Body Composition and Physical Activity among Omani Adults: A Population-Based Study
Bibliographic record
Abstract
Background: In the last century, huge advances were made in the understanding of physical activity (PA), sedentary life style, and physical fitness (PF). The present study aimed to address the role PA and PF in relation to health and wellbeing among Omani adult's population. Subjects: The survey used complex, multi-stage, stratified, clustered samples of healthy Omani adults (n=100), non-institutionalized populations, to collect information about the anthropometric and physical activity. All study participants provided written informed consent. Results: It was observed that the age and PA are not significantly difference between males and females study subjects. Occupation, body mass index (BMI), and resting basal metabolic rate (RBMR) were significantly difference between male and females. The enrolled female subjects had higher BMI, and there was a negative relationship between male and female study subjects in BMI, percent body fat, and waist to hip ratio. Conclusion: Increasing PA will result in increasing the level of PF and thus reducing fatness. Body composition is the relative amount of fat, muscle, bone, and other vital parts of the body, a component that represents health in the PF and is increased with low PA.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".