Dietary and Physical Activity Modifications Intervention for Older People
Bibliographic record
Abstract
OBJECTIVE: Diet and physical activity modification such as specialized gymnastic, Taichi, or yoga could reduce either blood glucose or HbA1C level in diabetes patients among older people. This study was a behaviorally based nutrition education intervention for older people in reducing their HbA1C and total cholesterol level in urban area in Indonesia. DESIGN: This was quasi experimental study with three-month behavioral intervention based on Social Cognitive Theory. SETTING: The study was held in Jagir Sub-district located in urban area of Surabaya. Preliminary study showed proportion of hypercholesterolemia and diabetes mellitus among older people in Jagir Sub-district was respectively 21.37% and 2.74%. PARTICIPANTS: 60 older people in Jagir Sub-district was divided into 5 groups consist of: control, physical activity education only, nutrition education only, combination of physical activity and nutrition education, and education material only group. INTERVENTIONS: The intervention was consisted of six sessions physical activity or nutrition education, or both of it performed by trained nutrition science students. MAIN OUTCOME MEASURES: The primary outcomes of this study were HbA1C (%) and total cholesterol (mg/dL) levels. ANALYSIS: The normal distributed or transformed data was analyzed using mixed factorial ANOVA in order to test the difference between groups.
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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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".