Maximal Fat Oxidation: Comparison between Treadmill, Elliptical and Rowing Exercises
Bibliographic record
Abstract
Fat oxidation during exercise is associated with cardio-metabolic benefits, but the extent of which whole-body exercise modality elicits the greatest fat oxidation remains unclear. We investigated the effects of treadmill, elliptical and rowing exercise on fat oxidation in healthy individuals. Nine healthy males participated in three, peak oxygen consumption tests, on a treadmill, elliptical and rowing ergometer. Indirect calorimetry was used to assess maximal oxygen consumption (V̇O2peak), maximal fat oxidation (MFO) rates, and the exercise intensity MFO occurred (Fatmax). Mixed venous blood was collected to assess lactate and blood gases concentrations. While V̇O2peak was similar between exercise modalities, MFO rates were higher on the treadmill (mean ± SD; 0.61 ± 0.06 g·min-1) compared to both the elliptical (0.41 ± 0.08 g·min-1, p = 0.022) and the rower (0.40 ± 0.08 g·min-1, p = 0.017). Fatmax values were also significantly higher on the treadmill (56.0 ± 6.2 %V̇O2peak) compared to both the elliptical (36.8 ± 5.4 %V̇O2peak, p = 0.049) and rower (31.6 ± 5.0 %V̇O2peak, p = 0.021). Post-exercise blood lactate concentrations were also significantly lower following treadmill exercise (p = 0.021). Exercising on a treadmill maximizes fat oxidation to a greater extent than elliptical and rowing exercises, and remains an important exercise modality to improve fat oxidation, and consequently, cardio-metabolic health.
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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 | 0.001 |
| 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".