Aerobic Testing and Training for Persons with Multiple Sclerosis: A Review with Clinical Recommendations
Bibliographic record
Abstract
Purpose: In this article, we review the evidence for aerobic training in persons with multiple sclerosis (MS). The effects of aerobic training on enhancing cardiovascular fitness, muscle strength, and health-related quality of life (HRQOL) and reducing risk factors for cardiovascular disease are summarized. Clinical considerations and recommendations for aerobic testing and training are also presented. Summary of Key Points: Persons with MS may limit their participation in physical activity, predisposing them to deconditioning and additional disability, previously attributed to the disease process itself. However, individuals with mild to moderate levels of disability who participate in aerobic training can improve aerobic fitness, muscle strength, and HRQOL and reduce the risk factors for cardiovascular disease. Aerobic testing and training are safe for these patients provided that they are adequately screened and managed for fatigue, dysautonomia, and thermosensitivity. These historical concerns should not preclude active participation in aerobic training as current evidence suggests that the benefits outweigh the risks. Conclusions: To minimize activity limitations and participation restrictions and enhance physical and psychosocial well-being, aerobic exercise should be promoted and included in the overall management plan of clients with MS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".