Role of Yoga on Sleep and Quality of Life among Elderly.
Bibliographic record
Abstract
In the elderly, the majority of health problems are addressed by health care. However, sleep and quality of life in old age are overlooked while managing old age challenges. The present study aimed to do a literary review to find the role of Yoga on sleep and quality of life among the elderly and to conduct a prospective pilot study aimed to assess the sleep quality and quality of life among elderly with mild cognitive impairment (MCI). In a thorough review on Google scholar and pub med, we come across a total of 20 clinical trials which assessed sleep and/or quality of life. In the prospective pilot study, twenty-seven community-dwelling elderly (aged 62.22±6.01, male-14) having MCI were recruited. Weekly, six sessions of Integrated Yoga (IY) were administered to all the participants for eight weeks. Each session was of 60 minutes. Assessment for MCI was done by using Montreal Cognitive Assessment (MoCA). Participants were assessed before and after intervention for change in sleep quality by using the Pittsburgh Sleep Quality Index (PSQI) and quality of life by Quality of life scale (CASP-19). A Shapiro-Wilk test shown significant improvement in Sleep quality W(26) = -3.76, P= 0.001, and quality of life W(26)= -4.29, P=0.001 at the end of eight weeks, compared to baseline scores. We conclude that Yoga intervention is an effective and potential tool to enhance sleep quality and quality of life among the elderly. However, generalizations of results have limitations. Further studies with strong methodology, large sample size and active control group, and objective outcomes might add value to this work.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".