Enhancing performance: unveiling the physiological impact of submaximal and supramaximal tests on mixed martial arts athletes in the −61 kg and −66 kg weight divisions
Bibliographic record
Abstract
This study delves into the intricate details of Mixed Martial Arts (MMA) by examining key variables such as maximal oxygen uptake (VO2 peak), aerobic energy (EAER), anaerobic energy (EAN), and accumulated O2 deficit (DOA). By investigating associations and comparing athletes in the −61 kg bantamweight and −66 kg featherweight weight divisions, we aim to shed light on their physiological characteristics. The sample consisted of 20 male volunteers separated into two paired groups: ten athletes in the category up to 61 kg (age: 27.7 ± 5.9 years old, height: 170.9 ± 3.4 cm, body mass: 72.8 ± 1.4 kg, fat percentage: 9.5% ± 3.0%, professional experience: 7.5 ± 7.1 years) and ten athletes up to 66 kg (age: 27.6 ± 2.9 years old, height: 176.0 ± 5.5 cm, body mass: 77.0 ± 1.5 kg, fat percentage: 7.85% ± 0.3%, professional experience: 5.5 ± 1.5 years). Remarkably, our findings revealed striking similarities between the two weight divisions. Furthermore, we discovered a negative correlation between VO2 peak and the number of MMA fights, indicating a potential impact of professional experience on aerobic capacity (r = −0.65, p < 0.01). Additionally, the number of fights exhibited negative correlations with anaerobic energy (r = −0.53, p < 0.05) and total energy cost (r = −0.54, p < 0.05). These results provide valuable insights for designing training programs in the context of MMA. While training both weight divisions together can be beneficial, it is equally crucial to incorporate specific weight-class-focused training to address each division’s unique physical demands and requirements.
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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 | 0.000 |
| 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".