Total energy expenditure of 10- to 12-year-old Japanese children measured using the doubly labeled water method
Bibliographic record
Abstract
To establish Japanese children’s estimated energy requirements, total energy expenditure (TEE) data measured using the doubly labeled water (DLW) method is needed. This study aimed to 1) obtain basic TEE data from Japanese children measured using DLW (TEE DLW ), 2) compare TEE DLW with TEE estimated by various estimation formulas to calculate their accuracy, and 3) develop a new equation to estimate TEE using body composition and pedometers. TEE was measured using DLW in 56 10- to 12-year-old Japanese children (33 boys, 23 girls). Physical activity level (PAL) was calculated by dividing TEE DLW by estimated resting energy expenditure. To assess their physical activity, participants wore pedometers during the 7-d DLW period. Total body water was calculated from 2 H and 18 O; fat-free mass (FFM) and fat mass (FM) were then determined. In boys and girls of normal weight, TEE DLW was 2067 ± 230 kcal/d and 1830 ± 262 kcal/d, respectively. Average PAL was 1.58 ± 0.17. FFM was strongly related to TEE ( r = 0.702, p < 0.01). After adjusting for FFM and FM, step count was significantly associated with TEE ( r = 0.707, p < 0.01). The TEE estimation formula used in the Dietary Reference Intakes (DRI) for the United States and Canada estimated TEE DLW with high accuracy (bias: 2.0%) in both sexes. We developed new equations for TEE consisting of FFM and step count, which accounted for 68% and 65% of TEE variance in boys and girls, respectively: boys, 47.1 × FFM (kg) + 0.0568 × step count (steps/d) – 122, and girls, 55.5 × FFM (kg) + 0.0315 × step count (steps/d) - 117. The TEE in 10- to 12-year-old Japanese children measured using DLW was approximately 7% lower for boys and 12% lower for girls compared to the current Japanese DRI. If PAL can be accurately determined, the equation in the DRI for the United States and Canada may be applicable to Japanese children. In addition, TEE could be predicted using FFM and step count.
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 | 0.000 | 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) | 0.000 | 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".