COMPARATIVE STUDY OF TOTAL ENERGY EXPENDITURE IN JAPANESE MEN USING DOUBLY LABELED WATER METHOD AGAINST ACTIVITY RECORD, HEART RATE MONITORING, AND ACCELEROMETER METHODS
Bibliographic record
Abstract
The purpose of this study was to examine total energy expenditure (TEE) in Japanese men under free-living conditions using various field methods, and compare these methods with the doubly labeled water method (DLW) . Ten Japanese men, aged 24.2±1.8 (mean±SD) yrs, were studied for 14 consecutive days. TEE was assessed by DLW, activity record method (AR ; using relative metabolic rate and calculated basal metabolic rate), heart rate monitoring methods (Two-line and FLEX-HR methods), and accelerometer method (AC) . Energy intake (EI) was also evaluated over the same period. Although TEE estimated by AR (2730±139 kcal⋅day-1), Two-line (2925±433 kcal⋅day-1), and FLEX-HR (2949±506 kcal⋅day-1) did not differ significantly from the TEE determined by DLW (2910±524 kcal⋅day-1), there was no significant relationship between each of these methods and DLW. In addition, El (2963±482 kcal⋅day-1) and TEE determined by DLW were similar, and there was a significant correlation (r=0.809, P<0.01) . Compared with DLW, AC (2697±541 kcal⋅day-1) underestimated TEE at the group level ; however, AC was the only method to show a significant correlation with DLW (r=0.871, P<0.001) . Therefore, it seems possible that AC would accurately estimate TEE at the individual level by improving both the instrument and its methodology.
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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 | 0.000 |
| 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) | 0.000 | 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 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".