Lean Body Mass and Self-Perceived Body Image among Youth in the United Arab Emirates.
Bibliographic record
Abstract
BACKGROUND AND AIM: Lean body mass may be a promising tool to screen body image disorders. This study aim was to explore the relationships between fat free mass index (FFM-I) on self-perceived body image and shape among university students in the United Arab Emirates. METHODS: Cross-sectional study, using questionnaires to evaluate demographics, body figure, shape and image dissatisfaction; in addition to anthropometrics like BMI and FFM-I. Accordingly, participants were categorized into adequate muscle mass (AMM) and low muscle mass (LMM) individuals. RESULTS: A total of 402 participants (50.4% females) aged between 18 and 25 years, were recruited. Almost third (33.8%) of the participants were overweight/obese, 81% had AMM; 48.5% and 76.3% of them were concerned about their body shape and image, respectively; 55.2% desired to be thinner. Males (M) had significantly higher BMI and body fat compared to females (F). Subjects with LMM, irrespective of sex, were underweight (49% F; 40.7% M), desired to be heavier (44.9% F; 74.1% M) and they had a lower agreement in their perceived BMI versus actual (k=0.024; poor) compared with those with AMM (k=0.408; fair); and in general males had a better agreement between their perceived and actual BMI compared to females (0.432, moderate vs. 0.308, fair). CONCLUSIONS: Our results conclude that female sex and LMM were associated with higher body image and shape dissatisfaction; thus, highlighting the importance of increasing awareness among youth to assess body composition and engage in muscle mass building activities as an effective step towards improving body image perception. (www.actabiomedica.it).
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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 | 0.001 | 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) | 0.002 | 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".