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Pre-Competition Habits and Injuries in Taekwondo Athletes

2002· article· en· W4239255676 on OpenAlexaffabout
Mohsen Kazemi, Young Soo Choung

Bibliographic record

VenueMedicine & Science in Sports & Exercise · 2002
Typearticle
Languageen
FieldMedicine
TopicSports injuries and prevention
Canadian institutionsCanadian Memorial Chiropractic College
Fundersnot available
KeywordsAthletesMedicinePhysical therapyDietingWeight lossObesity

Abstract

fetched live from OpenAlex

Introduction: No research has been conducted on the way taekwondo athletes prepare themselves prior to competition. The objective of this retrospective survey, therefore, was to assess training characteristics, competition preparation habits and injury profiles of taekwondo athletes. Methods: Subjects for this study were Canadian male and female taekwondo athletes participating in a national tournament. Questionnaires, comprising items on training characteristics, diet, and injuries sustained during training and competition, were handed out to 60 athletes prior to competition. Results: Twenty-eight questionnaires (46.7%) were returned. The mean age, weight and height of the athletes were 22 years, 149 lbs and 68.6 inches, respectively. Most of the respondents (75%) had 6 years or more experience in taekwondo. Fifty-seven percent of the athletes were practicing taekwondo for 8 or more years, 18% for 6–7 years and 21% for 4–5 years. Fifty-four percent of the athletes dieted before competition. Fifty percent of them did not eat but drank, 33% neither ate nor drank and 17% did not drink but ate. Thirty-six percent did aerobic exercises in addition to dieting to make the weight. Thirty-nine percent practiced 5–6 times per week, 25% 4 times, 21% 2–3 times and 14% 7 or more times per week. Fifty-four percent practiced 2 hours per session, 18% 1 hour, 18% 3 hours and 11% 4 or more. Twenty-five percent sparred 1–2 times per week, 53.5% 3–4 times per week and 21% 5 times or more per week. Forty-one percent stretched before and 57% stretched before and after training. Fifty-seven percent of athletes always did warm-up exercises, whereas 43% sometimes warmed up. Sixty-four percent of athletes sometimes, 21% always and 14% never did cool-down exercises. The location of first injuries reported were 46.5% to the lower extremities, 18% upper extremities, 10.8% back, 3.6% head with 21.4% not reporting any injuries. Forty-five percent of these injuries were sprains/strains, 32% contusions, 14% fractures, and 5% concussions. Fifty-nine percent of the first injuries were incurred during training versus 41% during competition. A hundred percent of the third to fifth injuries reported happened during training. Discussion: As expected in a weight-categorized sport, more than half of the competitors dieted to make weight prior to competition. The adverse effects on the taekwondo athlete's health and performance of dehydration techniques to lose weight were highlighted before (Pieter and Taaffe, 1991), especially when practiced over several years as was also suggested for judoka (judo athletes) (Maslen et al., 1993). Not surprisingly, the lower extremities received most of the injuries reported. Sprains and strains were the most common injuries followed by contusions, which was also found in an earlier study on competition injuries in Canadian taekwondo athletes (Kazemi and Pieter, 2000). When considering all the reported injuries, the frequency of injuries was higher during training. However, in the absence of any exposure data, it cannot be concluded that the risk of injury is higher during training (Kazemi and Pieter, 2000). The sample size of this study was small and hence any definitive conclusions would be premature. Further research with more subjects should also consider the relationship between training and competition injuries, and dieting. In addition, the relationship between warm-up and cool-down routines, and injuries should be investigated.

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.012
GPT teacher head0.273
Teacher spread0.261 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations18
Published2002
Admission routes2
Has abstractyes

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