Physiological, biochemical, anthropometric, and biomechanical influences on exercise economy in humans
Bibliographic record
Abstract
Interindividual variation in running and cycling exercise economy ( EE ) remains unexplained although studied for more than a century. This study is the first to comprehensively evaluate the importance of biochemical, structural, physiological, anthropometric, and biomechanical influences on running and cycling EE within a single study. In 22 healthy males ( VO 2 max range 45.5‐72.1 mL·min −1 ·kg −1 ), no factor related to skeletal muscle structure (% slow‐twitch fiber content, number of capillaries per fiber), mitochondrial properties (volume density, oxidative capacity, or mitochondrial efficiency), or protein content ( UCP 3 and MFN 2 expression) explained variation in cycling and running EE among subjects. In contrast, biomechanical variables related to vertical displacement correlated well with running EE , but were not significant when taking body weight into account. Thus, running EE and body weight were correlated ( R 2 =.94; P <.001), but was lower for cycling EE ( R 2 =.23; P <.023). To separate biomechanical determinants of running EE , we contrasted individual running and cycling EE considering that during cycle ergometer exercise, the biomechanical influence on EE would be small because of the fixed movement pattern. Differences in cycling and running exercise protocols, for example, related to biomechanics, play however only a secondary role in determining EE . There was no evidence for an impact of structural or functional skeletal muscle variables on EE . Body weight was the main determinant of EE explaining 94% of variance in running EE , although more than 50% of the variability of cycling EE remains unexplained.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".