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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.015 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".