Impact of Yoga in Transformation of Quality of Life of Alzheimer’s disease cases
Bibliographic record
Abstract
Abstract Background Alzheimer’s disease (AD) constitutes a formidable challenge, profoundly affecting the fabric of individuals' lives and intricately entwined with disturbances in their quality of life. This study embarks on a mission to unravel the transformative potential of a carefully crafted 12‐week yoga intervention, specifically tailored to address the multifaceted dimensions of quality of life. Our aim is to unravel the impact of a yoga intervention on their overall quality of life with AD and mild cognitively impairment subjects. Method A case control yoga interventional study was conducted on 30 subjects (male‐18 and female‐12) were enrolled from the department of neurology and neurocognitive assessments were done in department of Anatomy, AIIMS, New Delhi, India. All the participants, aged >60 years were recruited. The 12‐week yoga intervention entailed daily hour‐long sessions over 6 days. The multifaceted evaluation encompassed the Geriatric Depression Scale (GDS) including the evaluation of 15 questions and the Montreal Cognitive Assessment (MoCA) scale including for overall quality of life assessment with measuring language, memory, attention, visuospatial, naming, delayed recall, abstraction, and orientation for quality‐of‐life of the AD subjects. Result The AD subjects cohort exhibited statistically significant enhancements (P < 0.001) in quality‐of‐life scores (GDS & MoCA) pre and post Yoga intervention. GDS scores of 15 questions experienced a remarkable transition from pre‐yoga (8.36 ± 2.7) to post‐yoga (5.13 ± 3.0). While total MoCA scores ascended from pre yoga (18.65 ± 4.13) to post yoga (25.06 ± 6.3). MoCA scores of individual points pre yoga includes language (1.10 ± 0.2), memory (no points), attention (3.54 ± 1.8), visuospatial (4.0 ± 1.30), naming (2.16 ± 0.4), delayed recall (3.55 ± 0.25), abstraction (0.50 ± 0.0), and orientation (3.80 ± 0.18). MoCA scores of individual points post yoga includes language (2.16 ± 0.6), memory (no points), attention (4.60 ± 1.3), visuospatial (4.90 ± 2.50), naming (2.85 ± 0.75), delayed recall (4.85 ± 0.50), abstraction (1.40 ± 0.20), and orientation (4.30 ± 0.45). Conclusion This discerning study illuminates the transformative potential of a 12‐week yoga program, showcasing significant enhancements in the quality of life of individuals grappling with mild to moderate Alzheimer’s disease.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".