant level of fatigue experienced by the athletes (p<0.001). Conclusions Although monitoring processes in team sports are today frequent, not all the load markers seem to have the same importance explaining the level of fatigue experienced by the athletes. Pre-practice level of muscle soreness and the number of jumps performed during the activity, a specific expression of external load in volleyball, reveal as the key elements to be controlled by coaches and practitioners to promote an optimal load adaptation.
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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 | 0.000 | 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".