Impact of a Holistic Health Intervention on the Well-Being of Elderly Thais
Bibliographic record
Abstract
OBJECTIVE: Mental stress is a major risk factor of metabolic diseases and impairs quality of life in the elderly. The goal of this study was to investigate whether a mindfulness-based intervention can improve health parameters and reduce stress-related hormones in elderly Thais. METHODS: A total of 192 participants were recruited from urban (n = 96) and rural areas (n = 96) in central region of Thailand. Creative visual art relaxation and meditative movement exemplified by Dao De Xin Xi exercise were introduced as monthly workshops and individual participants were encouraged to practice regularly for three months. General health status, levels of blood pressure (BP), fasting blood sugar (FBS), cortisol and dehydroepiandrosteone (DHEA) were evaluated before and after completing the intervention. RESULTS: This mindfulness-based intervention was effective as assessed by several biological health parameters. However, the degree of effectiveness differed between participants in urban and rural groups. In urban group, BPs were significantly reduced in hypertension and pre-hypertension subgroups of participants, and levels of DHEA in low DHEA participants were significantly increased after the intervention. In contrast, FBS levels in diabetic and pre-diabetic participants in the rural group, but not in the urban group, were significantly decreased after the intervention. We found a trend toward improvement of cortisol levels in both groups. However, the levels of glycated hemoglobin in either group remained unchanged. CONCLUSION: This mindfulness-based intervention to elderly people improved health parameters related to metabolic diseases. Therefore, applying the intervention in primary healthcare may help promote the well-being in elderly.
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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 | 0.000 |
| 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 | 0.001 |
| Research integrity | 0.000 | 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".