Tracking Athlete Wellness And Its Relationship With Activities During A Season In Female Soccer Players
Bibliographic record
Abstract
Performance analysis creates a foundation for performance staff to display findings to coaches and aid in understanding how training loads impact the wellness of each player. Applying an appropriate training load and allowing sufficient recovery will improve an athlete’s performance, while reducing the risk of overtraining, injury, and illness. Monitoring individual load and recovery is a critical part of this process and not solely dependent on physical observations. Overtraining can manifest in an array of symptoms that also includes changes in mood, sleep disturbances, stress, and more generalized fatigue. PURPOSE: To examine the effects of different activities during a season on daily wellness dimensions. METHODS: 25 female soccer players (21±2y) completed daily morning self-administered questionnaires consisting of 5 dimensions of wellness (i.e. fatigue, sleep, muscle soreness, stress, and mood) on a 0 (feeling the worst) - 100 (feeling the best) scale on their computers or mobile devices. Activity on the previous day (i.e. off-day, game, practice, or double practice) was used as an independent variable in assessing wellness scores. RESULTS: Type of day did not have a significant effect on fatigue (p=0.842), sleep (p=0.395), or mood (p=0.499). Post hoc analyses revealed self-reported muscle soreness to be significantly worse (p=0.029) after game days (n=8) than off-days (n=19) (difference score = 12) and self-reported stress to be significantly worse (p=0.049) after practice days (n=11) than after off-days (n=19) (difference score = 7). In all dimensions, there was a trend for positive self-reports to be best after off-days and worst after days of double practice. CONCLUSION: This study provides evidence that a quick self-administered questionnaire can provide important information about an athlete’s wellness. Moreover, off-days (i.e. no activity) are important parts of programming as they generally positively affect the physical and mental health recovery of athletes. Nonetheless, adherence to survey completion declined and value assigned to activities changed throughout the season. As such, future research is needed to further the understanding of how athlete wellness is impacted by and can impact performance during activities across a competitive athletic season.
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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 | 0.001 | 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) | 0.001 | 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".