A systematic review and meta-analysis of the effects of non-pharmacological interventions on quality of life in adults with multiple sclerosis
Bibliographic record
Abstract
BACKGROUND: Multiple Sclerosis (MS) is a chronic debilitating disease that targets the central nervous system. Globally it is estimated that 2.8 million people live with MS (2018) and as there is no known cure; therefore, identifying methods to increase a patient's quality of life (QoL) is of considerable importance. Non-pharmacological interventions are a viable and effective option to increase QoL in patients with MS, however, to date, the literature lacks a complete systematic review of these interventions. METHODS: A literature search was conducted for studies published up until March 4th 2022 in Scopus, Web of Science, CINAHL Plus, The Cochrane Library, Medline, and Embase. Studies were included if they were randomized control trials (RCTs) assessing a non-pharmacological intervention in adults with MS and measured QoL using the MSQOL-54, SF-36 or MSQLI tools for at least two time points. Quality assessment of each study was completed as well as a review of publication bias. Where possible, meta-analysis was conducted using a random effects model and for other studies a qualitative synthesis was presented. RESULTS: Thirty studies were included in the meta-analysis and eleven studies were summarized qualitatively. The pooled effects across all non-pharmacological interventions showed a modest improvement in both the physical and mental components of QoL, with a standardized mean difference (SMD) of 0.44 (95% CI 0.26-0.61) and 0.42 (95% CI 0.24-0.60), respectively. Non-pharmacological interventions based around a physical activity were found to be particularly effective in improving both the physical composite score (PCS) and mental composite score (MCS), with an SMD of 0.40 (95% CI 0.14-0.66) and 0.31 (95% CI 0.08-0.55), respectively. Interventions incorporating balance exercises presented a significant advantageous solution for improving QoL, with an SMD of 1.71 (95% CI 1.22, 2.20) and 1.63(95% CI 1.15-2.12) for PCS and MCS respectively. CONCLUSIONS: This systematic review and meta-analysis identified that non-pharmacological interventions can be an effective method of improving QoL in patients with MS, especially modalities with a physical activity component and balance interventions.
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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 | 0.022 | 0.053 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.044 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".