An injury profile of amateur and semi-professional KwaZulu-Natal triathletes
Bibliographic record
Abstract
Background: A triathlon comprises of a combination of swimming, cycling and running. Triathlons are usually classified as sprint distance, Olympic distance, and Ironman or ultra-distance. Triathlon was popularized in the 2000 Summer Olympic Games and, despite this, there is insufficient data relating to injuries in the South African context. This study aimed to determine the injury profile of amateur and semi-professional Kwa-Zulu Natal triathletes. Methods: This Institutional Research Board approved, cross sectional study, included 80 active members of the Kwa-Zulu Natal Triathlon Association. All triathletes completed a questionnaire on training and injury profiles; with only those having had a musculoskeletal complaint additionally completing a clinical consultation. In order to assess associations between presence of injury and explanatory variables, binary logistic regression using backward selection based on likelihood ratios was used. Data was described using frequency tables for categorical data and summary statistics for continuous data. Odds ratios was reported and a p value <0.05 was considered statistically significant. For triathletes reporting injuries, linear regression was used for factors associated with injury severity. Results: Fifty seven triathletes responded giving a response rate of 71% (68% male, 32% female). The point and period (year) prevalence of triathlon-related musculoskeletal pain was 17.5% and 68.4% respectively. The ranking of the most common site of injury in the last 12 months included the knee (64%), low back (21%) and thigh (18%); with females having had a significantly higher risk of injury than males (p=.019). Additionally, injury risk also increased with weight (p=.055), number of triathlons undertaken in the previous year (p=.031), number of triathlons in the last 4 months (p=.009) and running distance during competition times (p=.011). Injury risk decreased with increasing distance of cycling (p=.061) and swimming (p=.030) in a competition, and length of training in- and off-season (p=.105 and p=.043 respectively). Strong trends were demonstrated between injury severity and long-slow training distance (p=.006) and weight (p=.006). By contrast to risk of injury, injury severity was negatively associated with weight, while a long-slow distance was positively associated with the severity of the injury. Of all the health professions, chiropractic was the most utilized health profession. Conclusion and recommendations: The results concur with previous research, but add insights into factors predisposing triathletes to injury. The most common injuries require investigation to develop preventative interventions to reduce injuries in triathletes. Health professionals require education about triathlon-related injuries to improve preventative and curative interventions.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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".