Effect of serious games over conventional therapy in the rehabilitation of people with multiple sclerosis – a systematic review and meta-analysis
Bibliographic record
Abstract
Purpose This meta-analysis aimed to quantify the effect of serious games over conventional therapy on upper-limb activity, balance, gait, fatigue, and cognitive functions in people with multiple sclerosis.Materials and methods Search strategies were developed for PubMed, Embase, Cochrane Library, and Scopus. Studies were selected if participants were adults with multiple sclerosis; the intervention consisted of a virtual reality serious game-based program; the control group received conventional therapy; outcomes included upper limb activity, balance, gait, fatigue, or cognitive functions; and used a randomized controlled trial design. Data were synthesized using a standardized mean difference with a random-effects model.Results From 2532 studies, seventeen trials were selected (n = 740). Overall, serious games programs effect on upper limb activity, gait, verbal memory, verbal fluency and attention seemed neutral. Balance functions appeared to be improved by semi-immersive virtual reality serious games (SMD = 0.48;95%CI = 0.12–0.84;p = 0.01;I2=0%), fatigue by treadmill serious games (SMD = 0.80;95%CI = 0.40–1.20;p < 0.001) and visuo-spatial memory by semi-immersive virtual reality general cognitive serious games (SMD = 0.35;95%CI = 0.04–0.65;p = 0.03;I2=0%).Conclusion This review suggests, with a very-low-to-low certainty of evidence, that while some specific serious games may improve balance, fatigue and visuo-spatial memory, their overall effect on upper limb activity, gait, and other cognitive functions appears neutral.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.023 | 0.036 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".