The Role of Active Rehabilitation in Concussion Management: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
PURPOSE: This study aimed to conduct a systematic review and meta-analysis of active rehabilitation on concussion management. We also examined moderator variables that may contribute to differences across studies: symptom scale, physical activity type, time of injury to recruitment, and mechanism of injury. METHODS: The standardized effect size of physical activity on concussion management was computed for 23 studies (29 effect sizes). Effect sizes were coded as positive when studies reported an improvement in symptom scores, which was represented by a decrease in postconcussive symptom scores. RESULTS: The overall effect size of physical activity on concussion recovery was large and positive (g = 1.03). Subthreshold aerobic activity provided the largest effect size (g = 1.71), whereas multimodal interventions had a moderate effect size (g = 0.70). All other moderator variables produced positive effect sizes ranging from g = 0.59 to g = 1.46. CONCLUSIONS: This systematic review and meta-analysis demonstrates that current evidence supports the notion that physical activity is beneficial in decreasing postconcussive symptoms in both the acute and chronic phases after concussion. The results indicate that unimodal subthreshold aerobic activity may be the best course of action compared with multimodal interventions. Despite this growing body of evidence, additional research is needed to determine the optimal intensity, duration, and time to initiation of aerobic exercise after concussion.
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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 | 0.023 | 0.056 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.033 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".