Epidemiology And Physical Dysfunction In University Badminton Athletes
Bibliographic record
Abstract
PURPOSE: For developing prevention programs of badminton injuries, epidemiological data on incidence of badminton injuries and physical dysfunction which might cause badminton injuries among badminton athletes should be well investigated. The purposes were to investigate the characteristics of badminton injuries/pain, and then to examine the association between physical function and badminton injuries/pain in university badminton athletes at national tournament level using medical check-ups. METHODS: A questionnaire survey and physical fitness tests of medical check-ups were performed among 51 university badminton athletes (25 males and 26 females) aged 18-22 years. The questionnaire survey asked for basic parameters including gender, age, height, weight, badminton experience, training hours of per day, training days of per week, warm-up, cool-down, and injuries/pain related to badminton. The physical fitness tests comprising of handgrip strength, heel buttock distance, straight leg raising, single leg stance, shoulder internal rotation and external rotation, and trunk flection, extension and rotation, were performed to evaluate physical function. Traumatic injuries, gradual-onset injuries, and pain were defined and assessed. Independent-samples t-test, pair-samples t-test, Mann-Whitney U-test and Wilcoxon’s rank-sum test were used for data analysis. RESULTS: In total, 280 injuries and pain were reported including 29 traumatic injuries, 46 gradual-onset injuries, and 205 pain. Injury incidence rate was 2.14 per 1000 athlete-hours of exposures. Knee was the most common injury site (0.46 per 1000 athlete-hours of exposures), followed by ankle and lower back. Shoulder was the most common pain site (28 cases, 13.7%), followed by lower back and foot. Athletes with present shoulder pain showed significantly greater straight leg raising angles (dominant: 90.7° vs 82.4°, p < 0.05; nondominant: 89.6° vs 81.4°, p < 0.05) and time of nondominant single leg stance (25.6 seconds vs 45.9 seconds, p < 0.05) compared with pain free athletes. CONCLUSIONS: Among university badminton athletes, badminton-associated pain was common, and shoulder was the most common pain site. Greater straight leg raising angle and weak balance ability might be risk factors for shoulder pain.
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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".