Validation of a portable bioelectrical impedance analyzer for the assessment of body composition
Bibliographic record
Abstract
One of the major challenges in field research has been the difficulty to adequately measure body composition, such as % body fat and fat-free mass (FFM). Therefore, the purpose of the present study was to investigate the convergent validity of the portable bioelectrical impedance body composition analyzer, the Inbody 230, with dual X-ray absorptiometry (DXA) (General Electric Lunar Prodigy). The study population consisted of 145 men and women (age, 44.6 ± 20 years; BMI, 24.5 ± 3.8 kg·m(-2)). We measured body composition (fat mass, % body fat, total FFM, trunk FFM, and appendicular FFM) using DXA and the Inbody 230. Results show strong significant correlations between both methods for fat mass, % body fat, total FFM, and trunk FFM (r = 0.94-0.99). Furthermore, we showed a modest significant correlation between both methods for appendicular FFM (r = 0.63). Finally, as shown by Bland-Altman analysis, no significant biases were observed between Inbody 230 and DXA for fat mass, % body fat, and total FFM. However, trunk and appendicular FFM were shown to have significant biases between the Inbody 230 and DXA. In conclusion, the present study indicated that the portable Inbody 230 may be an acceptable device to measure fat mass, % body fat, and total FFM (except for women) in healthy adults. In addition, there appears to be a systematic bias for the estimation of trunk and appendicular FFM with the Inbody 230 in men and women.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".