Motor-cognitive dual-task training for cognitive function in patients with Parkinson's disease: a meta-analysis
Bibliographic record
Abstract
Objective To explore the effect of motor-cognitive dual-task training on cognitive function in patients with Parkinson's disease (PD). Methods Following the PRISMA guidelines, randomized controlled trials (RCT) evaluating the effect of motor-cognitive dual-task training on cognitive function in PD patients were systematically searched in the Cochrane Library, Web of Science, Embase, PubMed, CNKI, Wanfang data, VIP and Chinese Biomedical Literature Database from inception to December 20, 2024. The methodological quality of the included studies was assessed using the Cochrane Handbook for Systematic Reviews of Interventions version 5.1.0 risk-of-bias tool and the PEDro scale. Meta-analysis was conducted using RevMan 5.4, and GRADE approach was applied to evaluate the quality of evidence. Result A total of ten studies involving 330 patients were included, with PEDro scores ranging from six to nine. Metaanalysis showed that motor-cognitive dual-task training significantly improved Montreal Cognitive Assessment (MoCA) scores (MD=2.05, 95%CI 0.38 to 3.73, P=0.02). There was no difference in Stroop Color and Word Test (SCWT) (reaction time and accuracy), Trail Making Test A (TMT-A) and Trail Making Test B (TMT-B) from the control group (P＞0.05). Subgroup analysis indicated that interventions lasting more than three months improved the MoCA scores (MD=3.64, 95%CI 3.13 to 4.16, P＜0.001). The GRADE rating of evidence was low for MoCA improvement, and moderate for improvements in TMT-A, TMT-B, and Stroop Color-Word Test (reaction time and accuracy). Conclusion Motor-cognitive dual-task training can improve overall cognitive function in patients with PD, as the intervention durations more than three months. The effect on visuospatial and executive function requires further investigation.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.023 | 0.047 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 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".