Exercise training and cognitive performance in persons with multiple sclerosis: A systematic review and multilevel meta-analysis of clinical trials
Bibliographic record
Abstract
Background: Cognitive impairment is common, debilitating, and poorly managed in persons with multiple sclerosis (pwMS). Exercise training might have positive effects on cognitive performance in pwMS, yet the overall magnitude, heterogeneity, and potential moderators remain unclear. Objective: This three-level meta-analysis aims to identify the effects of exercise training and those of exercise modalities on global and domain-specific cognitive performance in pwMS. Methods: MEDLINE, PsycInfo, SportDiscus, CENTRAL, and EMBASE were screened for randomized and non-randomized clinical trials from inception to 27 January 2020, yielding 3091 articles. Based on titles and abstracts, 75 articles remained in the selection process. After full-text evaluation, 13 studies were finally selected (PROSPERO pre-registered). Results: The pooled effect of exercise training on the global cognitive performance was null ( g = 0.04, 95% confidence interval (CI): –0.11 to 0.18) and no significant differences were displayed among domains. Heterogeneity within studies was null ([Formula: see text]= 0.0%) and between studies was low ([Formula: see text]= 25.1%). None of the moderators (exercise modalities, age, Expanded Disability Status Scale (EDSS), supervision, cognitive domains) reached significance. However, the exercise volume explained most of the overall heterogeneity (slope = 4.651 × 10 −5 , [Formula: see text] = 100%, [Formula: see text] = 52.34%). Conclusion: These results do not support the efficacy of exercise training on global or domain-specific cognitive performance in pwMS. Future studies are needed to determine whether higher training dose are beneficial.
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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 | 0.024 | 0.054 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.025 | 0.050 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 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".