A systematic review of aerobic and resistance exercise and inflammatory markers in people with multiple sclerosis
Bibliographic record
Abstract
Inflammation is a driver in the demyelination process in patients with multiple sclerosis (MS) and can influence disability levels. Both single and repeated bouts of exercise can decrease inflammatory markers in people with MS (PwMS). This systematic review evaluates whether exercise can influence inflammation and disability in individuals with MS. Experimental studies were reviewed that had to meet the following eligibility requirements: a sample of PwMS, an intervention of exercise (either aerobic, resistance, or a combination of each), and an outcome that included at least one inflammatory (cytokine) reaction. The main outcome measure was an evaluation of inflammation, as indicated by a change in any cytokine level. Other measures included muscle strength, balance, flexibility, walking ability, disability statues, and quality of life (QOL). A total of nine studies were included in the final review. Exercise interventions included predominantly cycling, although a few resistance training trials were mentioned. Small decreases were found in IL-17 and IFN-γ after exercise. Functional outcome measures and perceived disability status were improved posttraining. We conclude that while interventions such as exercise may impact QOL, they do not have a significant influence on inflammation associated with MS. Exercise is an accessible alternative that not only helps to decrease impairments but also limit the restrictions associated with participation in society. While functional outcomes after exercise improved, these improvements may not be attributable to changes in levels of cytokines or inflammatory markers.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".