Predicting Energy Expenditure in Elders with the Metabolic Cost of Activities
Bibliographic record
Abstract
INTRODUCTION: Measuring free-living energy expenditure in aging human is a considerable challenge. The objective of this study was to predict total energy expenditure (TEE) in elders by combining the metabolic cost of activities and accelerometer outputs. METHODS: Seventeen elders (7 women, 10 men) aged 60 to 78 yr were recruited. Body composition was measured by dual x-ray absorptiometry. Doubly labeled water was used as the criterion standard to measure TEE on a 7-d time frame. During the same period, participants wore a uniaxial accelerometer (Caltrac) to estimate TEE. Resting metabolic rate and metabolic costs of sitting, standing, and walking (1, 3, and 5 km·h(-1)) were measured by indirect calorimetry. RESULTS: There was no correlation between Caltrac's outputs and doubly labeled water measurement of TEE. The best predictors of TEE were fat-free mass, the metabolic cost of standing, and the metabolic cost of walking at 3 km·h(-1) (r = 0.78, P < 0.01). CONCLUSIONS: Our results suggest that TEE may be estimated with good accuracy using fat-free mass, the cost of standing still, and the cost of walking at 3 km·h(-1). These predictors are easy to measure in older adults. Further work is needed to confirm our findings and develop prediction equation with these parameters.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.001 | 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".