Comparison of Foot-to-Foot and Hand-to-Foot Bioelectrical Impedance Methods in a Population with a Wide Range of Body Mass Indices
Bibliographic record
Abstract
BACKGROUND: Several techniques are currently used for measurement of body composition. Bioelectrical impedance assessment (BIA) is a simple, noninvasive method of assessing body composition. We aimed to compare multifrequency hand-to-foot (HF-BIA) and foot-to-foot (FF-BIA) bioelectrical impedance analysis techniques to assess fat-free mass (FFM) in a population with a wide range of body mass indices (BMI). METHODS: This was a cross-sectional study of 198 adult subjects. Anthropometric and BIA measures (HF-BIA with Hydra ICF/ECF, Xitron Technologies and FF-BIA with Tanita, model TBF-300A) were recorded after a 12-h fast. RESULTS: Participants had a mean age of 42 years and BMI of 33.50.7 (range, 17.7-65.6) kg/m2. Mean FFM with HF-BIA (FFM BIA/HF) and FF-BIA (FFM BIA/FF) were 61.31.3 kg and 58.10.9 kg, respectively (P < 0.001). In subjects with BMI <25 kg/m2, FFM BIA/FF was not significantly different compared to FFMBIA/HF (-0.2 kg; P=0.8). However, FFM BIA/FF was significantly lower in subjects with BMI 25-30 kg/m2 (-2.0 kg; P=0.009), 30-34 kg/m2 (-1.8 kg; P¼0.04), 34-42 kg/m2 (-4.7 kg; P<0.001) and >42 kg/m2 (-8.0 kg; P=0.001). Pearson correlations between both methods were very high for FFM (r=0.92), fat mass (r=0.91), and % fat mass (r=0.85), all P<0.001. Correlation coefficients for FFM were high in each quintile of BMI. FFM BIA/FF was the only significant independent predictor of FFM BIA/HF (P<0.001) in linear regression analyses using clinical and FF-BIA variables, but introducing BMI in the model added precision. CONCLUSION: FFM BIA/FF correlates closely with FFM BIA/HF across all quintiles of BMI, but FF-BIA gives lower FFM in overweight and obese subjects.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".