Lessons learned from clinical trials of exercise and physical activity in people with MS – guidance for improving the quality of future research
Bibliographic record
Abstract
The body of research on exercise and physical activity among persons with multiple sclerosis(MS) has expanded rapidly in quantity, but not necessarily quality, over the past 20+ years. There is evidence for beneficial effects of exercise and physical activity on immune cells and neurotrophic factors, brain structure and function, walking and cognitive performance, fatigue, depression, and pain, and quality of life among persons with MS. Nevertheless, there is heterogeneity in the outcomes of exercise and physical activity, and a recognition of substantial challenges for improving the effectiveness of those behaviors in MS. To move the field forward, members of the National MS Society physical wellness research working-group pooled collective experiences for identifying challenges, logistic complexities, and opportunities for researchers in designing and conducting interventions of exercise and physical activity among persons with MS. We examined the scope of our experiences and identified collective "lessons learned" regarding the behaviors themselves; study design features and stage of research; study setting, safety, and sample selection; and implementation of randomized controlled trials(RCTs) and treatment fidelity. This paper provides a resource that can inform researchers, particularly new investigators or established investigators transitioning into MS, on conducting high-quality RCTs on exercise and physical activity in MS.
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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 | 0.384 | 0.509 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.013 | 0.010 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.014 | 0.028 |
| Open science | 0.008 | 0.007 |
| Research integrity | 0.014 | 0.020 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".