Normal weight men and women overestimate exercise energy expenditure.
Bibliographic record
Abstract
AIM: The limited potential of exercise to induce weight loss could be partly due to the overestimation of the energy cost of exercise. The objectives of this study were twofold: 1) to investigate whether men and women are able to accurately estimate exercise energy expenditure (EE); and 2) to determine whether they have the ability to accurately compensate for the EE of exercise during a buffet-type meal. METHODS: Sixteen (8 men, 8 women) moderately active (VO2 peak=45.4±7.7 mL.kg-1.min-1), normal weight (BMI=22.8±3.3 kg/m2) individuals, aged 20-35 years, were studied. They were blinded to two randomly assigned experimental conditions: a 200 and a 300 kcal (measured by indirect calorimetry) exercise sessions that were performed on a treadmill at the same intensity (50% of VO2 peak). At the end of each exercise session individuals were asked to estimate EE of the exercise sessions and to then eat the caloric equivalent of their estimated exercise EE from a buffet-type meal. RESULTS: Estimated EE was higher than measured EE for both the 200 kcal (825.0±1061.8 vs. 200.1±0.7 kcal, P<0.05) and 300 kcal (896.9±952.4 vs. 300.2±0.7 kcal, P<0.05) sessions. Further, post-exercise energy intake was higher than measured EE for the 200 kcal (556.8±204.4 vs. 200.1±0.7 kcal, P<0.001) and the 300 kcal (607.2±166.5 vs. 300.2±0.7 kcal, P<0.001) sessions. Although post-exercise energy intake was lower than estimated EE, no significant differences were noted. CONCLUSION: These results suggest that normal weight individuals overestimate EE during exercise by 3-4 folds. Further, when asked to precisely compensate for exercise EE with food intake, the resulting energy intake is still 2 to 3 folds greater than the measured EE of exercise.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".