Effects of Acute and 2-Hour Postphysical Activity on the Estimation of Body Fat Made by the Bod Pod
Bibliographic record
Abstract
The Bod Pod has been found to be reliable/valid against several criterion methods, including hydrostatic weighing and dual-energy x-ray absorptiometry, and under different conditions, such as clothing, dehydrated states, and body temperature changes. However, questions remain regarding the effects of an acute bout of exercise. Therefore, the purpose was to determine the effects of an acute bout of exercise on the estimations made by the Bod Pod. Participants (15 men and 22 women) were of age 18-27 years and were currently exercising. Baseline Bod Pod measures were completed followed by a 30-minute cycling trial at 75% of maximum heart rate. Bod Pod measures were taken immediately after exercise and 2 hours after exercise. Differences between men and women were found at baseline between height (p < 0.001), weight (p < 0.001), body volume (BV; p < 0.001), and body density (Db; p < 0.001). Among men, body mass (p < 0.001), body fat percentage (%BF; p < 0.001), and BV (p < 0.001) decreased, whereas Db (p < 0.001) and body temperature (p < 0.001) increased directly after exercise; body mass (p < 0.001) and BV (p < 0.001) remained lower after 2 hours. Among women, body mass (p < 0.001) and BV (p < 0.001) decreased, whereas thoracic gas volume (p = 0.014) and temperature (p < 0.001) increased directly after exercise; body mass (p < 0.001) and BV (p < 0.001) remained lower, whereas %BF (p < 0.001) and Db (p = 0.006) remained higher 2 hours after exercise. These results suggest that a single bout of exercise immediately before Bod Pod testing seems to alter the estimate of %BF, and continues to affect the prediction 2 hours after exercise in 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".