Injury Epidemiology, Prevention, and Rehabilitation in Student Triple Jump Athletes Insights from a Decade of Research
Bibliographic record
Abstract
The purpose of the study. The triple jump is one of the most technically demanding and high-impact track and field events, exposing student athletes to significant injury risks. The complex biomechanical demands, coupled with developmental and academic pressures, increase the vulnerability of young athletes to acute and overuse injuries, particularly in the lower extremities. This systematic review aims to synthesize a decade of evidence on injury epidemiology, prevention, and rehabilitation in student triple jump athletes. The goal is to evaluate risk factors, assess the effectiveness of prevention and rehabilitation strategies, and identify research gaps to inform future practices and interventions. Materials and methods. A systematic review was conducted in accordance with PRISMA guidelines. Six major databases (PubMed, SPORTDiscus, CINAHL, Web of Science, Cochrane Library, and PEDro) were searched from January 2010 to September 2024. Inclusion criteria targeted studies involving student athletes aged 16–25, focusing on triple jump injury patterns, prevention, and rehabilitation. Twenty-three studies met the eligibility criteria. Data extraction and quality assessment were performed independently by two reviewers, using validated tools such as the Cochrane Risk of Bias Tool and Newcastle-Ottawa Scale.. Results. The review revealed that injury rates escalate from 1.33 per 10,000 athlete exposures in high school athletes to 8.65 per 1,000 exposures at the collegiate level. Lower extremity injuries were most prevalent, with the thigh, ankle, and knee most commonly affected. Muscle strains and ligament sprains dominated injury types. Evidence-based prevention strategies, particularly neuromuscular and eccentric strengthening programs, reduced injury risk by 35–50%. Rehabilitation protocols emphasizing progressive agility, trunk stabilization, and eccentric training demonstrated superior outcomes and reduced reinjury rates. Most injuries (95.1%) were managed successfully with conservative treatment. Conclusions. Student triple jump athletes face substantial injury risk due to extreme biomechanical loads. Multicomponent prevention programs and comprehensive, criterion-based rehabilitation protocols are effective in reducing injury incidence and recurrence. However, gaps remain in event-specific research, long-term outcome studies, and implementation strategies. Future work should focus on prospective, large-scale studies and the integration of technology-driven monitoring and injury prediction tools.
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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 | 0.025 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| 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".