Inhale, Exhale: The Therapeutic Effect of Yoga on Physiological and Psychological Symptoms in Patients with Parkinson’s Disease
Bibliographic record
Abstract
Background: Parkinson’s disease (PD) has a high prevalence in Canada, thus research on this neurodegenerative condition is essential to further our knowledge on the etiopathogenesis of this condition. PD is characterized by a loss of dopamine in the cells. This neurotransmitter functions by sending signals from the brain to the muscles to control movements. As a result of the lack of dopamine, there are several symptoms including tremors, bradykinesia, rigidity. Currently, dopaminergic therapy is the standard for managing the physical motor symptoms associated with PD. This therapy does not address the psychological or quality of life of a patient with the disease. Yoga practice has been shown to improve fatigue, stress, depression and well-being in patients with chronic illnesses. Recent research suggests that yoga can improve physiological and psychological symptoms in PD. Objectives: The aim of this structured literature review is to determine whether the literature indicates that yoga has a positive therapeutic benefit in physiological and psychological symptoms among patients diagnosed with Parkinson’s disease. Methods: We performed a comprehensive search in the PubMed and Web of science electronic databases and searched the references from the articles found to review relevant articles in English, French and Portuguese, using keywords "yoga” and “Parkinson’s" and “quality of life”. Inclusion and exclusion criteria of the articles were predetermined and reviewed. Results: A total of 126 articles published from 2000 to 2017 were available. Of 126 articles, 7 articles were included in this review. Based on the available literature, yoga could be considered as an effective adjuvant for the patients with PD. Conclusion:The existing evidence supports that the practice of yoga has shown to be effective in improving various physiological and psychological symptoms in PD. Limited literature is available on the topic therefore further research needs to be completed.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".