Exercise training improves participation in persons with multiple sclerosis: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Although previous studies have examined the effects of exercise training on other International Classification of Functioning, Disability and Health (ICF) component levels in persons with multiple sclerosis (MS), the effects of exercise training on participation remain unclear. The objectives of this review were to: (1) characterize systematically the use of outcome measures that capture participation in exercise training studies; (2) quantify the effect of exercise training on participation in persons with MS. METHODS: A search of 6 electronic databases (CINAHL, SPORTDiscuss, Embase, MEDLINE, Cochrane Central, and Scopus) was conducted to identify controlled and noncontrolled trials involving exercise training and participation in persons with MS. Search strings were built from Medical Subject Headings and CINAHL headings. ICF linking rules were used to identify participation chapters and categories captured. Meta-analysis was used to quantify the effect of exercise training on participation in randomized controlled trials comparing exercise effects to no intervention/usual care. RESULTS: We included 49 articles involving controlled and noncontrolled exercise trials in the systematic review of outcome measures. We captured 16 different outcome measures that captured all 9 participation chapters and identified 89 unique participation categories. Across these 16 outcome measures, mobility was the most commonly represented participation chapter, with 108 items. A subsample of 23 randomized controlled trials was included in the meta-analysis. An overall effect of 0.60 (standard error = 0.12, 95% confidence interval: 0.36-0.84, z = 4.9, p < 0.001) was calculated, indicating a moderate, positive effect of exercise training on participation. CONCLUSION: The current review provides information that can be used to guide the selection of outcome measures that capture participation in studies of exercise training in persons with MS. Exercise training has a positive effect on outcomes that capture participation, providing further evidence for the role of exercise training in promoting and maintaining engagement in everyday life.
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 | 0.016 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.034 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".