The Association Between Running Injuries and Training Parameters: A Systematic Review
Bibliographic record
Abstract
OBJECTIVE: To synthesize the current evidence on the incidence of running-related injuries (RRIs) and their association with training parameters (distance, duration, frequency, intensity), as well as recent changes in training parameters. DATA SOURCES: Searches were conducted in MEDLINE/Ovid, CINAHL, Embase, and SPORTDiscus from their inception through July 7, 2020. STUDY SELECTION: Included articles had to report prospective data on RRIs and training parameters or any changes in parameters and be published in English or French. Two reviewers independently screened the titles, abstracts, and full texts. DATA EXTRACTION: Two independent raters performed data extraction and quality assessment using QualSyst, a quality appraisal tool. DATA SYNTHESIS: A total of 36 articles that involved 23 047 runners were included. Overall, 6043 runners (26.2%) sustained an RRI (incidence range = 8.8%-91.3%). The incidence of RRI was 14.9% in novice runners (range = 9.4%-94.9%), 26.1% in recreational runners (range = 17.9%-79.3%), and 62.6% in competitive runners (range = 52.6%-91.3%). The 3 most frequently injured body parts were the knee (25.8%), foot/ankle (24.4%), and lower leg (24.4%). Overall, evidence about the association between weekly running distance, duration, frequency, intensity, or specific changes in training parameters and the onset of RRIs was conflicting. CONCLUSIONS: Despite high rates of RRIs, current evidence does not consistently link RRIs with specific training parameters or recent changes in training parameters. Therefore, caution should be taken when recommending optimal parameters or progressions. Given the multifactorial nature of RRIs, future studies also need to consider the interactions between training parameters as well as psychosocial, hormonal, lifestyle, and recovery outcomes to better understand the onset of RRIs.
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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 | 0.013 | 0.072 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.007 |
| Bibliometrics | 0.016 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".