Advancements and Challenges in Exercise Training for Multiple Sclerosis: Comprehensive Review and Future Directions for Randomized Controlled Trials
Bibliographic record
Abstract
Exercise training represents a health behavior for the treatment and management of the multi-faceted manifestations of multiple sclerosis (MS). This paper provides a comprehensive overview of evidence from randomized controlled trials (RCTs) regarding benefits, safety, participation, and guidelines for exercise training in MS, based on systematic reviews and meta-analyses. The paper then provides our opinions based on extensive experience regarding challenges for improving and expanding future RCTs that will advance our understanding of exercise training in MS. The comprehensive review of evidence from RCTs indicates that exercise training yields substantial improvements in aerobic and muscle fitness, mobility, fatigue and depression, quality of life, and participation outcomes. There is a non-significant increase in the risk of adverse events or serious adverse events with exercise training compared with control conditions or healthy populations. Rates of adherence and compliance with exercise training (i.e., participation) approximate 80% and 70%, respectively. The current prescriptive guidelines suggest 2-3 days per week of aerobic and resistance exercise training as the minimal dose for safely benefiting from exercise training in MS. We propose 10 important topics as avenues for expanding the body of research and improving its scope for evidence-based practice in MS. Overall, the research on exercise training in MS is strong, but it can get stronger. The expansion and advancement of evidence are critical for moving exercise training into the clinical armamentarium of MS disease treatment and management.
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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 | 0.043 | 0.071 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.012 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".