Effects of psychological therapies in people with multiple sclerosis: a systematic review and network meta-analysis of randomized controlled trials
Bibliographic record
Abstract
BACKGROUND: People with MS (PwMS) often experience high levels of emotional distress. Interventions to improve emotional wellbeing in PwMS are needed; however, the effectiveness of diverse psychological therapies in PwMS on cumulative outcomes is not well understood. This systematic review and network meta-analysis (NMA) aims to evaluate the effectiveness of psychological therapies in PwMS. METHODS: Five databases were searched (CINAHL, Cochrane Central Register of Controlled Trials, EMBASE, MEDLINE ALL, APA PsycINFO) from 1984 to April 2025. Randomized controlled trials that compared a psychological intervention with any comparator in PwMS were included. Two independent reviewers performed data extraction and risk of bias analysis. A standard random-effects Bayesian consistency NMA was fitted. Outcome domains included mental health, cognition, physical health, quality of life (QoL), and health economics. RESULTS: After screening, 111 studies were included in the review, 89 included in the meta-analysis, and 77 included in the NMA (n = 3911 participants). Cognitive behavioural therapy was found to be most effective in improving outcomes overall, with the greatest changes in mean difference (MD 11.0, 95% CI 5.72-16.2) when compared with control conditions. Psychological therapies significantly improved mental health outcomes (SMD 1.41; 95% CI 1.00-1.83; p < 0.00001), physical health outcomes (SMD 1.08; 95% CI 0.41-1.74; p < 0.00001), QoL (SMD 0.77; 95% CI 0.21-1.34), and cognition (SMD 0.66, 95% CI 0.24-1.07) in PwMS. CONCLUSIONS: This systematic review and NMA found that psychological interventions are broadly effective in PwMS across mental health, cognitive, physical and QoL outcome domains, and provides strong support for their use in the care of PwMS.
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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 | 0.033 | 0.080 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.028 | 0.039 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| 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".