A Large-Scale Evaluation into the Accuracy of Bioelectrical Impedance Devices for Assessing Body Composition: A 10-Year Analysis in Canadian University Students
Bibliographic record
Abstract
Background: The assessment of body composition can be conducted using a variety of devices, each providing practitioners unique advantages such as cost effectiveness, efficiency, accessibility, and accuracy. The purpose of this study was to evaluate the precision of three bioelectrical impedance analysis (BIA) devices when compared to air displacement plethysmography (BOD POD). Methods: A large-scale study involving 617 undergraduate kinesiology students (♀ = 342 | ♂ = 275) participated in this study. Participants had a mean ( SD) age of 21.2 2.0 y, a height of 171.9 9.9 cm, and a weight of 71.6 15.2 kg. Each participant completed a body composition assessment using the following BIA equipment: Tanita BC-568, Tanita BF-683W, and InBody 230 to estimate body fat (%). Air plethysmography (BOD POD) was used as the criterion method for deriving body composition. Order of assessments were randomized and were performed 1hr apart on the same day. Results: All data sets were positively skewed and determined to be non-normally distributed after conducting a Shapiro-wilk assessment (p < 0.05). Statistically significant differences in the mean absolute error between the criterion and each BIA device were observed: Tanita-568 (MAE = 4.8, p < 0.0001), Tanita-683 (MAE = 5.8, p < 0.0001) and InBody 230 (MAE = 4.6, p = 0.046). The calculated effect size between each BIA device and the criterion revealed trivial differences (ES 0.16). Conclusion: Overall, the InBody 230 BIA device displayed the strongest agreement with the BOD POD.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".