Calorie Estimation in Adults Differing in Body Weight Class and Weight Loss Status
Bibliographic record
Abstract
PURPOSE: Ability to accurately estimate calories is important for weight management, yet few studies have investigated whether individuals can accurately estimate calories during exercise or in a meal. The objective of this study was to determine if accuracy of estimation of moderate or vigorous exercise energy expenditure and calories in food is associated with body weight class or weight loss status. METHODS: Fifty-eight adults who were either normal weight (NW) or overweight (OW), and either attempting (WL) or not attempting weight loss (noWL), exercised on a treadmill at a moderate (60% HRmax) and a vigorous intensity (75% HRmax) for 25 min. Subsequently, participants estimated the number of calories they expended through exercise and created a meal that they believed to be calorically equivalent to the exercise energy expenditure. RESULTS: The mean difference between estimated and measured calories in exercise and food did not differ within or between groups after moderate exercise. After vigorous exercise, OW-noWL overestimated energy expenditure by 72% and overestimated the calories in their food by 37% (P < 0.05). OW-noWL also significantly overestimated exercise energy expenditure compared with all other groups (P < 0.05) and significantly overestimated calories in food compared with both WL groups (P < 0.05). However, among all groups, there was a considerable range of overestimation and underestimation (-280 to +702 kcal), as reflected by the large and statistically significant absolute error in calorie estimation of exercise and food. CONCLUSIONS: There was a wide range of underestimation and overestimation of calories during exercise and in a meal. Error in calorie estimation may be greater in overweight adults who are not attempting weight loss.
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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) | 0.000 | 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".