Canadian recommendations underestimate energy needs of women over fifty years as determined by doubly-labelled water
Bibliographic record
Abstract
Accurate estimations of energy requirements at the population level are crucial because of disease processes associated with energy imbalance. The present objective was to compare energy expenditure with existing Recommended Nutrient Intakes for Canadians (RNIC) and determine whether the RNIC provides a true index of energy requirement in middle-aged and elderly Canadian women. A second objective was to compare energy expenditure and the RNIC to Food and Agriculture Organization, World Health Organization, United Nations University (FAO/WHO/UNU) predictions. Seventy-six women were recruited for the study (67.3 ± 11.5 y, 63 ± 11.7 kg, BMI 24.8 ± 4.4 kg·m-2). The two-point doubly-labelled water (DLW) method was used over 13 days to assess energy expenditure while subjects carried out their routine activities. Subjects were stratified to enable age specific requirements for middle-aged and elderly women. At weight maintenance, energy needs were underestimated using the RNIC (7.1 ± 1.6 MJ·d-1, 1698 ± 391 kcal·d-1) compared to total energy expenditure (10.0 ± 3.2 MJ·d-1, 2395 ± 746 kcal·d-1) as determined by DLW as a whole and for each age group. The RNIC recommendations were lower than the FAO/WHO/UNU estimations even for light activity. Results indicate that mean energy expenditure was 29% greater than the RNIC recommendations created using formulas based on age and weight, whereas the FAO/WHO/ UNU estimations closely approximated energy expenditure based on heavy activity in women 49-79 y and light activity in women over 80 y old. These data suggest a systematic underestimation of Canadian energy recommendations for women.Key words: energy expenditure, doubly-labelled water, energy intake, dietary assessment, humans.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".