Ambient temperature influences metabolic substrate oxidation curves during running and cycling in healthy men
Bibliographic record
Abstract
Abstract Fat oxidation in cold environments and carbohydrate (CHO) use in hot environments are increased during exercise at steady‐state submaximal workloads. However, the influence of cold and heat on fat and CHO oxidation curves remain unknown. We therefore examined the influence of a cold and warm ambient temperature on fat and CHO oxidation across a wide range of exercise intensities during treadmill and cycle ergometer exercise. Nine, young, healthy, male subjects completed four trials, during which they performed an incremental peak oxygen consumption (⩒O2peak) test on a cycle ergometer or treadmill in a 4.6°C or 34.1°C environment. Substrate oxidation, maximal fat oxidation rate (MFO), and exercise intensity where MFO occurs (Fatmax) were assessed via indirect calorimetry. MFO was significantly greater in the cold vs. warm during the treadmill exercise (0.66 ± 0.31 vs. 0.43 ± 0.23 g min−1; p = 0.02) but not during cycling (0.45 ± 0.24 vs. 0.29 ± 0.11 g min−1; p = 0.076). MFO was also greater during treadmill vs. cycling exercise, irrespective of ambient temperature (0.57 g min−1 vs. 0.37 g min−1; p = 0.04). Fatmax was greater in the cold vs. warm for both treadmill (57 ± 20 vs. 37 ± 17%⩒O2peak; p = 0.025) and cycling (62 ± 28 vs. 36 ± 13%⩒O2peak; p = 0.003). Multiple, linear, mixed‐effects regressions revealed a strong influence of ambient temperature on substrate oxidation. We demonstrated that exercising in a cold environment increases MFO and Fatmax, predominantly during treadmill exercise. These results validate the implication of ambient temperature on energy metabolism over a wide range of exercise intensities.
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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".