Strategies to prevent and manage running-related knee injuries: a systematic review of randomised controlled trials
Bibliographic record
Abstract
OBJECTIVE: To evaluate the effectiveness of interventions to prevent and manage knee injuries in runners. DESIGN: Systematic review and meta-analysis. DATA SOURCES: MEDLINE, EMBASE, CINAHL, Web of Science and SPORTDiscus up to May 2022. ELIGIBILITY CRITERIA FOR SELECTING STUDIES: Randomised controlled trials (RCTs) with a primary aim of evaluating the effectiveness of intervention(s) to prevent or manage running-related knee injury. RESULTS: Thirty RCTs (18 prevention, 12 management) analysed multiple interventions in novice and recreational running populations. Low-certainty evidence (one trial, 320 participants) indicated that running technique retraining (to land softer) reduced the risk of knee injury compared with control treadmill running (risk ratio (RR) 0.32, 95% CI 0.16 to 0.63). Very low-certainty to low-certainty evidence from 17 other prevention trials (participant range: 24 -3287) indicated that various footwear options, multicomponent exercise therapy, graduated running programmes and online and in person injury prevention education programmes did not influence knee injury risk (RR range: 0.55-1.06). In runners with patellofemoral pain, very low-certainty to low-certainty evidence indicated that running technique retraining strategies, medial-wedged foot orthoses, multicomponent exercise therapy and osteopathic manipulation can reduce knee pain in the short-term (standardised mean difference range: -4.96 to -0.90). CONCLUSION: There is low-certainty evidence that running technique retraining to land softer may reduce knee injury risk by two-thirds. Very low-certainty to low-certainty evidence suggests that running-related patellofemoral pain may be effectively managed through a variety of active (eg, running technique retraining, multicomponent exercise therapy) and passive interventions (eg, foot orthoses, osteopathic manipulation). PROSPERO REGISTRATION NUMBER: CRD42020150630.
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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 | 0.032 | 0.097 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.018 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".