The effect of exercise on mental health and health-related quality of life in individuals with multiple sclerosis: A Systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: A large body of evidence has tested the effect of exercise interventions on mental health and health-related quality of life (HRQoL) in individuals with multiple sclerosis (PwMS). OBJECTIVE: To determine the effect of exercise interventions on mental health and HRQoL in PwMS. METHODS: We searched four databases up to April 2023, and included randomized controlled trials that: 1) involved PwMS ≥18 years old; 2) delivered an exercise intervention; 3) measured subjective well-being, psychological well-being, social well-being, or HRQoL as outcomes. We reported standardized differences in means (d) with a 95 % confidence interval (CI), for continuous outcomes and an incidence rate ratio (IRR) with a 95 % CI for dichotomous outcomes. RESULTS: Forty-nine studies (n = 2,057 participants) were included. Exercise improved overall well-being (d = 0.78; 95 % CI 0.483, 1.077; moderate certainty evidence), subjective well-being (d = 0.666; 95 % CI 0.405, 0.928; moderate certainty evidence), social well-being (d = 1.046; 95 % CI 0.569, 1.523; low certainty evidence), and HRQoL (d = 0.568; 95 % CI 0.396, 0.74; moderate certainty evidence). CONCLUSION: Exercise interventions can improve well-being and HRQoL in PwMS. Future studies should focus on PwMS ≥ 65 years or with higher level of impairments.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.030 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".