Effectiveness of Yijinjing on cognitive and motor functions in patients with Parkinson’s disease: study protocol for a randomized controlled trial
Bibliographic record
Abstract
Background: Parkinson's disease (PD) is a common neurodegenerative disorder that affects motor and non-motor functions, significantly reducing patients' quality of life. No effective drug-based treatments are known to solve this problem. Non-drug therapies such as Yijinjing exercise have shown potential in improving cognitive and motor functions in PD patients. However, solid evidence must still be provided to support their clinical efficacy. This study aims to evaluate the clinical efficacy of Yijinjing exercise interventions in PD patients and explore the underlying mechanisms between the cognitive and motor functions in PD. Methods: This is a single-center randomized controlled trial in which 96 eligible PD patients will be randomly assigned to receive either Yijinjing exercise group or brisk walking group or control group in a ratio of 1:1:1. Interventions (Yijinjing exercise or brisk walking training, 40 min per session) will be provided in 3 sessions per week (Monday, Wednesday, Friday) for 12 weeks, with a total of 36 sessions. After the treatment, there will be a 1-month follow-up period. The primary outcomes will be measured using the Montreal Cognitive Assessment (MoCA) and the Unified Parkinson's Disease Rating Scale motor section (UPDRS-III). Secondary outcomes include balance function, executive function, walking function, sleep quality, and quality of life. Additionally, the prefrontal cerebral and sensorimotor cortex blood oxygen signal level will be collected to explore the underlying mechanisms. All outcomes will be assessed at baseline, at the end of 12 weeks of treatment and after an additional 1-month follow-up period. Discussion: The results of the study protocol will provide high-quality evidence for the potential of intervention measures based on the Yijinjing exercise to improve the cognitive and activity levels of Parkinson's disease patients. We envision the Yijinjing exercise as a non-pharmacological family activity that can provide a new and more effective method for the treatment of Parkinson's disease patients or those at risk. Clinical trial registration: This study was approved by the Ethics Committee of the Second Rehabilitation Hospital of Shanghai (2020-05-01). The trial has been registered in the China Clinical Trials Registry (ChiCTR2200055636).
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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 | 0.023 | 0.019 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.015 | 0.006 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.047 | 0.007 |
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".