Effects of an individual 12-week community-located “start-to-run” program on physical capacity, walking, fatigue, cognitive function, brain volumes, and structures in persons with multiple sclerosis
Bibliographic record
Abstract
Background: Exercise therapy studies in persons with multiple sclerosis (pwMS) primarily focused on motor outcomes in mid disease stage, while cognitive function and neural correlates were only limitedly addressed. Objectives: This pragmatic randomized controlled study investigated the effects of a remotely supervised community-located “start-to-run” program on physical and cognitive function, fatigue, quality of life, brain volume, and connectivity. Method: In all, 42 pwMS were randomized to either experimental (EXP) or waiting list control (WLC) group. The EXP group received individualized training instructions during 12 weeks (3×/week), to be performed in their community aiming to participate in a running event. Measures were physical (VO 2max , sit-to-stand test, Six-Minute Walk Test (6MWT), Multiple Sclerosis Walking Scale-12 (MSWS-12)) and cognitive function (Rao’s Brief Repeatable Battery (BRB), Paced Auditory Serial Attention Test (PASAT)), fatigue (Fatigue Scale for Motor and Cognitive Function (FSMC)), quality of life (Multiple Sclerosis Impact Scale-29 (MSIS-29)), and imaging. Brain volumes and diffusion tensor imaging (DTI) were quantified using FSL-SIENA/FIRST and FSL-TBSS. Results: In all, 35 pwMS completed the trial. Interaction effects in favor of the EXP group were found for VO 2max , sit-to-stand test, MSWS-12, Spatial Recall Test, FSMC, MSIS-29, and pallidum volume. VO 2max improved by 1.5 mL/kg/min, MSWS-12 by 4, FSMC by 11, and MSIS-29 by 14 points. The Spatial Recall Test improved by more than 10%. Conclusion: Community-located run training improved aerobic capacity, functional mobility, visuospatial memory, fatigue, and quality of life and pallidum volume in pwMS.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".