Prevalence and risk factors for back pain in sports: a systematic review with meta-analysis
Bibliographic record
Abstract
Objectives We aimed to determine the prevalence of low back pain (LBP) in sport, and what risk factors were associated with LBP in athletes. Design Systematic review with meta-analysis. Data sources Literature searches from database inception to June 2019 in Medline, Embase, Cumulated Index to Nursing and Allied Health Literature (CINAHL), Web of Science and Scopus, supplemented by grey literature searching. Eligibility criteria Studies evaluating prevalence of LBP in adult athletes across all sports. Results Eighty-six studies were included (30 732, range 20–5958, participants), of which 45 were of ‘high’ quality. Definitions of LBP varied widely, and in 17 studies, no definition was provided. High-quality studies were pooled and the mean point prevalence across six studies was 42%; range 18%–80% (95% CI 27% to 58%, I 2 =97%). Lifetime prevalence across 13 studies was 63%; range 36%–88% (95% CI 51% to 74%, I 2 =99%). Twelve-month LBP prevalence from 22 studies was 51%; range 12%–94% (95% CI 41% to 61%, I 2 =98%). Comparison across sports was limited by participant numbers, study quality and methodologies, and varying LBP definitions. Risk factors for LBP included history of a previous episode with a pooled OR of 3.5; range 1.6–4.0 (95% CI 1.9 to 6.4). Statistically significant associations were reported for high training volume, periods of load increase and years of exposure to the sport. Conclusion LBP in sport is common but estimates vary. Current evidence is insufficient to identify which sports are at highest risk. A previous episode of LBP, high training volume, periods of load increase and years of exposure are common risk factors.
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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 | 0.057 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.032 | 0.059 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".