The Influence of Aerobic Exercise Therapy on Patient-Reported Outcome Measures Following Concussion: A Scoping Review
Bibliographic record
Abstract
Background/Objectives: The purpose of this scoping review was to identify the effects of exercise therapy on patient-reported outcome measure(s) (PROM(s)) after concussion, and to highlight the current knowledge gaps in the literature. Methods: A search of EMBASE, MEDLINE, SCOPUS, BIOSIS, and Cochrane Libraries was performed on 1 December 2023 (from database inception) and updated on 19 June 2025 to review the available literature. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) extension for scoping reviews was used to report the scoping review. An online review management system was used to remove duplicates and perform article screening. Results: Following the removal of duplicates, 5434 articles were identified for screening. After applying the inclusion/exclusion criteria, 27 articles remained for data extraction. The available literature has found that applying structured aerobic exercise following concussion can improve patient-reported symptom severity compared with control groups. Conclusions: There is currently a paucity of research reporting on the impact of aerobic exercise therapy following concussion on PROMs aside from patient-reported symptom severity. Additional PROMs recommended as common data elements should be included in future studies investigating the effects of aerobic exercise therapy following 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 | 0.026 | 0.093 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.013 | 0.013 |
| 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) | 0.004 | 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".