Assessing Energy Expenditure in Male Endurance Athletes
Bibliographic record
Abstract
PURPOSE: The correct assessment of energy expenditure (EE) in athletes is important to ensure that dietary energy intake is sufficient. In general, athletes are individuals with especially high levels of total EE (TEE) and exercise-related EE (ExEE). The SenseWear Pro3 Armband (SWA) is a multisensor device for the individual assessment of EE, but data on the validity for higher exercise intensities are missing. The aim of the study was to validate the SWA for the assessment of TEE and ExEE in endurance athletes. METHODS: The SWA was worn by 14 male endurance athletes for 7 d during a regular training period, and TEE was measured in parallel with the doubly labeled water method. Two controlled exercise trials (treadmill running=2.4-4.8 m·s, stationary bicycling=140-380 W) were performed, during which indirect calorimetry was used to assess ExEE. RESULTS: TEE assessed with the SWA and TEE measured with the doubly labeled water method were significantly correlated (r=0.73, P<0.01), but there were a proportional bias and considerably wide limits of agreement (-1368 to 1238 kcal·d). The error of TEE assessed with the SWA was related to the athletes' individual lactate thresholds (P<0.05). During running and bicycling, ExEE was significantly underestimated for most exercise intensities, and the underestimation increased with exercise intensity (P<0.001). CONCLUSIONS: According to our results, the SWA does not provide valid results of TEE and ExEE in endurance athletes because of the underestimation of EE at higher exercise intensities. It seems necessary to develop exercise-specific prediction equations to improve EE measurements in athletes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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".