Yoga Interventions Used for the Rehabilitation of Stroke, Parkinson's Disease, and Multiple Sclerosis: A Scoping Review of Clinical Research
Bibliographic record
Abstract
Objectives: The current body of literature was reviewed to compile and describe yoga interventions that have been applied in clinical research and neurologic rehabilitation settings with patients affected by stroke, Parkinson's disease (PD), and multiple sclerosis (MS). Design: Available literature on yoga therapy (YT) was mapped following a five-stage framework to identify key concepts, knowledge gaps, and evidence to inform practice. Publications were identified through Medline, CINAHL, EMBASE, and PsycINFO. Selected studies required subjects with a clinical diagnosis of stroke, PD, and MS to participate in a yoga intervention and have physical, cognitive, and/or psychosocial outcome measures assessed. Results: A total of 50 studies were included in this review. Study characteristics, patient demographics, description of the yoga intervention, reported outcome measures and the main findings were extracted from the studies. Conclusion: Implementing YT in neurorehabilitation can help health care professionals integrate a more holistic approach that addresses the fundamental physical and psychological challenges of living with a chronic and debilitating neurologic disorder. The included studies described yogic interventions consisting of group or individual therapy sessions lasting 60–75 min that were carried out one to three times per week for 8–12 consecutive weeks across all three conditions. All studies described in this scoping review used different yoga protocols confirming the lack of specific interventional parameters available for implementing yoga into the rehabilitation of individuals affected by stroke, PD, or MS.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".