Food group preferences and energy balance in moderately obese postmenopausal women subjected to brisk walking program
Bibliographic record
Abstract
The objective of the study was to examine the effects of a 16-week walking program on food group preferences and energy balance of sedentary, moderately obese (body mass index, 29-35 kg/m(2)), postmenopausal Caucasian women, aged 60 ± 5 years old. One hundred and fifty-six volunteers were subjected to 3 sessions/week of 45 min of walking at 60% of heart rate reserve. Total energy intake (TEI) and food group preferences (3-day dietary record), total energy expenditure (TEE, 3-day physical activity diary), cardiorespiratory fitness (2-km walking test), anthropometry, and body composition (bioelectrical impedance) were measured before and after walking. Data were statistically analyzed using an ANOVA with repeated measures on 1 factor (time). The modest increase in TEE of 151 ± 24 kcal/day (p < 0.0001) leads to body weight, fat mass losses, and waist girth reduction (p < 0.0001). TEI remained unchanged despite a slight decrease in carbohydrate intake and a minor increase in protein intake (p < 0.05). Analysis of food records revealed a decreased consumption of fruits (p < 0.05) and sweet and fatty foods (p < 0.01), but an increase in oil consumption (p < 0.0001) after walking. Women with the highest body weight loss showed the greatest reduction in the consumption of fruits, sugar, sweet foods, and fatty foods (p < 0.05). Women with the greatest fat mass loss showed the highest decrease in fatty food intake (p < 0.05). In conclusion, although our walking program changed some food group consumption patterns, body weight loss was primarily because of the increased TEE.
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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 | 0.000 |
| 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".