Illness Monitoring in Team Sports Using a Web-Based Training Diary
Bibliographic record
Abstract
OBJECTIVE: Use of Web-based data recording systems has received little attention in sport. An "online" training diary could provide a valuable alternative to pen-paper methods in the regular assessment of physical activity and illness occurrence in athletes. The objective of this study was to design and implement a user-friendly and efficient system to monitor incidences of illness in team sport athletes. DESIGN: Prospective monitoring study over a 48-week rugby season. Players were asked to register presence/absence of weekly illness symptoms with medical staff and also use an online training diary. Submitted self-reported diary illness data were compared with illness complaint data recorded by medical staff. Diary response rates were calculated from the number of completed diary entries against the number of available/required entries over the season. SETTING: Web-based training diary. PARTICIPANTS: Thirty professional rugby union players. INTERVENTION: Comparison of gastrointestinal and upper respiratory illnesses (URIs) reported by players using an online diary and to medical staff. MAIN OUTCOME MEASURES: Incidences of URIs. RESULTS: The diary response rate in the reporting of weekly illnesses was 79% over the study period. Discrepancy existed between the number of self-reported URIs by players using the diary (118 URI incidences) compared with those reported to medical staff (23 URI incidences). Totaling all URI episodes (those self-reported + those registered by medical staff) revealed that players reported just 19% of URI episodes to medical staff. CONCLUSIONS: Players tend to underreport incidences of banal infections. Closer monitoring of self-reported illnesses using a similar system in the present study may provide a better alternative to previous methods in nonclinical illness assessment.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".