Relation between Lifestyle and Socio-Demographic Factors and Body Composition among the Elderly
Bibliographic record
Abstract
BACKGROUND: Aging is accompanied by various changes that can cause changes in diet and body composition resulting sometimes in malnutrition and disability in the elderly. Changes in body composition among the elderly are mainly the result of physical inactivity and nutrition. This study was designed to examine the relationship between lifestyle and socio-demographic factors and body composition. METHOD: A cross-sectional study was carried out with 380 elderly people aged 60 and over in district 5 of Tehran, Iran. Their body composition was measured by Bioelectrical Impedance Analysis and the Actigraph device was used for assessing physical activity patterns. A three-day food recall was conducted to measure their intake of energy and macronutrients. Lifestyle and socio-demographic information were collected by interview using a pretested questionnaire. RESULTS: Overweight, obesity and central obesity were more prevalent in women than in men (p<0.001). Moreover, 57.1% and 18.7% of participants had high and very high fat mass index, respectively. High fat mass index was seen in 47% of men and 37.5% of women who had normal body mass index (BMI). Meanwhile, age, gender, physical activity, energy intake, the percentage of energy from fat and protein, educational level, job, television watching time, smoking, chronic diseases, and taking medicine were significantly associated with anthropometric measurements (p<0.05). CONCLUSION: Overweight, obesity and high body fat percentage were common among the aged. Considering the factors that are significantly associated with body composition, programs that can increase their awareness about the dietary balance and suitable physical activity should be organized to address these problems.
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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".