Bioelectrical impedance analysis (BIA) of body segment composition as an obesity assessment tool among Jordanians
Bibliographic record
Abstract
Bioelectrical impedance analysis (BIA) is a body composition assessing technique for estimating body components; fat, and muscle mass, and body water (BW) by measuring the electrical resistance in ohm (Ω) of those components with response to the flow of a small electrical current through the body. Bioelectrical impedance analysis was used to investigate the relationships between segmental body composition, obesity and body mass index (BMI). The analysis in this study involved the use of InBody 370S equipment to conduct the body composition study of 48 adult participants (30 females and 18 males) at the Applied Science Private University (ASU) School of Pharmacy, Department of Clinical Nutrition and Dietetics, between October, 2019 and January, 2020, Jordan. Anthropometric parameters such as weight, height, body mass index (BMI), and waist-to-hip (W/H) ratio for each individual were measured. The values indicated that BMI of 26.5 for males and 25.42 for females and body composition such as body fat mass (BFM), protein, and visceral fat level (VFL) of females of BMI > 30 was 39.82 compared to 32.60 for males of the same category. The VFL for female individuals was higher than males (13.0 and 17.5) of the same BMI category. The results of BIA analysis, lean body mass, and body muscle mass were found to be significantly (p <0.05 ) correlated in obese adult females but not in obese males, where the body fat mass (BFM) of females with BMI > 30 was 39.82±4.35 compared to 32.6±3.67 for males of the same group. Additionally, the BFM (−3.86**), segmental muscle mass (SMM) was 0.784**, and total body water (TBW) of −0.658** were all indirectly correlated at (p <0.01) with the BIA impedance evaluated as Z(Ω) at 250 kHz. However, the lean mass of the left arm ( −0.662**) and left leg muscle (− 0.514**) were found to be significantly correlated (p < 0.01) and lower in identified obese females compared to non-obese individuals. The findings demonstrate the strong correlation between lean and fat segmental analysis values, and the level of obesity and segmental analysis for male and female individuals can be taken into consideration in obesity and disease prevention. Key words: BIA, obesity, segmental analysis, body composition, BMI, WHR, VFL
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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 | 0.001 |
| 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".