Efficacy of Yijinjing-inspired exercises on sleep disorders in Parkinson’s disease: a controlled fNIRS study
Bibliographic record
Abstract
Background Sleep disorders are prevalent in Parkinson's disease (PD) patients, significantly impacting their quality of life and rehabilitation outcomes. This controlled trial aimed to investigate the impact of Yijinjing-inspired exercises on sleep disorders in PD patients, utilizing functional near-infrared spectroscopy (fNIRS) to assess neurophysiological changes. Methods Ninety-six PD patients were allocated to control, exercise, or music therapy groups for eight weeks. The Pittsburgh Sleep Quality Index (PSQI), along with the Montreal Cognitive Assessment (MoCA), Mini-Mental State Examination (MMSE), Unified Parkinson's Disease Rating Scale (UPDRS), and Parkinson's Disease Questionnaire (PDQ-39) were used to assess outcomes. fNIRS measured neurophysiological changes post-intervention. Results The exercise group demonstrated substantial improvement in sleep quality after Bonferroni correction (PSQI: mean Δ = -1.78 ± 0.99, P < 0.001; Cohen's d = 1.45). Moderate effect sizes were observed in cognition (MoCA: d = 0.43) and motor function (UPDRS: d = 0.40), though these did not retain statistical significance after correction. Between-group analysis revealed greater PSQI reduction in exercise versus control (Δ = -1.19 ± 0.85 vs. -0.19 ± 1.53; P = 0.001 after Bonferroni adjustment, Cohen's d = 0.87), but not versus music therapy (P = 0.018 > 0.0167). fNIRS confirmed cortical reorganization in dorsolateral prefrontal cortex (Brodmann Area 9; Channel 3) and primary motor cortex (Brodmann Area 4; Channel 9) at FDR-corrected P < 0.05. The control group showed no statistically significant changes post-correction (all P > 0.01). Discussion Yijinjing training may improve sleep quality in Parkinson's disease patients, with preliminary evidence of neuroplastic adaptation. Consideration could be given to exploring its integration into comprehensive rehabilitation approaches.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".